bluuebunny/crossref_metadata_2025 huggingface dataset:
Complex Network Methods for Plastic Deformation Dynamics in Metals
Authors: Kiv Arnold , Bryukhanov Arkady , Soloviev Vladimir , Bielinskyi Andrii , Kavetskyy Taras , Dyachok Dmytro , Donchev Ivan , Lukashin Viktor
Published: January 2023

Plastic deformation of DC04 steel is regarded as a nonlinear, complex, irreversible, and self-organized process. The stress–strain time series analysis provided the possibility to identify areas of (quasi-)elastic deformation, plastic deformation, and necking. The latter two regions are the most informative. The area of inelastic deformation is reflected by collective, self-organized processes that lead to the formation of pores, and finally, the development of microcracks and a general crack as the cause of sample failure. Network measures for the quantitative assessment of the structural deformations in metals are proposed. Both spectral and topological measures of network complexity were found to be especially informative. According to our results, they can be used not only to classify the stages of plastic deformation, but also, they can be applied as a precursor of the material destruction process.
Coarse-Grained Effective Hamiltonian via the Magnus Expansion for a Three-Level System
Authors: Macrì Nicola , Giannelli Luigi , Paladino Elisabetta , Falci Giuseppe
Published: January 2023

Quantum state processing is one of the main tools of quantum technologies. While real systems are complicated and/or may be driven by non-ideal control, they may nevertheless exhibit simple dynamics approximately confined to a low-energy Hilbert subspace. Adiabatic elimination is the simplest approximation scheme allowing us to derive in certain cases an effective Hamiltonian operating in a low-dimensional Hilbert subspace. However, these approximations may present ambiguities and difficulties, hindering a systematic improvement of their accuracy in larger and larger systems. Here, we use the Magnus expansion as a systematic tool to derive ambiguity-free effective Hamiltonians. We show that the validity of the approximations ultimately leverages only on a proper coarse-graining in time of the exact dynamics. We validate the accuracy of the obtained effective Hamiltonians with suitably tailored fidelities of quantum operations.
DNA Code from Cyclic and Skew Cyclic Codes over F4[v]/⟨v3⟩
Authors: Prakash Om , Singh Ashutosh , Verma Ram Krishna , Solé Patrick , Cheng Wei
Published: January 2023

The main motivation of this work is to study and obtain some reversible and DNA codes of length n with better parameters. Here, we first investigate the structure of cyclic and skew cyclic codes over the chain ring R:=F4[v]/⟨v3⟩. We show an association between the codons and the elements of R using a Gray map. Under this Gray map, we study reversible and DNA codes of length n. Finally, several new DNA codes are obtained that have improved parameters than previously known codes. We also determine the Hamming and the Edit distances of these codes.
A Dynamic Programming Algorithm for Finding an Optimal Sequence of Informative Measurements
Authors: Loxley Peter N. , Cheung Ka-Wai
Published: January 2023

An informative measurement is the most efficient way to gain information about an unknown state. We present a first-principles derivation of a general-purpose dynamic programming algorithm that returns an optimal sequence of informative measurements by sequentially maximizing the entropy of possible measurement outcomes. This algorithm can be used by an autonomous agent or robot to decide where best to measure next, planning a path corresponding to an optimal sequence of informative measurements. The algorithm is applicable to states and controls that are either continuous or discrete, and agent dynamics that is either stochastic or deterministic; including Markov decision processes and Gaussian processes. Recent results from the fields of approximate dynamic programming and reinforcement learning, including on-line approximations such as rollout and Monte Carlo tree search, allow the measurement task to be solved in real time. The resulting solutions include non-myopic paths and measurement sequences that can generally outperform, sometimes substantially, commonly used greedy approaches. This is demonstrated for a global search task, where on-line planning for a sequence of local searches is found to reduce the number of measurements in the search by approximately half. A variant of the algorithm is derived for Gaussian processes for active sensing.
A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting
Authors: Varone Giuseppe , Ieracitano Cosimo , Çiftçioğlu Aybike Özyüksel , Hussain Tassadaq , Gogate Mandar , Dashtipour Kia , Al-Tamimi Bassam Naji , Almoamari Hani , Akkurt Iskender , Hussain Amir
Published: January 2023

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials’ shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam γ-ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to 1006 kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete’s γ-ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and R2score were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE.
Educational Inequity and Skill Formation Differences Experienced by Floating Rural Students in the Process of Urbanization: A Case Study from a School Perspective
Authors: Cao Xiaojie , Ji Siduo , Liu Xinqiao
Published: January 2023

In the process of urbanization in China, the migrant worker population entering cities is an important force in building cities. The children of these migrant workers who do not have the qualifications to participate in college entrance examinations in the city generally become floating rural students. The education problem of the children of the migrant worker population entering the city is still insufficiently considered, and the education inequality and skill formation defects faced by floating rural students are worth paying attention to. This study selected P Middle School in Daxing District of Beijing as a case and took “input–process–output” as the thread to investigate and analyze the school’s source of students and enrollment situation, survival strategy and student graduation destination. It tried to present the original ecology of the school’s survival situation from the micro level and further interpret the education inequality and skill formation of floating rural students from the perspective of the school’s survival. Through the case study, we have found that the academic achievement of students in privately run schools for migrant workers’ children is not high. The level of teachers in these schools is low, and teacher turnover is high, resulting in a significant gap in the quality of education compared to public schools. The main source of funding for these schools is donations from members of the community, and government funding is inadequate. Floating rural students in privately run schools for migrant workers’ children have poor graduation destinations, with a low percentage of students going on to key high schools, and some students are forced to become returning children, facing institutional barriers to upward mobility through education. These aspects have led to education inequality and possible defects in the skill formation of floating rural students. We hope to clarify and grasp the actual situation of privately run schools for migrant workers’ children and put forward corresponding policy recommendations to help bridge the educational inequity in China.
Structuring the Post-COVID-19 Process of Digital Transformation of Engineering Education in the Russian Federation
Authors: Shestakova Irina , Morgunov Vladimir
Published: January 2023

The temporality of the socio-technological transformations of industry 4.0 has exacerbated the problem of introducing and using digital technologies in the educational process. The development in this area is due to the presence of unique global challenges, the availability of digital devices, and the experience of distance learning due to the COVID-19 pandemic. As part of the work, a statistical study was conducted aimed at establishing the average level of proficiency in the basic skills of independent search for information on the Internet. A classification of digital solutions is formulated, and an attempt is made to synthesize criteria for the successful integration of digital solutions into the educational process, the following groups are distinguished: organizational, auxiliary, structural, and specialized. Independent searching for information is considered an element of the educational process, which changes almost uncontrollably, unlike other solutions in the field of digitalization of education. The process of digitalization is presented as a set of four elements, the successful implementation of which will allow the effective implementation of digital technologies in the educational process. An element of digital infrastructure is highlighted; an element of organization, an element of integrating teachers into a new digital educational environment, and a student element. It is concluded that it is necessary to introduce courses of additional competencies dedicated to working with information; the idea of integrating master’s students into research aimed at solving modern global challenges with the help of digital technologies is proposed.
SmaEPho–Smart Photometry in Education 4.0
Authors: Geuer Lena , Lauer Frederik , Kuhn Jochen , Wehn Norbert , Ulber Roland
Published: January 2023

Digitization offers new perspectives for educational research to identify the effects of visualizations regarding cognitive processing. In addition, new types of data can be generated, expanding the possibilities for visualizing cognitive processes and understanding human learning. Digital twins are already used in Industry 4.0, as an additional visualization to a real object, for data mining and data analysis for process optimization. The increasing integration of digital twins in the industrial sector requires the formulation of corresponding educational goals to ensure high-quality and future-oriented education. Therefore, future generations must be introduced to technologies from industry during their education. In this paper, an intelligent photometric measurement system called SmaEPho with a digital twin for science, technology, engineering, and mathematics (STEM) learning is presented. In addition to its function as a photometric measurement device, an intelligent sensor technology allows for data generation on the user’s usage behavior. The digital twin reflects and visualizes these data in real-time. This enables a variety of new didactic and methodological approaches in teaching. A first study evaluating the hardware and tracking components of SmaEPho shows that the deviation accuracy of the measurement system is sufficient for experimental applications in schools. Another study with n=52 students confirmed the excellent usability of the SmaEPho hardware platform. These research results lay the foundation for a variety of future research questions on data analysis and machine learning algorithms with the aim of increasing the quality of education. The use of intelligent digital twins as an element of digitization in educational contexts offers the extended possibility of identifying cognitive processing steps using this technology.
Educational Psychology Aspects of Learning with Chatbots without Artificial Intelligence: Suggestions for Designers
Authors: Černý Michal
Published: January 2023

Chatbots without artificial intelligence can play the role of practical and easy-to-implement learning objects in e-learning environments, allowing a reduction in social or psychological isolation. This research, with a sample of 79 students, explores the principles that need to be followed in designing this kind of chatbot in education in order to ensure an acceptable outcome for students. Research has shown that students interacting with a chatbot without artificial intelligence expect similar psychological and communicative responses to those of a live human, project the characteristics of the chatbot from the dialogue, and are taken aback when the chatbot does not understand or cannot help them sufficiently. The study is based on a design through research approach, in which students in information studies and library science interacted with a specific chatbot focused on information retrieval, and recorded their experiences and feelings in an online questionnaire. The study intends to find principles for the design of chatbots without artificial intelligence so that students feel comfortable interacting with them.
Relationships between Self-Efficacy, Job Instability, Decent Work, and Life Satisfaction in A Sample of Italian, Swiss, and Spanish Students
Authors: Zammitti Andrea , Moreno-Morilla Celia , Romero-Rodríguez Soledad , Magnano Paola , Marcionetti Jenny
Published: January 2023

Recent research has shown that self-efficacy has a positive relationship with life satisfaction and with the perception of access to decent work. On the other hand, a perception of instability regarding the profession is negatively correlated with these dimensions. Few authors have studied these constructs within the same research. Therefore, the aim of the study was to fill this gap in the literature by testing a structural equation model in which the perception of access to decent work could mediate between perceived self-efficacy in one’s training and life satisfaction, and between perceived instability of the profession and life satisfaction. Data was collected through an online research survey. Five hundred and seventeen university students (104 males and 413 females) aged between 18 and 30 years (M = 22.50; ds = 2.61) from three different countries participated: 181 were Italian, 173 were Swiss, and 163 were Spanish. The results only partially confirmed our model. The idea of finding a decent work mediates the relationship between perceived job instability and life satisfaction, but not between self-efficacy and life satisfaction. Perceived self-efficacy together with the idea of finding a decent work have a direct effect on life satisfaction. In career development, counselors must take into account what the perception of job instability entails for students, which may be demotivating and not allow future workers to imagine a decent job.
Environment-Friendly Ascorbic Acid Fuel Cell
Authors: Hasan Md. Mahmudul
Published: January 2023

Recently, ascorbic acid (AA) has been studied as an environment-friendly fuel for energy conversion devices. This review article has deliberated an overview of ascorbic acid electrooxidation and diverse ion exchange types of AA-based fuel cells for the first time. Metal and carbon-based catalysts generated remarkable energy from environment-friendly AA fuel. The possibility of using AA in a direct liquid fuel cell (DLFC) without emitting any hazardous pollutants is discussed. AA fuel cells have been reviewed based on carbon nanomaterials, alloys/bimetallic nanoparticles, and precious and nonprecious metal nanoparticles. Finally, the obstacles and opportunities for using AA-based fuel cells in practical applications have also been incorporated.
Time Jitter Analysis of an Optical Signal Based on Gated On-Off Optical Sampling and Dual-Dirac Modeling
Authors: Huang Tao , Fan Zhiqiang , Su Jun , Qiu Qi
Published: January 2023

A time jitter analysis method for an optical signal based on gated on-off optical sampling and dual-Dirac modeling is proposed and demonstrated experimentally. The optical signal under test is firstly sampled by an optical sampling pulse train generated through the gating on-off modulation of a Mach–Zehnder modulator (MZM). The sampled pulse is then broadened using optical true-time delay and electrical low-pass filtering to reduce its bandwidth to match the sample rate of a low-speed electrical analog-to-digital converter (ADC), which is used to quantify the sampled pulse. An eye diagram is obtained from the quantified data and used to plot a time jitter histogram. Finally, the dual-Dirac model is introduced to analyze the time jitter histogram to obtain the total jitter (TJ), including the deterministic jitter (DJ) and random jitter (RJ). In the experiment, a 19.05 ps TJ, including a 13.20 ps DJ and a 5.85 ps RJ, is measured for a 2.5 GHz optical signal using the proposed time jitter analysis method. The results agree well with those measured with a commercial real-time oscilloscope.
Age-Associated Changes on Gait Smoothness in the Third and the Fourth Age
Authors: Pau Massimiliano , Bernardelli Giuseppina , Leban Bruno , Porta Micaela , Putzu Valeria , Viale Daniela , Asoni Gesuina , Riccio Daniela , Cerfoglio Serena , Galli Manuela , Cimolin Veronica
Published: January 2023

Although gait disorders represent a highly prevalent condition in older adults, the alterations associated with physiologic aging are often not easily differentiable from those originated by concurrent neurologic or orthopedic conditions. Thus, the detailed quantitative assessment of gait patterns represents a crucial issue. In this context, the study of trunk accelerations may represent an effective proxy of locomotion skills in terms of symmetry. This can be carried out by calculating the Harmonic Ratio (HR), a parameter obtained through the processing of trunk accelerations in the frequency domain. In this study, trunk accelerations during level walking of 449 healthy older adults (of age > 65) who were stratified into three groups (Group 1: 65–74 years, n = 175; Group 2: 75–85 years, n = 227; Group 3: >85 years, n = 47) were acquired by means of a miniaturized Inertial Measurement Unit located in the low back and processed to obtain spatio-temporal parameters of gait and HR, in antero-posterior (AP), medio-lateral (ML) and vertical (V) directions. The results show that Group 3 exhibited a 16% reduction in gait speed and a 10% reduction in stride length when compared with Group 1 (p < 0.001 in both cases). Regarding the cadence, Group 3 was characterized by a 5% reduction with respect to Groups 1 and 2 (p < 0.001 in both cases). The analysis of HR revealed a general trend of linear decrease with age in the three groups. In particular, Group 3 was characterized by HR values significantly lower (−17%) than those of Group 1 in all three directions and significantly lower than Group 2 in ML and V directions (−10%). Taken together, such results suggest that HR may represent a valid measure to quantitatively characterize the progressive deterioration of locomotor abilities associated with aging, which seems to occur until the late stages of life.
E-Government Development—A Key Factor in Government Administration Effectiveness in the European Union
Authors: Doran Nicoleta Mihaela , Puiu Silvia , Bădîrcea Roxana Maria , Pirtea Marilen Gabriel , Doran Marius Dalian , Ciobanu George , Mihit Lavinia Daniela
Published: January 2023

The phenomenon of digitisation of the public sector is an irreversible process that affects both the way public institutions are organised and the communication relationships between people and institutions. The COVID-19 pandemic represented a challenge and a strong impetus in accelerating the digitisation process of public administration at the global level such that it is currently difficult to make a clear distinction between governance and e-governance. The purpose of this research is to investigate the impact of the intensification of the digitisation process of public services in increasing the efficiency of governments at the level of the member states of the European Union, based on a cluster analysis. A robust least squares regression method was used to estimate the effects of the three dimensions of the e-government development index (EGDI) on government effectiveness. The results of the analysis highlighted the fact that the skills of the population in using online services determine the increase in a double percentage of government efficiency in intensively digitised states compared to states where the digitisation of public services is less developed. The development of the telecommunications infrastructure also has a significant positive impact on the efficiency of the government. However, online services offered by public authorities have proven to negatively influence government efficiency in both clusters.
A Meta-Model to Predict and Detect Malicious Activities in 6G-Structured Wireless Communication Networks
Authors: Oleiwi Haider W. , Mhawi Doaa N. , Al-Raweshidy Hamed
Published: January 2023

The rapid leap in wireless communication systems incorporated a plethora of new features and challenges that accompany the era of 6G and beyond being investigated and developed. Recently, machine learning techniques were widely deployed in many fields, especially wireless communications. It was used to improve network traffic performance regarding resource management, frequency spectrum optimization, latency, and security. The studies of modern wireless communications and anticipated features of ultra-densified ubiquitous wireless networks exposed a risky vulnerability and showed a necessity for developing a trustworthy intrusion detection system (IDS) with certain efficiency/standards that have not yet been achieved by current systems. IDSs lack acceptable immunity against repetitive, updatable, and intelligent attacks on wireless communication networks, significantly concerning the modern infrastructure of 6G communications, resulting in low accuracies/detection rates and high false-alarm/false-negative rates. For this objective principle, IDS system complexity was reduced by applying a unique meta-machine learning model for anomaly detection networks was developed in this paper. The five main stages of the proposed meta-model are as follows: the accumulated datasets (NSL KDD, UNSW NB15, CIC IDS17, and SCE CIC IDS18) comprise the initial stage. The second stage is preprocessing and feature selection, where preprocessing involves replacing missing values and eliminating duplicate values, leading to dimensionality minimization. The best-affected subset feature from datasets is selected using feature selection (i.e., Chi-Square). The third step is represented by the meta-model. In the training dataset, many classifiers are utilized (i.e., random forest, AdaBoosting, GradientBoost, XGBoost, CATBoost, and LightGBM). All the classifiers undergo the meta-model classifier (i.e., decision tree as the voting technique classifier) to select the best-predicted result. Finally, the classification and evaluation stage involves the experimental results of testing the meta-model on different datasets using binary-class and multi-class forms for classification. The results proved the proposed work’s high efficiency and outperformance compared to existing IDSs.
Technical Study of Deep Learning in Cloud Computing for Accurate Workload Prediction
Authors: Ahamed Zaakki , Khemakhem Maher , Eassa Fathy , Alsolami Fawaz , Al-Ghamdi Abdullah S. Al-Malaise
Published: January 2023

Proactive resource management in Cloud Services not only maximizes cost effectiveness but also enables issues such as Service Level Agreement (SLA) violations and the provisioning of resources to be overcome. Workload prediction using Deep Learning (DL) is a popular method of inferring complicated multidimensional data of cloud environments to meet this requirement. The overall quality of the model depends on the quality of the data as much as the architecture. Therefore, the data sourced to train the model must be of good quality. However, existing works in this domain have either used a singular data source or have not taken into account the importance of uniformity for unbiased and accurate analysis. This results in the efficacy of DL models suffering. In this paper, we provide a technical analysis of using DL models such as Recurrent Neural Networks (RNN), Multilayer Perception (MLP), Long Short-Term Memory (LSTM), and, Convolutional Neural Networks (CNN) to exploit the time series characteristics of real-world workloads from the Parallel Workloads Archive of the Standard Workload Format (SWF) with the aim of conducting an unbiased analysis. The robustness of these models is evaluated using the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error metrics. The findings of these highlight that the LSTM model exhibits the best performance compared to the other models. Additionally, to the best of our knowledge, insights of DL in workload prediction of cloud computing environments is insufficient in the literature. To address these challenges, we provide a comprehensive background on resource management and load prediction using DL. Then, we break down the models, error metrics, and data sources across different bodies of work.
A Novel Fusion Approach Consisting of GAN and State-of-Charge Estimator for Synthetic Battery Operation Data Generation
Authors: Wong Kei Long , Chou Ka Seng , Tse Rita , Tang Su-Kit , Pau Giovanni
Published: January 2023

The recent success of machine learning has accelerated the development of data-driven lithium-ion battery state estimation and prediction. The lack of accessible battery operation data is one of the primary concerns with the data-driven approach. However, research on battery operation data augmentation is rare. When coping with data sparsity, one popular approach is to augment the dataset by producing synthetic data. In this paper, we propose a novel fusion method for synthetic battery operation data generation. It combines a generative, adversarial, network-based generation module and a state-of-charge estimator. The generation module generates battery operation features, namely the voltage, current, and temperature. The features are then fed into the state-of-charge estimator, which calculates the relevant state of charge. The results of the evaluation reveal that our method can produce synthetic data with distributions similar to the actual dataset and performs well in downstream tasks.
A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit
Authors: Lu Wanjie , Shi Chun , Fu Hua , Xu Yaosong
Published: January 2023

The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and the BiGRU parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on landmark isometric mapping (L-Isomap) and Improved Sand Cat Swarm Optimization (ISCSO) to optimize the BiGRU (ISCSO-BiGRU). Firstly, L-Isomap is used to extract features from DGA feature quantities. In addition, ISCSO is further proposed to optimize the BiGRU parameters to build an optimal diagnosis model based on BiGRU. For the ISCSO, four improvement methods are proposed. The traditional sand cat swarm algorithm is improved using logistic chaotic mapping, the water wave dynamic factor, adaptive weighting, and the golden sine strategy. Then, benchmarking functions are used to test the optimization performance of ISCSO and the four algorithms, and the results show that ISCSO has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on L-Isomap and ISCSO-BiGRU is obtained. Using the model for fault diagnosis, the example simulation results show that using L-ISOMP to filter and downscale the model inputs can better improve model performance. The results are compared with the SCSO-BiGRU, WOA-BiGRU, GWO-BiGRU, and PSO-BiGRU fault diagnosis models. The results show that the fault diagnosis rate of ISCSO-BiGRU is 94.8%, which is 11.69%, 10.39%, 7.14%, and 5.9% higher than that of PSO-BiGRU, GWO-BiGRU, WOA-BiGRU, and SCSO-BiGRU, respectively, and validate that the proposed method can effectively improve the fault diagnosis performance of transformers.
Human Pose Estimation via Dynamic Information Transfer
Authors: Li Yihang , Shi Qingxuan , Song Jingya , Yang Fang
Published: January 2023

This paper presents a multi-task learning framework, called the dynamic information transfer network (DITN). We mainly focused on improving the pose estimation with the spatial relationship of the adjacent joints. To benefit from the explicit structural knowledge, we constructed two branches with a shared backbone to localize the human joints and bones, respectively. Since related tasks share a high-level representation, we leveraged the bone information to refine the joint localization via dynamic information transfer. In detail, we extracted the dynamic parameters from the bone branch and used them to make the network learn constraint relationships via dynamic convolution. Moreover, attention blocks were added after the information transfer to balance the information across different granularity levels and induce the network to focus on the informative regions. The experimental results demonstrated the effectiveness of the DITN, which achieved 90.8% PCKh@0.5 on MPII and 75.0% AP on COCO. The qualitative results on the MPII and COCO datasets showed that the DITN achieved better performance, especially on heavily occluded or easily confusable joint localization.
Energy Modeling and Techno-Economic Feasibility Analysis of Greenhouses for Tomato Cultivation Utilizing the Waste Heat of Cryptocurrency Miners
Authors: Asgari Nima , McDonald Matthew T. , Pearce Joshua M.
Published: January 2023

Greenhouses extend growing seasons in upper latitudes to provide fresh, healthy food. Costs associated with carbon-emission-intensive natural gas heating, however, limit greenhouse applications and scaling. One approach to reducing greenhouse heating costs is electrification by using waste heat from cryptocurrency miners. To probe this potential, a new quasi-steady state thermal model is developed to simulate the thermal interaction between a greenhouse and the environment, thereby estimating the heating and cooling demands of the greenhouse. A cryptocurrency mining system was experimentally evaluated for heating potential. Using these experimental values, the new thermal model was applied to the waste heat of the three cryptocurrency mining systems (1, 50, and 408 miners) for optimally sized greenhouses in six locations in Canada and the U.S.: Alberta, Ontario, Quebec, California, Texas, and New York. A comprehensive parametric study was then used to analyze the effect of various parameters (air exchange rate, planting area, lighting allowance factor, and photoperiod) on the thermal demands and optimal sizing of greenhouses. Using waste heat from cryptocurrency mining was found to be economically profitable to offset natural gas heating depending on the utility rates and Bitcoin value in a wide range of scenarios.
Analysis of the Situation of Renewable and Non-Renewable Energy Consumption in the European Union
Authors: Anghelache Constantin , Anghel Mădălina Gabriela , Iacob Ștefan Virgil , Pârțachi Ion , Rădulescu Irina Gabriela , Brezoi Alina Gabriela
Published: January 2023

In this article, the authors present the results of research undertaken in relation to the situation regarding renewable and non-renewable energy reserves in the European Union, as well as the way in which this energy is consumed. The general view presented regarding energy resources around the world is that oil, natural gas and coal are being exhausted at an alarming rate and if we continue to exploit these oil resources at our current pace, we will see a massive depletion in energy resources over the next 41 years. The authors also focus on representing the intensity of greenhouse gas emissions from energy consumption, demonstrating that it has shown a slight decrease in the European Union. The resources and consumption of renewable and non-renewable energy were analyzed in close interdependence with these indicators under study, such as final energy consumption, renewable energy and total energy production, in order to give a correct interpretation of how these resources are used. At the same time, starting from the fact that the world economy is currently facing a cluster of crises (pandemic, financial-economic, energy, general resources), it was deemed important to highlight the fact that the total production of energy demonstrated an oscillating trend during this period.
Transformers-Based Encoder Model for Forecasting Hourly Power Output of Transparent Photovoltaic Module Systems
Authors: Sherozbek Jumaboev , Park Jaewoo , Akhtar Mohammad Shaheer , Yang O-Bong
Published: January 2023

Solar power generation is usually affected by different meteorological factors, such as solar radiation, cloud cover, rainfall, and temperature. This variability has shown a negative impact on the large-scale integration of solar energy into energy supply systems. For successful integration of solar energy into the electrical grid, it is necessary to predict the accurate power generation by solar panels. In this work, solar power generation forecasting for two types of solar system (non-transparent and transparent panels) was configured by the smart artificial intelligence (AI) modelling. For deep learning models, the dataset obtained from the target value of electricity generation in kWh and other features, such as weather conditions, solar radiance, and insolation. In PV power generation values from non-transparent and transparent solar panels were collected from 1 January to 31 December 2021 with an hourly interval. To prove the efficiency of the proposed model, several deep learning approaches RNN models, such as LSTM, GRU, and transformers models, were implemented. Transformers model for forecasting power generation expressed the best model for non-transparent and transparent solar panels with lower error rates for MAE 0.05 and 0.04, and RMSE 0.24 and 0.21, respectively. The proposed model showed efficient performance and proved effective in forecasting time-series data.
Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm
Authors: Yang Fanjie , Zeng Yun , Qian Jing , Li Youtao , Xie Shihao
Published: January 2023

Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter identification method depending on the adaptive grey wolf algorithm with an information-sharing search strategy (ISIAGWO) is proposed to solve the problem of low accuracy and slow identification speed of multiple parameters in DFIG. The easily obtainable generator outlet current was selected as the observed quantity, and the trajectory sensitivity analysis was performed on the five electrical parameters of the DFIG to derive its discriminability. Finally, the parameter recognition of the DFIG was carried out using the ISIAGWO algorithm in the MATLAB/Simulink simulation software, applying the proposed calculation functions. The experimental results show that the ISIAGWO algorithm has better identification accuracy, stability, and convergence for DFIG’s generator parameter identification.
Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm
Authors: Alsharif Abdulgader , Tan Chee Wei , Ayop Razman , Al Smin Ahmed , Ali Ahmed Abdussalam , Kuwil Farag Hamed , Khaleel Mohamed Mohamed
Published: January 2023

The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.
Optimal Scheduling of Integrated Community Energy Systems Based on Twin Data Considering Equipment Efficiency Correction Models
Authors: Ye Zeli , Huang Wentao , Huang Jinfeng , He Jun , Li Chengxi , Feng Yan
Published: January 2023

The economics of integrated community energy system (ICES) dispatch schemes are influenced by the accuracy of the parameters of the different energy-conversion-equipment models. Traditional equipment efficiency correction models only take into account the historical load factors and variations in the environmental factors, ignoring the fact that the input data do not come from the actual operating data of the equipment, which affects the accuracy of the equipment models and therefore reduces the economics of ICES dispatch solutions. Therefore, this paper proposes an optimal scheduling of a community-integrated energy system based on twin data, considering a device-correction model that combines an energy hub model and a twin data correction model. Firstly, a dynamic energy hub (DEH) model with a correctable conversion efficiency is developed based on the twin data; secondly, a physical model of the system and a digital twin are established, with the prediction data as the input of the digital twin and the twin data as the output. Polynomial regression (PR) and a back propagation neural network (BPNNS) are used to process the twin data to accurately extract the equipment conversion efficiency. Considering the lack of accuracy of traditional prediction methods, a prediction model combining a long- and short-term-memory neural network and digital-twin technology is constructed for renewable energy generation and load prediction. The simulation results show that using twin data to correct the equipment efficiency reduces the average absolute error and average relative error by 4.6706 and 1.18%, respectively, when compared with the use of historical data. Compared with the actual total cost of the dispatch, the total cost of the dispatch after the equipment efficiency correction was reduced by USD 850.19.
The Role of Biogas Potential in Building the Energy Independence of the Three Seas Initiative Countries
Authors: Ślusarz Grzegorz , Twaróg Dariusz , Gołębiewska Barbara , Cierpiał-Wolan Marek , Gołębiewski Jarosław , Plutecki Philipp
Published: January 2023

Increasing biogas production in the Three Seas Initiative countries (3SI) is a good way to reduce greenhouse gas emissions and to increase energy self-sufficiency by replacing some of the fossil energy sources. An assessment of the biogas production potential carried out for the 3SI at the NUTS 1 and NUTS 2 level shows that the potential of this energy carrier was stable for the period (from 2010–2021). The results showed that it can cover from approximately 10% (Hungary, Slovakia) to more than 34% (Estonia, Slovenia) of natural gas consumption; moreover, there is strong variation in the value of potential at the regional level (NUTS 2) in most of the countries studied. The biogas production forecast was carried out with the ARIMA model using four regressors, which are GDP, biogas potential utilisation, natural gas consumption and investments in RES (renewable energy sources) infrastructure, including changes in the EU energy policy after 24 February 2022. In the most promising scenario (four regressors), the results obtained for the period from 2022–2030 predict a rapid increase in biogas production in the 3SI countries, from 32.4 ± 11.3% for the Czech Republic to 138.7 ± 27.5% for Estonia (relative to 2021). However, in the case of six countries (Bulgaria, Lithuania, Hungary, Austria, Poland and Romania) the utilisation of 50% of the potential will most likely occur in the fifth decade of the 21st century. The above results differ significantly for those obtained for three regressors, where the highest rise is predicted for Bulgaria at 33.5 ± 16.1% and the lowest for Slovenia, at only 2.8 ± 14.4% (relative to 2021).
Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan
Authors: Ho Cheng-Yu , Cheng Ke-Sheng , Ang Chi-Hang
Published: January 2023

The Taiwan Strait contains a vast potential for wind energy. However, the power grid balance is challenging due to wind energy’s uncertainty and intermittent nature. Wind speed forecasting reduces this risk, increasing the penetration rate. Machine learning (ML) models are adopted in this study for the short-term prediction of wind speed based on the complex nonlinear relationships among wind speed, terrain, air pressure, air temperature, and other weather conditions. Feature selection is crucial for ML modeling. Finding more valuable features in observations is the key to improving the accuracy of prediction models. The random forest method was selected because of its stability, interpretability, low computational cost, and immunity to noise, which helps maintain focus on investigating the essential features from vast data. In this study, several new exogenous features were found on the basis of physics and the spatiotemporal correlation of surrounding data. Apart from the conventional input features used for wind speed prediction, such as wind speed, wind direction, air pressure, and air temperature, new features were identified through the feature importance of the random forest method, including wave height, air pressure difference, air-sea temperature difference, and hours and months, representing the periodic components of time series analysis. The air–sea temperature difference is proposed to replace the wind speed difference to represent atmosphere stability due to the availability and adequate accuracy of the data. A random forest and an artificial neural network model were created to investigate the effectiveness and generality of these new features. Both models are superior to persistence models and models using only conventional features. The random forest model outperformed all models. We believe that time-consuming and tune-required sophisticated models may also benefit from these new features.
Insights into the Application of Machine Learning in Reservoir Engineering: Current Developments and Future Trends
Authors: Wang Hai , Chen Shengnan
Published: January 2023

In the past few decades, the machine learning (or data-driven) approach has been broadly adopted as an alternative to scientific discovery, resulting in many opportunities and challenges. In the oil and gas sector, subsurface reservoirs are heterogeneous porous media involving a large number of complex phenomena, making their characterization and dynamic prediction a real challenge. This study provides a comprehensive overview of recent research that has employed machine learning in three key areas: reservoir characterization, production forecasting, and well test interpretation. The results show that machine learning can automate and accelerate many reservoirs engineering tasks with acceptable level of accuracy, resulting in more efficient and cost-effective decisions. Although machine learning presents promising results at this stage, there are still several crucial challenges that need to be addressed, such as data quality and data scarcity, the lack of physics nature of machine learning algorithms, and joint modelling of multiple data sources/formats. The significance of this research is that it demonstrates the potential of machine learning to revolutionize the oil and gas sector by providing more accurate and efficient solutions for challenging problems.
Tautomerism of β-Diketones and β-Thioxoketones
Authors: Hansen Poul Erik
Published: January 2023

The present overview concentrates on recent developments of tautomerism of β-diketones and β-thioxoketones, both in solution and in the solid state. In particular, the latter has been a matter of debate and unresolved problems. Measurements of 13C, 17O, and 2H chemical shifts have been used. Deuterium isotope effects on chemical shifts are proposed as a tool in the study of this problem. Photoconversion of β-diketones and β-thioxoketones are discussed in detail, and the incorporation of β-diketones into molecules with fluorescent properties is assessed. Finally, docking studies of β-diketones are scrutinized with an emphasis on correct tautomeric structures and knowledge about barriers to interconversion of tautomers.