bluuebunny/crossref_metadata_2025 huggingface dataset:
Organic–Inorganic Hybrid Film MLAs Built on the Silicon Solar Cells to Improve the Photoelectric Conversion Efficiency
Authors: Zhang Xuehua , Bi Yang , Liu Shun , Zhang Wei , Hu Fangren
Published: March 2022

Light trapping micro-nano structures have been widely used to optimize the function of solar cell devices, especially microlens arrays (MLAs). In this article, we first prepared composite films by using sol-gel technology and the spin coating method, and then constructed heteromorphic MLAs on the surface of the composite films by using thermal reflow and UV nanoimprint technology; the substrate used was the silicon solar cell. Finally, the performance of the cells was improved. Optical transmission properties and surface morphology of the organic–inorganic hybrid films were detected by using a UV-Vis spectrometer and atomic force microscopy, respectively; it was revealed that the hybrid films had relatively excellent optical transmission performance in the visible light range. Surface structure of the hybrid film MLAs were detected by using SEM. At the same time, the optical imaging capabilities of MLAs were studied by using optical microscopy. Besides, the contact angles of the MLAs were also measured. It can be clearly seen that the prepared MLAs have a regular arrangement, clean appearance, and good imaging capabilities (from the actual test results). Finally, the various parameters of the silicon solar cells with hybrid film MLAs were studied. In addition, the power conversion efficiency (PCE) values increased by about 10.48% for the silicon solar cell with circular MLAs, compared to the silicon solar cell without a structure. The results show a concise and effective method to prepare organic–inorganic hybrid film MLAs on silicon solar cells, with significant improvement in photoelectric conversion efficiency.
The Nitrogen Bond, or the Nitrogen-Centered Pnictogen Bond: The Covalently Bound Nitrogen Atom in Molecular Entities and Crystals as a Pnictogen Bond Donor
Authors: Varadwaj Pradeep R. , Varadwaj Arpita , Marques Helder M. , Yamashita Koichi
Published: March 2022

The nitrogen bond in chemical systems occurs when there is evidence of a net attractive interaction between the electrophilic region associated with a covalently or coordinately bound nitrogen atom in a molecular entity and a nucleophile in another, or the same molecular entity. It is the first member of the family of pnictogen bonds formed by the first atom of the pnictogen family, Group 15, of the periodic table, and is an inter- or intra-molecular non-covalent interaction. In this featured review, we present several illustrative crystal structures deposited in the Cambridge Structure Database (CSD) and the Inorganic Crystal Structure Databases (ICSD) to demonstrate that imide nitrogen is not the only instance where nitrogen can act as an electrophilic agent. Analysis of a set of carefully chosen illustrative crystal systems shows that a covalently bound nitrogen atom in a variety of molecular entities features a σ-hole or even a π-hole, and these have the ability to sustain attractive engagements with negative sites to form inter- and/or intramolecular interactions that drive, or assist, the formation of a crystalline phase.
Investigation of Low-Calcium Circulating Fluidized Bed Fly Ash on the Mechanical Strength and Microstructure of Cement-Based Material
Authors: Zhang Wenyan , Wang Shuai , Zhao Liya , Ran Junsheng , Kang Wenjing , Feng Chunhua , Zhu Jianping
Published: March 2022

This present study mainly focuses on the influence of low-calcium circulating fluidized bed fly ash (LCFA) on the mechanical property and microstructure of cement-based materials under different curing conditions. The mechanical properties test was conducted by changing variable parameters, such as LCFA content, the internal mixing ratio of LCFA and fly ash (FA), and dry and water curing conditions. Further, the hydration products and pore structure were analyzed using XRD, FT-IR, TG-DTG, NI, SEM, and BET micro-testing technology. The strength development law of LCFA on cement-based materials is discussed. The research results show that LCFA has a certain degree of self-hardening and can be used as a cementitious material in cement-based materials. Still, the loose and porous microstructure of LCFA leads to higher water requirements, which reduces the fluidity of cement-based pastes. Water curing is favorable for promoting the development of LCFA on the long-term compressive strength of cement-based materials. When the LCFA was added to the cement, the optimal substitution ratio was 20%, and the compressive strength at 91 days reached 101 MPa. In the case of compounding LCFA and FA, when the internal mixing ratio of LCFA/FA was 3 and the total content was 20%, the mechanical properties were the highest, and the compressive strength at 91 days was 92 MPa. The microscopic analysis result shows that the cumulative hydration heat of the samples decreased significantly with the increase of dosage of LCFA. The main hydration products of cement-based materials mixed with LCFA were AFt, C-S-H gel, and Ca(OH)2. AFt and C-S-H gels are critical to the strength development of OPC-LCFA samples. The active Al2O3 and active SiO2 in LCFA were involved in hydration reactions to promote the formation of C-A-H and C-S-H gel and effectively promote the development of the mechanical properties. Overdosages of LCFA would reduce the ettringite formation rate. FA is not conducive to AFt formation in the hydration process of OPC-FA samples.
Dielectric Properties of Mg2TiO4-Doped Ca0.65Sr0.35Zr0.65Ti0.35O3 with High Withstand Voltage and Low Loss
Authors: Liu Yun , Huang Binbin , Chen Xiaoyang , Yu Ping
Published: March 2022

Mg2TiO4-doped Ca0.65Sr0.35Zr0.65Ti0.35O3 (CSZT) thin films with different Mg2TiO4 concentrations were deposited on the LaNiO3(LNO)/p-Si substrate using radio-frequency magnetron sputtering technology. The dielectric response of the prepared x% Mg2TiO4-CSZT thin films with frequency, voltage, and temperature was systematically studied. The tanδ and leakage current density of CSZT thin films were reduced effectively by introducing Mg2TiO4 content. The prepared 6% Mg2TiO4-CSZT thin film, due to its low loss (tanδ ~0.01 at 1 MHz), satisfied temperature stability (TCC ~−68 ppm/°C, from −55 °C to 205 °C), high withstand voltage (>160 V), and small leakage current density (about 3.34 × 10−6 A/cm2 at operating voltage of 160 V). This may be useful for capacitor materials in the next generation of portable electronic systems.
Mechanisms Generating Dichotomies in the Life Strategies of Heterotrophic Marine Prokaryotes
Authors: Thingstad Tron Frede , Øvreås Lise , Vadstein Olav
Published: March 2022

Understanding the mechanisms that generate and maintain diversity in marine prokaryotic communities is one of the main challenges for contemporary marine microbiology. We here review how observational, experimental, and theoretical evidence converge on the conclusion that the marine pelagic community of heterotrophic prokaryotes consists of organisms with two main types of life strategies. We illustrate this dichotomy by SAR11 and Vibrio spp. as typical representatives of the two strategies. A theory for life strategy dichotomy exists in classical r/K-selection. We here discuss an additional dichotomy introduced by what we term S/L-selection (for Small and Large, respectively). While r/K-selection focuses on the role of environmental disturbances, steady-state models suggest that high abundance at species level should be closely related to a low trade-off between competition and defense. We summarize literature indicating that the high availability of organic C is an essential environmental factor favoring Vibrio spp. and suggest that the essence of the generalized L-strategy is to reduce the competition-predator defense trade-off by using non-limiting organic C to increase size. The “streamlining” theory that has been suggested for the S-strategist SAR11 proposes the opposite: that low trade-off is achieved by a reduction in size. We show how this apparent contradiction disappears when the basic assumptions of diffusion-limited uptake are considered. We propose a classification scheme that combines S/L and r/K-selection using the two dimensions of organic C availability and environmental disturbance. As organic C in terrestrial runoff and size of the oligotrophic oceanic gyres are both changing, habitat size for both S- and L-strategists are affected by global change. A theory capturing the main aspects of prokaryote life strategies is therefore crucial for predicting responses of the marine microbial food web to climate change and other anthropogenic influences.
Early Pregnancy Serum Concentration of Secreted Frizzled-Related Protein 4, Secreted Frizzled-Related Protein 5, and Chemerin in Obese Women Who Develop Gestational Diabetes Mellitus
Authors: Beernink Rik H. J. , Schuitemaker Joost H. N. , Faas Marijke M. , Poston Lucilla , White Sara L.
Published: March 2022

Background: The aim of this study was to evaluate whether secreted frizzled-related protein 4 (sFRP4), secreted frizzled-related protein 5 (sFRP5), and chemerin serum concentrations in early pregnancy are associated with the development of gestational diabetes mellitus (GDM) in an obese cohort. In previous studies, increased sFRP4 and chemerin, and decreased sFRP5 concentrations were associated with the development of GDM in normal and overweight women. Methods: In this exploratory case control study, sFRP4, sFRP5, and chemerin serum concentrations were determined by ELISA in 50 obese women who developed GDM and 100 uncomplicated control pregnancies. Serum samples were obtained between 15+0–18+6 weeks’ gestational age and based on a priori known associations with the development of GDM, body mass index (BMI) and maternal age were selected for adjustment in multivariate analyses. Results: In this obese cohort (median BMI 35.7 kg/m2, IQR 33.2–40.3 kg/m2), the biochemical markers showed no association with GDM: sFRP5 odds ratio (OR) 0.44 (95% confidence interval (CI) 0.01–23.18, p = 0.687), sFRP4 OR 0.55 (95% CI 0.09–3.52, p = 0.528), and chemerin OR 3.47 (95% CI 0.05–227.72, p = 0.560). Adjustment for BMI and maternal age did not influence the association. None of the markers were significantly correlated with insulin resistance (HOMA2-IR). Conclusion: No association was found between sFRP4, sFRP5, or chemerin concentration and the development of GDM in a cohort of obese pregnant women. The absence of the association may indicate that these proteins play a lesser biological role in the pathophysiology of GDM in obese women.
Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation
Authors: Martini Chiara , Di Maria Bernardo , Reverberi Claudio , Tuttolomondo Domenico , Gaibazzi Nicola
Published: March 2022

The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random “snapshot” assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.
The Effect of Varying Interval Definitions on the Prevalence of SARS-CoV-2 Reinfections: A Retrospective Cross-Sectional Cohort Study
Authors: Euser Sjoerd M. , Weenink Tieme , Prins Jan M. , Haverkort Milly , Manders Irene , van Lelyveld Steven , Herpers Bjorn L. , Sinnige Jan , Kalpoe Jayant , Snijders Dominic , Cohen Stuart James , Slijkerman Megelink Fred , Kapteijns Erik , den Boer Jeroen , Wagemakers Alex , Souverein Dennis
Published: March 2022

Background: We assessed the SARS-CoV-2 reinfection rate in a large patient cohort, and evaluated the effect of varying time intervals between two positive tests on assumed reinfection rates using viral load data. Methods: All positive SARS-CoV-2 samples collected between 1 March 2020 and 1 August 2021 from a laboratory in the region Kennemerland, the Netherlands, were included. The reinfection rate was analyzed using different time intervals between two positive tests varying between 2 and 16 weeks. SARS-CoV-2 PCR crossing point (Cp) values were used to estimate viral loads. Results: In total, 679,513 samples were analyzed, of which 53,366 tests (7.9%) were SARS-CoV-2 positive. The number of reinfections varied between 260 (0.52%) for an interval of 2 weeks, 89 (0.19%) for 4 weeks, 52 (0.11%) for 8 weeks, and 37 (0.09%) for a minimum interval of 16 weeks between positive tests. The median Cp-value (IQR) in the second positive samples decreased when a longer interval was chosen, but stabilized from week 8 onwards. Conclusions: Although the calculated reinfection prevalence was relatively low (0.11% for the 8-week time interval), choosing a different minimum interval between two positive tests resulted in major differences in reinfection rates. As reinfection Cp-values stabilized after 8 weeks, we hypothesize this interval to best reflect novel infection rather than persistent shedding.
Decreased Intrathecal Concentrations of Free Light Chains Kappa in Multiple Sclerosis Patients Taking Very High Effective Disease-Modifying Treatment
Authors: Süße Marie , Konen Franz Felix , Schwenkenbecher Philipp , Budde Kathrin , Nauck Matthias , Grothe Matthias , Hannich Malte Johannes , Skripuletz Thomas
Published: March 2022

Free light chains kappa (FLCκ) in cerebrospinal fluid (CSF) are a part of the intrathecal immune response. This observational study was conducted to investigate the effects of different disease-modifying therapies (DMT) on the humoral intrathecal immune response in the CSF of patients with multiple sclerosis (MS). FLCκ were analyzed in CSF and serum samples from MS patients taking DMT (n = 60) and those in a control cohort of treatment-naïve MS patients (n = 90). DMT was classified as moderately effective (including INFß-1a, INFß-1b, glatiramer acetate, dimethyl fumarate, teriflunomide, triamcinolone); highly effective (including fingolimod, daclizumab) and very highly effective (alemtuzumab, natalizumab, rituximab/ocrelizumab, mitoxantrone). FLCκ were measured using a nephelometric FLCκ kit. Intrathecal FLCκ and IgG concentrations were assessed in relation to the hyperbolic reference range in quotient diagrams. Intrathecal FLCκ concentrations and IgG concentrations were significantly lower in samples from the cohort of MS patients taking very highly effective DMT than in samples from the cohort of MS patients taking highly effective DMT and in the treatment-naïve cohort (FLCκ: p = 0.004, p < 0.0001 respectively/IgG: p = 0.013; p = 0.021). The reduction in FLCκ could contribute to an anti-inflammatory effect in the CNS through this mechanism. There was no difference in the appearance of CSF-specific oligoclonal bands (p = 0.830). Longitudinal analyses are required to confirm these results.
"I Just Stay in the House So I Don't Need to Explain": A Qualitative Investigation of Persons with Invisible Disabilities
Authors: Hendry Gillian , Wilson Claire , Orr Mairi , Scullion Rebecca
Published: March 2022

Research has shown that persons with disabilities continually face discrimination. More research attention has focused on individuals’ experiences of visible disability, despite evidence that there are higher numbers worldwide of people with invisible disabilities. As such, persons with invisible disabilities can feel under-represented in disability literature. A qualitative study was conducted to address this. Twenty-five persons with an invisible disability were recruited to take part in focus groups and interviews aimed at understanding the lived experience of invisible disability on social life and within the workplace. Data were analyzed using Braun and Clarke’s reflexive thematic analysis, identifying themes of (1) Incongruity between looking and feeling, (2) The impact of others, (3), Adaptation, (4) Talking about disability, (5) (Un)supported and (un)accepted, and (6) Discrimination/legislation. The findings indicate that the language, attitudes and behaviour of others are important to support inclusion in the social and working lives of those with invisible disabilities. Persons without a disability should be willing to talk about disability, see the strengths of those with an invisible disability and be mindful of language used around visibility. Suggestions relating to what we can do to be better support those with an invisible disability in society are discussed.
The Potential of a Thick Present through Undefined Causality and Non-Locality
Authors: Capurso Alessandro
Published: March 2022

This paper elaborates on the interpretation of time and entanglement, offering insights into the possible ontological nature of information in the emergence of spacetime, towards a quantum description of gravity. We first investigate different perspectives on time and identify in the idea of a “thick present” the only element of reality needed to describe evolution, differences, and relations. The thick present is connected to a spacetime information “sampling rate”, and it is intended as a time symmetric potential bounded between a causal past of irreversible events and a still open future. From this potential, spacetime emerges in each instant as a space-like foliation (in a description based on imaginary paths). In the second part, we analyze undefined causal orders to understand how their potential could persist along the thick present instants. Thanks to a C-NOT logic and the concept of an imaginary time, we derive a description of entanglement as the potential of a logically consistent open choice among imaginary paths. We then conceptually map the imaginary paths identified in the entanglement of the undefined orders to Closed Time-like Curves (CTC) in the thick present. Considering a universe described through information, CTC are interpreted as “memory loops”, elementary structures encoding the information potential related to the entanglement in both time and space, manifested as undefined causality and non-locality in the emerging foliation. We conclude by suggesting a possible extension of the introduced concepts in a holographic perspective.
Adversarially Training MCMC with Non-Volume-Preserving Flows
Authors: Liu Shaofan , Sun Shiliang
Published: March 2022

Recently, flow models parameterized by neural networks have been used to design efficient Markov chain Monte Carlo (MCMC) transition kernels. However, inefficient utilization of gradient information of the target distribution or the use of volume-preserving flows limits their performance in sampling from multi-modal target distributions. In this paper, we treat the training procedure of the parameterized transition kernels in a different manner and exploit a novel scheme to train MCMC transition kernels. We divide the training process of transition kernels into the exploration stage and training stage, which can make full use of the gradient information of the target distribution and the expressive power of deep neural networks. The transition kernels are constructed with non-volume-preserving flows and trained in an adversarial form. The proposed method achieves significant improvement in effective sample size and mixes quickly to the target distribution. Empirical results validate that the proposed method is able to achieve low autocorrelation of samples and fast convergence rates, and outperforms other state-of-the-art parameterized transition kernels in varieties of challenging analytically described distributions and real world datasets.
New Modulation Technique in Smart Grid Interfaced Multilevel UPQC-PV Controlled via Fuzzy Logic Controller
Authors: Kenjrawy Hassan , Makdisie Carlo , Houssamo Issam , Mohammed Nabil
Published: March 2022

The use of sensitive electronic devices has increased recently; hence, power quality has become an important factor in electrical power systems. The various disturbances occurring in the electrical network (harmonics, voltage swells, sags, etc.) may lead to technical-economic damage that negatively impacts the quality of the supplied power. Therefore, the use of the Unified Power Quality Conditioner (UPQC) is a promising solution to limit such damage. In this paper, a nine-level structure of the proposed standardized power quality conditioner is analyzed. The conditioner is connected between a photovoltaic system (UPQC-PV) and a smart grid. Then, a new approach to generate switches for the switches of both the series and parallel converters is proposed. This modulation technique relies on an adaptive hysteresis band (AHB) that is determined via a fuzzy logic controller in order to obtain the required modified output voltage with minimum distortion. Simulation results, using MATLAB/Simulink, of different disturbance scenarios that may occur in the network are presented to verify the accuracy of the proposed fuzzy-logic-based AHB control system. Compared with the conventional SPWM, it is found that the proposed AHB modulation technique significantly improves the power quality in terms of producing less total harmonic distortion in both the voltage and current waveforms.
Application of Cyber-Physical System Technology on Material Color Discrimination
Authors: Shyr Wen-Jye , Juan Hou-Chueh , Tsai Chih-Yu , Chang Yu-Jia
Published: March 2022

With the innovative advance in science and technology, manufacturing production methods have made considerable progress. However, before the production process is actually implemented, it is important to examine whether the design can meet the actual need. By applying cyber-physical system technology to test the production process, the development problems of the actual construction can be avoided. Based on the existing components, this study incorporated the cyber-physical system via innovative integration. In addition to the human–machine interface, this was employed as the operating spindle to integrate the material color identification system of the physical organization. This study also adopted the automated virtual factory constructed by the simulation software of Factory IO with an aim to achieve the technical application.
Adaptive Fuzzy PID Control Strategy for Vehicle Active Suspension Based on Road Evaluation
Authors: Han Shi-Yuan , Dong Jia-Feng , Zhou Jin , Chen Yue-Hui
Published: March 2022

The paper proposes a fuzzy proportion integral differential (PID) control strategy based on road estimation to meet the different performance requirements of active suspension for comfort and safety under different road conditions in view of the uncertainty of vehicle suspension parameters and random road perturbations. First, flexible neural tree (FNT) is used to estimate the vertical disturbance of road within uncertain vehicle parameters in which the Fourier transform of the autocorrelation function is used to fit the road power spectrum density (PSD). In order to compensate for the delay and realize the real-time estimate of road conditions, an online fuzzy evaluation strategy for road condition is developed by integrating the fitted road PSD and the real-time suspension performance. After that, an adaptive fuzzy PID control strategy is developed based on the adaptive suspension control performance, which is supposed to realize the adaptive adjustment of control parameters for different road conditions. The simulation experimental results show that the online fuzzy evaluation strategy can effectively reflect the change in road condition, and the proposed adaptive fuzzy PID control strategy can work for adjusting the parameters adaptively, according to the changes in road conditions and therefore meet the control performance requirements under different road conditions.
A General Parameter Optimization Method for a Capacitive Power Transfer System with an Asymmetrical Structure
Authors: Xia Jinglin , Yuan Xinmei , Lu Sizhao , Dai Weiju , Li Tong , Li Jun , Li Siqi
Published: March 2022

Capacitive power transfer (CPT) is an attractive wireless power transfer (WPT) technology and it has been widely studied in many applications. Symmetrical structures and high-order compensation networks are always produced as optimization results and common configurations for high-efficiency CPT systems. However, in space-limited scenarios, an asymmetric structure tends to be a better choice. The related large number of high-order asymmetric system parameters is a key problem in parameter design. In this study, a general parameter design method that is based on reactive power optimization is proposed for an electric field resonance-based CPT system with an asymmetric six-plate coupler. The reactive power in the compensation network was analyzed and optimized under the constraint of transferred power. With equal reactive power, the optimization complexity was significantly reduced and the optimized system parameters were provided. To validate the effectiveness of the proposed method, a 1 MHz, 3.2 kW asymmetric CPT protype with 100 mm gap distance was implemented. The results indicate that, with the optimized parameters, high system efficiency can be achieved when the system’s volume is reduced. At the rated power, about 95% DC–DC overall efficiency was achieved through a 6-pF coupling capacitor.
Flexible Design of Low-Delay MEC-VLC Integrating Network Based on Attocell Overlap for IIoT
Authors: Xue Jingshu , Ye Ziwei , Zhang Haiyong , Zhu Yijun
Published: March 2022

Recently, multi-access edge computing (MEC) cooperating with fifth-generation (5G) mobile communication technology or WiFi has been widely discussed for low-delay systems. However, for the Industrial Internet of Things, which raises higher requirements on system delay, security, capacity, etc., visible light communication (VLC) has better adaptability due to its controllable attocells. Therefore, we establish a computation and transmission integrated system with MEC-VLC as the main body. To solve the imbalance of resource utilization caused by users’ movement in intensive attocells, we propose a series of flexible design schemes based on access points’ cooperation in attocell overlapping areas. We formulate the overlap-based low-delay flexible system design as an optimization problem and then design the system based on it. Specifically, we first give an attocell-associated congestion judgment criterion and correspondingly propose a user discard algorithm. After that, we offer an iterative optimization method for task assignment, which adjusts computing-transmitting units’ cooperation mode to enhance the overall time delay. Then, the computing and transmitting resources are jointly allocated for delay reduction. Finally, our simulation demonstrates that the overlap-based design has a lower user discard ratio than the traditional distance-based system. The maximum delay and standard deviation are also reduced. Consequently, the flexible design based on attocell overlap can improve the reliability, capacity, and fairness of the low-delay integrating system.
Roach Infestation Optimization MPPT Algorithm for Solar Photovoltaic System
Authors: Pradhan Chittaranjan , Senapati Manoj Kumar , Ntiakoh Nicholas Kakra , Calay Rajnish Kaur
Published: March 2022

Solar photovoltaic (PV) power has several advantages such as free availability, absence of rotating parts, can be easily integrated with building architecture, and need little maintenance. However, the PV cell current–voltage (I–V) characteristics are non-linear and power generated from a PV array depends on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, partial shading, and the presence of system uncertainties and load variation conditions of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is realized that the RIO algorithm has improved search performance in terms of finding the optimal solution and faster convergence characteristics than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO, perturb and observe (P&O), and incremental conductance (IC)-based MPPT schemes.
Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch
Authors: Thanh Vo-Van , Su Wencong , Wang Bin
Published: March 2022

Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method.
Resilience-Oriented Framework for Microgrid Planning in Distribution Systems
Authors: Son Eun-Tae , Bae In-Su , Kim Sung-Yul , Kim Dong-Min
Published: March 2022

Recently, it has been suggested that microgrids (MGs) can improve the resilience of distribution systems. However, predictions about future faults are uncertain. This makes calculating the exact value of the benefits of system resilience enhancement close to impossible at the time of MG planning. Therefore, this paper proposes a framework for MG planning, which focuses on resilience estimation. To consider the uncertainties of future failure events, the proposed method for estimating the resilience utilized the Monte Carlo simulation. In addition, an optimal scenario was estimated using a cost–benefit analysis and constraints on the expected value of resilience enhancement. In the case study, an actual MG installation at D-university was evaluated to obtain the optimal MG planning scenario. The results show that the capacity and installation locations of the distributed generators (DGs) impact the resilience enhancement. The proposed method can effectively derive the optimal MG planning scenario by evaluating the possibility of future operations based on the segmentation of both the system configuration and type of DG to improve the resilience of distribution systems.
Technical and Economic Analysis of the Implementation of a Self-Sustainable Briquetting Process for Electric Generation
Authors: Martins Rosa , Cardenas-Rodriguez Haylemar de Nazaret , Oliveira Levy Ely Lacerda , Bonaldi Erik Leandro , Assuncao Frederico de Oliveira , Lambert-Torres Germano , Villa-Nova Helcio Francisco , Sant’Ana Wilson Cesar , Borges-da-Silva Luiz Eduardo , Bomfin Clodoaldo Barboza , Haddad Jamil , Santos Afonso Henriques Moreira
Published: March 2022

The wood industry is an essential part of the economy of some regions in Brazil. Although the excess of wood residue is an environmental concern, it is also an alternative source for electricity generation. This allows for compliance with current legislation to minimize environmental impacts such as strategies to control the emission of pollutants and the decarbonization in the wood exploration activity. Despite this, the thermoelectric plants based on wood residues face problems associated with the low efficiency in generation due to the high moisture content of the residues, and challenges related to transport and storage. In this sense, this work is to evaluate the application of a self-sustainable briquetting plant as an alternative for solving the problems associated with the high moisture content, transport, and storage of wood residues. The aspects related to the construction of the briquetting plant and economic indicators associated with the economic feasibility, such as, the estimation of the net present value over the project lifecycle, internal rate of return and pay-back period, are also presented and discussed. The results demonstrated the feasibility of the plant mainly due to the better energy/volume ratio of the briquette (drying and compaction) and the transportation cost savings associated to the density of the compacted material.
Characteristics and Formation Mechanism of the Lower Paleozoic Dolomite Reservoirs in the Dongying Depression, Bohai Bay Basin
Authors: Zhang Xuemei , Li Qing , You Xuelian , Ma Lichi , Jing Anyu , Tian Wen , Wen Lang
Published: March 2022

The Lower Paleozoic carbonate strata experience multi-stage tectonic activity and post-depositional volcanic activity in the Dongying Depression, Bohai Bay basin. These tectonic and magmatic activities have caused the reservoir to undergo severe diagenesis, resulting in strong reservoir heterogeneity. This study aims to identify the characteristics of dolomite, various reservoir spaces’ characteristics, the origin of different types of dolomite, and the porosity evolution. According to crystal size and morphology, dolomites can be divided into three kinds of matrix dolomites and four kinds of dolomite cements. The petrology and geochemistry of the dolomite suggests that matrix dolomite is formed from seawater. The medium-to-coarse-crystalline dolomite cement (D3) has a higher 87Sr/86Sr ratio (0.7119 to 0.7129) and a higher homogenization temperature (>125 °C), suggesting that the fluid for the precipitation of D3 is a mixed fluid formed by hydrothermal fluid eroding the 87Sr-rich feldspar sandstone. The strikingly negative δ18O values (−23.7 to −25.7‰ VPDB) of saddle dolomite (D4) indicate that D4 precipitated from hydrothermal fluids and the Mg2+ source may be due to dissolution of the host dolomite that formed in the evaporation environment. The reservoir spaces of the target strata in the study area mainly include fractures, dissolution vugs, intercrystalline pores, and moldic pores. Dissolution is the basis for forming high-quality dolomite reservoirs. The faults and fractures provided favorable conditions for dissolution. Hydrothermal fluid and organic acid were the main dissolution fluids for the dolomite reservoir, which were beneficial to the development of secondary pores. In the study area, organic acid dissolution was shown to contribute more than hydrothermal dissolution in the study area.
Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model
Authors: Shohan Md Jamal Ahmed , Faruque Md Omar , Foo Simon Y.
Published: March 2022

Load forecasting (LF) is an essential factor in power system management. LF helps the utility maximize the utilization of power-generating plants and schedule them both reliably and economically. In this paper, a novel and hybrid forecasting method is proposed, combining a long short-term memory network (LSTM) and neural prophet (NP) through an artificial neural network. The paper aims to predict electric load for different time horizons with improved accuracy as well as consistency. The proposed model uses historical load data, weather data, and statistical features obtained from the historical data. Multiple case studies have been conducted with two different real-time data sets on three different types of load forecasting. The hybrid model is later compared with a few established methods of load forecasting found in the literature with different performance metrics: mean average percentage error (MAPE), root mean square error (RMSE), sum of square error (SSE), and regression coefficient (R). Moreover, a guideline with various attributes is provided for different types of load forecasting considering the applications of the proposed model. The results and comparisons from our test cases showed that the proposed hybrid model improved the forecasting accuracy for three different types of load forecasting over other forecasting techniques.
Solidified-Air Energy Storage: Conceptualization and Thermodynamic Analysis
Authors: Hiller Sandro , Hartmann Christian , Hebenstreit Babette , Arzbacher Stefan
Published: March 2022

Grid-scale electrical energy storage (EES) is a key component in cost-effective transition scenarios to renewable energy sources. The requirement of scalability favors EES approaches such as pumped-storage hydroelectricity (PSH) or compressed-air energy storage (CAES), which utilize the cheap and abundant storage materials water and air, respectively. To overcome the site restriction and low volumetric energy densities attributed to PSH and CAES, liquid-air energy storage (LAES) has been devised; however, it suffers from a rather small round-trip efficiency (RTE) and challenging storage conditions. Aiming to overcome these drawbacks, a novel system for EES is developed using solidified air (i.e., clathrate hydrate of air) as the storable phase of air. A reference plant for solidified-air energy storage (SAES) is conceptualized and modeled thermodynamically using the software CoolProp for water and air as well as empirical data and first-order approximations for the solidified air (SA). The reference plant exhibits a RTE of 52% and a volumetric storage density of 47 kWh per m3 of SA. While this energy density relates to only one half of that in LAES plants, the modeled RTE of SAES is comparable already. Since improved thermal management and the use of thermodynamic promoters can further increase the RTEs in SAES, the technical potential of SAES is in place already. Yet, for a successful implementation of the concept—in addition to economic aspects—questions regarding the stability of SA must be first clarified and challenges related to the processing of SA resolved.
A Study on Energy Tax Reform for Carbon Pricing Using an Input-Output Table for the Analysis of a Next-Generation Energy System
Authors: Nakano Satoshi , Washizu Ayu
Published: March 2022

Carbon pricing, such as a carbon tax, is an invisible hand that leads to the construction of a sustainable low-carbon society, and precise analysis of the impact of carbon pricing on each sector of the economy is indispensable for its design. In this study, an equilibrium price model based on the 2015 input-output table was used for the analysis of next-generation energy systems (2015 IONGES) and the effect of the introduction of a carbon tax on the price of the industrial sector was assessed. Based on the existing energy-related tax system in Japan, the introduction of a carbon tax is regarded as an increase in the tax for global-warming countermeasures (TGWC) in the petroleum and coal tax (PCT). While existing energy-related taxes are designed to place a relatively heavy burden on the transportation sector, tax reform of the petroleum and coal tax has a relatively large effect on raising prices in energy-conversion and energy-intensive sectors. As a result, the reform of the energy-related tax may promote the introduction of energy-saving technology and decarbonization technology, both in the transportation sector and in a wider range of sectors, and may work to correct the unfairness of the tax burden between sectors.
Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities
Authors: Ali Muhammad , Prakash Krishneel , Macana Carlos , Bashir Ali Kashif , Jolfaei Alireza , Bokhari Awais , Klemeš Jiří Jaromír , Pota Hemanshu
Published: March 2022

Demographic factors, statistical information, and technological innovation are prominent factors shaping energy transitions in the residential sector. Explaining these energy transitions requires combining insights from the disciplines investigating these factors. The existing literature is not consistent in identifying these factors, nor in proposing how they can be combined. In this paper, three contributions are made by combining the key demographic factors of households to estimate household energy consumption. Firstly, a mathematical formula is developed by considering the demographic determinants that influence energy consumption, such as the number of persons per household, median age, occupancy rate, households with children, and number of bedrooms per household. Secondly, a geographical position algorithm is proposed to identify the geographical locations of households. Thirdly, the derived formula is validated by collecting demographic factors of five statistical regions from local government databases, and then compared with the electricity consumption benchmarks provided by the energy regulators. The practical feasibility of the method is demonstrated by comparing the estimated energy consumption values with the electricity consumption benchmarks provided by energy regulators. The comparison results indicate that the error between the benchmark and estimated values for the five different regions is less than 8% (7.37%), proving the efficacy of this method in energy consumption estimation processes.
Digitalization—The Engine of Sustainability in the Energy Industry
Authors: Mihai Florin , Aleca Ofelia Ema , Stanciu Andrei , Gheorghe Mirela , Stan Mirela
Published: March 2022

The goal of this paper is to conduct a bibliometric analysis of the scientific literature about the sustainability of digitalization in the energy sector in order to capture the main challenges and trends in the transition towards it. The bibliometric analysis of the scientific literature was carried out by interrogating the Scopus database, using a set of keywords considered relevant for the analyzed field and for the goal of the proposed research. The purpose of the study was, on the one hand, the depth of the research into these topics during the 2013–2021 period, in terms of the number of scientific papers, the topic, abstract and keywords associated, the geographical area of origin and the authors’ affiliation and on the other hand, an analysis of the existence of possible links between these topics formulated through three hypotheses. The results obtained reveal the researchers’ concerns for digitalization in the energy sector, the existing correlations between the keywords analyzed and the tendencies registered in the field of digitalization in the energy industry in order to ensure higher sustainability.
Semi-Empirical Approach to Gas Flow Velocity Measurement by Means of the Thermal Time-of-Flight Method—Further Investigation
Authors: Sobczyk Jacek
Published: March 2022

This paper describes a study to expand the knowledge as to whether a thermal wave anemometer can be used to measure the velocity of flowing gases or gas mixtures in situ. For this purpose, several series of measurements were performed in laboratory conditions using both the previously used probe and other probes of similar design. The probes were not modified mechanically or electrically in any way. The obtained results were compared with each other, and on this basis, the optimal, though purely empirical, form of the calibration function was determined (4). The analysis of the relative differences between the measured and set velocity values showed that they do not exceed 1% in the velocity range from 0.05 to 2.5 m/s. Lowering the sensitivity of the method for velocities below approx. 0.05 m/s results in a rapid increase in the observed deviations, reaching 15% for 0.015 m/s. The conducted research also revealed an increased resistance of the proposed measurement method to small flow disturbances, both longitudinal and transverse, and a reduced sensitivity to non-optimal positioning of the probe in relation to the flow direction, relative to the methods using both detectors.
Grid-Connected PV System with Reactive Power Management and an Optimized SRF-PLL Using Genetic Algorithm
Authors: Aldbaiat Bashar , Nour Mutasim , Radwan Eyad , Awada Emad
Published: March 2022

This paper presents a two-stage grid-connected PV system with reactive power management capability. The proposed model can send phase-shifted current to the grid during a low-voltage ride through (LVRT) to recover the voltage levels of the grid’s feeders. The novelty of the proposed algorithm, unlike the common methods, is that it does not need to disable the maximum power point tracking (MPPT) state while managing active and reactive power injection simultaneously. Moreover, the new method promotes a safety factor by offering overcurrent protection to the PV inverter. The phase-locked loop based on the synchronous reference frame (SRF-PLL) is optimized using a genetic algorithm (GA). The settling time of SRF-PLL’s step response is minimized, and the frequency dynamics are improved to enhance synchronization during LVRT. The system’s performance is tested and verified using MATLAB/Simulink simulations. The obtained results prove the effectiveness of the proposed control algorithm in managing reactive power interventions. The optimized phase-locked loop shows robust performance and is compared to the conventional low-gain PLL to spot the enhancement.