bluuebunny/crossref_metadata_2025 huggingface dataset:
State of the Art in Combination Immuno/Radiotherapy for Brain Metastases: Systematic Review and Meta-Analysis
Authors: Najafi Masoumeh , Jahanbakhshi Amin , Gomar Marzieh , Iotti Cinzia , Giaccherini Lucia , Rezaie Omid , Cavallieri Francesco , Deantonio Letizia , Bardoscia Lilia , Botti Andrea , Sardaro Angela , Cozzi Salvatore , Ciammella Patrizia
Published: April 2022

Objectives: Common origins for brain metastases (BMs) are melanoma, lung, breast, and renal cell cancers. BMs account for a large share of morbidity and mortality caused by these cancers. The advent of new immunotherapeutic treatments has made a revolution in the treatment of cancer patients and particularly, as a new concept, if it is combined with radiotherapy, may lead to considerably longer survival. This systematic review and meta-analysis aimed to evaluate the survival rate and toxicities of such a combination in brain metastases. Methods: To perform a systematic review of the literature until January 2021 using electronic databases such as PubMed, Cochrane Library, and Embase; the Newcastle–Ottawa Scale was used to evaluate the quality of cohort studies. For data extraction, two reviewers extracted the data blindly and independently. Hazard ratio with 95% confidence interval (CI), fixed-effect model, and inverse-variance method was calculated. The meta-analysis has been evaluated with the statistical software Stata/MP v.16 (The fastest version of Stata). Results: In the first step, 494 studies were selected to review the abstracts, in the second step, the full texts of 86 studies were reviewed. Finally, 28 studies were selected consisting of 1465 patients. The addition of IT to RT in the treatment of brain metastasis from melanoma and non-small-cell lung carcinoma was associated with a 39% reduction in mortality rate and has prolonged overall survival, with an acceptable toxicity profile. The addition of IT to RT compared with RT alone has a hazard ratio of 0.39(95% CI 0.34–0.44). Conclusions: A combination of immuno/radiotherapy (IR) for the treatment of patients with BMs from melanoma and non-small-cell lung carcinoma has prolonged overall survival and reduced mortality rate, with acceptable toxicity. In terms of timing, RT seems to have the best effect on the result when performed before or simultaneously with immunotherapy.
Chloroplast Genome of Salvia Sect. Drymosphace: Comparative and Phylogenetic Analysis
Authors: Su Ting , Geng Yan-Fei , Xiang Chun-Lei , Zhao Fei , Wang Mei , Gu Li , Hu Guo-Xiong
Published: April 2022

Sect. Drymosphace is one of eight sections of Salvia subg. Glutinaria and includes 13 species and one dubious species that hold great economic value. Although the section is well supported, interspecific relationships remain unresolved. Moreover, most of this section’s plastome information remains unknown. In this study, we sequenced and assembled eight sect. Drymosphace plastomes and conducted comparative analyses within this section. The length of plastid genome sequences ranged from 151,330 bp to 151,614 bp, with 80 protein-coding, 30 tRNA, and four rRNA genes being annotated. The plastomes were found to be as conservative as other Lamiaceae species, showing high consistency and similarity in terms of gene content, order, and structure. Within the sect. Drymosphace, single-copy regions were more variable than IR regions, and the intergenic regions were more variable than the coding regions; nine hypervariable regions were detected, and some of them may be useful for the phylogenetic analysis of Salvia. The topologies inferred from all of the data sets indicated that sect. Drymosphace was monophyletic and that S. honania was sister to S. meiliensis. Compared to previous studies involving more sect. Drymosphace species, phylogenomic analyses can improve the phylogenetic resolution considerably.
Network Analysis Using Markov Chain Applied to Wildlife Habitat Selection
Authors: Dhakal Thakur , Lim Sang-Jin , Park Yung-Chul , Heo Muyoung , Lee Sang-Hee , Hong Sungwon , Kim Eui-Kyeong , Chon Tae-Soo
Published: April 2022

In the present study, behavioral states for habitat selection are examined using a discrete-time Markov chain (DTMC) combined with a network model with wildlife movement data. Four male boars (Sus scrofa Linnaeus) at the Bukhansan National Park in South Korea were continuously tracked with an interval of approximately 2 h to 313 days from June 2018 to May 2019. The time-series movement positions were matched with covariates of environmental factors (leaf types and water) in field conditions. Stationary probabilities were used to quantify the habitat selection preference of wild boars, including maximum probability (0.714) with the “broadleaf without water habitat” where in-degree centrality was at its maximum (0.54), but out-degree centrality was low and even (0.17) for all states. Betweenness was the maximum for the “needleleaf without water habitat”, suggesting its role as a bridging habitat between other habitats. Out-closeness scores presented the highest values in the “broadleaf without water habitat” (0.26). Similarly, the first hitting time to the habitat was shortest at the “broadleaf without water habitat” (3.64–5.16 h) and slightly longer than one day in other examined habitats, including “broadleaf with water,” “needleleaf without water,” and “no-leaf without water”. The network model using the Markov chain provided information on both local movement behavior and general resource-use patterns of wild boars in field conditions.
Effects of Waterbird Herbivory on Dominant Perennial Herb Carex thunbergii in Shengjin Lake
Authors: Wang Xin , Zhao Jinming , Xu Wenjing , Ye Xiaoxin
Published: April 2022

Abundant food resources in riparian zones provide efficient foraging sites for waterbirds. Herbivory is a key ecosystem process that has widely recognized effects on primary production and vegetation structure and composition. However, there is limited understanding of impacts of waterbird herbivory on riparian zone vegetation. In this study, a bird exclosure experiment with five levels of foraging intensities (no foraging, very little foraging, light foraging, moderate foraging and heavy foraging) was set up in Shengjin Lake to study the effects of waterbird foraging on the community structure of sedge meadows and individual traits of the dominant plant Carex thunbergii. Foraging intensity had little effect on community structure. The dry mass of C. thunbergii decreased with the increasing foraging time. Waterbird foraging reduced leaf dry mass under heavy foraging by 27.7% and root dry mass by 45.6% compared to CK (no foraging). Waterbird foraging increased allocation to shoot growth but had a weak effect on elemental allocation of C. thunbergii. The foraging intensity significantly affected the morphological traits of C. thunbergii. The results of structural equation modeling showed that RSR (root: shoot ratio represents the ratio of dry mass) and RL (root length) are key traits in driving the dry mass decline in the presence of bird foraging. This study may contribute to a better understanding of the adaptability of perennial herb plants to waterbird foraging and maintain the healthy development of wetland ecosystems.
Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences
Authors: Hassanien Mohamed A. , Singh Vivek Kumar , Puig Domenec , Abdel-Nasser Mohamed
Published: April 2022

Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.
Robustness and Flexibility of Neural Function through Dynamical Criticality
Authors: Magnasco Marcelo O.
Published: April 2022

In theoretical biology, robustness refers to the ability of a biological system to function properly even under perturbation of basic parameters (e.g., temperature or pH), which in mathematical models is reflected in not needing to fine-tune basic parameter constants; flexibility refers to the ability of a system to switch functions or behaviors easily and effortlessly. While there are extensive explorations of the concept of robustness and what it requires mathematically, understanding flexibility has proven more elusive, as well as also elucidating the apparent opposition between what is required mathematically for models to implement either. In this paper we address a number of arguments in theoretical neuroscience showing that both robustness and flexibility can be attained by systems that poise themselves at the onset of a large number of dynamical bifurcations, or dynamical criticality, and how such poising can have a profound influence on integration of information processing and function. Finally, we examine critical map lattices, which are coupled map lattices where the coupling is dynamically critical in the sense of having purely imaginary eigenvalues. We show that these map lattices provide an explicit connection between dynamical criticality in the sense we have used and “edge of chaos” criticality.
Passive Light Source Monitoring for Sending or Not Sending Twin-Field Quantum Key Distribution
Authors: Qian Xuerui , Zhang Chunhui , Yuan Huawei , Zhou Xingyu , Li Jian , Wang Qin
Published: April 2022

Twin-field quantum key distribution (TF-QKD) can break the repeaterless linear bound and possess the measurement-device-independent security, and thus seems very promising in practical applications of quantum secure communication. In most reported TF-QKD protocols, light sources are assumed to possess trusted and fixed photon number distributions (PND), which are unrealistic assumptions in practical applications. Fortunately, the light source monitoring (LSM) method is proposed to circumvent this problem by actively adjusting the attenuation coefficient and monitoring the photon number distribution of light sources. However, the active light source monitoring (ALSM) method may induce additional modulation errors due to imperfect attenuation devices, deteriorating practical performances of TF-QKD systems. In this manuscript, we propose a passive light source monitoring (PLSM) scheme for TF-QKD, and employ the sending-or-not-sending (SNS) TF-QKD as an example for illustration. Simulation results show that our present work can greatly exceed both the original SNS protocol and the ALSM scheme when light source fluctuations and modulation errors are taken into account.
Soyasapogenol-B as a Potential Multitarget Therapeutic Agent for Neurodegenerative Disorders: Molecular Docking and Dynamics Study
Authors: Iqbal Danish , Rizvi Syed Mohd Danish , Rehman Md Tabish , Khan M. Salman , Bin Dukhyil Abdulaziz , AlAjmi Mohamed F. , Alshehri Bader Mohammed , Banawas Saeed , Zia Qamar , Alsaweed Mohammed , Madkhali Yahya , Alsagaby Suliman A. , Alturaiki Wael
Published: April 2022

Neurodegenerative disorders involve various pathophysiological pathways, and finding a solution for these issues is still an uphill task for the scientific community. In the present study, a combination of molecular docking and dynamics approaches was applied to target different pathways leading to neurodegenerative disorders such as Alzheimer’s disease. Initially, abrineurin natural inducers were screened using physicochemical properties and toxicity assessment. Out of five screened compounds, a pentacyclic triterpenoid, i.e., Soyasapogenol B appeared to be the most promising after molecular docking and simulation analysis. Soyasapogenol B showed low TPSA (60.69), high absorption (82.6%), no Lipinski rule violation, and no toxicity. Docking interaction analysis revealed that Soyasapogenol B bound effectively to all of the targeted proteins (AChE, BuChE MAO-A, MAO-B, GSK3β, and NMDA), in contrast to other screened abrineurin natural inducers and inhibitors. Importantly, Soyasapogenol B bound to active site residues of the targeted proteins in a similar pattern to the native ligand inhibitor. Further, 100 ns molecular dynamics simulations analysis showed that Soyasapogenol B formed stable complexes against all of the targeted proteins. RMSD analysis showed that the Soyasapogenol B–protein complex exhibited average RMSD values of 1.94 Å, 2.11 Å, 5.07 Å, 2.56 Å, 3.83 Å and 4.07 Å. Furthermore, the RMSF analysis and secondary structure analysis also indicated the stability of the Soyasapogenol B–protein complexes.
Dynamics of the Natural Regeneration of Vegetation in an Anthropized Forest in Côte d’Ivoire, West Africa
Authors: Kouman Kouame Jean Marc , Kouakou Akoua Tamia Madeleine , Kpangui Kouassi Bruno , Bamba Issouf , Barima Yao Sadaiou Sabas , Bogaert Jan
Published: April 2022

Ivorian classified forests have been highly anthropized by cocoa farming. In an attempt to provide guidance to the government on approaches to the restoration of the forest while respecting the aspirations of local populations, permanent plots were set up in the classified forest of Haut-Sassandra, and were monitored and measured for 3 years. This study was intended to analyze the evolution of the vegetation of permanent plots in the classified forest of Haut-Sassandra from 2018 to 2021. The results show that the vegetation evolves with the cessation of some agricultural activities. These plantations are colonized by pioneer species during the first three years of the abandonment of agricultural activities. Mortality rates increased by 477.59% and recruitment rates were reduced by 61.87% in regularly maintained plantations compared to their condition three years ago. However, the plantations with no agricultural activities and those which were not maintained but harvested had the highest recruitment rates of pioneer and heliophilous individuals. In sum, tree species could recolonize the classified forest of Haut-Sassandra if clearing is prohibited in cocoa farms. However, the populations could continue to harvest the pods from the cocoa trees which are already established in the classified forest of Haut-Sassandra.
Advanced Fusion-Based Speech Emotion Recognition System Using a Dual-Attention Mechanism with Conv-Caps and Bi-GRU Features
Authors: Maji Bubai , Swain Monorama , Mustaqeem Mustaqeem
Published: April 2022

Recognizing the speaker’s emotional state from speech signals plays a very crucial role in human–computer interaction (HCI). Nowadays, numerous linguistic resources are available, but most of them contain samples of a discrete length. In this article, we address the leading challenge in Speech Emotion Recognition (SER), which is how to extract the essential emotional features from utterances of a variable length. To obtain better emotional information from the speech signals and increase the diversity of the information, we present an advanced fusion-based dual-channel self-attention mechanism using convolutional capsule (Conv-Cap) and bi-directional gated recurrent unit (Bi-GRU) networks. We extracted six spectral features (Mel-spectrograms, Mel-frequency cepstral coefficients, chromagrams, the contrast, the zero-crossing rate, and the root mean square). The Conv-Cap module was used to obtain Mel-spectrograms, while the Bi-GRU was used to obtain the rest of the spectral features from the input tensor. The self-attention layer was employed in each module to selectively focus on optimal cues and determine the attention weight to yield high-level features. Finally, we utilized a confidence-based fusion method to fuse all high-level features and pass them through the fully connected layers to classify the emotional states. The proposed model was evaluated on the Berlin (EMO-DB), Interactive Emotional Dyadic Motion Capture (IEMOCAP), and Odia (SITB-OSED) datasets to improve the recognition rate. During experiments, we found that our proposed model achieved high weighted accuracy (WA) and unweighted accuracy (UA) values, i.e., 90.31% and 87.61%, 76.84% and 70.34%, and 87.52% and 86.19%, respectively, demonstrating that the proposed model outperformed the state-of-the-art models using the same datasets.
Investigation of ON-State Breakdown Mechanism in AlGaN/GaN HEMTs with AlGaN Back Barrier
Authors: Li Yuchen , Huang Sen , Wang Xinhua , Jiang Qimeng , Liu Xinyu
Published: April 2022

The temperature-dependent ON-state breakdown BVON loci of AlGaN/GaN high-electron-mobility transistors (HEMTs) with an AlGaN back barrier were investigated using the gate current extraction technique. The impact ionization of acceptor-like traps was revealed to be responsible for the ON-state breakdown in HEMTs as a 2D electron gas (2DEG) channel is marginally turned on. The characteristic electric field Ei of impact ionization was extracted, exhibiting a U-shaped temperature dependence from 40 to −30 ∘C, with minimum Ei occurring at −10 ∘C. The impurity scattering effect of acceptor-like traps in AlGaN/GaN heterostructures is suggested to be responsible for the negative temperature dependence of BVON and Ei below −10 ∘C.
Formal Verification and Analysis of 5G AKA Protocol Using Mixed Strand Space Model
Authors: Xiao Yuelei , Gao Shan
Published: April 2022

The 5th generation mobile communication technology (5G) authentication and key management (AKA) protocol specified by the 3rd generation partnership project (3GPP) includes three cases because it introduces synchronization failure and message authentication code (MAC) failure procedures. Thus, there may be interactions between these cases, forming vulnerabilities that do not exist in any single case. However, this is not fully considered in the existing formal analysis and improvement of the 5G AKA protocol. To solve this problem, this paper formally analyzes the security of the latest version of the 5G AKA protocol based on the mixed strand space model for mixed protocols and finds many new attacks, including cross attacks for mixed cases. Then, a secure and efficient primary authentication and key agreement protocol for 5G networks is proposed, which is named the 5G-AKA’. In the 5G-AKA’ protocol, the pre-shared key between the user equipment (UE) and the home network (HN) is replaced with a derivation key of the pre-shared key, the challenge–response mechanism between the serving network (SN) and the HN is added, the subscription permanent identifier (SUPI) of the UE is added to the second message between the SN and the HN, and the MAC failure is replaced with a timeout mechanism on the HN. Finally, the 5G-AKA’ protocol is proved secure in the mixed strand space model and can overcome these attacks of the latest version of the 5G AKA protocol. Additionally, the comparative analysis shows that the 5G-AKA’ protocol is better than the recently improved 5G AKA protocols in security, and the 5G-AKA’ protocol is efficient and is backward compatible with the 5G AKA protocol.
GPR-Based Framework for Statistical Analysis of Gate Delay under NBTI and Process Variation Effects
Authors: Bu Aiguo , Wang Rongke , Jia Shuhao , Li Jie
Published: April 2022

With the aggressive scaling of feature size, Negative Bias Temperature Instability (NBTI) and Process Variation (PV) have become major issues for circuit reliability and yield. In this paper, we analyze the variation of gate delay by jointly considering NBTI and PV effects. Using Gaussian Process Regression (GPR) learning interface, a Statistical Gate Delay Extraction (SGDE) framework is proposed. Typical types of logic gates are simulated with commercial 28 nm technology for verifying the performance of SGDE in the experiment. Compared to the golden data, the results show that our proposed approach achieves minimal loss of accuracy with significant runtime speed-up.
Application of Fractional-Order PI Controllers and Neuro-Fuzzy PWM Technique to Multi-Rotor Wind Turbine Systems
Authors: Benbouhenni Habib , Bizon Nicu , Colak Ilhami , Thounthong Phatiphat , Takorabet Noureddine
Published: April 2022

In recent years, the methods of controlling electrical machines have been witnessing increasing development to reduce torque and electric current fluctuations in electrical power generation systems from renewable sources such as wind energy. The generation of electric power from wind plants imposes the need for an efficient and more robust method in order to obtain fewer ripples in active and reactive power. In this work, a new fractional-order proportional-integral (FOPI) controller and intelligent PWM (IPWM) technique are proposed to control an existing asynchronous generator (AG) in variable-speed multi-rotor wind turbines (VSMRWTs). This proposed method depends on combining or using two methods, namely nonlinear area and fractional calculus, to obtain a more robust method and to reduce current and torque ripples. In the framework of this study, the electric power generation system consists of a 1.5 MW AG and VSMRWTs. The AG is controlled using a simpler and easily accomplished method called direct vector control, based on FOPI controllers and the IPWM technique (DVC-FOPI-IPWM). The maximum power point tracking (MPPT) method is used to generate the maximum energy from the VSMRWTs. The proposed DVC-FOPI-IPWM technique is modeled in the Matlab/Simulink platform to obtain good quality current and active power. Simulation results show that the proposed strategy reduces the ripples of torque, current, and active power compared to the classical technique. Moreover, the reduction ratio is about 85%, 99%, and 93.33% for the current, active power, and torque, respectively.
On the Derivation of Winograd-Type DFT Algorithms for Input Sequences Whose Length Is a Power of Two
Authors: Raciborski Mateusz , Cariow Aleksandr
Published: April 2022

Winograd’s algorithms are an effective tool for calculating the discrete Fourier transform (DFT). These algorithms described in well-known articles are traditionally represented either with the help of sets of recurrent relations or with the help of products of sparse matrices obtained on the basis of various methods of the DFT matrix factorization. Unfortunately, in the mentioned papers, it is not shown how the described relations were obtained or how the presented factorizations were found. In this paper, we use a simple, understandable and fairly unified approach to the derivation of the Winograd-type DFT algorithms for the cases N = 8, N = 16 and N = 32. It is easy to verify that algorithms for other lengths of sequences that are powers of two can be synthesized in a similar way.
A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation
Authors: Gallego Pareja Luis A. , López-Lezama Jesús M. , Gómez Carmona Oscar
Published: April 2022

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.
Grey Correlation Analysis of Transportation Carbon Emissions under the Background of Carbon Peak and Carbon Neutrality
Authors: Sun Yanming , Liu Shixian , Li Lei
Published: April 2022

Transportation carbon emission reduction has become an important driving point for China to achieve carbon peak and carbon neutrality. Based on the three-dimensional grey correlation analysis model, taking the five factors affecting transportation carbon emissions, namely, population, GDP, tertiary industry, energy structure and logistics scale, as the research object, the transportation carbon emissions of China’s low-carbon pilot and nonpilot provinces from 2010 to 2019 are calculated based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission accounting method. The time series grey correlation degree and regional grey correlation degree of each influencing factor and traffic carbon emission are obtained using the provincial data, so as to provide policy suggestions for China to achieve the goal of “carbon peak and carbon neutrality” in the field of transportation. The results show that the descending order of the five influencing factors on transportation carbon emissions is: energy structure, logistics scale, population, GDP and tertiary industry. From 2010 to 2019, the time series grey correlation degree between the five influencing factors and transportation carbon emissions shows a fluctuating downward trend, but the impact of demographic factors has become more and more obvious in the past two years; According to the difference of grey correlation degree in different regions, the traffic development of various provinces in China is different, so it is necessary to formulate relevant policies individually.
Determinants of Electric Cars Purchase Intention in Poland: Personal Attitudes v. Economic Arguments
Authors: Sobiech-Grabka Katarzyna , Stankowska Anna , Jerzak Krzysztof
Published: April 2022

Urban e-mobility, seen as a part of complex and multidimensional European Green Deal plan, is essential for cities. However, it cannot be implemented without a common social commitment accompanied by a shared, strong belief in its advantages. Even if urban authorities and central governments would encourage their citizens to buy or share an electric vehicle (EV), the shift to EV will not be significant without people convinced that the idea of becoming zero-emission is economically viable and rational to them privately. This is especially true and important in countries like Poland—which is classified as an “EV readiness straggler”. The main purpose of this study is to develop a robust forecasting model with the aid of advanced machine learning methods. Based on the survey conducted, we identified factors useful for predicting consumer behaviour in terms of willingness to purchase an EV. The proposed machine-learning tool (specifically, the Random Forest algorithm) will allow automotive companies to more effectively target factors supporting the promulgation of urban individual e-mobility.
Optimal Scheduling Model of a Battery Energy Storage System in the Unit Commitment Problem Using Special Ordered Set
Authors: Do Insu , Lee Siyoung
Published: April 2022

Nonlinear characteristics of a battery energy storage system (BESS) may cause errors in the stored energy between the operation plan and the actual operation. These errors may hinder the reliability of the power system especially in environments such as microgrids with limited power generation resources and high uncertainty. This study proposes a method to alleviate the occurrence of such errors in the charging/discharging scheduling process of the BESS by piecewise linearizing its nonlinear characteristics. Specifically, the stored energy in a BESS that changes nonlinearly according to the size of the charging/discharging power was modeled using the special ordered set of the type 2 (SOS2) method. The proposed model and the typical BESS-operation models with constant power conditioning system (PCS) input/output power efficiency were applied to the unit commitment (UC) problem in a microgrid environment, and the results were compared with the actual operation results. The proposed model operated similarly to the actual operation compared to the typical model, reducing the error in charging/discharging energy. Consequently, the proposed model was made cost-effective by reducing the cost of error correction and reduced the risk of deviating from operating range of the BESS. This study demonstrates that the proposed method can efficiently solve the operational problems caused by the nonlinear characteristics of BESS.
Predicting Energy Savings of the UK Housing Stock under a Step-by-Step Energy Retrofit Scenario towards Net-Zero
Authors: Bennadji Amar , Seddiki Mohammed , Alabid Jamal , Laing Richard , Gray David
Published: April 2022

The UK has one of the least energy-efficient housing stocks in Europe. By 2030, the emissions from UK homes need to fall by at least 24% from 1990 levels to meet the UK’s ambitious goal, which is reaching net-zero emissions. The originality of this paper is to apply the building typology approach to predict energy savings of the UK housing stock under a step-by-step energy retrofit scenario, targeting the Passive House Standard for refurbishments of existing buildings, namely the EnerPHit “Quality-Approved Energy Retrofit with Passive House Components.” The typologies consist of twenty reference buildings, representative of five construction ages and four building sizes. The energy balance of the UK residential buildings was created and validated against statistical data. A building stock retrofit plan specifying the order in which to apply energy efficiency measures was elaborated, and energy savings were calculated. The predicted total energy demand for the UK residential building stock for the year 2022 is 37.7 MTOE, and the carbon emissions estimation is 65.33 MtCO2e. The energy-saving potential is 87%, and carbon reductions are about 76%, considering all the steps of renovation applied. It has been demonstrated that the step that provides the biggest savings across the housing stock is the one that involves replacing windows, draught-proofing, and installing mechanical ventilation with heat recovery.
Numerical Study of Knocking Combustion in a Heavy-Duty Engine under Plateau Conditions
Authors: Li Haiying , Zhang Xiaoqin , Li Chaofan , Cao Rulou , Zhu Weiqing , Li Yaozong , Liu Fengchun , Li Yufeng
Published: April 2022

Diesel engine combustion becomes very rough and can lead even lead to deflagration under high altitude conditions, which is harmful to component durability. In this study, the effects of altitude on the main combustion characteristics—in-cylinder fluid flow, spray behavior, and pressure and temperature distribution—were analyzed with CFD. A numerical model was built on the CONVERGE platform and validated with the optical spray behavior and pressure trace measured by the test bench. The simulation results indicated that the decreases in compression pressure and temperature at 4.5 km led to an over 4 °CA longer ignition delay than those of 1 and 3 km. The combustion efficiency decreased from 90% to 47% when the combustion changed from normal combustion to knocking combustion due to severe spray impingement. The processes of end-gas ignition, sequential combustion, and pressure oscillation in knocking combustion were revealed by the numerical modeling results. These results indicate that super-knocking combustion exists in both spark-ignition (SI) engines and compression-ignition (CI) engines.
Velocity Sensor Fault-Tolerant Controller for Induction Machine Using Intelligent Voting Algorithm
Authors: Alyoussef Fadi , Akrad Ahmad , Sehab Rabia , Morel Cristina , Kaya Ibrahim
Published: April 2022

Nowadays, induction machines (IMs) are widely used in industrial and transportation applications (electric or hybrid ground vehicle or aerospace actuators) thanks to their significant advantages in comparison to other technologies. Indeed, there is a large demand for IMs because of their reliability, robustness, and cost-effectiveness. The objective of this paper is to improve the reliability and performance of the three-phase induction machine in case of mechanical sensor failure. Moreover, this paper will discuss the development and proposal of a fault-tolerant controller (FTC), based on the combination of a vector controller, two virtual sensors (an extended Kalman filter, or EKF, and a sliding mode observer, or SMO) and a neural voting algorithm. In this approach, the vector controller is based on a new structure of a back-stepping sliding mode controller, which incorporates a double integral sliding surface to improve the performance of the induction machine in faulty operation mode. More specifically, this controller improves the machine performance in terms of having a fast response, fewer steady-state errors, and a robust performance in the existence of uncertainty. In addition, two voting algorithms are suggested in this approach. The first is based on neural networks, which are insensitive to parameter variations and do not need to set a threshold. The second one is based on fuzzy logic. Finally, validation is carried out by simulations in healthy and faulty operation modes to prove the feasibility of the proposed FTC.
Study on the Effect of Spoiler Columns on the Heat Dissipation Performance of S-Type Runner Water-Cooling Plates
Authors: Zheng Xiongfei , Hu Xue , Zhang Lixin , Zhang Xinwang , Chen Feng , Mai Chunliang
Published: April 2022

To solve the problem of low heat dissipation efficiency for the conventional S-type runner water-cooling plate of the fan converter IGBT module, two new water-cooling plates were designed with rectangular and elliptical column structures in the S-shaped runner of the water-cooling plate. The heat dissipation performance, the fluidity of cooling water, and pressure drop of different spoiler column structures were compared using Fluent software for the simulation and experiment. The comparative results show, compared with the water-cooling plate without a spoiler column in the flow channel of the control group, that the spoiler column structure in the flow channel significantly improved the heat dissipation performance of the water-cooling plate. When the inlet velocity of the water-cooling plate was 2 m/s, the highest temperature inside the water-cooling plate with a rectangular spoiler column structure was 12.25 °C, lower than the control water-cooling plate. The highest temperature inside the water-cooled plate with an elliptical structure was 12.40 °C, lower than the control water-cooled plate. The obstructive effect of the elliptical spoiler column structure on water flow was smaller than in the rectangular spoiler column structure. The fluidity of the cooling water inside the elliptical spoiler column structure water-cooling plate was better. When the inlet velocity of the water-cooling plate was 2 m/s, the cooling water flowing through the former was 282 L more than the latter in half an hour. Compared to the pressure drop, we found that in the design group, the pressure drop of the water-cooled plate with a rectangular spoiler column structure was 40,988.3 Pa. The pressure drop of the water-cooled plate with an elliptical spoiler column structure was 25,576.6 Pa. The difference between the two was 15,411.7 Pa, which proves that the energy loss inside the latter is smaller. To further explore the relationship between the heat dissipation and energy consumption of the two types of water-cooled plates, the comprehensive evaluation index η was calculated, ηb = 26.2, ηc = 31.6; therefore, ηb was significantly smaller than ηc. The overall performance of the water-cooled plate with an elliptical spoiler column structure was superior.
An Operation Strategy of ESS for Enhancing the Frequency Stability of the Inverter-Based Jeju Grid
Authors: Jung Jin-Yong , Cho Yoon-Sung , Min Jae-Hyun , Song Hwachang
Published: April 2022

As environmental pollution deteriorates, the acceleration of decarbonization worldwide is underway. Several countries, including Korea, are incorporating new and renewable energy into existing systems, and various methods of decarbonization are being researched. With changes in the control environment, the expansion of the system linkage of new and renewable energy can cause system instability problems. This paper introduces an operation plan that uses the fast response characteristics of energy storage system(s) (ESS) to improve stability and review system stability as a result of the expansion of new and renewable energy on Jeju Island. Frequency stability was ensured through an analysis of frequency response characteristics in the event of a system accident, and an efficient ESS operation plan was simulated by changing the ESS parameters. Through this simulation, stable ESS operation and hunting in the frequency recovery process were minimized through cooperation between the existing and new ESS via a wide dead-band range and speed adjustment rate correction, and the lowest frequency was improved.
A Probabilistic Model for Minimization of Solar Energy Operation Costs as Well as CO2 Emissions in a Multi-Carrier Microgrid (MCMG)
Authors: Ranjbarzadeh Hassan , Tafreshi Seyed Masoud Moghaddas , Ali Mohd Hasan , Kouzani Abbas Z. , Khoo Suiyang
Published: April 2022

This paper proposes a probabilistic model with the aim to reduce the solar energy operation cost and CO2 emissions of a multi-carrier microgrid. The MCMG in this study includes various elements such as combined heat and power (CHP), electrical heat pump (EHP), absorption chiller, solar panels, and thermal and electrical storages. A MILP model is proposed to manage the commitment of energy producers, energy storage equipment, the amount of selling/buying of energy with the upstream network, and the energy consumption of the responsible electrical loads for the day-ahead optimal operation of this microgrid. The proposed operation model is formulated as a multi-objective optimization model based on two environmental and economic objectives, using a weighted sum technique and a fuzzy satisfying approach. In this paper, the 2 m + 1-point estimate strategy has been used to model the uncertainties caused by the output power of solar panels and the upstream power supply price. In order to evaluate the performance of the proposed model, and also for minimizing cost and CO2 emissions, the simulation was conducted on two typical cold and hot days. Numerical results show the proposed model’s performance and the effect of electrifying the heating and cooling of the microgrid through the EHP unit on greenhouse gas emissions in the scenarios considered.
Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach
Authors: Wibbeke Jelke , Teimourzadeh Baboli Payam , Rohjans Sebastian
Published: April 2022

In these days, when complex, IT-controlled systems have found their way into many areas, models and the data on which they are based are playing an increasingly important role. Due to the constantly growing possibilities of collecting data through sensor technology, extensive data sets are created that need to be mastered. In concrete terms, this means extracting the information required for a specific problem from the data in a high quality. For example, in the field of condition monitoring, this includes relevant system states. Especially in the application field of machine learning, the quality of the data is of significant importance. Here, different methods already exist to reduce the size of data sets without reducing the information value. In this paper, the multidimensional binned reduction (MdBR) method is presented as an approach that has a much lower complexity in comparison on the one hand and deals with regression, instead of classification as most other approaches do, on the other. The approach merges discretization approaches with non-parametric numerosity reduction via histograms. MdBR has linear complexity and can be facilitated to reduce large multivariate data sets to smaller subsets, which could be used for model training. The evaluation, based on a dataset from the photovoltaic sector with approximately 92 million samples, aims to train a multilayer perceptron (MLP) model to estimate the output power of the system. The results show that using the approach, the number of samples for training could be reduced by more than 99%, while also increasing the model’s performance. It works best with large data sets of low-dimensional data. Although periodic data often include the most redundant samples and thus provide the best reduction capabilities, the presented approach can only handle time-invariant data and not sequences of samples, as often done in time series.
A Study of Total-Factor Energy Efficiency for Regional Sustainable Development in China: An Application of Bootstrapped DEA and Clustering Approach
Authors: Li Yang , Liu An-Chi , Wang Shu-Mei , Zhan Yiting , Chen Jingran , Hsiao Hsiao-Fen
Published: April 2022

Total-factor energy efficiency (TFEE) is widely used to measure energy efficiency under the data envelopment analysis (DEA) framework, but the efficiencies obtained from DEA are structurally biased upward, and thus TFEE tends to overestimate energy efficiency. This research thus applies the bootstrapped DEA approach to correct the bias of TFEE. Using a dataset consisting of 30 provinces of China in the period 2016–2019, the bootstrapped-based test supports technology with variable returns to scale. The biased-corrected TFEE also indicates that energy consumption on average can be scaled down by 42.36%, rather than the biased value of 19.4%. The bootstrapped clustering partitions provinces into three groups: Cluster 1, with Guizhou as the representative medoid, includes half of the superior coastal provinces in terms of actual energy consumption and TFEE and half of the competitive inland provinces, whereas Cluster 3 outperforms Cluster 2 in terms of TFEE, but the actual energy consumption is higher, with Shandong and Hebei as the representative medoids, respectively. Lastly, empirical results imply that the northeast and central regions need more government attention and resources to practice sustainable development and improve TFEE.
Economic Evaluation of Oil and Gas Projects: Justification of Engineering Solutions in the Implementation of Field Development Projects
Authors: Ponomarenko Tatiana , Marin Eugene , Galevskiy Sergey
Published: April 2022

The condition of the oil and gas sector is characterized by the complication of geological settings, and there is more competition in energy markets due to the current trend towards decarbonization. These circumstances require oil and gas companies to become more flexible and improve their project economic feasibility studies. The purpose of the research was to develop a methodological approach for the economic evaluation of oil and gas projects that includes choosing and substantiating the choice of engineering solutions that can be used to make decisions on subsequent investing. A comparative analysis of various economic evaluation methods applied to oil and gas projects included the DCF model, DPNV model with modifications, binary discounting, and reverse discounting. Taking into account the specific features of oil and gas projects, the authors provide a rationale for using a risk-free rate to calculate project outflows and a combination of reverse rates to calculate inflows. In addition, the theory of real options was applied to assess and account for geological and technical risks. These risks cause changes in the production rate when making engineering decisions. Using the proposed methodological approach, oil and gas companies will be able to evaluate in more detail and explain the influence of engineering solutions on the net present value of the project. This is the result of better consideration of geological and technical risks. The proposed approach is relevant both before project implementation and during production.
Analysis of Polarization and Depolarization Currents of Samples of NOMEX®910 Cellulose–Aramid Insulation Impregnated with Synthetic Ester
Authors: Krotowski Adam , Wolny Stefan
Published: April 2022

The paper presents the results of laboratory tests regarding the impact of accelerated thermal ageing of cellulose–aramid insulation samples saturated with electrical-insulating synthetic ester on the polarization and depolarization current characteristics being analyzed in the time domain. In the tests, electro-technical paper from DuPontTM was used, commercially designated as NOMEX®910. Laboratory-accelerated ageing consisted of heating with air a supply of samples of not-impregnated cellulose–aramid insulation for a specified time at specified temperatures. The temperatures and the warming time were selected so as to faithfully represent the degree of the thermal degradation of the cellulose fibers that takes place during normal operation of the material in power transformers. To determine the described impact of the ageing process in the insulation samples on the dielectric properties, the Polarization and Depolarization Method was applied. In the measurements, the MIC-15k1 high resistance meter from SONEL S.A. was used.
Experimental and Numerical Study on the Effect of Hydrogen Addition on Laminar Burning Velocity of Ethanol–Air Mixtures
Authors: Zhou Jianxi , Lu Chenyu , Xu Cangsu , Yu Zitao
Published: April 2022

To understand the effect of hydrogen addition on the laminar burning velocity (LBV) of ethanol–air mixtures, experiments were conducted in a constant volume combustion chamber with the high-speed schlieren photography technique. The experiments were carried out under the equivalence ratios (ERs) of 0.7–1.4, an initial temperature of 400 K, an initial pressure of 0.1 MPa, and hydrogen fractions of 30% and 90% by volume, respectively. The effects of ER, initial temperature, initial pressure, and hydrogen fractions on the LBV were investigated. Moreover, adiabatic flame temperature (AFT), heat release rate (HRR), flow rate sensitivity analysis, and ROP (rate of production) analysis were also performed. Results showed that LBV increased with increasing hydrogen addition and temperature but decreased with increasing pressure. The hydrogen addition significantly increased the HRR of ethanol–hydrogen–air flames. The sensitivity analysis showed that R5 (O2 + H = O + OH) significantly influenced the LBV.