bluuebunny/crossref_metadata_2025 huggingface dataset:
Impacts of Marketing Strategy and Social Media Activity on the Profitability of Online Wine Shops: The Case of Hungary
Authors: Balogh Jeremiás , Mizik Tamás
Published: November 2022

Today, the wine business can benefit significantly from the advantages of online sales and the use of social media. However, while the online wine business is growing and the number of online buyers is increasing in Hungary, the impact of online marketing and social media use on their profitability is understudied. The research aims to apply qualitative data collection techniques such as web-content analysis to capture the elements of online marketing as an engine of stimulating profitability. For this reason, the top 12 Hungarian online wine shops were analyzed. The results revealed that the Hungarian online wine business is highly concentrated, the five main players (Vinotrade, Törley, Grape-Vine, Borháló, and Bortársaság) attract most buyers and account for the highest profit rate. Using online marketing channels and social media, wine shops can positively influence their profitability. In addition to the classic online marketing tools of the Internet (blogs, newsletters), social networks (Facebook, Instagram, Twitter, and Pinterest) of wine shops become highly relevant to boost wine sales. However, offline platforms (wine tastings, dinners, picnics) are still used by Hungarian online wine business. Online shops with strong retail and wholesale connections were better off, and the retail pillar becomes more important.
Physical Sciences Teachers’ Enactment of Simulations in 5E Inquiry-Based Science Teaching
Authors: Makamu Gloria , Ramnarain Umesh
Published: November 2022

The aim of this study was to investigate the pedagogical actions of Physical Sciences teachers when enacting simulations in 5E inquiry-based science teaching for current electricity. Three grade 10 teachers from three high schools who were teaching at schools where ICT resources are available participated in this study. Data was collected by means of lesson observations and interviews. The lesson observation and interview transcripts were coded to generate themes. The results of this study showed that the simulations enable teachers to afford learners with opportunities to engage in hands-on inquiry based on the 5E model. The hands-on activities that students engage in on the simulation help them to explain phenomena from evidence and also allow them to acquire autonomy from the teacher. When students are hands-on, they get the chance to test their hypothesis and also to develop their understanding of the phenomenon that is investigated. Through the use of the simulated activity, teachers were able to support leaners to reflect on activities to reconcile their new knowledge with previous ideas. While it is acknowledged that PhET simulated activity is not a substitute for hands-on practical work in a laboratory, the findings show it can be a powerful tool for supporting inquiry learning.
Barriers to Digital Higher Education Teaching and How to Overcome Them—Lessons Learned during the COVID-19 Pandemic
Authors: Draxler-Weber Nicole , Packmohr Sven , Brink Henning
Published: November 2022

The COVID-19 pandemic forced a transition to digital teaching in higher education institutions (HEIs) as itwas the only safe method for higher education (HE) teaching during the pandemic. However, this crisis emphasized the barriers students face worldwide. For digital HE teaching to survive in the future, these barriers should be overcome. The present paper aimed to systematically identify these barriers and present recommendations to overcome them. For this purpose, a quantitative survey (n = 369) was conducted with students in three countries, and qualitative student statements were analyzed. Possible countermeasures for corresponding barriers are described, and related stakeholders are identified. Thus, the study provided an overview of recommendations for stakeholders to overcome the barriers. The recommendations to resolve most barriers entail offering hybrid formats, adjusting lecture design, and ensuring proper communication.
An Innovative Biomedical Research Training Model: Rationale, Design, and Evaluation
Authors: Sheikhattari Payam , Mehravaran Shiva , Apata Jummai , Silver Gillian , Murphy Shamara , Hurtado Sylvia , Kamangar Farin
Published: November 2022

Much is told regarding the need for greater diversity in the biomedical research workforce in terms of race, ethnicity, and socioeconomic status. However, there are few evidence-based models that are tested and can have significant effects in this regard. Thus, there is a need for development and evaluation of innovative models that may help train a more diverse biomedical research workforce. In this study, we provided the rationale, conceptual model, and preliminary evaluation of a program called “A Student-Centered Entrepreneurship Development (ASCEND)”. This training program was designed, implemented, and evaluated between 2017 and 2020 at Morgan State University, Baltimore, Maryland, United States. The program’s conceptual model is based on four stages: Attraction and Inspiration, Ideation and Innovation, Research Implementation, and Career Growth. Results of the comparative survey between 50 students who participated in ASCEND and 86 non-member controls showed an increase in science identity, academic self-concept, science self-efficacy, and peer support. The only domain that did not show a larger increase in participants in our program compared to controls was social self-concept. In addition, a total of 59 students submitted 48 research concepts, and 16 undergraduate student projects were funded. Of participants in the Health Research Concepts Competition, 39 students graduated, and 13 were pursuing graduate programs in STEM fields at the time of evaluation. The number of research projects and trainees who started a graduate degree were also reported. The ASCEND training model fosters an entrepreneurial mindset among undergraduate students. Such a program might be effective in diversifying the biomedical research workforce. While this preliminary evaluation indicates the efficacy of the ASCEND model, there is a need for further long-term and multi-center evaluations with the trainees’ research productivity and receipt of independent funding as outcomes.
Time–Frequency Analysis of Experimental Measurements for the Determination of EMI Noise Generators in Power Converters
Authors: Oyarzun Javier , Aizpuru Iosu , Baraia-Etxaburu Igor
Published: November 2022

In the context of recent decades in which there has been great development in power electronics systems with increasingly better operating characteristics, the study of the factors that affect the behaviour of these systems in terms of electromagnetic interference (EMI) is mandatory. Within this general perspective, it is essential to know the time and frequency characteristics of the switching signals, as they are the main sources of EMI noise. This work analyses the suitability of different spectral analysis techniques, specifically short-time Fourier transform (STFT) and continuous wavelet transform (CWT), to extract the frequency characteristics of the switching signals of a converter. Thus, a test bench based on a half-bridge was designed as a preliminary step in the development of a model of noise generators. After the analysis of the main parameters of these techniques, a comparison of the results obtained was carried out. It was concluded that both techniques are considered valid and complementary; not only that, but they overcome some limitations of the fast Fourier transform (FFT) as they offer the possibility of determining which are the frequency components associated with different types of events occurring at different times.
Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach
Authors: Starace Giuseppe , Tiwari Amber , Colangelo Gianpiero , Massaro Alessandro
Published: November 2022

This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and open source platforms are implemented for cloud data transmission, data storage and data processing. Advanced data analytics is performed by the seasonal autoregressive integrated moving average (SARIMA) method and a long short-term memory (LSTM) neural network with an accurate calculation performance about energy predictions. The proposed results are developed within the framework of the R&D project Data System Platform for Smart Communities (D-SySCOM), which is oriented to a smart public building application. The main goal of the work was to define a guideline-matching energy efficiency with wellness in public indoor environments, by providing modular low-cost solutions which are easily implementable for advanced data processing. The implemented technologies are suitable to define an efficient organizational user protocol based on energy efficiency and worker wellness. The estimated performance of mean square error (MSE) of 0.01 of the adopted algorithms proves the efficiency of the implemented building monitoring system in terms of energy consumption forecasting. In addition, the possibility of designing and implementing a modular low-cost hardware–software system was demonstrated utilizing open source tools in a way that was oriented to smart buildings approaches.
Numerical Modelling of Dynamic Electromagnetic Problems Based on the Time-Domain Finite Integration Technique
Authors: Lou Zhuochen , Wu Xiongwei , Hou Junming , Zhang Jianan , You Jianwei , Cui Tiejun
Published: November 2022

Developing numerical methods to solve dynamic electromagnetic problems has broad application prospects. In computational electromagnetics, traditional numerical methods are commonly used to deal with static electromagnetic problems. However, they can hardly be applied in the modeling of time-varying materials and moving objects. So far, the studies on numerical methods that can efficiently solve dynamic electromagnetic problems are still very limited. In this paper, a numerical method called the time-domain finite integration technique (TDFIT) is extended to tackle this problem via the introduction of time-varying iterative coefficients. In order to validate the effectiveness of the proposed algorithm, three numerical examples are demonstrated, including two microstrip structures with a time-varying medium and a rapidly rotating structure. The numerical results reveal that the time-varying medium can induce a nonlinear spectrum shift, and the radar cross section (RCS) of a rapidly rotating structure is highly dependent on the rotating speed. The proposed algorithm opens a new avenue for the exploration of many intriguing phenomena in fundamental physics, including frequency conversion and magnetless nonreciprocity. Meanwhile, it can also lead to a wide range of promising practical applications, such as active electron devices, space-time metamaterials, and hypersonic vehicles.
A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier
Authors: Alimisis Vassilis , Mouzakis Vassilis , Gennis Georgios , Tsouvalas Errikos , Dimas Christos , Sotiriadis Paul P.
Published: November 2022

Electromyography is a diagnostic medical procedure used to assess the state of a muscle and its related nerves. Electromyography signals are monitored to detect neuromuscular abnormalities and diseases but can also prove useful in decoding movement-related signals. This information is vital to controlling prosthetics in a more natural way. To this end, a novel analog integrated voting classifier is proposed as a hand gesture recognition system. The voting classifiers utilize 3 separate centroid-based classifiers, each one attached to a different electromyographic electrode and a voting circuit. The main building blocks of the architecture are bump and winner-take-all circuits. To confirm the proper operation of the proposed classifier, its post-layout classification results (91.2% accuracy) are compared to a software-based implementation (93.8% accuracy) of the same voting classifier. A TSMC 90 nm CMOS process in the Cadence IC Suite was used to design and simulate the following circuits and architectures.
A New Decentralized PQ Control for Parallel Inverters in Grid-Tied Microgrids Propelled by SMC-Based Buck–Boost Converters
Authors: Jasim Ali M. , Jasim Basil H. , Neagu Bogdan-Constantin
Published: November 2022

Nowadays, the microgrid (MG) concept is regarded as an efficient approach to incorporating renewable generation resources into distribution networks. However, managing power flows to distribute load power among distribution generators (DGs) remains a critical focus, particularly during peak demand. The purpose of this paper is to control the adopted grid-tied MG performance and manage the power flow from/to the parallel DGs and the main grid using discrete-time active/reactive power (PQ) control based on digital proportional resonant (PR) controllers. The PR controller is used to eliminate harmonics by acting as a digital infinite-impulse response (IIR) filter with a high gain at the resonant frequency. Additionally, the applied PR controller has fast reference signal tracking, responsiveness to grid frequency drift, and no steady-state error. Moreover, this paper describes the application of robust nonlinear sliding mode control (SMC)-technique-based buck–boost (BB) converters. The sliding adaptive control scheme is applied to prevent the output voltage error that occurs during DG failure, load variations, or system parameter changes. This paper deals with two distinct case studies. The first one focuses on applying the proposed control for two parallel DGs with and without load-changing conditions. In the latter case, the MG is expanded to include five DGs (with and without DG failure). The proposed control technique has been compared with the droop control and model predictive control (MPC) techniques. As demonstrated by the simulation results in MATLAB software, the proposed method outperformed the others in terms of both performance analysis and the ability to properly share power between parallel DGs and the utility grid.
Construction of a Character Dataset for Historical Uchen Tibetan Documents under Low-Resource Conditions
Authors: Zhang Ce , Wang Weilan , Zhang Guowei
Published: November 2022

The construction of a character dataset is an important part of the research on document analysis and recognition of historical Tibetan documents. The results of character segmentation research in the previous stage are presented by coloring the characters with different color values. On this basis, the characters are annotated, and the character images corresponding to the annotation are extracted to construct a character dataset. The construction of a character dataset is carried out as follows: (1) text annotation of segmented characters is performed; (2) the character image is extracted from the character block based on the real position information; (3) according to the class of annotated text, the extracted character images are classified to construct a preliminary character dataset; (4) data augmentation is used to solve the imbalance of classes and samples in the preliminary dataset; (5) research on character recognition based on the constructed dataset is performed. The experimental results show that under low-resource conditions, this paper solves the challenges in the construction of a historical Uchen Tibetan document character dataset and constructs a 610-class character dataset. This dataset lays the foundation for the character recognition of historical Tibetan documents and provides a reference for the construction of relevant document datasets.
PEASE: A PUF-Based Efficient Authentication and Session Establishment Protocol for Machine-to-Machine Communication in Industrial IoT
Authors: Gong Xiang , Feng Tao , Albettar Maher
Published: November 2022

Machine-to-machine (M2M) communication is one of the critical technologies of the industrial Internet of Things (IoT), which consists of sensors, actuators at the edge, and servers. In order to solve the security and availability problems regarding communication between edge devices with constrained resources and servers in M2M communication, in this study we proposed an authentication and session establishment protocol based on physical unclonable functions (PUFs). The scheme does not require clock synchronization among the devices, and it circumvents the situation where the authentication phase has to use a high computational overhead fuzzy extractor due to PUF noise. The protocol contains two message interactions, which provide strong security and availability while being lightweight. The security modelling is based on CPN Tools, which verifies security attributes and attack resistance in the authentication phase. After considering the design of the fuzzy extractor and scalability, the proposed scheme significantly reduces the computational overhead by more than 93.83% in the authentication phase compared with other schemes using PUFs. Meanwhile, under the guarantee of availability, the communication overhead is maintained at a balanced and reasonable level, at least 19.67% lower than the solution using XOR, hashing, or an elliptic curve.
Customized Imperialist Competitive Algorithm Methodology to Optimize Robust Miller CMOS OTAs
Authors: Galembeck Egon Henrique Salerno , Gimenez Salvador Pinillos , Moreto Rodrigo Alves de Lima
Published: November 2022

The design and optimization of the analog complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs) are intrinsically complicated and depend heavily on the designer’s experience, and are associated with very long design and optimization-cycle times. In addition, in order to the analog and radiofrequency (RF) CMOS IC work suitably in practice, it is necessary to perform robustness analyses (RAs) through Simulation Program with Integrated Circuit Emphasis (SPICE) simulations, which result in still-higher design and optimization cycle times and therefore represent the biggest bottleneck to the launching of new electronic products. In this context, this manuscript aims to present, for the first time, the use of a custom imperialist competitive algorithm (ICA) in order to reduce the design and optimization-cycle times of analog CMOS ICs. In this study, we implement some Miller CMOS operational transconductance amplifiers (OTAs) using the computational tool named iMTGSPICE, considering two different bulk CMOS IC manufacturing processes from Taiwan Semiconductor Company (TSMC) (180 nm and 65 nm nodes) and two evolutionary optimization methodologies of artificial intelligence, i.e., ICA and a genetic algorithm (GA). The main result obtained by this work shows that, by using an ICA-customized evolutionary algorithm to perform the design and optimization processes of Miller CMOS OTAs, it is possible to reduce the design and optimization-cycle times by up to 83% in relation to those implemented with the GA-customized evolutionary algorithm, achieving practically the same electrical performance.
Thermal Biometric Features for Drunk Person Identification Using Multi-Frame Imagery
Authors: Koukiou Georgia
Published: November 2022

In this work, multi-frame thermal imagery of the face of a person is employed for drunk identification. Regions with almost constant temperature on the face of sober and drunk persons are thoroughly examined for their capability to discriminate intoxication. Novel image processing approaches as well as feature extraction techniques are developed to support the drunk identification procedure. These techniques constitute novel ideas in the theory of image analysis and algorithm development. Nonlinear anisotropic diffusion is employed for a light smoothing on the images before feature extraction. Feature vector extraction is based on morphological operations performed on the isothermal regions on the face. The classifier chosen to verify the drunk person discrimination capabilities of the procedure is a Support Vector Machine (SVM). Obviously, the isothermal regions on the face change their shape and size with alcohol consumption. Consequently, intoxication identification can be carried out based only on the thermal signatures of the drunk person, while the signature of the corresponding sober person is not needed. A sample of 41 participants who drank in a controlled alcohol consumption procedure was employed for creating the database, which contains 4100 thermal images. The proposed method for intoxication identification achieves a success rate of over 86% and constitutes a fast non-invasive test that can replace existing breathalyzer check up.
Limitations of Nature-Inspired Algorithms for Pricing on Digital Platforms
Authors: Sanchez-Cartas J. Manuel , Sancristobal Ines P.
Published: November 2022

Digital platforms have begun to rely more on algorithms to perform basic tasks such as pricing. These platforms must set prices that coordinate two or more sides that need each other in some way (e.g., developers and users or buyers and sellers). Therefore, it is essential to form correct expectations about how both sides behave. The purpose of this paper was to study the effect of different levels of information on two biology-inspired metaheuristics (differential evolution and particle swarm optimization algorithms) that were programmed to set prices on multisided platforms. We assumed that one platform always formed correct expectations (human platform) while the competitor always used a generic version of particle swarm optimization or differential evolution algorithms. We tested different levels of information that modified how expectations were formed. We found that both algorithms might end up in suboptimal solutions, showing that algorithms needed to account for expectation formation explicitly or risk setting nonoptimal prices. In addition, we found regularity in the way algorithms set prices when they formed incorrect expectations that can help practitioners detect cases in need of intervention.
Design Research Insights on Text Mining Analysis: Establishing the Most Used and Trends in Keywords of Design Research Journals
Authors: Nusir Muneer , Louati Ali , Louati Hassen , Tariq Usman , Zitar Raed Abu , Abualigah Laith , Gandomi Amir H.
Published: November 2022

Design research topics attract exponentially more attention and consideration among researchers. This study is the first research article that endeavors to analyze selected design research publications using an advanced approach called “text mining”. This approach speculates its results depending on the existence of a research term (i.e., keywords), which can be more robust than other methods/approaches that rely on contextual data or authors’ perspectives. The main aim of this research paper is to expand knowledge and familiarity with design research and explore future research directions by addressing the gaps in the literature; relying on the literature review, it can be stated that the research area in the design domain still not built-up a theory, which can unify the field. In general, text mining with these features allows increased validity and generalization as compared to other approaches in the literature. We used a text mining technique to collect data and analyzed 3553 articles collected in 10 journals using 17,487 keywords. New topics were investigated in the domain of design concepts, which included attracting researchers, practitioners, and journal editorial boards. Such issues as co-innovation, ethical design, social practice design, conceptual thinking, collaborative design, creativity, and generative methods and tools were subject to additional research. On the other hand, researchers pursued topics such as collaborative design, human-centered design, interdisciplinary design, design education, participatory design, design practice, collaborative design, design development, collaboration, design theories, design administration, and service/product design areas. The key categories investigated and reported in this paper helped in determining what fields are flourishing and what fields are eroding.
Implanting Intelligence in 5G Mobile Networks—A Practical Approach
Authors: Malik Sumbal , Khan Manzoor Ahmed , Aadam  , El-Sayed Hesham , Khan Jalal , Ullah Obaid
Published: November 2022

With the advancement in various technological fronts, we are expecting the design goals of smart cities to be realized earlier than expected. Undoubtedly, communication networks play the crucial role of backbone to all the verticals of smart cities, which is why we are surrounded by terminologies such as the Internet of Things, the Internet of Vehicles, the Internet of Medical Things, etc. In this paper, we focus on implanting intelligence in 5G and beyond mobile networks. In this connection, we design and develop a novel data-driven predictive model which may serve as an intelligent slicing framework for different verticals of smart cities. The proposed model is trained on different machine learning algorithms to predict the optimal network slice for a requested service resultantly assisting in allocating enough resources to the slice based on the traffic prediction.
Metal-Organic Framework Adsorbent Materials in HVAC Systems: General Survey and Theoretical Assessment
Authors: Rocchetti Andrea , Lippi Martina , Socci Luca , Gullo Paride , Khorshidi Vahid , Talluri Lorenzo
Published: November 2022

In this paper, the use of Metal-Organic Framework (MOF) materials as an option for the energy efficiency enhancement of HVAC systems is investigated. In particular, the possibility of using MOFs as dehumidifying materials to reduce the latent load associated with the moisture content of the airflows is studied. A literature review is proposed, highlighting the benefits of using MOFs instead of other adsorbents (e.g., silica-gel) and discussing the unique features (high water uptake capacity and low regeneration temperatures) that make MOFs a preferential desiccant. The possibility to finely tune these properties is also underlined, reporting some explicative examples. A theoretical proposal of a psychrometric transformation, to be performed in a HVAC system equipped with a MOF-Assisted Dehumidifier (MAD), is presented. This transformation is compared with a traditional one (cooling and dehumidification operated by a cooling coil with low temperatures of the coolant). The preliminary numerical simulations, conducted on a reference case study in Florence, Italy, show an estimated energy saving of 30–50%, leading us to consider the use of this technology as a very competitive one in the air-conditioning sector.
Experimental Investigation on the Effect of the Staggered Impeller on the Unsteady Pressure Pulsations Characteristic in a Pump
Authors: Ni Dan , Wang Feifan , Gao Bo , Zhang Yang , Huang Shiyuan
Published: November 2022

High−energy pressure pulsation induced by rotor−stator interaction (RSI) is the primary source of flow−induced vibration noise in the pump, affecting the pump’s stability and system operation. In order to find an effective method to suppress the pressure pulsation in the pump caused by RSI, a new staggered impeller is proposed in this paper, which can significantly suppress the pressure pulsation energy. The unsteady pressure pulsation characteristic of the original impeller and the staggered impeller scheme are measured and analyzed under different working flow conditions. The results show that although the hydraulic performance of the model pump decreases to a certain extent when the staggered impeller is used, the pressure pulsation energy in the pump decreases significantly. Under 0.8QN–1.2QN working flow conditions, the energy suppression effect of the blade passing frequency (fbpf) amplitude is higher than 80% with the staggered impeller scheme. The Root Mean Square (RMS) values for distribution of pressure pulsation in different frequency bands varies greatly, and the pressure pulsation energy near the tongue is prominent. On a broader frequency band (0–6 fbpf), the pressure pulsation energy of the staggered impeller scheme is smaller than that of the original impeller scheme. With the expansion of the frequency band, the pressure pulsation energy decreased steadily, with a minimum decrease of 37.33%.
A Novel High-Speed Permanent Magnet Synchronous Motor for Hydrogen Recirculation Side Channel Pumps in Fuel Cell Systems
Authors: Ma Kang , Liu Ye , Wei Ziqiang , Yang Jianfei , Guo Baocheng
Published: November 2022

In hydrogen recirculation side channel pumps, the motor rotor is exposed to a high-pressure mixture of steam and hydrogen, which makes hydrogen embrittlement occur in permanent magnets (PMs). A protective coating is necessary for the PMs in high-pressure hydrogen. However, in the process of sleeve interference installation, the protective coating of the PMs is easily damaged. This paper proposes two surface-mounted insert permanent magnet (SIPM) synchronous motor topologies, SIPM1 and SIPM2, in which the retaining sleeves can be eliminated and the PM protective coating is safe in the assembling process. A dovetail PM and rotor core structure is used to protect the PM with higher rotor strength without retaining the sleeve. The electromagnetic performance of the motors with different rotors, including airgap flux density, output torque, torque ripple, and energy efficiency is compared and optimized. It is concluded that the output torque of the SIPM motor can be promoted by 22.4% and torque ripple can be reduced by 2.9%, while the PM volume remains the same as that of the conventional SPM motor. At the same time, the SIPM motor can have lower harmonic contents of back electromotive force (EMF) and rotor loss compared to the SPM motor with a retaining sleeve. Furthermore, the stress of the PM is analyzed under conditions of PM glue action and failure. The proposed SIPM2 has the ability to operate safely at high-speed and high-temperature operating conditions when the PM glue fails.
Methods of Forecasting Electric Energy Consumption: A Literature Review
Authors: Klyuev Roman V. , Morgoev Irbek D. , Morgoeva Angelika D. , Gavrina Oksana A. , Martyushev Nikita V. , Efremenkov Egor A. , Mengxu Qi
Published: November 2022

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).
Risk Assessment of Industrial Energy Hubs and Peer-to-Peer Heat and Power Transaction in the Presence of Electric Vehicles
Authors: Valipour Esmaeil , Nourollahi Ramin , Taghizad-Tavana Kamran , Nojavan Sayyad , Alizadeh As’ad
Published: November 2022

The peer-to-peer (P2P) strategy as a new trading scheme has recently gained attention in local electricity markets. This is a practical framework to enhance the flexibility and reliability of energy hubs, specifically for industrial prosumers dealing with high energy costs. In this paper, a Norwegian industrial site with multi-energy hubs (MEHs) is considered, in which they are equipped with various energy sources, namely wind turbines (WT), photovoltaic (PV) systems, combined heat and power (CHP) units (convex and non-convex types), plug-in electric vehicles (EVs), and load-shifting flexibility. The objective is to evaluate the importance of P2P energy transaction with on-site flexibility resources for the industrial site. Regarding the substantial peak power charge in the case of grid power usage, this study analyzes the effects of P2P energy transaction under uncertain parameters. The uncertainties of electricity price, heat and power demands, and renewable generations (WT and PV) are challenges for industrial MEHs. Thus, a stochastically based optimization approach called downside risk constraint (DRC) is applied for risk assessment under the risk-averse and risk-neutral modes. According to the results, applying the DRC approach increased by 35% the operation cost (risk-averse mode) to achieve a zero-based risk level. However, the conservative behavior of the decision maker secures the system from financial losses despite a growth in the operation cost.
Innovation Input, Climate Change, and Energy-Environment-Growth Nexus: Evidence from OECD and Non-OECD Countries
Authors: Li Zhuohang , Shen Tao , Yin Yifen , Chen Hsing Hung
Published: November 2022

With economic growth and rising incomes, increasing consumption of fossil energy is leading to environmental pollution and climate change, which requires increased innovative inputs to promote the efficiency of renewable energy use. Considering the important impact of innovation input and climate change on renewable energy consumption, greenhouse gas emissions, and green economic growth, this study uses simultaneous equation and sys-GMM model to explore the dynamic nexus of innovation input, climate change, and energy-environment-growth in OECD and non-OECD countries, with panel data covering 2000 to 2019. The empirical results show that renewable energy consumption in non-OECD countries significantly promoted green economic growth, while OECD countries did the opposite. Moreover, renewable energy consumption significantly reduces greenhouse gas emissions caused by climate change, especially for OECD countries. When the level of economic growth exceeds a certain inflection point, greenhouse gas emissions begin to turn from positive to negative, which further verifies the EKC hypothesis. In addition, this study found that innovation input has significantly increased renewable energy consumption, reduced greenhouse gas emissions, and promoted green economic growth in OECD countries. Finally, this study also found that the impact of innovation input in OECD and non-OECD countries on the energy-environment-growth nexus is greater in the short term and more significant in the medium and long term, while the impact of climate change on the energy-environment nexus in OECD and non-OECD countries is more significant in the medium and long term.
Application of Digitalization in Real-Time Analysis of Drilling Dynamics Using Along-String Measurement (ASM) Data along Wired Pipes
Authors: Gomar Mostafa , Elahifar Behzad
Published: November 2022

An automated drilling system requires a real-time evaluation of the drilling bit during drilling to optimize operation and determine when to stop drilling and switch bits. Furthermore, in the dynamic modeling of drill strings, it is necessary to take into account the interactions between drilling bits and rock. To address this challenge, a hybrid approach that combines physics-based models with data analytics has been developed to handle downhole drilling measurements in real time. First, experimental findings were used to formulate mathematical models of cutter–rock interaction in accordance with their geometrical characteristics, rock properties, and drilling parameters. Specifically, these models represent the normal and contact forces of polycrystalline diamond compact cutters (PDCs). Experimental data are analyzed utilizing deep learning, nonlinear regression, and genetic algorithms to fit nonlinear equations to data points. Following this, the recursive least square was implemented as a data analytic method to integrate real-time drilling data, drilling bit models, and mathematical models. Drilling data captured by the along-string measurement system (ASM) is implemented to estimate cutting and normal forces, torque, and specific energy at the bit. The unique aspect of this research is our approach in developing a detailed cutter–rock interaction model that takes all design and operation parameters into account. In addition, the applicability of the algorithm is demonstrated by real-time assessments of drilling dynamics, utilizing downhole digital data, that enable the prediction of drilling events and problems related to drilling bits.
Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid
Authors: Ali Asad , Fakhar Muhammad Salman , Kashif Syed Abdul Rahman , Abbas Ghulam , Khan Irfan Ahmad , Rasool Akhtar , Ullah Nasim
Published: November 2022

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper.
Electrified Process Heating in Textile Wet-Processing Industry: A Techno-Economic Analysis for China, Japan, and Taiwan
Authors: Hasanbeigi Ali , Zuberi M. Jibran S.
Published: November 2022

The textile industry accounts for approximately 2% of global greenhouse gas emissions. There is a significant opportunity to decarbonize the textile industry by electrification of process heating where low- or zero-carbon electricity is used. Electrified process heating can be achieved through cross-cutting technologies without modifying the textile process equipment and/or through replacing the existing equipment with technologies that employ electromagnetic or resistance heating techniques for specific end-use applications. This paper aims to investigate the potential for electrification of process heating in the textile wet-processing industry in three of the top textile-producing and exporting regions in the world. To do this, two separate technology pathways, i.e., electrification through (a) industrial heat pumps and (b) textile end-use processes are developed and analyzed. The results show that the total potential final energy and CO2 savings due to electrification in both scenarios could be substantially large due to the lower energy intensity of the electrified heating systems. Moreover, the costs per unit of textile production are found to be lower in the case of industrial heat pumps compared to other systems. It is concluded that wide-scale electrification of process heating in the textile wet-processing industry will require major changes to the electricity system and individual sites, and the coordination efforts among different stakeholders to plan these changes must be intensified.
Research on the Law of Surge Pressure during Tripping of HTHP Wells under the Influence of Tool Joints
Authors: Shi Jiangang , Yu Buwen , Song Xianzhi , Guo Yong
Published: November 2022

Accurate surge pressure prediction during tripping is significant to ensure drilling safety. Based on the theory of wellbore hydraulics and heat transfer, a surge pressure prediction model considering the influence of HTHP and joints is established in this paper. The finite difference method is used to solve the wellbore flow model. Compared with the measured surge pressure, it was found that the error between the predicted and measured values after considering the above factors was only 0.89%. The influence of dimensionless joint outer diameter, joint length and drill string tripping speed on the surge pressure was further analyzed. The results show that the existence of joints increases the surge pressure. When the dimensionless joint outer diameter was increased from 0.70 to 0.91, the surge pressure increased by 76%. Neglecting the effect of the joint will cause a large error in calculating the surge pressure. In addition, the surge pressure is positively correlated with the drill string tripping speed. However, with the decrease in drill string tripping speed, the surge pressure will gradually tend to a stable value. This study provides a theoretical reference for the hydraulic design of HTHP well tripping.
How Does the Accessibility of Floating Wind Farm Sites Compare to Existing Fixed Bottom Sites?
Authors: Rowell David , Jenkins Brian , Carroll James , McMillan David
Published: November 2022

Offshore wind is poised for huge growth in the coming years, the UK government has set targets of 40 GW of offshore wind, including 1 GW of floating wind, to be installed in the UK by 2030. Many proposed wind development sites are in deeper waters, farther from shore and will therefore need to be developed as floating wind sites. Developing sites in deeper waters provides access to higher, more consistent wind speeds, however this also means increased wave heights and tougher operating conditions. This makes the challenge of site accessibility critical to the goal of lowering the costs of offshore wind. Accessibility is the amount of time that vessels can safely access a given site. The objective of this study was to make a comparison of the accessibility of potential future floating wind sites to existing fixed bottom wind farm sites. Accessibility was calculated by developing Matlab code using established techniques and definitions found in the literature. A case study was then completed using sites in Scottish waters proposed for development as part of the Scotwind leasing plan. The majority of the Scotwind sites will need to be developed as floating wind sites due to the large water depths. This study assesses the accessibility of the Scotwind leasing sites and compares them to a typical fixed bottom site. The study found that accessibility will be a greater challenge for floating farm wind sites compared to fixed bottom sites. Increased access to vessels that can operate in larger wave heights will likely be necessary to operate and maintain floating wind farm sites.
Water Availability Assessment from Power Generation Reservoirs in the Rio Grande Operated by Furnas, Brazil
Authors: da Luz Marta Pereira , Lins da Silva Jefferson , Higuera-Castro Edna Lizeth , Ribeiro Luciano Ferreira
Published: November 2022

Analyzing water availability from energy generation reservoirs is a determining factor in inferring the economic and development capacity of countries that operate primarily with hydroelectric generation. This is because the reservoirs configure the battery of this type of electric power generation system. In this context, the main interference variables are climate, consumptive use, operation, land use and occupation, topography, sediment input and geology. The present article analyzed rainfall data and the consumptive uses of five hydroelectric plants in the Rio Grande, operated by Furnas in Brazil. These two variables are chosen due to the frequent correlation between the rainfall regime and reservoir level and the scarcity of studies considering the influence of consumptive uses in this storage process. Data from 1994 to 2021 were analyzed from publicly available sources. The information was treated using the Mann–Kendall trend test. Having obtained the results, it was observed that there is a difference in behavior between accumulation and run-of-river plants, the rainfall regime over the years analyzed showed no trend, while the consumptive uses tend to grow. This shows the need for policies that encompass the combination of multiple uses so that the reservoirs are sustainable.
Simulation of Electromagnetic Implosion of Metal Shells to Obtain Supercritical Fluids
Authors: Antonov Valery , Kalinin Nicolay
Published: November 2022

This study analyzes the conditions for creating the energy density necessary to obtain supercritical fluids of substances with parameters (temperature T > 1 eV, density N > 1022 cm−3, specific energy density ε > 100 kJ/g). The calculations are carried out on the basis of the one-dimensional (1D) two-temperature (2T) magneto hydrodynamic radiation model, which takes into account the physical processes occurring in the energy storage, switching system and the pulsed plasma load-a cylindrical compressible conductive shell. Developing a mathematical model, we assumed that physical processes were self-consistent. The simulation results were presented as time dependences of the main process parameters. Calculations showed that it becomes possible to sharpen the radiation pulse and pressure in the shock wave. As a result, we formulated the requirements for a laboratory energy source to establish the characteristics of a current pulse flowing through a conductive cylindrical shell and its dimensions (radius and thickness) necessary to achieve the goal.
CFD Modelling and Visual Analysis of Heat Transfer and Flow Pattern in a Vertical Two-Phase Closed Thermosyphon for Moderate-Temperature Application
Authors: Sadeghi Kaveh , Kahani Mostafa , Ahmadi Mohammad Hossein , Zamen Mohammad
Published: November 2022

A heat pipe is an energy-efficient heat transfer device that relies on evaporation and condensation processes for energy transfer. The main purpose of this study is to simulate a two-phase closed thermosyphon, at moderate temperature, that can be used in industrial applications such as steam power plants. After creating a computational network in the Gambit software, the thermosyphon is simulated in Fluent software using the VOF model. Special oil is employed as the working fluid. Based on the CFD results, the efficiency of the system reaches approximately 96%, and the thermal resistance decreases to 0.54 K/W. The contours of the boiling and evaporation process at differing filling ratios, ranging between 30–90%, is visually investigated and the best performance is obtained for 30% of the filling ratio in thermosyphon. At higher filling ratios, more giant bubbles are generated in thermosyphon, which can attach to the inner wall of the system and reduce the thermal performance. The steady-state condition is obtained 84 s after the start of the process.