bluuebunny/crossref_metadata_2025 huggingface dataset:
HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images
Authors: Yuan Yanchao , Li Cancheng , Zhang Ke , Hua Yang , Zhang Jicong
Published: November 2022

Carotid artery stenotic plaque segmentation in ultrasound images is a crucial means for the analysis of plaque components and vulnerability. However, segmentation of severe stenotic plaques remains a challenging task because of the heterogeneities of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated HRU-Net transfer learning method for segmenting carotid plaques, using the limited images. The HRU-Net is based on the U-Net encoder–decoder paradigm, and cross-domain knowledge is transferred for plaque segmentation by fine-tuning the pretrained ResNet-50. Moreover, a cropped-blood-vessel image augmentation is customized for the plaque position constraint during training only. Moreover, hybrid atrous convolutions (HACs) are designed to derive diverse long-range dependences for refined plaque segmentation that are used on high-level semantic layers to exploit the implicit discrimination features. The experiments are performed on 115 images; Firstly, the 10-fold cross-validation, using 40 images with severe stenosis plaques, shows that the proposed method outperforms some of the state-of-the-art CNN-based methods on Dice, IoU, Acc, and modified Hausdorff distance (MHD) metrics; the improvements on metrics of Dice and MHD are statistically significant (p < 0.05). Furthermore, our HRU-Net transfer learning method shows fine generalization performance on 75 new images with varying degrees of plaque stenosis, and it may be used as an alternative for automatic noisy plaque segmentation in carotid ultrasound images clinically.
Reduction in Radiation Exposure of CT Perfusion by Optimized Imaging Timing Using Temporal Information of the Preceding CT Angiography of the Carotid Artery in the Stroke Protocol
Authors: Deak Zsuzsanna , Schuettoff Lara , Lohse Ann-Kathrin , Fabritius Matthias , Reidler Paul , Forbrig Robert , Kunz Wolfgang , Dimitriadis Konstantin , Ricke Jens , Sabel Bastian
Published: November 2022

(1) Background: CT perfusion (CTP) is a fast, robust and widely available but dose-exposing imaging technique for infarct core and penumbra detection. Carotid CT angiography (CTA) can precede CTP in the stroke protocol. Temporal information of the bolus tracking series of CTA could allow for better timing and a decreased number of scans in CTP, resulting in less radiation exposure, if the shortening of CTP does not alter the calculated infarct core and penumbra or the resulting perfusion maps, which are essential for further treatment decisions. (2) Methods: 66 consecutive patients with ischemic stroke proven by follow-up imaging or endovascular intervention were included in this retrospective study approved by the local ethics committee. In each case, six simulated, stepwise shortened CTP examinations were compared with the original data regarding the perfusion maps, infarct core, penumbra and endovascular treatment decision. (3) Results: In simulated CTPs with 26, 28 and 30 scans, the infarct core, penumbra and PRR values were equivalent, and the resulting clinical decision was identical to the original CTP. (4) Conclusions: The temporal information of the bolus tracking series of the carotid CTA can allow for better timing and a lower radiation exposure by eliminating unnecessary scans in CTP.
The Impact of Coronavirus Disease 2019 on Maternal and Fetal Wellbeing in New Mexico
Authors: Emery Tiffany , Baillie Kati , Myers Orrin , Ko Hellen , Maxwell Jessie R.
Published: November 2022

Coronavirus disease 2019 (COVID-19) has been shown to affect the vasculature, including placental changes. Insults to the placenta, especially in the first and second trimester, can affect placental functionality with a resultant impact on fetal growth and wellbeing. Thus, we explored the relationship between antenatally acquired maternal COVID-19 infection and neonatal birth characteristics. A retrospective chart review was completed using the University of New Mexico electronic medical record system. ICD-10 codes were used to identify individuals that had a positive pregnancy test and positive COVID-19 screening test between 1 March 2020 to 24 March 2021. Chi-square and nonparametric Wilcoxon analyses were used, with p < 0.05 considered significant. A total of 487 dyad charts was analyzed, with 76 (16%) individuals identified as being COVID-19-positive (CovPos) during pregnancy. CovPos mothers were significantly more likely to deliver via a cesarean section compared to CovNeg mothers (33% vs. 20%, p < 0.01). There was a significant difference in gestational age at delivery, with infants born to CovPos individuals born at an earlier gestational age than those born to CovNeg individuals (37.6 vs. 38.5 weeks; p < 0.01). Our findings showed differences in maternal and infant characteristics following COVID-19 infection during pregnancy. Additional investigations are required to further delineate these relationships with a focus on potential long-term impacts on the neonate.
Establishment and Systematic Evaluation of Gastric Cancer Classification Model Based on Pyroptosis
Authors: Kadasah Sultan F.
Published: November 2022

Background: Gastric cancer (GC) is considered the fifth most prevalent type of cancer and the third leading cause of cancer deaths worldwide. This in-depth investigation was performed to generate fresh concepts for the clinical classification, diagnosis, and prognostic evaluation of GC. Methods: The data were retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Unsupervised cluster analysis was used to divide up the GC patients using pyroptosis-related differentially expressed genes (DEGs), which were discovered to be significantly linked with GC prognosis. The therapeutic importance of pyroptosis in GC patients was discovered using PCA analysis of genes associated with pyroptosis. The models were then carefully scrutinized. Results: Three hub genes, ELANE, IL6, and TIRAP, exhibit significant predictive importance among the 15 pyroptosis-related genes. Unsupervised clustering analysis revealed that the DEGs were enriched in the pathway of cytokine–cytokine receptor interactions, and Clusters 1 and 2 had statistically distinct prognoses. PCA analysis revealed significant differences in the area under the curve, immunological checkpoints, immunogenic cell death, and prognostic value between the high- and low-risk groups. Conclusions: These two GC classification models, based on pyroptosis, have significant clinical value for patients with GC.
Lectin Analysis of SARS-CoV-2-Positive Nasopharyngeal Samples Using GLYcoPROFILE® Technology Platform
Authors: Seničar Mateja , Roubinet Benoît , Daniellou Richard , Prazuck Thierry , Landemarre Ludovic
Published: November 2022

Nasopharyngeal samples are currently accepted as the standard diagnostic samples for nucleic acid amplification testing and antigenic testing for the SARS-CoV-2 virus. In addition to the diagnostic capacity of SARS-CoV-2-positive crude nasopharyngeal samples, their qualitative potential for direct glycan-specific analysis, in order to uncover unique glycol profiles, was assessed. In this study we provide glycan characterization of SARS-CoV-2-positive and -negative nasopharyngeal samples directly from lectin interactions. Although with limited throughput, this study evaluated the clinical sensitivity and specificity of the GLYcoPROFILE® technology platformon45crude nasopharyngeal samples collected between November 2020 and April 2022. Each GLYcoPROFILE® of 39 SARS-CoV-2-positive samples was compared toglycoprofiling on a panel of 10 selected lectins and the results were paralleled with SARS-CoV-2-negative samples’ results. The GLYcoPROFILE® showed a clear distinction between positive and negative samples with WFA, GSL-II, PHA-L (GlcNAc-specific) and BPA (GalNAc-specific) highlighted as relevant lectins in SARS-CoV-2-positive samples. In addition, a significant, positive statistical correlation was found for these lectins (p < 0.01).
Results of Numerical Modeling of Blood Flow in the Internal Jugular Vein Exhibiting Different Types of Strictures
Authors: Rashid Anas , Iqrar Syed Atif , Rashid Aiman , Simka Marian
Published: November 2022

The clinical relevance of nozzle-like strictures in upper parts of the internal jugular veins remains unclear. This study was aimed at understanding flow disturbances caused by such stenoses. Computational fluid dynamics software, COMSOL Multiphysics, was used. Two-dimensional computational domain involved stenosis at the beginning of modeled veins, and a flexible valve downstream. The material of the venous valve was considered to be hyperelastic. In the vein models with symmetric 2-leaflets valve without upstream stenosis or with minor 30% stenosis, the flow was undisturbed. In the case of major 60% and 75% upstream stenosis, centerline velocity was positioned asymmetrically, and areas of reverse flow and flow separation developed. In the 2-leaflet models with major stenosis, vortices evoking flow asymmetry were present for the entire course of the model, while the valve leaflets were distorted by asymmetric flow. Our computational fluid dynamics modeling suggests that an impaired outflow from the brain through the internal jugular veins is likely to be primarily caused by pathological strictures in their upper parts. In addition, the jugular valve pathology can be exacerbated by strictures located in the upper segments of these veins.
Global Stability of the Curzon-Ahlborn Engine with a Working Substance That Satisfies the van der Waals Equation of State
Authors: Pacheco-Paez Juan Carlos , Chimal-Eguía Juan Carlos , Páez-Hernández Ricardo , Ladino-Luna Delfino
Published: November 2022

In this paper, we show an analysis of the global stability of a Curzon–Ahlborn engine considering that the working substance of the engine satisfies the Van der Waals equation of state, which is more general than the ideal gas case. We use the Lyapunov stability theory for the case where the engine operates at a maximum power output. We analyze the steady state of the intermediate temperatures as well as the asymptotic behavior of the performance of the engine. Additionally, we study the relationship between the inherent time delay by analyzing the dynamic properties of the system and the stability of the steady state. We present illustrative graphs of the obtained results. Finally, we include a brief discussion of the obtained results and appropriate conclusions.
Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting
Authors: Al-qaness Mohammed A. A. , Ewees Ahmed A. , Abualigah Laith , AlRassas Ayman Mutahar , Thanh Hung Vo , Abd Elaziz Mohamed
Published: November 2022

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine–cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.
Quantum Mutual Information, Fragile Systems and Emergence
Authors: Navarrete Yasmín , Davis Sergio
Published: November 2022

In this paper, we present an analytical description of emergence from the density matrix framework as a state of knowledge of the system, and its generalized probability formulation. This description is based on the idea of fragile systems, wherein the observer modifies the system by the measurement (i.e., the observer effect) in order to detect possible emergent behavior. We propose the use of a descriptor, based on quantum mutual information, to calculate if subsystems of systems have inner correlations. This may contribute to a definition of emergent systems in terms of emergent information.
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation
Authors: Yucel Meric , Bagis Serdar , Sertbas Ahmet , Sarikaya Mehmet , Ustundag Burak
Published: November 2022

A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy–entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced—seconds versus hours—encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare.
Better Heisenberg Limits, Coherence Bounds, and Energy-Time Tradeoffs via Quantum Rényi Information
Authors: Hall Michael J. W.
Published: November 2022

An uncertainty relation for the Rényi entropies of conjugate quantum observables is used to obtain a strong Heisenberg limit of the form RMSE≥f(α)/(⟨N⟩+12), bounding the root mean square error of any estimate of a random optical phase shift in terms of average photon number, where f(α) is maximised for non-Shannon entropies. Related simple yet strong uncertainty relations linking phase uncertainty to the photon number distribution, such as ΔΦ≥maxnpn, are also obtained. These results are significantly strengthened via upper and lower bounds on the Rényi mutual information of quantum communication channels, related to asymmetry and convolution, and applied to the estimation (with prior information) of unitary shift parameters such as rotation angle and time, and to obtain strong bounds on measures of coherence. Sharper Rényi entropic uncertainty relations are also obtained, including time-energy uncertainty relations for Hamiltonians with discrete spectra. In the latter case almost-periodic Rényi entropies are introduced for nonperiodic systems.
Thermodynamically Consistent Models for Coupled Bulk and Surface Dynamics
Authors: Jing Xiaobo , Wang Qi
Published: November 2022

We present a constructive paradigm to derive thermodynamically consistent models coupling the bulk and surface dynamics hierarchically following the generalized Onsager principle. In the model, the bulk and surface thermodynamical variables are allowed to be different and the free energy of the model comprises the bulk, surface, and coupling energy, which can be weakly or strongly non-local. We illustrate the paradigm using a phase field model for binary materials and show that the model includes the existing thermodynamically consistent ones for the binary material system in the literature as special cases. In addition, we present a set of such phase field models for a few selected mobility operators and free energies to show how boundary dynamics impart changes to bulk dynamics and vice verse. As an example, we show numerically how reactive transport on the boundary impacts the dynamics in the bulk using a reactive transport model for binary reactive fluids by adopting a structure-preserving algorithm to solve the model equations in a rectangular domain.
Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles
Authors: Huang Zhaolong , Li Qiting , Zhao Junling , Song Meimei
Published: November 2022

Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs’ collision avoidance model.
Lending Technologies, Firm Characteristics and Small Business Efficiency in South Africa
Authors: Mbedzi Edson , Simatele Munacinga
Published: November 2022

Internal factors of Small, Micro and Medium Enterprises (SMMEs) determine their technical efficiency, while external funding characteristics improve the quality of internal factors. Since the type of lending institutions and lending technologies primarily influence the lending decisions of financial institutions, firms’ technical efficiency may be linked to such external factors. Literature on determinants of the technical efficiency of SMMEs mainly focuses on internal factors excluding the financial access paradigm which stifles the effectiveness of internal factors on technical efficiency. Based on a sample of 321 randomly selected SMMEs from Eastern Cape Province in South Africa, the study measures technical efficiency using Data Enveloping Analysis and differentiates technical efficiency among firms using Post Hoc Test Pairwise Comparisons derived from factorial ANOVA. Both main and interaction effects were captured in the analysis. Our results, which pinpoint four main findings, show technical efficiency paths followed by firms vary significantly as a result of both internal and external factors. In particular, the effects of other factors are amplified by race. As a consequence, three main contributions emerge from the study.
An Efficient Machine Learning Approach for Diagnosing Parkinson’s Disease by Utilizing Voice Features
Authors: Rana Arti , Dumka Ankur , Singh Rajesh , Rashid Mamoon , Ahmad Nazir , Panda Manoj Kumar
Published: November 2022

Parkinson’s disease (PD) is a neurodegenerative disease that impacts the neural, physiological, and behavioral systems of the brain, in which mild variations in the initial phases of the disease make precise diagnosis difficult. The general symptoms of this disease are slow movements known as ‘bradykinesia’. The symptoms of this disease appear in middle age and the severity increases as one gets older. One of the earliest signs of PD is a speech disorder. This research proposed the effectiveness of using supervised classification algorithms, such as support vector machine (SVM), naïve Bayes, k-nearest neighbor (K-NN), and artificial neural network (ANN) with the subjective disease where the proposed diagnosis method consists of feature selection based on the filter method, the wrapper method, and classification processes. Since just a few clinical test features would be required for the diagnosis, a method such as this might reduce the time and expense associated with PD screening. The suggested strategy was compared to PD diagnostic techniques previously put forward and well-known classifiers. The experimental outcomes show that the accuracy of SVM is 87.17%, naïve Bayes is 74.11%, ANN is 96.7%, and KNN is 87.17%, and it is concluded that the ANN is the most accurate one with the highest accuracy. The obtained results were compared with those of previous studies, and it has been observed that the proposed work offers comparable and better results.
HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images
Authors: Song Huina , Wu Han , Huang Jianhua , Zhong Hua , He Meilin , Su Mingkun , Yu Gaohang , Wang Mengyuan , Zhang Jianwu
Published: November 2022

Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively.
Selection and Dimensioning of Energy Storage Systems for Standalone Communities: A Review
Authors: Symeonidou Maria , Papadopoulos Agis M.
Published: November 2022

The European Union’s energy and climate policies are geared on reducing carbon dioxide emissions and advancing sustainable energy, focusing on a faster propagation of renewable energy sources to decarbonize the energy sector. The management of locally produced energy, which can be implemented by a microgrid capable of either being linked to the main grid or operating independently, is equally crucial. Additionally, it seems that electricity storage is the only practical way to manage energy effectively within a microgrid. Energy storage is hence one of the main technological parameters upon which future energy management has to be based. Especially during crisis periods (such as the COVID-19 pandemic or the ongoing energy crisis), storage is a valuable tool to optimize energy management, particularly from renewables, in order to successfully cover demand fluctuation, hence achieving resilience, while at the same time reducing overall energy costs. The purpose of the paper is to analyze and present, in brief, the state-of-the-art of the energy storage systems that are available on the market and discuss the upcoming technological improvements of the storage systems and, in particular, of batteries. The analysis will focus on the storage systems that can be used within a stand-alone community such as a microgrid, but not limited to it. In the analysis, short- and long-term storage options are discussed, as well as varying storage capacities of the different technologies. The analysis is based on contemporary optimization tools and methods used for standalone communities. Understanding the state-of-the-art of energy storage technology is crucial in order to achieve optimum solutions and will form the base for any further research.
Novel Functionalities of Smart Home Devices for the Elastic Energy Management Algorithm
Authors: Powroźnik Piotr , Szcześniak Paweł , Sobolewski Łukasz , Piotrowski Krzysztof
Published: November 2022

Energy management in power systems is influenced by such factors as economic and ecological aspects. Increasing the use of electricity produced at a given time from renewable energy sources (RES) by employing the elastic energy management algorithm will allow for an increase in “green energy“ in the energy sector. At the same time, it can reduce the production of electricity from fossil fuels, which is a positive economic aspect. In addition, it will reduce the volume of energy from RES that have to be stored using expensive energy storage or sent to other parts of the grid. The model parameters proposed in the elastic energy management algorithm are discussed. In particular, attention is paid to the time shift, which allows for the acceleration or the delay in the start-up of smart appliances. The actions taken by the algorithm are aimed at maintaining a compromise between the user’s comfort and the requirements of distribution network operators. Establishing the value of the time shift parameter is based on GMDH neural networks and the regression method. In the simulation studies, the extension of selected activities related to the tasks performed in households and its impact on the user’s comfort as well as the response to the increased generation of energy from renewable energy sources have been verified by the simulation research presented in this article. The widespread use of the new functionalities of smart appliance devices together with the elastic energy management algorithm is planned for the future. Such a combination of hardware and software will enable more effective energy management in smart grids, which will be part of national power systems.
Heat Transport in Rotating Annular Duct: A Short Review
Authors: Piton Maxime , Huchet Florian , Cazacliu Bogdan , Le Corre Olivier
Published: November 2022

Heat transport in rotating processes finds a wide range of application in which academic issues in the fluid mechanics and heat transfer areas are here reported. This paper discusses successive works from the seminal paper of Taylor (1923) to recent numerical results established from a broad range of methods such as DNS, LES, RANS or LB methods. The flow regimes identification is thus reported in Taylor–Couette geometry. The role of the axial flow rates in the apparition, stabilization and destruction of the large-scale of the turbulent structures is depicted in the case of Taylor–Couette–Poiseuille geometry. In a non-isothermal condition, a discussion is held on the various exponent values found in the scaling relationships relying on the Nusselt number as a function of the Rayleigh or Reynolds numbers according to the regimes of thermal convection.
Prospect of Post-Combustion Carbon Capture Technology and Its Impact on the Circular Economy
Authors: Olabi A. G. , Wilberforce Tabbi , Sayed Enas Taha , Shehata Nabila , Alami Abdul Hai , Maghrabie Hussein M. , Abdelkareem Mohammad Ali
Published: November 2022

The sudden increase in the concentration of carbon dioxide (CO2) in the atmosphere due to the high dependency on fossil products has created the need for an urgent solution to mitigate this challenge. Global warming, which is a direct result of excessive CO2 emissions into the atmosphere, is one major issue that the world is trying to curb, especially in the 21st Century where most energy generation mediums operate using fossil products. This investigation considered a number of materials ideal for the capturing of CO2 in the post-combustion process. The application of aqueous ammonia, amine solutions, ionic liquids, and activated carbons is thoroughly discussed. Notable challenges are impeding their advancement, which are clearly expatiated in the report. Some merits and demerits of these technologies are also presented. Future research directions for each of these technologies are also analyzed and explained in detail. Furthermore, the impact of post-combustion CO2 capture on the circular economy is also presented.
Evaluation of Photovoltaic Consumption Potential of Residential Temperature-Control Load Based on ANP-Fuzzy and Research on Optimal Incentive Strategy
Authors: Lu Siyue , Li Teng , Yan Xuefeng , Yang Shaobing
Published: November 2022

Temperature-control loads, such as residential air conditioners (ACs) and electric water heaters (EWHs), have become important demand response resources in the power system. However, due to the impact of various factors on users’ response behavior, it has been difficult for power grid operators to accurately evaluate the response potential under complex factor relationships to derive optimal incentive strategy. Therefore, it cannot achieve a win-win economic benefit between the grid and users. In this paper, a method combining Analytic Network Process (ANP) and Fuzzy logical inference is proposed to predict the user’s willingness firstly by taking residential AC load as an example. The weight of each factor affecting users’ willingness is analyzed, and main factors are selected as inputs of fuzzy logic inference to derive the willingness of the resident to actively regulate the AC. Then, this method is applied in evaluating the response potential of certain residential area in Beijing according to the survey. By further considering users’ house size and the sacrificed comfort temperature under the incentive strategy, the power potential curve of the AC load under different incentives is obtained by using the first-order equivalent thermal parameter (ETP) model and the regulation willingness. Finally, with the objective of maximizing the consumption of the photovoltaic (PV) power, the optimal operation is achieved through the coordinated regulation of residential ACs and EWHs based on the potential curve, and the corresponding optimal incentive strategy for the flexible temperature-control loads is obtained. Simulation results show that the optimal incentive strategy proposed not only increases the PV consumption ratio to 98.35% with an increase of 24.71%, but also maximizes the economic benefits of both sides of the power grid and users. This method of deriving incentive strategy can be used as a reference for grid companies to formulate the incentive strategy to realize optimal operation, such as the maximum new energy consumption.
Research and Application of Surface Throttling Technology for Ultra-High-Pressure Sour Natural Gas Wells in Northwestern Sichuan Basin
Authors: Luo Zhaoqian , Liu Qilin , Yang Fan , Li Ziyuan , Wang Huanhuan , Wang Bo , Peng Zhouyu , Jia Wenlong
Published: November 2022

The gas reservoirs in the Northwestern Sichuan Basin have great development potential. However, their production efficiency is seriously reduced by the complex surface multistage throttling process and frequent hydrate plugging caused by the ultra-high-pressure sour gas. Considering the prevention and control of hydrates, properly designing the throttling temperature and pressure to optimize the surface production process is the key to solving this problem. First, this work presents a method for predicting the hydrate formation conditions of ultra-high-pressure sour natural gas based on the vdW-P (van der Waals–Platteeuw) model and the CPA (cubic-plus-association) equation of state (EoS) and considering the association between acid gases (H2S, CO2) and H2O. Secondly, the throttling temperature prediction method was developed by coupling the isenthalpic throttling model and the modified Lee–Kesler EoS mixing rule; the maximum throttling pressure difference calculation method was constructed based on the critical flow principle. Finally, field cases are given to illustrate the distribution process of assigning the maximum throttling pressure differences. The results show that the proposed model can accurately predict the hydrate formation conditions and throttling temperature of ultra-high-pressure sour natural gas, and reveal their changing laws. The surface throttling technology of the ultra-high-pressure sour gas wells Long 004-X1 and Long 016-H1 was optimized from five-stage to three-stage, and the application of the fixed throttle valve in the field is successfully demonstrated.
Sugarcane Biomass as a Source of Biofuel for Internal Combustion Engines (Ethanol and Acetone-Butanol-Ethanol): A Review of Economic Challenges
Authors: Algayyim Sattar , Yusaf Talal , Hamza Naseer , Wandel Andrew , Fattah I. , Laimon Mohamd , Rahman S.
Published: November 2022

The objective of this review is to provide a deep overview of liquid biofuels produced from sugarcane bagasse and to address the economic challenges of an ethanol and acetone-butanol-ethanol blend in commercial processes. The chemistry of sugarcane bagasse is presented. Pretreatment technologies such as physical, chemical pretreatment, biological, and combination pretreatments used in the fermentation process are also provided and summarised. Different types of anaerobic bacteria Clostridia (yeast) are discussed to identify the ingredient best suited for sugarcane bagasse, which can assist the industry in commercializing ethanol and acetone-butanol-ethanol biofuel from biomass sugarcane. The use of an acetone-butanol-ethanol mixture and ethanol blend in internal combustion engines is also discussed. The literature then supports the proposal of the best operating conditions for fermentation to enhance ethanol and acetone-butanol-ethanol plant efficiency in the sugar waste industry and its application in internal combustion engines.
Research on ZVS Phase-Shifted Full-Bridge Broadband Inverter Based on Auxiliary Current Source
Authors: Zhao Yuezhang , Xiao Quan , Zhang Zihao , Zhao Xueting , Lin Deyan
Published: November 2022

Phase-shifted full-bridge topologies are widely used in medium- and high-power DC/DC converters due to their small size and high switching frequency. However, there are few studies on the application of broadband inverters. In the traditional phase-shift full-bridge inverter with a fully resonant load, the problem of current commutation which leads to hard switching is often encountered, to overcome such an issue, an auxiliary current source network is introduced to realize the zero-voltage turn-on of the lagging bridge arm. The working modes of the converter are analyzed in detail, and the parameters of the auxiliary current source network are designed. The simulation verification is carried out by MATLAB/Simulink in a wide frequency range from 10 kHz to 500 kHz. Finally, an experimental circuit board is designed, and the experimental results show that the topology can achieve soft switching in a frequency range from 10 kHz to 200 kHz and has a certain applicability.
Soil Organic Carbon Distribution, Enzyme Activities, and the Temperature Sensitivity of a Tropical Rainforest in Wuzhishan, Hainan Island
Authors: Wang Xiaofang , Li Jialing , Xing Guitong , Mai Siwei , Liu Wenjie , Jiang Yamin , Xu Wenxian , Yang Qiu , Yang Huai , Lu Jingli , Long Wenxing
Published: November 2022

Soil carbon (C) distribution, which is deeply correlated with soil extracellular enzyme activity and its temperature sensitivity (Q10), are important for predicting the soil organic carbon (SOC) pools under climate warming. However, the high spatial heterogeneity in tropical forest soils makes the predicted results vary significantly. Thus, a total of 87 soil samples of three forest types, eight slope orientations, and four vegetation types were selected from Wuzhishan in Hainan Island, China. SOC distribution, the activities of six soil enzymes, and Q10 at 10, 20, 30, and 40 °C were investigated to supplement the tropical data. The results showed that: (1) SOC ranged from 24.82 to 87.72 g/kg. SOC in the primary forest was significantly higher than that of the secondary forest, and SOC of the cloud forests and hilltop scrub at higher elevations was significantly higher than that of the lowland rainforests and montane rainforests at lower elevations. However, the amount of recalcitrant carbon in the primary forest was the lowest. (2) Under lab temperature conditions, the acid phosphatase and β-1,4-glucosidase in the plantation forest were significantly higher than that of the secondary forest, and the polyphenol oxidase and catalase in the plantation forest were significantly higher than that of the primary forest. Enzyme stoichiometry analysis indicated that microbials were limited by nitrogen in the study area. The six soil enzyme activities were strongly correlated with SOC and total nitrogen. (3) The Q10 of soil enzymes ranged from 0.61 to 1.92 under three temperature gradients. Most hydrolases enzymes (Q10 > 1) showed a positive response with temperatures from 10 to 30 °C, and showed a negative response (Q10 < 1) with temperatures from 30 to 40 °C. We concluded that the negative response of enzyme Q10 with global warming would slow down the SOC decomposition. Primary tropical forests could still sequester SOC; however, their ability to do so may be vulnerable to climate change, as the amount of soil C quality index was low.
Dynamics of Enzyme Activities during the Decomposition of Castanopsis carlesii Leaf Litter in the Forest Canopy and Forest Floor in a Mid-Subtropical Area
Authors: Zhu Ling , Wei Wentao , Wu Ruobing , Zhang Xiaoyue , Guo Hongrong , Wang Dingyi , Wu Fuzhong
Published: November 2022

Enzyme activity plays a pivotal role in leaf litter decomposition, but the variations have not been well addressed in the forest canopy with amounts of leaf litter. Therefore, eight enzymes related to carbon, nitrogen, and phosphorus mineralization were checked during Castanopsis carlesii leaf litter decomposition in the forest canopy and on the forest floor from April 2021 to February 2022. The results displayed that most enzyme activities were lower in the forest canopy compared to the forest floor during litter decomposition, except for acid phosphatase, polyphenol oxidase, and peroxidase activities. Moreover, enzyme stoichiometry and enzyme vector features indicated that the microbes in both habitats were limited by carbon and phosphorus during litter decomposition. Much stronger carbon limitation was detected on the forest floor, while phosphorus limitation was higher in the forest canopy. Phosphorus limitation was weakened, but carbon limitation was strengthened in the forest canopy with leaf litter decomposition. Additionally, the redundancy analysis revealed that air temperature dominated the variations in enzyme activities during litter decomposition in the forest canopy, and litter mass-loss rate in each period explained much more dynamics on the forest floor compared with those in the forest canopy. These results provide new insight into a comprehensive understanding of litter decomposition in subtropical forests.
The Effect of Quercetin on the Growth, Development, Nutrition Utilization, and Detoxification Enzymes in Hyphantria cunea Drury (Lepidoptera: Arctiidae)
Authors: Gao Yi-Lin , Pan Zhong-Yu , Meng Xiang , Yuan Yu-Fei , Li Hong-Yun , Chen Min
Published: November 2022

Hyphantria cunea Drury (Lepidoptera: Arctiidae) is a worldwide quarantine pest that has a wide range of host plants. Quercetin is a secondary metabolite involved in chemical defense processes in plants. To understand how H. cunea adapt to quercetin in its host plants, we determined the effects of quercetin on larval mortality, growth, nutritional indices, and the activity or content of detoxification enzymes in H. cunea larvae by feeding them an artificial diet containing different concentrations of quercetin. Our results showed that 0.50% quercetin treatment significantly prolonged the development duration of H. cunea larvae and inhibited growth of H. cunea. Nutritional indices analysis indicated that quercetin significantly affected nutrient use, including effects on the approximate digestibility, consumption index, relative growth rate, and efficiency of conversion of ingested food to body substance. Furthermore, our results revealed that quercetin reduced the content of carboxylesterases, and increased the activity or content of glutathione S-transferases, UDP-glucuronosyltransferases, and ATP-binding cassette transporters in H. cunea larvae. These results provide a foundation for revealing the adaptation that H. cunea use to adapt to quercetin in host plants.
Pollen Morphological Inter- and Intraspecific Variability in Selected Species of Rubus L. (Rosaceae)
Authors: Lechowicz Kacper , Bocianowski Jan , Wrońska-Pilarek Dorota
Published: November 2022

Pollen morphology is one of the important vehicles in resolving some taxonomic problems on the family, genera or species level and has become part of the many disciplines and collaborative approaches in plant systematics and evolution. Palynological studies on the large and taxonomically highly complex genus Rubus L. have been limited, particularly concerning pollen variability. The aim of current study was to investigate the scope of inter- and intraspecific variability of the studied species based on pollen morphology, as well as verify taxonomic usefulness of pollen traits in distinguishing the studied taxa. The sixteen native Poland and Europe Rubus species were studied. Over a dozen quantitative and qualitative features of 2100 pollen (70 samples of 30 pollen each) were analyzed. Exine ornamentation and pollen size proved to be most useful pollen traits in the assessment of variability in the studied Rubus species. Pollen traits did not confirm the currently adopted taxonomical division of the genus Rubus into subgenera, sections and series. The greatest interspecific variability was found for traits P, E, Le and d, while variability was lowest in the case of Exp, Exe, P/E, Le/P, d/E, Exp/P and Exe/E. Intraspecific variability was determined by the following features ordered from those least to most variable: P, E, d, Le, d/E, Exp/P, Exp, P/E and Le/P.
On the Rarefied Thermally-Driven Flows in Cavities and Bends
Authors: Mousivand Mostafa , Roohi Ehsan
Published: November 2022

This study examined rarefied thermally-driven flow in a square cavity (Case 1) and rectangular bend (Case 2), with various uniform wall temperatures in two dimensions. We employed the direct simulation Monte Carlo (DSMC) to solve problems with a wide range of Knudsen numbers Kn = 0.01 to 10, and the discrete unified gas kinetic scheme (DUGKS) solver was used at Kn = 0.01. The scenario was that, in case 1, the bottom side and its opposite were set hot, and the other sides were set cold. Diffuse reflector boundary conditions were set for all walls. The imposed temperature differences created four primary vortices. The results of the continuum set of equations of the slow non-isothermal flow (SNIT) solver proved that the primary vortices in the square cavity were caused by nonlinear thermal stress effects, and other smaller vortices appearing at Kn = 0.01, 0.1 were brought about by thermal creep processes. As the Kn increased, vortices generated by thermal creep disappeared, and eddies created by nonlinear thermal stress occupied the cavity. In case 2, i.e., a rectangular bend, two sides were set cold, and the others were hot. Two primary vortices were formed, which were caused by nonlinear thermal stress effects. The direction of streamlines in the two main vortices was opposite, from the warm to the cold zone, as some eddies on the left were counterclockwise, and others were clockwise.
Natural Materials for 3D Printing and Their Applications
Authors: Su Chunyu , Chen Yutong , Tian Shujing , Lu Chunxiu , Lv Qizhuang
Published: November 2022

In recent years, 3D printing has gradually become a well-known new topic and a research hotspot. At the same time, the advent of 3D printing is inseparable from the preparation of bio-ink. Natural materials have the advantages of low toxicity or even non-toxicity, there being abundant raw materials, easy processing and modification, excellent mechanical properties, good biocompatibility, and high cell activity, making them very suitable for the preparation of bio-ink. With the help of 3D printing technology, the prepared materials and scaffolds can be widely used in tissue engineering and other fields. Firstly, we introduce the natural materials and their properties for 3D printing and summarize the physical and chemical properties of these natural materials and their applications in tissue engineering after modification. Secondly, we discuss the modification methods used for 3D printing materials, including physical, chemical, and protein self-assembly methods. We also discuss the method of 3D printing. Then, we summarize the application of natural materials for 3D printing in tissue engineering, skin tissue, cartilage tissue, bone tissue, and vascular tissue. Finally, we also express some views on the research and application of these natural materials.
The Feasibility of Shellac Wax Emulsion Oleogels as Low-Fat Spreads Analyzed by Means of Multidimensional Statistical Analysis
Authors: Puşcaş Andreea , Mureşan Vlad
Published: November 2022

Shellac wax-based oleogel emulsions were studied with a three level two factorial design in order to find an optimal formulation for a spread formulation. Rheological, textural, colorimetry, and stability analysis were conducted to assess the performance of oleogel emulsions. FTIR spectra were also compared. The similarities between the samples were studied using cluster analysis. Analysis of variance (ANOVA) demonstrates that (i) the texture is influenced by the wax concentration, (ii) the rheology and stability by both the considered numeric factors (wax and water concentration) and their interaction, and (iii) the color by both factors. The emulsions containing 7% (m/m) shellac oleogels behaved like the strongest systems, (G′ & GLVR > 30,000 Pa) and exhibited the highest value of the G′-G″ cross-over. The lowest oil binding capacity (OBC) was 99.88% for the sample with 3% (m/m) shellac and 20% (m/m) water. The whiteness index (Windex) varied between 58.12 and 78.50. The optimization process indicated that a formulation based on 4.29% (m/m) shellac wax and 24.13% (m/m) water was suitable as a low-fat spread.