bluuebunny/crossref_metadata_2025 huggingface dataset:
Stan and BART for Causal Inference: Estimating Heterogeneous Treatment Effects Using the Power of Stan and the Flexibility of Machine Learning
Authors: Dorie Vincent , Perrett George , Hill Jennifer L. , Goodrich Benjamin
Published: December 2022

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well. These methods have also shown promise for the related task of identifying heterogeneous treatment effects. However, the estimation of both overall and heterogeneous treatment effects can be hampered when data are structured within groups if we fail to correctly model the dependence between observations. Most machine learning methods do not readily accommodate such structure. This paper introduces a new algorithm, stan4bart, that combines the flexibility of Bayesian Additive Regression Trees (BART) for fitting nonlinear response surfaces with the computational and statistical efficiencies of using Stan for the parametric components of the model. We demonstrate how stan4bart can be used to estimate average, subgroup, and individual-level treatment effects with stronger performance than other flexible approaches that ignore the multilevel structure of the data as well as multilevel approaches that have strict parametric forms.
Binary Classification Quantum Neural Network Model Based on Optimized Grover Algorithm
Authors: Zhao Wenlin , Wang Yinuo , Qu Yingjie , Ma Hongyang , Wang Shumei
Published: December 2022

We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classification task can solve the problem of the classical classifier, this paper proposes a binary quantum neural network classifical model based on an optimized Grover algorithm based on partial diffusion. Trial and error is adopted to extend the partial diffusion quantum search algorithm with the known proportion of target solutions to the unknown state, and to apply the characteristics of the supervised learning of the quantum neural network to binary classify the classified data. Experiments show that the proposed method can effectively retrieve quantum states with similar features. The test accuracy of BQM retrieval under the depolarization noise at the 20th period can reach 97% when the depolarization rate is 0.1. It improves the retrieval accuracy by about 4% and 10% compared with MSE and BCE in the same environment.
Exploring Long-Term Anomalies in the Vegetation Cover of Peri-Urban Parks Using the Fisher-Shannon Method
Authors: Telesca Luciano , Aromando Angelo , Faridani Farid , Lovallo Michele , Cardettini Gianfranco , Abate Nicodemo , Papitto Giancarlo , Lasaponara Rosa
Published: December 2022

The main goal of this study was to evaluate the potential of the Fisher-Shannon statistical method applied to the MODIS satellite time series to search for and explore any small multiyear trends and changes (herein also denoted as inner anomalies) in vegetation cover. For the purpose of our investigation, we focused on the vegetation cover of three peri-urban parks close to Rome and Naples (Italy). For each of these three areas, we analyzed the 2000–2020 time variation of four MODIS-based vegetation indices: evapotranspiration (ET), normalized difference vegetation index (NDVI), leaf area index (LAI), and enhanced vegetation index (EVI). These data sets are available in the Google Earth Engine (GEE) and were selected because they are related to the interactions between soil, water, atmosphere, and plants. To account for the great variability exhibited by the seasonal variations while identifying small multiyear trends and changes, we devised a procedure composed of two steps: (i) application of the Singular Spectrum Analysis (SSA) to each satellite-based time series to detect and remove the annual cycle including the seasonality and then (ii) analysis of the detrended signals using the Fisher-Shannon method, which combines the Shannon entropy and the Fisher Information Measure (FIM). Our results indicate that among all the three pilot test areas, Castel Volturno is characterized by the highest Shannon entropy and the lowest FIM that indicate a low level of order and organization of vegetation time series. This behaviour can be linked to the degradation phenomena induced by the parasite (Toumeyella parvicornis) that has affected dramatically the area in recent years. Our results were nicely confirmed by the comparison with in situ analyzed and independent data sets revealing the existence of subtle, small multiyear trends and changes in MODIS-based vegetation indices.
The Geometry of Generalized Likelihood Ratio Test
Authors: Cheng Yongqiang , Wang Hongqiang , Li Xiang
Published: December 2022

The generalized likelihood ratio test (GLRT) for composite hypothesis testing problems is studied from a geometric perspective. An information-geometrical interpretation of the GLRT is proposed based on the geometry of curved exponential families. Two geometric pictures of the GLRT are presented for the cases where unknown parameters are and are not the same under the null and alternative hypotheses, respectively. A demonstration of one-dimensional curved Gaussian distribution is introduced to elucidate the geometric realization of the GLRT. The asymptotic performance of the GLRT is discussed based on the proposed geometric representation of the GLRT. The study provides an alternative perspective for understanding the problems of statistical inference in the theoretical sense.
Curriculum Reinforcement Learning Based on K-Fold Cross Validation
Authors: Lin Zeyang , Lai Jun , Chen Xiliang , Cao Lei , Wang Jun
Published: December 2022

With the continuous development of deep reinforcement learning in intelligent control, combining automatic curriculum learning and deep reinforcement learning can improve the training performance and efficiency of algorithms from easy to difficult. Most existing automatic curriculum learning algorithms perform curriculum ranking through expert experience and a single network, which has the problems of difficult curriculum task ranking and slow convergence speed. In this paper, we propose a curriculum reinforcement learning method based on K-Fold Cross Validation that can estimate the relativity score of task curriculum difficulty. Drawing lessons from the human concept of curriculum learning from easy to difficult, this method divides automatic curriculum learning into a curriculum difficulty assessment stage and a curriculum sorting stage. Through parallel training of the teacher model and cross-evaluation of task sample difficulty, the method can better sequence curriculum learning tasks. Finally, simulation comparison experiments were carried out in two types of multi-agent experimental environments. The experimental results show that the automatic curriculum learning method based on K-Fold cross-validation can improve the training speed of the MADDPG algorithm, and at the same time has a certain generality for multi-agent deep reinforcement learning algorithm based on the replay buffer mechanism.
Optimizing Multiple Entropy Thresholding by the Chaotic Combination Strategy Sparrow Search Algorithm for Aggregate Image Segmentation
Authors: Wang Mengfei , Wang Weixing , Li Limin , Zhou Zhen
Published: December 2022

Aggregate measurement and analysis are critical for civil engineering. Multiple entropy thresholding (MET) is inefficient, and the accuracy of related optimization strategies is unsatisfactory, which results in the segmented aggregate images lacking many surface roughness and aggregate edge features. Thus, this research proposes an autonomous segmentation model (i.e., PERSSA-MET) that optimizes MET based on the chaotic combination strategy sparrow search algorithm (SSA). First, aiming at the characteristics of the many extreme values of an aggregate image, a novel expansion parameter and range-control elite mutation strategies were studied and combined with piecewise mapping, named PERSSA, to improve the SSA’s accuracy. This was compared with seven optimization algorithms using benchmark function experiments and a Wilcoxon rank-sum test, and the PERSSA’s superiority was proved with the tests. Then, PERSSA was utilized to swiftly determine MET thresholds, and the METs were the Renyi entropy, symmetric cross entropy, and Kapur entropy. In the segmentation experiments of the aggregate images, it was proven that PERSSA-MET effectively segmented more details. Compared with SSA-MET, it achieved 28.90%, 12.55%, and 6.00% improvements in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM), and the feature similarity (FSIM). Finally, a new parameter, overall merit weight proportion (OMWP), is suggested to calculate this segmentation method’s superiority over all other algorithms. The results show that PERSSA-Renyi entropy outperforms well, and it can effectively keep the aggregate surface texture features and attain a balance between accuracy and speed.
Advancing Educational Equity Research, Policy, and Practice
Authors: Wilcox Kristen Campbell , Lawson Hal A.
Published: December 2022

As student populations worldwide become more diverse and many are challenged by poverty and social exclusion, educational equity’s importance grows, both as a core value and as a driver for school improvement and redesign. While national and local policies vary, as do resources in support of equity outcomes, it is timely to identify, synthesize, and evaluate the available research with special attention to commonalities, similarities, and lessons learned. Mindful of selectivity, we summarize eighteen years’ worth of equity-focused research and development conducted under the sponsorship of a state-funded initiative in the USA called NYKids. Utilizing practice-embedded research methodologies, research teams have identified, described, and interpreted seven keynote features of equity-oriented schools. They also have implemented research-practice partnerships that facilitate equity-oriented school improvement. This holistic, evidence-based, and practice-centered approach can be classified as “a strong equity approach”, albeit a selective one, when alternatives worldwide are surveyed.
Human Emotions Recognition, Analysis and Transformation by the Bioenergy Field in Smart Grid Using Image Processing
Authors: Chhabra Gunjan , Onyema Edeh Michael , Kumar Sunil , Goutham Maganti , Mandapati Sridhar , Iwendi Celestine
Published: December 2022

The passage of electric signals throughout the human body produces an electromagnetic field, known as the human biofield, which carries information about a person’s psychological health. The human biofield can be rehabilitated by using healing techniques such as sound therapy and many others in a smart grid. However, psychiatrists and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results, confirming the transformation. The present research was based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically, the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on the prediction capability of the proposed module and their overall satisfaction. The participants were strongly satisfied with the prediction capability (~81%) of the proposed module and future recommendations (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In the future, experimentation using different therapies and integrating more advanced techniques are anticipated to open new gateways in healthcare.
Energy Saving Implementation in Hydraulic Press Using Industrial Internet of Things (IIoT)
Authors: Sumit  , Gupta Deepali , Juneja Sapna , Nauman Ali , Hamid Yasir , Ullah Inam , Kim Taejoon , Tag eldin Elsayed Mohamed , Ghamry Nivin A.
Published: December 2022

With the growing cost of electrical energy, the necessity of energy-saving implementation in industries based on energy audits has become a major focus area. Energy audit results indicate energy-saving potential in an application and require the physical presence of the auditor’s team for monitoring and analyzing the energy consumption data. The use of Industrial Internet of Things (IIoT) for remote data monitoring and analysis is growing and new industrial applications based on IIoT are being developed and used by various industrial sectors. Possibilities of a mixed method of physical and remote energy audit using IIoT in industrial applications and its advantages as proposed in this research work needs to be explored. Existing hydraulic press machines running with direct online starter (DOL) can be run with variable speed drive (VSD) for energy saving but this requires an extensive energy audit. Key electrical and operational parameters of the hydraulic pump motor were monitored and analyzed remotely using IIoT in this research work by operating the hydraulic press with DOL and VSD motor control methods one by one. The input power factor of the hydraulic pump motor showed an improvement from 0.79 in DOL control to 0.9 in VSD control at different motor loads. The hydraulic pump motor starting current showed a reduction of 84% with VSD control. The hydraulic pump motor’s continuous current was reduced by 40% and 65% during the loading and unloading cycle, respectively, with VSD control. Electrical consumption was reduced by 24% as a result of operating the hydraulic pump motor at 35 Hz with VSD control without impacting the performance of the hydraulic press. These results indicated more efficient control by changing to VSD control in comparison with DOL control. A combination of physical and remote energy audits as performed in this research work using the proposed IIoT framework can be utilized for implementing energy saving in hydraulic presses thus motivating industries to adopt available more energy-efficient technologies at a faster pace.
Using Machine Learning and Software-Defined Networking to Detect and Mitigate DDoS Attacks in Fiber-Optic Networks
Authors: Alwabisi Sulaiman , Ouni Ridha , Saleem Kashif
Published: December 2022

Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used to perform specific tasks at remote locations. During the COVID-19 pandemic, many telecom companies advised their employees to manage the network using the public internet (e.g., working from home while connected to an IoT network). Theses IoT devices mostly have weak security algorithms that are easily taken-over by hackers, and therefore can generate Distributed Denial of Service (DDoS) attacks in FONs. A DDoS attack is one of the most severe cyberattack types, and can negatively affect the stability and quality of managing networks. Nowadays, software-defined networks (SDN) constitute a new approach that simplifies how the network can be managed through a centralized controller. Moreover, machine learning algorithms allow the detection of incoming malicious traffic with high accuracy. Therefore, combining SDN and ML approaches can lead to detecting and stopping DDoS attacks quickly and efficiently, especially compared to traditional methods. In this paper, we evaluated six ML models: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. The accuracy reached 100% while detecting DDoS attacks in FON with two approaches: (1) using SVM with three features (SOS, SSIP, and RPF) and (2) using Random Forest with five features (SOS, SSIP, RPF, SDFP, and SDFB). The training time for the first approach was 14.3 s, whereas the second approach only requires 0.18 s; hence, the second approach was utilized for deployment.
Saliency Guidance and Expansion Suppression on PuzzleCAM for Weakly Supervised Semantic Segmentation
Authors: Chang Rong-Hsuan , Guo Jing-Ming , Seshathiri Sankarasrinivasan
Published: December 2022

The semantic segmentation model usually provides pixel-wise category prediction for images. However, a massive amount of pixel-wise annotation images is required for model training, which is time-consuming and labor-intensive. An image-level categorical annotation is recently popular and attempted to overcome the above issue in this work. This is also termed weakly supervised semantic segmentation, and the general framework aims to generate pseudo masks with class activation mapping. This can be learned through classification tasks that focus on explicit features. Some major issues in these approaches are as follows: (1) Excessive attention on the specific area; (2) for some objects, the detected range is beyond the boundary, and (3) the smooth areas or minor color gradients along the object are difficult to categorize. All these problems are comprehensively addressed in this work, mainly to overcome the importance of overly focusing on significant features. The suppression expansion module is used to diminish the centralized features and to expand the attention view. Moreover, to tackle the misclassification problem, the saliency-guided module is adopted to assist in learning regional information. It limits the object area effectively while simultaneously resolving the challenge of internal color smoothing. Experimental results show that the pseudo masks generated by the proposed network can achieve 76.0%, 73.3%, and 73.5% in mIoU with the PASCAL VOC 2012 train, validation, and test set, respectively, and outperform the state-of-the-art methods.
Recurrent Convolutional Neural Network-Based Assessment of Power System Transient Stability and Short-Term Voltage Stability
Authors: Tapia Estefania Alexandra , Colomé Delia Graciela , Rueda Torres José Luis
Published: December 2022

Transient stability (TS) and short-term voltage stability (STVS) assessment are of fundamental importance for the operation security of power systems. Both phenomena can be mutually influenced in weak power systems due to the proliferation of power electronic interface devices and the phase-out of conventional heavy machines (e.g., thermal power plants). There is little research on the assessment of both types of stability together, despite the fact that they develop over the same short-term period, and that they can have a major influence on the overall transient performance driven by large electrical disturbances (e.g., short circuits). This work addresses this open research challenge by proposing a methodology for the joint assessment of TS and STVS. The methodology aims at estimating the resulting short-term stability state (STSS) in stable, or unstable conditions, following critical events, such as the synchronism loss of synchronous generators (SG) or the stalling of induction motors (IM). The estimations capture the mechanisms responsible for the degradations of TS and STVS, respectively. The paper overviews the off-line design of the data-driven STSS classification methodology, which supports the design and training of a hybrid deep neural network RCNN (recurrent convolutional neural network). The RCNN can automatically capture spatial and temporal features from the power system through a time series of selected physical variables, which results in a high estimation degree for STSS in real-time applications. The methodology is tested on the New England 39-bus system, where the results demonstrate the superiority of the proposed methodology over other traditional and deep learning-based methodologies. For reference purposes, the numerical tests also illustrate the classification performance in special situations, when the training is performed by exclusively using measurements from generation and motor load buses, which constitute locations where the investigated stability can be observed.
Hybrid Model-Based BESS Sizing and Control for Wind Energy Ramp Rate Control
Authors: Tadie Abebe Tilahun , Guo Zhizhong , Xu Ying
Published: December 2022

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly.
Small-Scale Solar-Powered Desalination Plants: A Sustainable Alternative Water-Energy Nexus to Obtain Water for Chile’s Coastal Areas
Authors: Cornejo-Ponce Lorena , Vilca-Salinas Patricia , Arenas-Herrera María Janet , Moraga-Contreras Claudia , Tapia-Caroca Héctor , Kukulis-Martínez Stavros
Published: December 2022

The natural potential of Chile—solar energy and 8 km of coastline—make the implementation of small-scale reverse osmosis desalination plants (RODPs) in coastal areas energetically supported with photovoltaic systems (PVs) feasible. This work considers a survey of the plants in Chile. As a demonstration of a RODP, a technical/economic evaluation is carried out, analyzing four possible cases in which different energy configurations are proposed: electric grid, diesel generator, and photovoltaic systems, without or with batteries. Finally, the challenges and opportunities of these plants are presented. The results obtained indicate that there are 39 plants in operation, which produce an average permeate water flow of Qp 1715 m3d−1. Solar Explorer, and Homer Pro software are used for a plant that generates 8 m3day−1 of permeate water, resulting in the conclusion that Case 3 is the most economically viable, as it has a useful life of 20 years and will have an annual solar contribution of more than 65%. The levelized cost of water production is 0.56 USDm−3 (RODP/PV) and 0.02 USDkW−1h−1 was obtained for the LCOE. Finally, this case contributes to the mitigation of climate change.
Membrane Electrode Assembly Degradation Modeling of Proton Exchange Membrane Fuel Cells: A Review
Authors: Dafalla Ahmed Mohmed , Wei Lin , Habte Bereket Tsegai , Guo Jian , Jiang Fangming
Published: December 2022

Proton exchange membrane fuel cells (PEMFCs) have been recognized as a promising power generation source for a wide range of automotive, stationary, and portable electronic applications. However, the durability of PEMFCs remains as one of the key barriers to their wide commercialization. The membrane electrode assembly (MEA) as a central part of a PEMFC, which consists of a proton exchange membrane with a catalyst layer (CL) and gas diffusion layer (GDL) on each side, is subject to failure and degradation in long-running and cycling load conditions. The real-time monitoring of the degradation evolution process through experimental techniques is challenging. Therefore, different numerical modeling approaches were proposed in the literature to assist the understanding of the degradation mechanisms in PEMFCs. To provide modeling progress in the addressed field, this paper briefly discusses the different degradation mechanisms occurring in the MEA. In particular, we present a detailed review of MEA degradation modeling research work, with special attention paid to the physical-based models (mechanistic models). Following the most recent relevant literature, the results showed that the combination of microstructure component models with macro-scale comprehensive PEMFC models provides a better understanding of degradation mechanisms when compared to single-scale degradation models. In this sense, it is concluded that in order to develop an accurate and efficient predictive degradation model, the different relevant scales ranging from nano- to macro-sized scales should be considered, and coupling techniques for multiscale modeling have to be advanced. Finally, the paper summarizes the degradation models for different MEA components. It is highlighted that the GDL chemical degradation models that describe damage accumulation are relatively limited. The paper provides a useful reference for the recent developments in the MEA degradation modeling of PEMFCs.
Study on the Accuracy of Fracture Criteria in Predicting Fracture Characteristics of Granite with Different Occurrence Depths
Authors: Liu Chenbo , Feng Gan , Xie Hongqiang , Wang Jilan , Duan Zhipan , Tao Ye , Lu Gongda , Xu Huining , Hu Yaoqing , Li Chun , Hu Yuefei , Wu Qiuhong , Chen Lu
Published: December 2022

The fracture network of a deep geothermal reservoir forms the place for heat exchange between injected fluid and rock mass with high temperature. The fracture resistance ability of reservoir rocks will affect the formation of fracture-network structure, heat exchange and transmission characteristics, and reservoir mechanical stability. However, there are few reports on the fracture toughness and trajectory prediction of geothermal reservoirs with different depths. In this paper, the modified maximum tangential stress criterion (MMTS) is analyzed. The results show that the experimental data are significantly different from the theoretical estimate of MMTS under the influence of different occurrence depths. It is found that the fracture process zone (FPZ) seriously affects the accuracy of predicting fracture initiation angle and mixed-mode (I+II) fracture toughness by MMTS. The FPZ value, considering the influence of different occurrence depths, is modified, and the accuracy of MMTS in predicting the fracture mechanical characteristics of granite is improved. In addition, the mechanical test results show that the Brazilian splitting strength (σt) of granite fluctuates increase with the increase in temperature. With the increase in deviatoric stress, the Brazilian splitting strength and the Brazilian splitting modulus of rock show a trend of first increasing, then decreasing, and then increasing.
Development and Experimental Validation of Novel Thevenin-Based Hysteretic Models for Li-Po Battery Packs Employed in Fixed-Wing UAVs
Authors: Suti Aleksander , Di Rito Gianpietro , Mattei Giuseppe
Published: December 2022

Lithium batteries employed in lightweight fixed-wing UAVs are required to operate with large temperature variations and, especially for the emerging applications in hybrid propulsion systems, with relevant transient loads. The detailed dynamic modelling of battery packs is thus of paramount importance to verify the feasibility of innovative hybrid systems, as well as to support the design of battery management systems for safety/reliability enhancement. This paper deals with the development of a generalised approach for the dynamic modelling of battery packs via Thevenin circuits with modular hysteretic elements (open circuit voltage, internal resistance, RC grids). The model takes into account the parameters’ dependency on the state of charge, temperature, and both the amplitude and sign of the current load. As a relevant case study, the modelling approach is here applied to the Li-Po battery pack (1850 mAh, 6 cells, 22.2 V) employed in the lightweight fixed-wing UAV Rapier X-25 developed by Sky Eye Systems (Cascina, Italy). The procedure for parameter identification with experimental measurements, obtained at different temperatures and current loads, is firstly presented, and then the battery model is verified by simulating an entire Hybrid Pulse Power Characterisation test campaign. Finally, the model is used to evaluate the battery performance within the altitude (i.e., temperature) envelope of the reference UAV. The experiments demonstrate the relevant hysteretic behaviour of the characteristic relaxation times, and this phenomenon is here modelled by inserting Bouc–Wen hysteresis models on RC grid capacitances. The maximum relative error in the terminal output voltage of the battery is smaller than 1% for any value of state of charge greater than 10%.
Numerical Modeling of Shell-and-Tube-like Elastocaloric Regenerator
Authors: Ahčin Žiga , Kabirifar Parham , Porenta Luka , Brojan Miha , Tušek Jaka
Published: December 2022

Elastocaloric cooling is considered an environmentally friendly future alternative to vapor-compression technology. Recently, a shell-and-tube-like elastocaloric regenerator loaded in compression has demonstrated record-breaking heat-pumping performance and fatigue-resistant operation. The aim of this work is thus to present a new 1D numerical model to simulate and optimize the operation of an elastocaloric regenerator with a shell-and-tube-like design. In the first part of this work, the superelastic and elastocaloric properties of a single NiTi tube, which serve as input data for the numerical model, were determined through experimental characterization and phenomenological modeling. In the second part, the results of the numerical model were compared with the experimentally obtained results. Relatively good agreement was found regarding the temperature span, cooling and heating power, and COP values, which indicates that the developed numerical model could be used for accurate optimization of shell-and-tube-like elastocaloric regenerators. Finally, the effects of operating conditions and hysteresis losses on the performance of the shell-and-tube-like elastocaloric regenerator are modeled and discussed. This work shows that the shell-and-tube-like elastocaloric regenerator with this configuration can achieve a maximum temperature span of more than 50 K at zero-thermal-load conditions and a maximum cooling/heating power of up to 4000 W·kg−1 and COP of about 4 (at zero temperature span).
Federated System for Transport Mode Detection
Authors: Cavalcante Iago C. , Meneguette Rodolfo I. , Torres Renato H. , Mano Leandro Y. , Gonçalves Vinícius P. , Ueyama Jó , Pessin Gustavo , Amvame Nze Georges D. , Rocha Filho Geraldo P.
Published: December 2022

Data on transport usage is important in a wide range of areas. These data are often obtained manually through costly and inaccurate interviews. In the last decade, several researchers explored the use of smartphone sensors for the automatic detection of transport modes. However, such works have focused on developing centralized machine learning mechanisms. This centralized approach requires user data to be transferred to a central server and, therefore, does not satisfy a transport mode detection mechanism’s practical response time and privacy needs. This research presents the Federated System for Transport Mode Detection (FedTM). The main contribution of FedTM is exploring Federated Learning on transport mode detection using smartphone sensors. In FedTM, both the training and inference process is moved to the client side (smartphones), reducing response time and increasing privacy. The FedTM was designed using a Neural Network for the classification task and obtained an average accuracy of 80.6% in three transport classes (cars, buses and motorcycles). Other contributions of this work are: (i) The use of data collected only on the curves of the route. Such reduction in data collection is important, given that the system is decentralized and the training and inference phases take place on smartphones with less computational capacity. (ii) FedTM and centralized classifiers are compared with regard to execution time and detection performance. Such a comparison is important for measuring the pros and cons of using Federated Learning in the transport mode detection task.
A Novel Temperature-Independent Model for Estimating the Cooling Energy in Residential Homes for Pre-Cooling and Solar Pre-Cooling
Authors: Heslop Simon , Yildiz Baran , Roberts Mike , Chen Dong , Lau Tim , Naderi Shayan , Bruce Anna , MacGill Iain , Egan Renate
Published: December 2022

Australia’s electricity networks are experiencing low demand during the day due to excessive residential solar export and high demand during the evening on days of extreme temperature due to high air conditioning use. Pre-cooling and solar pre-cooling are demand-side management strategies with the potential to address both these issues. However, there remains a lack of comprehensive studies into the potential of pre-cooling and solar pre-cooling due to a lack of data. In Australia, however, extensive datasets of household energy measurements, including consumption and generation from rooftop solar, obtained through retailer-owned smart meters and household-owned third-party monitoring devices, are now becoming available. However, models presented in the literature which could be used to simulate the cooling energy in residential homes are temperature-based, requiring indoor temperature as an input. Temperature-based models are, therefore, precluded from being able to use these newly available and extensive energy-based datasets, and there is a need for the development of new energy-based simulation tools. To address this gap, a novel data-driven model to estimate the cooling energy in residential homes is proposed. The model is temperature-independent, requiring only energy-based datasets for input. The proposed model was derived by an analysis comparing the internal free-running and air conditioned temperature data and the air conditioning data for template residential homes generated by AccuRate, a building energy simulation tool. The model is comprised of four linear equations, where their respective slope intercepts represent a thermal efficiency metric of a thermal zone in the template residential home. The model can be used to estimate the difference between the internal free-running, and air conditioned temperature, which is equivalent to the cooling energy in the thermal zone. Error testing of the model compared the difference between the estimated and AccuRate air conditioned temperature and gave average CV-RMSE and MAE values of 22% and 0.3 °C, respectively. The significance of the model is that the slope intercepts for a template home can be applied to an actual residential home with equivalent thermal efficiency, and a pre-cooling or solar pre-cooling analysis is undertaken using the model in combination with the home’s energy-based dataset. The model is, therefore, able to utilise the newly available extensive energy-based datasets for comprehensive studies on pre-cooling and solar pre-cooling of residential homes.
Fuel Consumption Dependence on a Share of Reduction Processes in Imperial Smelting Furnace
Authors: Bernasowski Mikolaj , Stachura Ryszard , Klimczyk Arkadiusz
Published: December 2022

The paper shows the use of novel modelling techniques adapted from ironmaking in the pyrometallurgical process of zinc production. Firstly, regarding the purpose to determine the boundary conditions of reduction processes taking part in the working volume of an Imperial Smelting Furnace (ISF), a deep thermochemical analysis was conducted. On this basis and using Ramm’s principles of direct and indirect reduction optimal share, the fuel rate minimization model was built. The model’s leading role is minimizing coke consumption in the ISF while maintaining the thermal state of the furnace at the correct level. In addition, the proposed presentation of the ISF thermal state shows in a unified way all the shortcomings in the correct process operation. Verification in real conditions on the ISF in Miasteczko Śląskie shows that model implementation can bring tangible benefits. Coke savings can reach over 30 kg per tonne of raw zinc.
Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer
Authors: Al-qaness Mohammed A. A. , Ewees Ahmed A. , Abd Elaziz Mohamed Abd , Samak Ahmed H.
Published: December 2022

It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high R2 results of 0.95, 0.96, 0.95, and 0.91 on the four datasets.
Research on Maintenance Strategies for Different Transmission Sections to Improve the Consumption Rate Based on a Renewable Energy Production Simulation
Authors: Hu Xiaojing , Min Haoling , Dai Sai , Cai Zhi , Yang Xiaonan , Ding Qiang , Yang Zhanyong , Xiao Feng
Published: December 2022

Renewable energy consumption is one of the most important factors in meeting the goal of “30 and 60” in China. However, the maintenance of the transmission section affects the amount of generation transfer, further affecting the consumption of renewable energy. Hence, in this study, a time-series renewable energy production simulation (REPS) is proposed in order to accurately predict the power generation in a simulated situation. According to the results of the REPS, the sensitivity of the different sections’ maintenance can be calculated and determined. The appropriate maintenance strategies can be selected for different situations by comparing the consumption rate; as an example, we conducted a case study. The results show that the quota in the transmission section has higher sensitivity; a larger quota indicates a greater sensitivity to the consumption rate. The results also show that a larger quota is more suitable for maintenance in February or November, since the consumption rate is higher regardless of if it is in a single-transmission-section maintenance strategy or in a two-section simultaneous maintenance strategy.
Testing the Indicators of Diesel Vehicles Operating on Diesel Oil and Diesel Biofuel
Authors: Matijošius Jonas , Orynycz Olga , Kovbasenko Sergii , Simonenko Vitalii , Shuba Yevheniy , Moroz Valentyn , Gutarevych Serhiy , Wasiak Andrzej , Tucki Karol
Published: December 2022

The growth in the number of cars and the increasing demand for fuels require scientific work to develop alternative fuels. The energy crisis, which is becoming more and more evident, is not unimportant. The manuscript presents an analysis of the possibility of using agricultural biofuels to power a diesel engine. The analysis was carried out in relation to the operation of the engine on conventional fuels. The D-241 engine under investigation is mainly used for agricultural tractors. During the tests carried out, the load characteristics of the diesel engine under investigation were determined as a function of fuel type and speed. The concentrations of CO, HC and NOx were analysed. Laboratory tests of the engine were carried out with a wide range of external loads to evaluate measurement errors for the measurement method. Experiments with the engine under investigation have shown that the hourly and specific biofuel consumption of diesel engines increases by an average of 11–16%. CO and NOx concentrations were found to be lower with increasing load compared to conventional diesel engines, while NOx concentrations are slightly increased. In all cases investigated, a decrease in exhaust development was observed.
Numerical Study on a Cylinder Vibrator in the Hydrodynamics of a Wind–Wave Combined Power Generation System under Different Mass Ratios
Authors: Sun Hongyuan , Wang Jiazheng , Lin Haihua , He Guanghua , Zhang Zhigang , Gao Bo , Jiao Bo
Published: December 2022

A hydrodynamic wind–wave combined power generation system is a new type of energy device that uses wind and ocean current energy to generate electricity. In this paper, the hydrodynamics of a wind–wave combined power generation system was simulated in Fluent. The fluid–structure coupling simulation of the vortex vibration of the cylindrical oscillator was realized using UDF and dynamic mesh technology. The Vortex-Induced Vibration (VIV) characteristics of the cylindrical oscillator were analyzed, and the reliability of the numerical simulation method was verified by comparing the amplitude and trajectory of the eddy-excited vibration with the classic experiments of Jauvtis and Williamson. The VIV characteristics of cylindrical oscillators with different mass ratios were studied in terms of vibration response, motion trajectory, and the streamwise equilibrium position. The effect of the mass ratio on the hydrodynamics of a wind–wave combined power generation system was simulated using spring damping, achieving the goal of carrying out preliminary research work simulating the wind–wave combined power generation device. Some useful conclusions were obtained through calculation, which provided data support for the corresponding platform device. This study shows that in cylindrical oscillators with different mass ratios, the overall trend at the same reduced velocity is that the larger the mass ratio, the smaller the crossflow amplitude. The cylindrical oscillators with mass ratios of one and two appear in the upper branch, while cylindrical oscillators with mass ratios of three and four do not appear, and with the increase in the mass ratio, the frequency ratio in the lower branch tends toward one. At the same reduced velocity, the lower the mass ratio, the larger the corresponding downstream equilibrium position, and the higher the energy acquisition efficiency.
Prediction of Solid Conversion Process in Direct Reduction Iron Oxide Using Machine Learning
Authors: Hosseinzadeh Masih , Mashhadimoslem Hossein , Maleki Farid , Elkamel Ali
Published: December 2022

The direct reduction process has been developed and investigated in recent years due to less pollution than other methods. In this work, the first direct reduction iron oxide (DRI) modeling has been developed using artificial neural networks (ANN) algorithms such as the multilayer perceptron (MLP) and radial basis function (RBF) models. A DRI operation takes place inside the shaft furnace. A shaft furnace reactor is a gas-solid reactor that transforms iron oxide particles into sponge iron. Because of its low environmental pollution, the MIDREX process, one of the DRI procedures, has received much attention in recent years. The main purpose of the shaft furnace is to achieve the desired percentage of solid conversion output from the furnace. The network parameters were optimized, and an algorithm was developed to achieve an optimum NN model. The results showed that the MLP network has a minimum squared error (MSE) of 8.95 × 10−6, which is the lowest error compared to the RBF network model. The purpose of the study was to identify the shaft furnace solid conversion using machine learning methods without solving nonlinear equations. Another advantage of this research is that the running speed is 3.5 times the speed of mathematical modeling.
Soil Fungal Diversity and Functionality Changes Associated with Multispecies Restoration of Pinus massoniana Plantation in Subtropical China
Authors: Wu Linfang , Zhou Luhong , Zou Bingzhang , Wang Sirong , Zheng Yong , Huang Zhiqun , He Ji-Zheng
Published: December 2022

Soil fungi play a critical role in the carbon and nutrient cycling of forest ecosystems. Identifying the composition of soil fungi in response to the broadleaf restoration of Pinus massoniana plantation is essential for exploring the mechanistic linkages between tree species and ecological processes, but remains unexplored. We compared the shifts in soil fungal diversity and guilds by high–throughput sequencing between two P. massoniana plantations at different stand ages, two modes of restoration with broadleaf trees, and a secondary forest in subtropical China. We found that soil fungal taxonomic and functional compositions significantly differed among forests. The highest Chao 1, Shannon, and phylogenetic diversity indices were consistently observed in the two P. massoniana monocultures, followed by the two modes of broadleaf mixing, and the secondary forests. Fungal communities transitioned from Ascomycota-dominated at P. massoniana plantations to Basidiomycota-dominated at other forests in the topsoil. Furthermore, saprotrophs and symbiotrophs were favoured in plantations and secondary forests, respectively. Soil pH exerted the most significant effect on the relative abundance of Ascomycota and Rozellomycota, as well as the saprotrophs. Moreover, the dominant phyla of Ascomycota, Mucoromycota, and Rozellomycota were negatively related to soil microbial biomass nitrogen, ammonium nitrogen, and total nitrogen contents; however, Mortierellomycota benefited from the elevated soil ammonium nitrogen content. On the other hand, soil nitrate nitrogen and available phosphorus contents strongly and negatively influenced the ectomycorrhizal fungi, while the other fungal guilds were mainly affected by soil pH. Our findings guide an evaluation of the consequences of forest restoration and contribute to an improved understanding of the mechanisms behind soil biogeochemical cycling in subtropical forest ecosystems.