bluuebunny/crossref_metadata_2025 huggingface dataset:
A Reliability-Enhanced Differential Sensing Amplifier for Hybrid CMOS/MTJ Logic Circuits
Authors: Wang Chengzhi , Yang Tianqi , Han Min , Wang Dongsheng
Published: January 2023

Recently, hybrid logic circuits based on magnetic tunnel junctions (MTJs) have been widely investigated to realize zero standby power. However, such hybrid CMOS/MTJ logic circuits suffer from a severe sensing reliability due to the limited tunnel magnetoresistance ratio (TMR ≤ 150%) of the MTJ and the large process variation in the deep sub-micrometer technology node. In this paper, a novel differential sensing amplifier (DSA) is proposed, in which two PMOS transistors are added to connect the discharging branches and evaluation branches. Owing to the positive feedback realized by these two added PMOS transistors, it can achieve a large sensing margin. By using an industrial CMOS 40 nm design kit and a physics-based MTJ compact model, hybrid CMOS/MTJ simulations have been performed to demonstrate its functionality and evaluate its performance. Simulation results show that it can achieve a smaller sensing error rate of 9% in comparison with the previously proposed DSAs with a TMR ratio of 100% and process variation of 10%, while maintaining almost the same sensing delay of 74.5 ps and sensing energy of 1.92 fJ/bit.
Descriptive Markers for the Cognitive Profiling of Desktop 3D Spaces
Authors: Sudár Anna , Csapó Ádám B.
Published: January 2023

3D virtual reality spaces, whether running on desktop environments or on immersive displays, have been noted to support a radically new and highly stimulating way of working with digital content in a variety of application domains. At the same time, research in recent decades has produced a number of experimental results showing that the use of 3D, as opposed to 2D interfaces, can lead to performance improvements from a wide range of aspects, including the ability to comprehend and retain knowledge, ability to work collaboratively in more creative and effective ways, and ability to carry out workflows integrating numerous sources of information in less time. In this paper, we first review the relevant literature; then, we describe an exploratory study that we carried out with test subjects, both in a 3D desktop virtual environment and in a 2D web-based environment, while collecting eye tracking data. In the study, subjects were presented with a set of multimedia content on a range of topics within the field of astronomy, based on which they were subsequently asked to fill out a set of questionnaires. By comparing the 2D and 3D cases in terms of correctness of answers, time taken to perform the task, pupil dilation measurements, subjects’ self-reported difficulty assessments, as well as various kinds of high-level interaction patterns employed during the task (in 3D), we were able to identify a set of descriptive markers which may be relevant to the prediction of users’ effectiveness in virtual reality workspaces. In a weaker sense, the results also seem to support previous research works claiming improved effectiveness in 3D spaces compared to 2D web-based interfaces, although further work is needed to more clearly identify the constraints within which such benefits can be guaranteed.
A New Varactor-Tuned 5.8 GHz Dielectric Resonator Band-Stop Filter for ITS and C-V2X Coexistence with Vehicular DSRC
Authors: Collodi Giovanni , Righini Monica , Cidronali Alessandro
Published: January 2023

In this paper, we introduce an electronically tuneable band-stop filter based on a dielectric resonator, along with its design principles and equivalent circuit model aimed at the coexistence of intelligent transport systems (ITS) and cellular vehicle-to-everything (C-V2X) communications operating at 5.9 GHz, with the widely spread vehicular dedicated short range communications (DSRC) at 5.8 GHz. The proposed architecture involves a dielectric resonator coupled via a microstrip transmission line to a planar ring resonator. The resonance is electronically tuned by varying the bias voltage of a varactor diode placed in the ring. This design is analyzed theoretically and experimentally. For validation, a filter operating at 5.8 GHz was designed. The prototype is capable of enhancing coexistence exhibiting to features. First, when inserted into the ITS transmitter chain, it reduces unwanted transmitter emissions in the out-of-band spectrum occupied by the DSRC carriers in the 5.8 GHz frequency band of at least 25 dB, with a minimal insertion loss at 5. 9 GHz of 1.6 dB; secondly, when inserted in the ITS receiver chain, it exhibits high linearity that prevents the generation of intermodulation products falling into the filter pass band. The prototype features more than 21 dBm of third-order input intercept points, and a tuning range of 26 MHz while maintaining a minimal loaded Q factor of 93. The prototype is discussed regarding the detailed equivalent circuit parameter extraction and their dependency upon the control voltage.
Online Education Management: A Multivariate Analysis of Students’ Perspectives and Challenges during Online Classes
Authors: Puiu Silvia , Idowu Samuel O. , Meghisan-Toma Georgeta-Madalina , Bădîrcea Roxana Maria , Doran Nicoleta Mihaela , Manta Alina Georgiana
Published: January 2023

The aim of the present study is to find solutions for better management of online education, starting from students’ perspectives regarding the challenges they encountered in the last two years when online courses were imposed during the COVID-19 pandemic. The research methodology we used was partial least squares structural equation modelling based on data collected by applying a survey among students in Romanian universities. The novelty of our study consists in the proposed model, which has five variables: communication problems specific to online education, professors’ skill in conducting online classes, the quality of online education, the stress felt by students during online education, and the technical requirements of online education. The results revealed that despite challenges during online classes students benefited from a high-quality education because they had the support of their professors, all the educational resources that they needed, a device to connect from, and a very good internet connection. These findings are helpful for managers in the higher education system to create better educational strategies meant to satisfy the educational needs of students in the digital age.
Photonic Multiple Microwave Frequency Measurement System with Single-Branch Detection Based on Polarization Interference
Authors: Zhu Wei , Li Jing , Yan Miaoxia , Pei Li , Ning Tigang , Zheng Jingjing , Wang Jianshuai
Published: January 2023

A photonic microwave frequency measurement system with single-branch detection based on polarization interference is proposed. In this scheme, a 15-line non-flat optical frequency comb (OFC) based on sawtooth signal modulation via a Mach–Zehnder modulator is generated. The intercepted microwave signal with multiple-frequency components can be measured by frequency down-conversion with this simple structure. This system can measure the multi-tone microwave signals in real time. The single-branch detection makes the system a simple and compact structure and avoids the unbalanced variation, as in a two-branches scheme. The blind area of the system can be solved by adjusting the comb-line spacing of the OFC. A simulation is carried out and related discussion is given. The result reveals that it can measure multi-tone microwave signals with a resolution of less than 2 MHz over 0.1–12 GHz.
Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System
Authors: Li Yangyang , Ramli Dzati Athiar
Published: January 2023

The estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency plane is obtained by rotating the time–frequency plane after the short-time Fourier transform. This plane represents the fine characteristics of the mixed signal in the time domain and the frequency domain. The rotation angle is determined by global searching for the minimum L1 norm to make the mixed signal sufficiently sparse. The obtained time–frequency points do not need single source point detection, reducing the calculation amount of the original algorithm, and the insensitivity to noise in the fractional domain improves the robustness of the algorithm in the noise environment. The simulation results show that the sparsity of the mixed signal and the estimation accuracy of the mixed matrix are improved. Compared with the existing mixed matrix estimation algorithms, the proposed method is effective.
Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
Authors: Hasan Muhammad Zulkifl , Mohd Hanapi Zurina
Published: January 2023

The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance.
A Novel Virus Capable of Intelligent Program Infection through Software Framework Function Recognition
Authors: Guo Wang , Shu Hui , Gu Yeming , Huang Yuyao , Zhao Hao , Li Yang
Published: January 2023

Viruses are one of the main threats to the security of today’s cyberspace. With the continuous development of virus and artificial intelligence technologies in recent years, the intelligentization of virus technology has become a trend. It is of urgent significance to study and combat intelligent viruses. In this paper, we design a new type of confirmatory virus from the attacker’s perspective that can intelligently infect software frameworks. We aim for structural software as the target and use BCSD (binary code similarity detection) to identify the framework. By incorporating a software framework functional structure recognition model in the virus, the virus is enabled to intelligently recognize software framework functions in executable files. This paper evaluates the BCSD model that is suitable for a virus to carry and constructs a lightweight BCSD model with a knowledge distillation technique. This research proposes a software framework functional structure recognition algorithm, which effectively reduces the recognition precision’s dependence on the BCSD model. Finally, this study discusses the next researching direction of intelligent viruses. This paper aims to provide a reference for the research of detection technology for possible intelligent viruses. Consequently, focused and effective defense strategies could be proposed and the technical system of malware detection could be reinforced.
A Composite Pipeline for Forwarding Low-Latency Traffic in SDN Programmable Data Planes
Authors: Ling Zhiyuan , Chen Xiao , Song Lei
Published: January 2023

With the rapid evolution of network technologies over recent years, emerging network services, especially industrial control networks, video conferencing, intelligent driving, and other scenarios, have put forward higher demand for the low-latency forwarding of network traffic. The existing flow caching and hardware acceleration methods only improve the overall forwarding performance of data-plane devices but cannot separate the forwarding process of low-latency traffic from others to reflect the priority of these flows. In this paper, we extend the POF southbound interface protocol and propose a marking method for low-latency flows, based on which we design a composite pipeline to achieve fast processing for low-latency traffic by introducing a fast-forwarding path. The experiments show that the fast path has a higher forwarding capability than the MAT pipeline in the POF Switch and can reduce the forwarding delay of low-latency flows by 62–68%. In a real network environment with a mixed traffic simulation, the reduction reaches 17–20% with no delay increment for the non-low-latency part.
A CC-Type IPT System Based on S/S/N Three-Coil Structure to Realize Low-Cost and Compact Receiver
Authors: Yang Lin , Shao Shuai , Wu Haiting , Jiang Shuai , Zhang Li
Published: January 2023

The characteristics of load-independent constant current (CC) output and zero phase angle (ZPA) operation are required in many scenarios of inductive power transfer (IPT) applications. However, the existing topologies with CC output characteristics usually need to introduce additional compensation components on the receiving side to compensate for reactive power and achieve the preset function. This not only increases the occupied space of the receiving side, but also increases the cost and weight. Therefore, this study proposes a new IPT system based on an S/S/N three-coil structure. The proposed system can achieve the CC output function and an operation nearly ZPA and zero-voltage switching (ZVS) through flexible parameter design. Moreover, there are no compensation components on the receiving side of the proposed system, which guarantees a low-cost, lightweight, and compact receiver. Firstly, a comprehensive analysis of the proposed S/S/N three-coil structure IPT system that implements CC output characteristics and ZPA operation is provided. Then, the conditions for realizing ZVS are discussed in terms of parameter design and the sensitivity of CC output characteristics to the changes in compensation capacitance parameters. Furthermore, the proposed S/S/N three-coil structure IPT system is compared with previous related studies to reflect its advantages. Finally, the correctness of the theory is verified by simulation and experiment.
Efficient Image Segmentation of Cardiac Conditions after Basketball Using a Deep Neural Network
Authors: Ma Jian , Li Wenfa
Published: January 2023

The evaluation of heart health status is the reference standard for measuring the intensity of exercise performed by different individuals. Thus, the effective analysis of heart conditions is an important research topic. In this study, we propose a system designed to segment images of the right ventricle. In this system, the right ventricle of the heart is segmented using an improved model called RAU-Net. The sensitivity and specificity of the network are enhanced by improving the loss function. We adopted an extended convolution rather than ordinary convolution to increase the receptive field of the network. In the network-sampling phase, we introduce an attention module to improve the accuracy of network segmentation. In the encoding and decoding stages, we also introduce three residual modules to solve the gradient explosion problem. The results of experiments are provided to show that the proposed algorithm exhibited better segmentation accuracy than an existing algorithm. Moreover, the algorithm can also be trained more rapidly and efficiently.
Efficient Training on Alzheimer’s Disease Diagnosis with Learnable Weighted Pooling for 3D PET Brain Image Classification
Authors: Xing Xin , Rafique Muhammad Usman , Liang Gongbo , Blanton Hunter , Zhang Yu , Wang Chris , Jacobs Nathan , Lin Ai-Ling
Published: January 2023

Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer’s disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training 3D-CNN is computationally expensive and with the potential of overfitting due to the small sample size available in the medical imaging field. Here we proposed a novel 3D-2D approach by converting a 3D brain image to a 2D fused image using a Learnable Weighted Pooling (LWP) method to improve efficient training and maintain comparable model performance. By the 3D-to-2D conversion, the proposed model can easily forward the fused 2D image through a pre-trained 2D model while achieving better performance over different 3D and 2D baselines. In the implementation, we chose to use ResNet34 for feature extraction as it outperformed other 2D CNN backbones. We further showed that the weights of the slices are location-dependent and the model performance relies on the 3D-to-2D fusion view, with the best outcomes from the coronal view. With the new approach, we were able to reduce 75% of the training time and increase the accuracy to 0.88, compared with conventional 3D CNNs, for classifying amyloid-beta PET imaging from the AD patients from the CU participants using the publicly available Alzheimer’s Disease Neuroimaging Initiative dataset. The novel 3D-2D model may have profound implications for timely AD diagnosis in clinical settings in the future.
A Comprehensive Multi-Factor Method for Identifying Overflow Fluid Type
Authors: Tao Zhenyu , Fan Honghai , Liu Yuhan , Ye Yuguang
Published: January 2023

Accurate identification of overflow fluid types facilitates timely and effective handling of onsite overflow accidents. Research into identifying the type of overflow fluid is limited, and there are only simple calculation models that do not consider enough effects; additionally, accuracy needs to be improved and the identification method is not perfect. If there is no drilling data, it is impossible to identify the overflow fluid. Therefore, this paper modifies the density calculation model of overflow fluid by considering the influence of the temperature, pressure field, and two-phase flow model, making the calculation result more accurate and universal, and puts forward a comprehensive method for auxiliary identification based on gas logging interpretation. This paper uses the gas state equation to verify the accuracy of the overflow density model; after verification using data from more than 20 overflowing wells, the method was found to be practical and had an accuracy rate of more than 90%. Thus, this study and the proposed method can provide guidance for dealing with overflow accidents in the field and any follow-up research.
Hybrid CSP—PV Plants for Jordan, Tunisia and Algeria
Authors: Benitez Daniel , Röger Marc , Kazantzidis Andreas , Al-Salaymeh Ahmed , Bouaichaoui Sofiane , Guizani AmenAllah , Balghouthi Moncef
Published: January 2023

Hybrid concentrated solar thermal power (CSP) and photovoltaic (PV) plants are gaining relevance because they combine their advantages: easy installation and low cost of PV plus dispatchability of CSP. This paper presents results of a techno-economic modelling of this hybrid approach for sites in Jordan, Tunisia and Algeria. Local boundary conditions such as meteorology, cost and electricity demand have been considered to determine the best configurations for these three sites. Different CSP technologies with thermal energy storage have been selected. Hybridization with natural gas has also been included. The optimization is done towards minimizing the LCOE while covering the electrical demand 24/7. Results are presented for different CO2 emissions ranges, as the use of fossil fuel has a strong impact on the LCOE and for environmental reasons, it may be preferred to be kept to a minimum. For most of the cases analyzed, the fraction of energy from PV that leads to minimum LCOE is lower than the energy from CSP. It is shown that for countries with a high fuel price, the use of natural gas reduces the LCOE until a share from this source of about 20%. A higher integration of fossil fuel for sites rich in solar irradiation is considered not advantageous if the price of natural gas is above EUR 40/MWh.
Salient Insights on the Performance of EU Member States on the Road towards an Energy-Efficient Future
Authors: Momete Daniela Cristina
Published: January 2023

Energy efficiency plays an important role in energy systems all over the world, representing the most available and cheapest source of energy. Progress in energy efficiency translates into reduced consumption, reduced dependency on external suppliers of fossil fuels, mitigation of climate change, and improvement of quality of life. Given its tremendous importance for energy systems and national economies, this paper aims to offer salient insights on the performance of the countries of the European Union (EU-27) towards an energy-efficient future, bearing in mind the quantitative milestones of sustainable development goal 7 (SDG7) of the Agenda for Sustainable Development of the United Nations. The current research offers a new perspective and delivers a comprehensive, robust, and succinct model which provides insights on the performance of EU-27 member states based on appropriate quantitative key performance indicators related to energy efficiency. The results show that about 19% of EU-27 member states are in a leading position, while about 41% of EU-27 member states have modest performance, and about 14% are still lagging. The findings of this paper may open discussions to find better ways to valorise energy efficiency, and may offer support for legislators and other stakeholders in energy systems. Moreover, by benchmarking different types of national practices, the countries which are still struggling to use energy-efficiency measures may find suitable ways to accelerate transition to a more energy-efficient future.
Participation of Renewable Energy Sources in the Frequency Regulation Issues of a Five-Area Hybrid Power System Utilizing a Sine Cosine-Adopted African Vulture Optimization Algorithm
Authors: Nayak Smruti Ranjan , Khadanga Rajendra Kumar , Panda Sidhartha , Sahu Preeti Ranjan , Padhy Sasmita , Ustun Taha Selim
Published: January 2023

In this article, a novel methodology is proposed by utilizing a technique which, in light of the change in the African vulture optimization known as Sine Cosine, adopted an African vulture optimization algorithm (SCaAVOA)-based tilt integral derivative (TID) regulator for the load frequency control (LFC) of a five-area power system with multi-type generations. At first, the execution of the Sine Cosine-adopted calculation is tried by contrasting it with the standard AVOA calculation while considering different standard benchmark functions. To demonstrate the superiority of the proposed SCaAVOA algorithm, the results are contrasted using different standard approaches. In the next stage, the proposed method is used in a five-area thermal power system and is likewise applied to a five-area, ten-unit system comprising different conventional sources as well as some renewable energy sources. The performance analysis of the planned regulator is completed for various system boundaries and loading conditions. It is seen that the said regulator is more viable in comparison to the other standard controllers.
A Generic Framework for Multiscale Simulation of High and Low Enthalpy Fractured Geothermal Reservoirs under Varying Thermodynamic Conditions
Authors: Wang Yuhang , HosseiniMehr Mousa , Marelis Arjan , Hajibeygi Hadi
Published: January 2023

We develop a multiscale simulation strategy, namely, algebraic dynamic multilevel (ADM) method, for simulation of fluid flow and heat transfer in fractured geothermal reservoirs under varying thermodynamic conditions. Fractures with varying conductivities are modeled using the projection-based embedded discrete fracture model (pEDFM) in an explicit manner. The developed ADM method allows the fine-scale system to be mapped to a discrete domain with an adaptive grid resolution via the use of the restriction and prolongation operators. The developed framework is used (a) to investigate the impacts of formulations with different primary variables on the simulation results, and (b) to assess the performance of ADM in a high-enthalpy reservoir by comparing the simulation results against those obtained from fine-scale grids. Results show that the two formulations produce similar results in the case of single-phase flow, which indicates that the molar formulation is a favorable option that can be applied to varying thermodynamic conditions. Moreover, the ADM can provide accurate solutions with only a fraction of fine-scale grids, e.g., for the studied case, the maximum error is by average 1.3 with only 42% of active cells, thereby improving the computational efficiency. This is promising for applying the developed method to field-scale geothermal systems.
A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs
Authors: Liu Zhiqiang , Niu Yanqi , Guo Caiyun , Jia Shitong
Published: January 2023

Under the background of the normalization of COVID-19 prevention and control and the rapid development of e-commerce, community group buying has occupied the market by providing low-priced, fast, and green consumer goods, but with it, the logistics and distribution volume of goods has also increased sharply. In order to reduce environmental pollution and the carbon emissions caused by transportation in the community group buying logistics distribution, it is necessary to investigate a suitable method to optimize vehicle distribution routes and reduce carbon emissions. Taking the lowest total costs of logistics and distribution and the smallest carbon emissions, this article introduces soft time window function and carbon emissions parameters, takes the delivery of goods from the community group buying distribution center in Wu’an Town, Hebei Province to customer points in 14 townships as an example, an optimization model for the distribution route of low carbon vehicles for community group buying based on improved genetic algorithm was constructed, AHP-EW fusion technology was used to calculate carbon emissions and cost weights, and compared with the traditional genetic algorithm and ant colony algorithm two typical heuristic algorithms, the feasibility of the proposed model and the advantages of the improved algorithm are verified, and the research results showed that it can reduce the costs and carbon emissions of vehicle distribution, provide decision-making reference for community group buying logistics enterprise distribution, and promote energy conservation and environmental sustainable development.
Modeling of Dynamic Operation Modes of IVG.1M Reactor
Authors: Irkimbekov Ruslan , Vurim Alexander , Vityuk Galina , Zhanbolatov Olzhas , Kozhabayev Zamanbek , Surayev Artur
Published: January 2023

This paper presents the results of a calculation code approach providing a solution to the point kinetics problem for the IVG.1M research reactor of the National Nuclear Center of the Republic of Kazakhstan and allowing the simulation of dynamic processes going on during reactor start-ups, including changes in the thermal state of all its elements, reactor regulator displacement, accumulation of absorbers in the fuel, and the beryllium reflector. A mathematical description of the IVG.1M point kinetics model is presented, which provides a calculation of the reactor neutron parameters, taking into account the dependence of reactivity effects on the temperature, changes in the isotopic composition of materials, and thermal expansion of core structural elements. An array of data values was formed of reactivity added by separate elements of the core when changing their thermal state and other reactor parameters, as well as an array of data with the parameters of heat exchange of coolant-based reactor structural elements. These are used in the process of solving the point kinetics problem to directly replace formal parameters, eliminating the need to calculate the values of these parameters at each calculation step. Preliminary calculations to form an array of values of reactivity effects was applied to the reactor by separate structural elements when their temperature changes were performed using the IVG.1M precision reactor calculation model. The model was validated by the reactor parameters in the critical state. Preliminary calculations to form an array of data with the parameters of heat exchange of coolant-based reactor structural elements were performed in ANSYS Fluent software using the calculation model that describes the IVG.1M reactor fuel element in detail. Validation of the developed calculation code based on the results of two start-ups of the IVG.1M reactor was performed and its applicability for the analysis of transient and emergency modes of reactor operation and evaluation of its safe operation limits was confirmed.
Perspective on Predictive Modeling: Current Status, New High-Order Methodology and Outlook for Energy Systems
Authors: Cacuci Dan Gabriel
Published: January 2023

This work presents a perspective on deterministic predictive modeling methodologies, which aim at extracting best-estimate values for model responses and parameters along with reduced predicted uncertainties for these best-estimate values. The two oldest such methodologies are the data-adjustment method, which stems from the nuclear energy field, and the data-assimilation method, which is implemented in the geophysical sciences. Both of these methodologies attempt to minimize, in the least-square sense, a user-defined functional that represents the discrepancies between computed and measured model responses. These two methodologies were briefly reviewed and shown to be inconsistent even to first-order in the sensitivities of the response to the model parameters. In contrast to these methodologies, it was shown that the “maximum entropy”-based predictive modeling methodology (called BERRU-PM) that was developed by the author not only dispenses with the subjective “user-chosen functional to be minimized” but is also inherently amenable to high-order formulations. This inherent potential was illustrated by presenting a novel, higher-order, MaxEnt-based predictive modeling methodology, labelled BERRU-PM-2+, which is complete and exact to second-order sensitivities and moments of both the a priori and posterior distributions of responses and parameters, while explicitly including third- and fourth-order sensitivities and correlations, thus indicating the mechanism for incorporating information of orders higher than second in predictive modeling. The presentation of this new predictive modeling methodology also aims at motivating a widespread application of predictive modeling principles and methodologies in the energy sciences for obtaining best-estimate results with reduced uncertainties.
Greywater as a Future Sustainable Energy and Water Source: Bibliometric Mapping of Current Knowledge and Strategies
Authors: Kordana-Obuch Sabina , Starzec Mariusz , Wojtoń Michał , Słyś Daniel
Published: January 2023

The use of greywater reduces the consumption of many resources and is an effective tool for achieving Sustainable Development Goals. In order to assess the rationality of its use as an alternative source of energy and water in buildings, a holistic literature review was carried out based on a bibliometric analysis of publications in these fields. The main bibliographic source was the Web of Science database. This review contributed to a better understanding of the analyzed research field. It also revealed trends in greywater energy recovery and recycling research, indicating that these are developing fields. In recent years, there has been a marked increase in the number of publications on the most popular ways of using greywater in buildings, with the territorial scope of research carried out in the greywater recycling domain being considerably larger than research on greywater energy recovery. The analysis revealed poor cooperation between different universities, especially in the field of greywater energy recovery. In light of previous literature reviews, some important research gaps and further proposals for future research were also identified. They concern, in particular, the simultaneous use of greywater as an alternative source of energy and water. Together with the findings of other researchers and people related to the subject matter, this review can contribute to the further development of greywater energy recovery systems and greywater recycling systems.
Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia
Authors: Barashid Khadijah , Munshi Amr , Alhindi Ahmad
Published: January 2023

The world’s technological and economic advancements have led to a sharp increase in the demand for electrical energy. Saudi Arabia is experiencing rapid economic and demographic growth, which is resulting in higher energy needs. The limits of fossil fuel reserves and their disruption to the environment have motivated the pursuit of alternative energy options such as wind energy. In order to regulate the power system to maintain safe and dependable operation, projections of current and daily power generation are crucial. Thus, this work focuses on wind power prediction and the statistical analysis of wind characteristics using wind data from a meteorological station in Makkah, Saudi Arabia. The data were collected over four years from January 2015 to July 2018. More than twelve thousand data points were collected and analyzed. Layout and wake effect studies were carried out. Furthermore, the near wake length downstream from the rotor disc between 1 and 5 rotor diameters (1D to 5D) was taken into account. Five robust machine learning algorithms were implemented to estimate the potential wind power production from a wind farm in Makkah, Saudi Arabia. The relationship between the wind speed and power produced for each season was carefully studied. Due to the variability in the wind speeds, the power production fluctuated much more in the winter. The higher the wind speed, the more significant the difference in energy production between the five farm layouts, and vice versa, whereas at a low wind speed, there was no significant difference in the power production in all of the near wake lengths of the 1D to 5D rotor diameters downstream from the rotor disc. Among the utilized prediction models, the decision tree regression was found to have the best accuracy values in all four utilized evaluation metrics, with 0.994 in R-squared, 0.025 in MAE, 0.273 in MSE, and 0.522 in RMSE. The obtained results were satisfactory and provide support for the construction of several wind farms, producing hundreds of megawatts, in Saudi Arabia, particularly in the Makkah Region.
Spatial Conflicts concerning Wind Power Plants—A Case Study of Spatial Plans in Poland
Authors: Nowak Maciej J. , Brelik Agnieszka , Oleńczuk-Paszel Anna , Śpiewak-Szyjka Monika , Przedańska Justyna
Published: January 2023

This article aims to determine the specificity of spatial conflicts related to spatial plans concerning wind power plants. To achieve the aim of the article, all spatial plans in force in Poland were analysed, distinguishing those which determine the possibility of realisation of wind power plants. The research concerns the whole country. The literature review carried out for this article verifies approaches to spatial conflicts and identifies how planning barriers to the implementation of wind power investments are defined. The results identified Polish municipalities where spatial plans containing provisions for implementing wind power plants have been enacted. Then, through survey research, an attempt was made to identify critical spatial conflicts occurring in these municipalities. The last part of the research involved identifying and analysing Polish court decisions concerning spatial plans permitting wind power plants. These were recognised as a particular stage of spatial conflicts. The article’s novelty is the attempt to isolate regional spatial conflicts concerning wind power plants comprehensively. This applies to a broader scientific discussion (also applicable to other countries). In addition, the treatment of court cases as the final stage of spatial conflicts related to the location of wind power plants should be considered innovative. An important contribution to the international discussion is the proposal for broader (quantitative) research on the role of courts in spatial planning. Possible classifications in court settlements of parties to spatial conflicts, reasons for spatial conflicts, and ways of ending conflicts have been proposed.
Green Hydrogen Production by Anion Exchange Membrane Water Electrolysis: Status and Future Perspectives
Authors: Falcão Daniela S.
Published: January 2023

Green hydrogen production, i.e., produced on a CO2-neutral basis through the electrolysis of water employing renewable electricity, has attracted increasing attention. The electricity required is generated from Renewable Energy Sources (RES), for example, wind energy, hydropower, or solar energy. Since neither the process of production nor the end products of H2 and O2 are harmful to the environment, green hydrogen is climate neutral. Developing electrolysis technology is, therefore, a research topic to follow. Anion Exchange Membrane (AEM) Water Electrolysis (WE) is an innovative technology that couples the advantages of the more mature technologies of Proton Exchange Membrane (PEM) and conventional alkaline electrolysis, with the potential to eliminate the drawbacks of both. AEMWE technology is in an evolutionary stage and involves more investigation on several research topics, such as membrane and catalyst development and stability, as well as alternative feeding solutions that do not compromise the availability of fresh water. These topics are addressed in this paper, mentioning the state-of-the-art materials, new promising ones, and providing future research directions to improve AEMWE towards a most mature technology.
Analysis and Evaluation of the Possibility of Electricity Production from Small Photovoltaic Installations in Poland
Authors: Izdebski Waldemar , Kosiorek Katarzyna
Published: January 2023

The production of electricity from photovoltaic (PV) systems is one of the significant opportunities for novel renewable energy sources. The PV systems can provide energy with minimum environmental harm; however, the usage of this energy source becomes strongly dependent on a wide range of social, economic, and technical factors. Based on the growing demand for renewable energy sources, the aim of the work focuses on the socio-economic analysis of possibilities of producing electricity from photovoltaic installations up to 10 kW in Poland. Based on expert research, the factors influencing energy production from PV systems were assessed based on factor three analysis (level II with 5 factors, level III with 15 factors). Using the expert-mathematical method, a hierarchy of economic, technical, and social factors of PV-based energy production was evaluated. The analysis of socio-economic factors indicated that the greatest impact on the PV systems development in Poland depends on energy purchasing costs and EU law regulations on renewable energy sources (RESs). The most influential factors were then used to forecast the possible cost-effectiveness of renewable energy production of home energy production. The study demonstrates the importance of renewable energy sources utilization and the cost-effectiveness of solar energy production in small PV systems in Poland.
Analysis of the Impact of the Level of Self-Consumption of Electricity from a Prosumer Photovoltaic Installation on Its Profitability under Different Energy Billing Scenarios in Poland
Authors: Kurz Dariusz , Nowak Agata
Published: January 2023

Renewable Energy Sources (RES) have been gaining popularity on a continuous basis and the current global political situation is only accelerating energy transformation in many countries. Objectives related to environmental protection and use of RES set by different countries all over the world as well as the European Union (EU) are becoming priorities. In Poland, after years of a boom in photovoltaic (PV) installations, the Renewable Energy Sources Act has been amended, resulting in a change to the billing system for electricity produced by individual prosumers. The change in the billing method, also in pursuance to the provisions of EU laws, has contributed to the inhibition of the PV installation market for fear of energy prices and investment payback time. In this paper, by using the Net Present Value (NPV) method, three mechanisms of billing of electricity from prosumer micro-installations—based on the net-metering principle and net-billing principle (using monthly and hourly prices)—have been analysed. Particular attention has also been paid to the aspects of electricity self-consumption and energy storages, which play a significant role in the economy of PV installations in the net-billing system.
Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals
Authors: Jaen-Cuellar Arturo Yosimar , Trejo-Hernández Miguel , Osornio-Rios Roque Alfredo , Antonino-Daviu Jose Alfonso
Published: January 2023

Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.
Gas–Liquid Flow Behavior in Condensate Gas Wells under Different Development Stages
Authors: Wang Weiyang , Zhu Wei , Li Mingzhong
Published: January 2023

The phase state prediction methods of condensate gas are relatively mature, but the effect of phase changes on gas–liquid mixture flow behavior and the liquid-carrying capacity of gas has not been researched in detail. This study applied PIPESIM software to predict the fluid phase properties under different development stages of a condensate gas reservoir in Shengli Oilfield and determined the phase diagram and physical properties of the well stream on the basis of the optimized equation of state (EOS). Then the influence of phase change characteristics on wellbore flow behavior and critical liquid-carrying gas velocity was analyzed. The study showed that compared with the early development stage, fewer heavy components are produced and the produced gas–liquid ratio is increased in the late stage of the condensate gas reservoir. In addition, the pressure loss of fluid is decreased, the critical liquid-carrying gas velocity and flow rate are reduced, and the liquid-lifting difficulty is reduced for gas. The reason is that the liquid density decreases obviously due to the phase change, while the gas density is almost unchanged, and the oil–gas surface tension decreases obviously, resulting in a decrease in the critical liquid-carrying gas velocity. At the same time, the variation in the gas compressibility factor is very small, which leads to a decrease in the critical liquid-carrying gas flow rate.
Cosimulation of Integrated Organic Photovoltaic Glazing Systems Based on Functional Mock-Up Unit
Authors: Riquelme Santiago , Gros Adrien , Klemz Bruno , Moura Luís Mauro , Mendes Nathan
Published: January 2023

This study presents an approach to simulating building-integrated photovoltaic glazing systems composed of semitransparent organic photovoltaic (ST-OPV) elements. The approach consists of a mathematical cosimulation model based on the energy balance of complex glazing systems, considering heat transfer as conduction, mixed convection, and radiation effects. The cosimulation method is based on a functional mock-up unit (FMU) developed in Python and the building simulation program Domus. This work aims at presenting a cosimulation technique that can be easily applied to building energy simulation tools for the assessment of photovoltaic energy generation in glazing systems. The cosimulation glazing model was verified according to ANSI/ASHRAE Standard 140-2011, and the zone temperature was kept within with a root medium square error (RMSE) of 1.45 °C. The simulated building with an ST-OPV system showed promising results and could be applied to near-zero energy buildings since each 6-m2 glazing has a power generation of around 77 W, equivalent to 9% of available solar resource.