bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Changes of the Intercanine and Intermolar Widths Following Palatal Expansion in the Mixed Dentition Patients with Bilateral Posterior Crossbite: A Systematic Review
Authors: Lim Yen Nie , Baharin Fadzlinda , Lin Galvin Sim Siang , Hassan Rozita , Tsai Milton Hongli , Wei Lim Chia , Yeoh Suzanne , Ping Mark Ko Xiang
Published: February 2023

This systematic review aimed to identify the intercanine and intermolar width changes following palatal expansion in bilateral posterior crossbite (PXB) in mixed dentition. This review was registered in the PROSPERO database (CRD42021275833). All randomized controlled trials (RCT) and non-RCT articles between 1980 and August 2022 on the palatal expansion of bilateral PXB in mixed dentition were searched in seven online databases (Google Scholar, Ovid, Web of Science, Scopus, EBSCOHost, Cochrane Library and PubMed). The risk of bias (RoB) of the articles included was analyzed using the Joanna Briggs Institute (JBI) critical appraisal tool. Three non-RCT studies were included and showed a low risk of bias. Meta-analysis on the changes in intercanine and intermolar widths was not performed due to study design heterogeneity. One study reported an over-correction of the bilateral PXB. There is a need for more RCT studies with standardized landmark measurements, outcome assessment methods and retention periods to investigate the interdental changes following palatal expansion.
SunMap: Towards Unattended Maintenance of Photovoltaic Plants Using Drone Photogrammetry
Authors: Hernández-López David , Oña Esteban Ruíz de , Moreno Miguel A. , González-Aguilera Diego
Published: February 2023

Global awareness of environmental issues has boosted interest in renewable energy resources, among which solar energy is one of the most attractive renewable sources. The massive growth of PV plants, both in number and size, has motivated the development of new approaches for their inspection and monitoring. In this paper, a rigorous drone photogrammetry approach using optical Red, Green and Blue (RGB) and Infrared Thermography (IRT) images is applied to detect one of the most common faults (hot spots) in photovoltaic (PV) plants. The latest advances in photogrammetry and computer vision (i.e., Structure from Motion (SfM) and multiview stereo (MVS)), together with advanced and robust analysis of IRT images, are the main elements of the proposed methodology. We developed an in-house software application, SunMap, that allows automatic, accurate, and reliable detection of hot spots on PV panels. Along with the identification and geolocation of malfunctioning PV panels, SunMap provides high-quality cartographic products by means of 3D models and true orthophotos that provide additional support for maintenance operations. Validation of SunMap was performed in two different PV plants located in Spain, generating positive results in the detection and geolocation of anomalies with an error incidence lower than 15% as validated by the manufacturer’s standard electrical tests.
Design and Implementation of UAV Velocity Controller Based on Reference Model Sliding Mode Control
Authors: Wang Qi , Wang Wei , Suzuki Satoshi , Namiki Akio , Liu Hongxun , Li Ziran
Published: February 2023

In recent years, multi-rotor unmanned aerial vehicles (UAV) have been widely applied for various applications; however, they are yet to be as commonly utilized in certain industrial transportation applications. Thus, this work designed and implemented a reference model-based integral sliding mode control (SMC) method applied to the velocity controller of a multi-rotor UAV. The designed controller was compared with an integral SMC scheme, and then the controller and modeling robustness were verified. Finally, the proposed method was applied to an industrial six-rotor UAV. Three experiments involving target-tracking, fixed-point hovering, and robustness verification were executed. A load of approximately 81.5% of the UAV’s self-weight was used to verify the robustness of the proposed scheme against parameter uncertainty. This work will serve as a meaningful reference for the application of the SMC in practical industrial applications.
Kinetics of Precipitation Processes at Non-Zero Input Fluxes of Segregating Particles
Authors: Schmelzer Jürn W. P. , Tropin Timur V. , Abyzov Alexander S.
Published: February 2023

We consider the process of formation and growth of clusters of a new phase in segregation processes in solid or liquid solutions in an open system when segregating particles are added continuously to it with a given rate of input fluxes, Φ. As shown here, the value of the input flux significantly affects the number of supercritical clusters formed, their growth kinetics, and, in particular, the coarsening behavior in the late stages of the process. The detailed specification of the respective dependencies is the aim of the present analysis, which combines numerical computations with an analytical treatment of the obtained results. In particular, a treatment of the coarsening kinetics is developed, allowing a description of the development of the number of clusters and their average sizes in the late stages of the segregation processes in open systems, which goes beyond the scope of the classical Lifshitz, Slezov and Wagner theory. As is also shown, in its basic ingredients, this approach supplies us with a general tool for the theoretical description of Ostwald ripening in open systems, or systems where the boundary conditions, like temperature or pressure, vary with time. Having this method at one’s disposal supplies us with the possibility that conditions can be theoretically tested, leading to cluster size distributions that are most appropriate for desired applications.
TREPH: A Plug-In Topological Layer for Graph Neural Networks
Authors: Ye Xue , Sun Fang , Xiang Shiming
Published: February 2023

Topological Data Analysis (TDA) is an approach to analyzing the shape of data using techniques from algebraic topology. The staple of TDA is Persistent Homology (PH). Recent years have seen a trend of combining PH and Graph Neural Networks (GNNs) in an end-to-end manner to capture topological features from graph data. Though effective, these methods are limited by the shortcomings of PH: incomplete topological information and irregular output format. Extended Persistent Homology (EPH), as a variant of PH, addresses these problems elegantly. In this paper, we propose a plug-in topological layer for GNNs, termed Topological Representation with Extended Persistent Homology (TREPH). Taking advantage of the uniformity of EPH, a novel aggregation mechanism is designed to collate topological features of different dimensions to the local positions determining their living processes. The proposed layer is provably differentiable and more expressive than PH-based representations, which in turn is strictly stronger than message-passing GNNs in expressive power. Experiments on real-world graph classification tasks demonstrate the competitiveness of TREPH compared with the state-of-the-art approaches.
System Integrated Information
Authors: Marshall William , Grasso Matteo , Mayner William G. P. , Zaeemzadeh Alireza , Barbosa Leonardo S. , Chastain Erick , Findlay Graham , Sasai Shuntaro , Albantakis Larissa , Tononi Giulio
Published: February 2023

Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (called a complex), which are then used to formulate a mathematical framework for assessing both the quality and quantity of experience. The explanatory identity proposed by IIT is that an experience is identical to the cause–effect structure unfolded from a maximally irreducible substrate (a Φ-structure). In this work we introduce a definition for the integrated information of a system (φs) that is based on the existence, intrinsicality, information, and integration postulates of IIT. We explore how notions of determinism, degeneracy, and fault lines in the connectivity impact system-integrated information. We then demonstrate how the proposed measure identifies complexes as systems, the φs of which is greater than the φs of any overlapping candidate systems.
Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic
Authors: Chen Jingyao , Yang Jie , Huang Shigao , Li Xin , Liu Gang
Published: February 2023

This study proposes a decomposed broad learning model to improve the forecasting accuracy for tourism arrivals on Hainan Island in China. With decomposed broad learning, we predicted monthly tourist arrivals from 12 countries to Hainan Island. We compared the actual tourist arrivals to Hainan from the US with the predicted tourist arrivals using three models (FEWT-BL: fuzzy entropy empirical wavelet transform-based broad learning; BL: broad Learning; BPNN: back propagation neural network). The results indicated that US foreigners had the most arrivals in 12 countries, and FEWT-BL had the best performance in forecasting tourism arrivals. In conclusion, we establish a unique model for accurate tourism forecasting that can facilitate decision-making in tourism management, especially at turning points in time.
Characterizing the Solar Activity Using the Visibility Graph Method
Authors: Zurita-Valencia Tomás , Muñoz Víctor
Published: February 2023

In this paper, the Sun and its behavior are studied by means of complex networks. The complex network was built using the Visibility Graph algorithm. This method maps time series into graphs in which every element of the time series is considered as a node and a visibility criterion is defined in order to connect them. Using this method, we construct complex networks for magnetic field and sunspots time series encompassing four solar cycles, and various measures such as degree, clustering coefficient, mean path length, betweenness centrality, eigenvector centrality and decay exponents were calculated. In order to study the system in several time scales, we perform both a global, where the network contains information on the four solar cycles, and a local analysis, involving moving windows. Some metrics correlate with solar activity, while others do not. Interestingly, those metric which seem to respond to varying levels of solar activity in the global analysis, also do in the moving windows analysis. Our results suggest that complex networks can provide a useful way to follow solar activity, and reveal new features on solar cycles.
Determinants Influencing Distance Learning at Health Technology Higher Education Institutions in Portugal
Authors: Teresa Ribeiro Ricardo , Cunha Gilda , Silva Carina , Medeiros Nuno , Viegas Claúdia , Ferro Amadeu , Poças Ilda , Raposo Hélder , Eiras Margarida
Published: February 2023

Introducing distance learning within higher education institutions (HEIs) is a key societal issue, especially in the health sector, due to its in vivo learning nature. Public policies play an important role in these digital environments. This study aims to identify the determinants influencing national public policies that foster digital learning transformation in Health HEIs in Portugal. A prospective survey, using the structural analysis of Godet’s method, is conducted, and data are gathered from different health sector stakeholders. Despite the efforts to increase digital literacy and funding toward digital learning in HEIs, a weak strategy and implementation of a national plan for distance learning in Health HEIs are still prevalent. The driver to success is grounded on national and international cooperation between health professionals, hospitals, and HEIs through transferability processes of innovative practices.
A Scientometric Review of Research Trends in Language Assessment Literacy
Authors: Wang Xuan , Zuo Jingying , Liu Feiyu , Sun Ze
Published: February 2023

This study provides a scientometric review of language assessment literacy (LAL) research published in the Web of Science (WoS) core collection between 2008, when LAL was first indexed as a combined phrase, and 2021. Six comprehensive analyses were conducted utilizing CiteSpace: publication and cited trends, dual-map overlay, most cited papers, co-authorship, co-occurrence, and co-citation. Results showed that the annual publications have shown a fluctuating upward trend that has undergone three stages: the initial (2008–2012), fluctuated (2013–2017), and steadily increasing stages (2018–2021). Professional conferences in language assessment and testing have significantly contributed to these development stages and are closely related to stage-critical milestones. “Teacher” appears as one of the most frequently researched stakeholder groups, and research on other stakeholders, especially students, has increased. “Psychology” and “Education” are major areas for both citing and cited journals and provide a solid foundation for LAL research. However, the interdisciplinary nature of LAL research still leaves room for exploration. This study provides a reference point for future research by identifying trends in LAL research and comparing existing studies. Furthermore, when authors and author linkage networks are used to build a research network, multi-dimensional indicators should be considered to guarantee the completeness of the network.
Should Improvisation Be Regularly Included in Music Lessons? A Single-Case Quasi-Experimental Study Exploring the Differences in the Electrical Activity of the Brain between Musical Improvisation and Sight-Reading
Authors: Mateos-Moreno Daniel , Erlanson Ernst
Published: February 2023

Thanks to advances in portable electroencephalography technology (PEEGT), investigating the states of the mind is a relatively new area of research with a promising future in music education. Our aim, drawing upon PEEGT, was to contribute to the study of the differences in brain activity between piano music improvisation and sight-reading. We used the EMOTIV Insight helmet to register brain activity in repeated instances of one purposefully selected case while pursuing these two activities in experimental, controlled conditions. Thereafter, we pursued descriptive and robust statistical analysis of the variables offered by the Emotiv software. The quantitative data resulting from our study were triangulated with the qualitative analysis of a logbook filled by the participant on his subjective experiences. While the quantitative results were not statistically significant in measuring differences between the experimental conditions, trends were indeed found in the data and triangulated by our qualitative results. Our study provides preliminary evidence that supports the value of regularly incorporating musical improvisation moments in music education. This, to increase the students’ excitement towards music lessons in cases that are similar to the case under scrutiny. Finally, implications and limitations are discussed in relation to the research design, the use of PEEGT technology, and the Emotiv software and hardware for investigating brain activity in pursuing musical activities.
Randomized Controlled Trial of Home Telemonitoring of Blood Pressure with an Adapted Tensiometer with SMS Capability
Authors: Calderón-Anyosa Renzo , Tincopa Jean Pierre , Raza Mabel , Cárcamo Cesar P.
Published: February 2023

Despite being a public health problem, less than a third of hypertensive patients manage to control blood pressure (BP). In this paper, we conducted a two-arm randomized controlled trial to investigate the efficacy of an SMS-based home BP telemonitoring system compared to usual care in patients with uncontrolled hypertension from a primary care center. This study was conducted between April and August 2018. Participants in the intervention arm used a custom-designed telemonitoring device for two weeks and were followed up for two additional weeks; controls were followed for 4 weeks. The main objective of this study is to evaluate the impact on blood pressure of a telemonitoring system using a blood pressure monitor adapted to send data via SMS to health providers in primary care centers for 4 weeks. In this trial, 38 patients were included in the analysis (18 in each arm), 68% were women, and the mean age was 68.1 [SD: 10.8 years], with no differences between arms. Among the results we found was that There was no significant difference in the change in systolic BP values between the control and intervention arm (−7.2 [14.9] mmHg vs. −16.3 [16.7] mmHg; p = 0.09). However, we found a significant difference in the change of diastolic BP (−1.2 [6.4] mmHg vs. −7.2 [9.8] mmHg; for the control and intervention arms, respectively p = 0.03). With all this, we conclude that an SMS-based home BP telemonitoring system is effective in reducing diastolic BP by working in conjunction with primary care centers. Our findings represent one of the first interventions of this type in our environment, being an important alternative for the control of high blood pressure.
Lightweight Pedestrian Detection Based on Feature Multiplexed Residual Network
Authors: Sha Mengzhou , Zeng Kai , Tao Zhimin , Wang Zhifeng , Liu Quanjun
Published: February 2023

As an important part of autonomous driving intelligence perception, pedestrian detection has high requirements for parameter size, real-time, and model performance. Firstly, a novel multiplexed connection residual block is proposed to construct the lightweight network for improving the ability to extract pedestrian features. Secondly, the lightweight scalable attention module is investigated to expand the local perceptual field of the model based on dilated convolution that can maintain the most important feature channels. Finally, we verify the proposed model on the Caltech pedestrian dataset and BDD 100 K datasets. The results show that the proposed method is superior to existing lightweight pedestrian detection methods in terms of model size and detection performance.
Emotion Recognition Based on the Structure of Narratives
Authors: Pólya Tibor , Csertő István
Published: February 2023

One important application of natural language processing (NLP) is the recognition of emotions in text. Most current emotion analyzers use a set of linguistic features such as emotion lexicons, n-grams, word embeddings, and emoticons. This study proposes a new strategy to perform emotion recognition, which is based on the homologous structure of emotions and narratives. It is argued that emotions and narratives share both a goal-based structure and an evaluation structure. The new strategy was tested in an empirical study with 117 participants who recounted two narratives about their past emotional experiences, including one positive and one negative episode. Immediately after narrating each episode, the participants reported their current affective state using the Affect Grid. The goal-based structure and evaluation structure of the narratives were analyzed with a hybrid method. First, a linguistic analysis of the texts was carried out, including tokenization, lemmatization, part-of-speech tagging, and morphological analysis. Second, an extensive set of rule-based algorithms was used to analyze the goal-based structure of, and evaluations in, the narratives. Third, the output was fed into machine learning classifiers of narrative structural features that previously proved to be effective predictors of the narrator’s current affective state. This hybrid procedure yielded a high average F1 score (0.72). The results are discussed in terms of the benefits of employing narrative structure analysis in NLP-based emotion recognition.
A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
Authors: Xu Fangyuan , Guo Xuan , Li Zeyu , Jia Hanbo , Wu Danyu , Wu Jin
Published: February 2023

This paper presents a 1-GS/s12-bit pipelined analog-to-digital converter (ADC) fabricated in 40-nm CMOS technology that optimizes the settling time, bit error rate, and robustness. This ADC uses an improved timing called pre-quantization timing (PQT), which implements quantization in half the time of the sampling phase to maximize the output-settling time of the operational amplifier (op-amp). A complete clocking scheme along with a delay lock loop (DLL) is proposed to generate an accurate timing no matter how process, voltage, and temperature (PVT) change. Based on PQT, a high-speed comparator circuit is adopted to obtain a bit error rate (BER) below 10−15. Sample and hold amplifier (SHA) is used to guarantee robustness over the wide input frequency. Furthermore, a low-cost automatic calibration is implemented to correct residual curves, and inter-stage gain errors are also corrected. This ADC achieves a signal-to-noise-and-distortion ratio (SNDR) of 57.3 dB and a spurious-free dynamic range (SFDR) of 78.5 dB at a 227 MHz input frequency. The measured differential nonlinearities (DNL) and integral nonlinearities (INL) after calibration are ±0.7 LSB and ±1.50 LSB, respectively. The power consumption of the ADC core is 97.6-mW, and the Walden figure of merit (FoM) is 172.9-fJ/conversion-step.
Relationship between Odor Adsorption Ability and Physical–Hydraulic Properties of Torrefied Biomass: Initial Study
Authors: Sobol Łukasz , Łyczko Jacek , Dyjakon Arkadiusz , Sroczyński Ryszard
Published: February 2023

Various techniques are implemented to reduce odor emission due to their potential multi-source nature. One modern approach is the use of thermochemically processed biomass to eliminate odors. Compared with raw biomass, processed biomass is characterized by greater porosity and an expanded specific surface. In these laboratory experiments, adsorption tests for a mixture of indole, 2,3-dimethylpyrazine, and 2,3,5-trimethylpyrazine are carried out using torreficates produced from biomass from the agri-food industry (walnut shells, orange peels, peach stones, and apple wood chips). This research is focused on the determination of the correlation between the physical-hydraulic properties of the torreficates and their ability to reduce the odors simulated by the selected compounds. The results indicate that 2,3-dimethylpyrazine and 2,3,5-trimethylpyrazine are not detected in any of the investigated low-temperature biochars. However, indole is detected in most materials, and its most significant quantities are adsorbed on torreficates made of orange peels (45.64 µg·mL−1 ± 40.02 µg·mL−1) and peach stones (61.26 µg·mL−1 ± 49.55 µg·mL−1). The performed analysis reveals that the highest correlation with the ability to adsorb indole is found for the average pore size (r = 0.66) and specific density (r = −0.63) as well as the content of fixed carbon (r = 0.66), which may prove the importance of physical-hydraulic properties in odor sorption by low-temperature torreficates.
Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method
Authors: Krishnamurthy Nandini K. , Sabhahit Jayalakshmi N. , Jadoun Vinay Kumar , Gaonkar Dattatraya Narayan , Shrivastava Ashish , Rao Vidya S. , Kudva Ganesh
Published: February 2023

In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 106 for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm.
Potential Use of Industrial Biomass Waste as a Sustainable Energy Source in the Future
Authors: Kalak Tomasz
Published: February 2023

Aspects related to the growing pollution of the natural environment and depletion of conventional fossil fuels have become the motive for searching for ecofriendly, renewable, and sustainable alternative energy sources. Particular attention is paid to industrial waste, especially waste of biomass materials, which can be converted into biofuels and energy that meets the growing needs of humanity. The use of biomass for energy purposes is less damaging to the environment, the materials are low-cost, locally available in large quantities, and create employment opportunities for workers in suburban and rural areas around the world. This article discusses issues related to the use of waste biomass materials as renewable energy sources. The current energy situation in the world is analyzed in terms of production, consumption, and investments in green energy. Types of biomass and individual physicochemical and energy properties of waste plant materials obtained for energy purposes are described. Currently available methods of converting biomass into energy, including mechanical, thermal, and biochemical techniques are discussed. The conducted analysis indicates the possibility of using it as a competitive source of electricity and heat. Understanding the properties of biomass materials allows us to understand the right way to use them for energy and reduce the consumption of fossil fuels in the future.
Study on Geothermal Genesis Mechanism and Model in the Western Margin of Ordos Basin
Authors: An Baizhou , Zeng Zhaofa , Sun Boyang , Bai Yadong , Yan Zhaotao , Zhao Jianwei , Yan Jiahe
Published: February 2023

Among so many fault basins around Ordos presenting good geothermal background, Yinchuan Plain is the key development and protection area of the Ecological and Economic Belt along the Yellow River in Ningxia and Yinchuan Metropolitan Area. The study of geothermal resources in this region is of great significance to the sustainable economic development of Yinchuan Plain. Most scholars suggested that Yinchuan Plain geothermal resources are low-to-medium temperature geothermal resources and have high value in terms of development and utilization due to their large reserves, good water quality conditions, and wide distribution. However, there is much controversy over the geothermal mechanism of this region. As an effective means to study geothermal mechanism, geophysical methods can be used to study regional basement uplift and structural distribution characteristics. Based on the discussion of regional geothermal geology background, this paper studies the distribution rules of Moho and Curie depths in the region by applying different geophysical data. The results show that the western margin of Yinchuan Plain is obviously uplifted. As inferred from the epicentral distribution of Yinchuan Plain section, there is a significant difference of the seismic bottom interface on the east and west sides of the Yellow River fault in Yinchuan Plain. The three-dimensional gravity inversion confirms basement uplift in this region. By referring to results obtained by the inversion of gravity and magnetic data and deep seismic exploration, the paper presents a comprehensive analysis of the regional geothermal geology conditions and proposes a geothermal conceptual model of Yinchuan Plain. The heat source comes from the earth heat flow and migrates to the thermal reservoir through the fault or fissure convection. The deep-large active faults provide a channel for deep heat flow to the shallow part, and the thermal fluid accumulates in the uplift area to form the high geothermal anomaly area. There should be two large-scale geological activities in the geotropics around Ordos. One is the basement uplift of the basins around Ordos platform, which provide the heat source. The other is the uplift of Ordos, which places the surrounding basin in an extensional environment and provides a channel for the heat source upwelling.
Sorption–Dilatometric Properties of Coal from a High-Methane Mine in a CO2 and CH4 Atmosphere
Authors: Baran Paweł , Kozioł Stanisław , Czerw Katarzyna , Smoliński Adam , Zarębska Katarzyna
Published: February 2023

Although highly developed countries are trying to diversify away from coal-based energy, many economies rely on this resource. Its consumption results in the production of carbon dioxide, which promotes global warming, necessitating its sequestration. This paper presents the sorption–dilatometric relationships of hard coal samples differing in vitrinite and inertinite content. The studies were carried out under isothermal conditions (298 K) at a free pressure drop complemented by measurements under non-isothermal conditions (298 K to 323 K). The tests were performed on an original apparatus, based on the operation of an Arduino microcontroller. For the natural porosity to be preserved and for a better representation of the behaviour of the coal–gas system, samples in the form of cuboidal blocks were used, making this apparatus unique worldwide. Based on the study, it appears that the difference in petrographic composition affects the behaviour of the coal structure, influencing differences in the sorption–dilatometric properties. In the case of the sample with higher vitrinite content, the amount of adsorbed gases is higher.
Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN
Authors: Kim Tae-Gyu , Lee Hoon , An Chang-Gyun , Yi Junsin , Won Chung-Yuen
Published: February 2023

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration.
Well Placement Optimization through the Triple-Completion Gas and Downhole Water Sink-Assisted Gravity Drainage (TC-GDWS-AGD) EOR Process
Authors: Al-Mudhafar Watheq , Wood David , Al-Obaidi Dahlia , Wojtanowicz Andrew
Published: February 2023

Gas and downhole water sink-assisted gravity drainage (GDWS-AGD) is a new process of enhanced oil recovery (EOR) in oil reservoirs underlain by large bottom aquifers. The process is capital intensive as it requires the construction of dual-completed wells for oil production and water drainage and additional multiple vertical gas-injection wells. The costs could be substantially reduced by eliminating the gas-injection wells and using triple-completed multi-functional wells. These wells are dubbed triple-completion-GDWS-AGD (TC-GDWS-AGD). In this work, we design and optimize the TC-GDWS-AGD oil recovery process in a fictitious oil reservoir (Punq-S3) that emulates a real North Sea oil field. The design aims at maximum oil recovery using a minimum number of triple-completed wells with a gas-injection completion in the vertical section of the well, and two horizontal well sections—the upper section for producing oil (from above the oil/water contact) and the lower section for draining water below the oil/water contact. The three well completions are isolated with hydraulic packers and water is drained from below the oil–water contact using the electric submersible pump. Well placement is optimized using the particle swarm optimization (PSO) technique by considering only 1 or 2 TC-GDWS-AGD wells to maximize a 12-year oil recovery with a minimum volume of produced water. The best well placement was found by considering hundreds of possible well locations throughout the reservoir for the single-well and two-well scenarios. The results show 58% oil recovery and 0.28 water cut for the single-well scenario and 63.5% oil recovery and 0.45 water cut for the two-well scenario. Interestingly, the base-case scenario using two wells without the TC-GDWS-AGD process would give the smallest oil recovery of 55.5% and the largest 70% water cut. The study indicates that the TC-GDWS-AGD process could be more productive by reducing the number of wells and increasing recovery with less water production.
Optimal Control of TBCC Engines in Mode Transition
Authors: He Zengming , Zhang Junlong , Sun Hongfei
Published: February 2023

This paper mainly studies the optimal control problem of turbine-based combined cycle (TBCC) engines in the mode-transition stage. Based on the TBCC scheme proposed by Xiamen University, an aerothermodynamic model is established as a verification model for the validity of control laws. To reduce the complexity of control design, a control-oriented linear parameter-varying (LPV) model with Mach number as a scheduling variable is established under a given flight path. The design of mode-transition points and distribution of air-flow-rate among paths during the mode-transition process are transformed into linear quadratic (LQ) optimal control problems for an LPV system under the initial and terminal as well as process constraints. By optimizing the opening of the splitters of the inlet and the fuel flow in each channel, the optimal mode-transition points are found to achieve coordinated control and complete the high-precision thrust tracking during the mode-transition process.
Evaluation of Different Optimization Techniques and Control Strategies of Hybrid Microgrid: A Review
Authors: Shezan Sk. A. , Kamwa Innocent , Ishraque Md. Fatin , Muyeen S. M. , Hasan Kazi Nazmul , Saidur R. , Rizvi Syed Muhammad , Shafiullah Md , Al-Sulaiman Fahad A.
Published: February 2023

Energy consumption is increasing rapidly; hence, the energy demand cannot be fulfilled using traditional power resources only. Power systems based on renewable energy, including solar and wind, are effective and friendly for the environment. Islanded hybrid microgrid systems (IHMS) are relatively new in this industry and combine two or more sustainable sources, such as wind turbines, solar photovoltaic (PV), and other renewable alternatives, ocean, wave, and geothermal energy, etc. While sustainable, long-lasting power sources are the best choice to satisfy the growing energy demands, they are still not yet ready to be used on a large scale due to their stochastic characteristics. Furthermore, integrating these sources into the existing energy system can cause high technical difficulties, due to the stochastic nature of solar and wind in the conventional grid system and common stand-alone framework. A review of research and applications of the effective hybridization of renewable energy sources is therefore essential to address those technical and economic issues and ensure system stability, reliability, and cost-effectiveness. This article discusses the challenges that might arise when a PV plant and a wind power station are combined to produce power for the conventional main grid or in a stand-alone system. In addition, this analysis provides light on optimization approaches for improving power quality and cost-effectiveness in a solar and wind integrated IHMS. Voltage fluctuation, frequency deviation, and the uncertain nature of solar irradiation and wind sources are significant challenges for both grid-connected and standalone hybrid systems. This study then provides an overview of the control strategies which might help enhance the integration of the IHMS in producing electricity for distribution to the grid-connected load and the islanded load. In this study, the possible issues that can hinder the smooth integration of these renewable sources have been discussed. Finally, this study discusses the recent platforms being used in IHMS as well as the potential of dispatch strategies on solar and wind-integrated IHMS.
Economic Operation Strategy of an EV Parking Lot with Vehicle-to-Grid and Renewable Energy Integration
Authors: Qi Jiwen , Li Li
Published: February 2023

The economic operation of an electric vehicle (EV) parking lot under different cases are explored in the paper. The parking lot is equipped with EV charging stations with a vehicle-to-grid (V2G) function, renewable energy sources (RESs), and energy storage system (ESS). An optimisation problem is formulated to maximise the profit of the parking lot from EV charging and feed-in energy to the grid under various charging modes while considering the uncertain factors, ESS degradation, and diverse EV parking conditions. The electricity market price, solar radiation and wind speed are considered as uncertain factors, and the scenred toolbox of MATLAB is used to generate scenarios. Based on the parking time of different EVs, the model classifies the EVs entering the charging station and dynamically determines the charging price according to their charging demand through a linear price-demand relationship. The efficacy of the proposed model is verified by the comparison with two other models under three different cases. It is shown that the proposed model gains the most profit based on the proposed V2G services and dynamic charging price.
Analysis of Losses in Two Different Control Approaches for S-S Wireless Power Transfer Systems for Electric Vehicle
Authors: Kumar Abhay , Bertoluzzo Manuele , Jha Rupesh Kumar , Sagar Amritansh
Published: February 2023

This paper presents the study and detailed analysis of converter losses at different stages together with the series-series (S-S) compensating coils in wireless power transfer (WPT) systems, via two distinct approaches to control the power converters. The two approaches towards wireless DC–DC power flow control are termed as the Single Active High-Frequency Wireless Power Transfer (SAHFWPT) system and the Dual Active High-Frequency Wireless Power Transfer (DAHFWPT) system. The operation of converters in SAHFWPT and DAHFWPT are controlled by the extended phase shift (EPS) and dual phase shift method respectively. The general schematic of the SAHFWPT system consists of an active bridge and a passive bridge, while the schematic of the DAHFWPT system consists of both active bridges. The efficiency evolutions of ideal SAHFWPT and DAHFWPT are far away from the real ones. Moreover, this article analyzes the operation and losses of the uni-directional power flow of the WPT system, i.e., from the DC bus in the primary side to the battery load in the secondary side. The loss estimation includes high-frequency switching losses, conduction losses, hard turn on and turn off coil losses, etc. Moreover, the efficiency of the WPT system depends on operation of the converter. A 50 W–3600 W Power range system at a resonant frequency of 85 kHz is implemented in MATLAB/SIMULATION to demonstrate the validity of the proposed method.
Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform
Authors: Tubeuf Carlotta , Birkelbach Felix , Maly Anton , Hofmann René
Published: February 2023

The increasing demand for flexibility in hydropower systems requires pumped storage power plants to change operating modes and compensate reactive power more frequently. In this work, we demonstrate the potential of applying reinforcement learning (RL) to control the blow-out process of a hydraulic machine during pump start-up and when operating in synchronous condenser mode. Even though RL is a promising method that is currently getting much attention, safety concerns are stalling research on RL for the control of energy systems. Therefore, we present a concept that enables process control with RL through the use of a digital twin platform. This enables the safe and effective transfer of the algorithm’s learning strategy from a virtual test environment to the physical asset. The successful implementation of RL in a test environment is presented and an outlook on future research on the transfer to a model test rig is given.
Evaluating the Degree of Tectonic Fracture Development in the Fourth Member of the Leikoupo Formation in Pengzhou, Western Sichuan, China
Authors: Xie Qiang , Li Gao , Yang Xu , Peng Hongli
Published: February 2023

The extent of fracture development is associated with the degree of enrichment of a natural gas reservoir and its productivity. Based on numerical simulation results of the paleotectonic stress field, a set of evaluation methods for determining the degree of development of reservoir tectonic fractures were established using rock rupture criteria. Taking the fourth member of the Leikoupo Formation in the Pengzhou area of western Sichuan as an example, a finite element (FE) method was employed to simulate the paleo-tectonic stress field during the period of fracture development, and the degree of tectonic fracture development was further evaluated using the above methods. The results indicated that effective fractures were created in the Himalayan period. In this time, mainly NE–NEE and nearly E–W strike tectonic fractures were developed in the target layer. The fractures were mainly low-angle and oblique fractures, while the high-angle fractures were less developed. According to the integrative fracture index (F), five typical fracture development areas were determined: the fault zone, and the northern, eastern, southeastern, and central regions of the study area. The reliability of the fracture prediction results was verified using fracture distribution statistics and gas production test results.
Technical-Economic Evaluation of Residential Wind and Photovoltaic Systems with Self-Consumption and Storage Systems in Portugal
Authors: Camilo Fernando M. , Santos Paulo
Published: February 2023

At present, a worldwide paradigm shift has become apparent, with more and more consumers consuming the energy generated by renewable energy sources (RES) systems, such as wind or photovoltaic (PV) energy, sometimes benefiting from appropriate incentives by individual governments. Consequently, it is necessary to carry out technical–economic assessments to understand the evolution of the viability of RES investments. Within the framework of an intelligent network control environment, the smart grid (SG) concept is associated with this model, and is an important tool in the management of energy distribution networks. This article aims to make a further contribution to this issue by analyzing the economic feasibility of investing in residential consumers, considering different RES configurations. Scenarios covered in this study include: “inject all on the low voltage network/consume all on the low voltage network”, self-consumption, net-metering, and storage systems. The economic study results in this article show that self-consumption with and without the injection of excess electricity into the grid is quite attractive. The bi-hourly tariff was found to be more profitable than other tariffs. Variable tariffs (bi or tri-hourly) are more profitable than fixed tariffs. It is also concluded that investment in storage systems is not yet an economically viable solution due to the high price of energy storage.
High-Resolution Load Forecasting on Multiple Time Scales Using Long Short-Term Memory and Support Vector Machine
Authors: Zhang Sizhe , Liu Jinqi , Wang Jihong
Published: February 2023

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is performed via blind tests and the test results are consistent.
Static and Dynamic Analysis of Conductor Rail with Large Cross-Sectional Moment of Inertia in Rigid Catenary Systems
Authors: Feng Xiaohe , Gao Shibin , Song Yang , Hu Zeyao , Chen Long , Liang Tao
Published: February 2023

The rigid catenary system is widely used in tunnels to power electric trains via contact with a pantograph. Due to gravity, the contact wire normally has a sag that may affect the dynamic interaction performance with a pantograph. To reduce the contact wire sag, the most efficient measure is to improve the moment of inertia of the conductor rail, which is used to clamp the contact wire. Six new types of conductor rail with large moments of inertia are developed based on a conventional conductor rail. Then both the static and dynamic analyses are conducted to investigate the performance of the new types of conductor rail with a big moment of inertia. The conductor rail’s 3D solid finite element model is built using a finite element approach. The vertical deflection and the stress distribution are comparatively analyzed among different types of conductor rail. The analysis results indicate that the vertical deflection and maximum stress are significantly reduced when using the conductor rail with a large moment of inertia. The best performance is observed when the conductor rail of case 1 is used. The maximum sag is reduced by 28.37%, and the maximum stress is decreased by 27.76% compared with the conventional conductor. Finally, a pantograph model is included to evaluate the dynamic performance of the conductor rail with large moments of inertia. The results indicate that contact force fluctuation is significantly reduced after the conductor rails with large moments of inertia are presented. The conductor rail of case 1 shows the best performance, which can reduce the contact force standard deviation by 32% and 27% at speeds of 160 km/h and 200 km/h.