bluuebunny/crossref_metadata_2025 huggingface dataset:
An Ancillary Method for Adrenal Venous Sampling in Cases in Which Right Adrenal Vein Sampling Is Difficult
Authors: Yamamoto Akira , Fukunaga Takeshi , Takeuchi Mitsuru , Nakamura Hiroki , Kanki Akihiko , Higaki Atsushi , Tamada Tsutomu
Published: February 2023

Catheterization of the right adrenal vein (rt.AdV) to obtain blood samples can often be difficult. The aim of the present study was to investigate whether blood sampling from the inferior vena cava (IVC) at its juncture with the rt.AdV can be an ancillary to sampling of blood directly from the rt.AdV. This study included 44 patients diagnosed with primary aldosteronism (PA) in whom AVS with adrenocorticotropic hormone (ACTH) was performed, resulting in a diagnosis of idiopathic hyperaldosteronism (IHA) (n = 24), and patients diagnosed with unilateral aldosterone-producing adenoma (APA) (n = 20; rt.APA = 8, lt.APA = 12). In addition to regular blood sampling, blood was also sampled from the IVC, as the substitute rt.AdV [S-rt.AdV]. Diagnostic performance with the conventional lateralized index (LI) and the modified LI using the S-rt.AdV were compared to examine the utility of the modified LI. The modified LI of the rt.APA (0.4 ± 0.4) was significantly lower than those of the IHA (1.4 ± 0.7) (p < 0.001) and the lt.APA (3.5 ± 2.0) (p < 0.001). The modified LI of the lt.APA was significantly higher than those of the IHA (p < 0.001) and rt.APA (p < 0.001). Likelihood ratios to diagnose rt.APA and lt.APA using the modified LI with threshold values of 0.3 and 3.1 were 27.0, and 18.6, respectively. The modified LI has the potential to be an ancillary method for rt.AdV sampling in cases in which rt.AdV sampling is difficult. Obtaining the modified LI is extremely simple, which might complement conventional AVS.
Intracranial Hemorrhage Detection Using Parallel Deep Convolutional Models and Boosting Mechanism
Authors: Asif Muhammad , Shah Munam Ali , Khattak Hasan Ali , Mussadiq Shafaq , Ahmed Ejaz , Nasr Emad Abouel , Rauf Hafiz Tayyab
Published: February 2023

Intracranial hemorrhage (ICH) can lead to death or disability, which requires immediate action from radiologists. Due to the heavy workload, less experienced staff, and the complexity of subtle hemorrhages, a more intelligent and automated system is necessary to detect ICH. In literature, many artificial-intelligence-based methods are proposed. However, they are less accurate for ICH detection and subtype classification. Therefore, in this paper, we present a new methodology to improve the detection and subtype classification of ICH based on two parallel paths and a boosting technique. The first path employs the architecture of ResNet101-V2 to extract potential features from windowed slices, whereas Inception-V4 captures significant spatial information in the second path. Afterwards, the detection and subtype classification of ICH is performed by the light gradient boosting machine (LGBM) using the outputs of ResNet101-V2 and Inception-V4. Thus, the combined solution, known as ResNet101-V2, Inception-V4, and LGBM (Res-Inc-LGBM), is trained and tested over the brain computed tomography (CT) scans of CQ500 and Radiological Society of North America (RSNA) datasets. The experimental results state that the proposed solution efficiently obtains 97.7% accuracy, 96.5% sensitivity, and 97.4% F1 score using the RSNA dataset. Moreover, the proposed Res-Inc-LGBM outperforms the standard benchmarks for the detection and subtype classification of ICH regarding the accuracy, sensitivity, and F1 score. The results prove the significance of the proposed solution for its real-time application.
Automatic Detection, Classification, and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial Neural Network Model
Authors: Liawrungrueang Wongthawat , Kim Pyeoungkee , Kotheeranurak Vit , Jitpakdee Khanathip , Sarasombath Peem
Published: February 2023

Background and Objectives: Intervertebral disc degeneration (IDD) is a common cause of symptomatic axial low back pain. Magnetic resonance imaging (MRI) is currently the standard for the investigation and diagnosis of IDD. Deep learning artificial intelligence models represent a potential tool for rapidly and automatically detecting and visualizing IDD. This study investigated the use of deep convolutional neural networks (CNNs) for the detection, classification, and grading of IDD. Methods: Sagittal images of 1000 IDD T2-weighted MRI images from 515 adult patients with symptomatic low back pain were separated into 800 MRI images using annotation techniques to create a training dataset (80%) and 200 MRI images to create a test dataset (20%). The training dataset was cleaned, labeled, and annotated by a radiologist. All lumbar discs were classified for disc degeneration based on the Pfirrmann grading system. The deep learning CNN model was used for training in detecting and grading IDD. The results of the training with the CNN model were verified by testing the grading of the dataset using an automatic model. Results: The training dataset of the sagittal intervertebral disc lumbar MRI images found 220 IDDs of grade I, 530 of grade II, 170 of grade III, 160 of grade IV, and 20 of grade V. The deep CNN model was able to detect and classify lumbar IDD with an accuracy of more than 95%. Conclusion: The deep CNN model can reliably automatically grade routine T2-weighted MRIs using the Pfirrmann grading system, providing a quick and efficient method for lumbar IDD classification.
Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review
Authors: Mohammed Mazin Abed , Abdulkareem Karrar Hameed , Dinar Ahmed M. , Zapirain Begonya Garcia
Published: February 2023

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.
Sensitivity of LiDAR Parameters to Aboveground Biomass in Winter Spelt
Authors: Montzka Carsten , Donat Marco , Raj Rahul , Welter Philipp , Bates Jordan Steven
Published: February 2023

Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.
RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark
Authors: Shuang Feng , Han Sheng , Li Yong , Lu Tongwei
Published: February 2023

Power line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. However, in the power line inspection based on computer vision, datasets have a significant impact on deep learning. The lack of public high-quality power scene data hinders the application of deep learning. To address this problem, we built a dataset for power line inspection scenes, named RSIn-Dataset. RSIn-Dataset contains 4 categories and 1887 images, with abundant backgrounds. Then, we used mainstream object detection methods to build a benchmark, providing reference for insulator detection. In addition, to address the problem of detection inefficiency caused by large model parameters, an improved YoloV4 is proposed, named YoloV4++. It uses a lightweight network, i.e., MobileNetv1, as the backbone, and employs the depthwise separable convolution to replace the standard convolution. Meanwhile, the focal loss is implemented in the loss function to solve the impact of sample imbalance. The experimental results show the effectiveness of YoloV4++. The mAP and FPS can reach 94.24% and 53.82 FPS, respectively.
Surrogate-Assisted Hybrid Meta-Heuristic Algorithm with an Add-Point Strategy for a Wireless Sensor Network
Authors: Pan Jeng-Shyang , Zhang Li-Gang , Chu Shu-Chuan , Shieh Chin-Shiuh , Watada Junzo
Published: February 2023

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems.
Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks
Authors: Yao Xiaohui , Yang Honghui , Sheng Meiping
Published: February 2023

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.
Characterizing the Impact of Communication on Cellular and Collective Behavior Using a Three-Dimensional Multiscale Cellular Model
Authors: Echlin Moriah , Aguilar Boris , Shmulevich Ilya
Published: February 2023

Communication between cells enables the coordination that drives structural and functional complexity in biological systems. Both single and multicellular organisms have evolved diverse communication systems for a range of purposes, including synchronization of behavior, division of labor, and spatial organization. Synthetic systems are also increasingly being engineered to utilize cell–cell communication. While research has elucidated the form and function of cell–cell communication in many biological systems, our knowledge is still limited by the confounding effects of other biological phenomena at play and the bias of the evolutionary background. In this work, our goal is to push forward the context-free understanding of what impact cell–cell communication can have on cellular and population behavior to more fully understand the extent to which cell–cell communication systems can be utilized, modified, and engineered. We use an in silico model of 3D multiscale cellular populations, with dynamic intracellular networks interacting via diffusible signals. We focus on two key communication parameters: the effective interaction distance at which cells are able to interact and the receptor activation threshold. We found that cell–cell communication can be divided into six different forms along the parameter axes, three asocial and three social. We also show that cellular behavior, tissue composition, and tissue diversity are all highly sensitive to both the general form and specific parameters of communication even when the cellular network has not been biased towards that behavior.
Non-Iterative Multiscale Estimation for Spatial Autoregressive Geographically Weighted Regression Models
Authors: Gao Shi-Jie , Mei Chang-Lin , Xu Qiu-Xia , Zhang Zhi
Published: February 2023

Multiscale estimation for geographically weighted regression (GWR) and the related models has attracted much attention due to their superiority. This kind of estimation method will not only improve the accuracy of the coefficient estimators but also reveal the underlying spatial scale of each explanatory variable. However, most of the existing multiscale estimation approaches are backfitting-based iterative procedures that are very time-consuming. To alleviate the computation complexity, we propose in this paper a non-iterative multiscale estimation method and its simplified scenario for spatial autoregressive geographically weighted regression (SARGWR) models, a kind of important GWR-related model that simultaneously takes into account spatial autocorrelation in the response variable and spatial heterogeneity in the regression relationship. In the proposed multiscale estimation methods, the two-stage least-squares (2SLS) based GWR and the local-linear GWR estimators of the regression coefficients with a shrunk bandwidth size are respectively taken to be the initial estimators to obtain the final multiscale estimators of the coefficients without iteration. A simulation study is conducted to assess the performance of the proposed multiscale estimation methods, and the results show that the proposed methods are much more efficient than the backfitting-based estimation procedure. In addition, the proposed methods can also yield accurate coefficient estimators and such variable-specific optimal bandwidth sizes that correctly reflect the underlying spatial scales of the explanatory variables. A real-life example is further provided to demonstrate the applicability of the proposed multiscale estimation methods.
N-States Continuous Maxwell Demon
Authors: Raux Paul , Ritort Felix
Published: February 2023

Maxwell’s demon is a famous thought experiment and a paradigm of the thermodynamics of information. It is related to Szilard’s engine, a two-state information-to-work conversion device in which the demon performs single measurements and extracts work depending on the state measurement outcome. A variant of these models, the continuous Maxwell demon (CMD), was recently introduced by Ribezzi-Crivellari and Ritort where work was extracted after multiple repeated measurements every time that τ is in a two-state system. The CMD was able to extract unbounded amounts of work at the cost of an unbounded amount of information storage. In this work, we built a generalization of the CMD to the N-state case. We obtained generalized analytical expressions for the average work extracted and the information content. We show that the second law inequality for information-to-work conversion is fulfilled. We illustrate the results for N-states with uniform transition rates and for the N = 3 case.
Quick Estimate of Information Decomposition for Text Style Transfer
Authors: Shibaev Viacheslav , Olbrich Eckehard , Jost Jürgen , Yamshchikov Ivan P.
Published: February 2023

A growing number of papers on style transfer for texts rely on information decomposition. The performance of the resulting systems is usually assessed empirically in terms of the output quality or requires laborious experiments. This paper suggests a straightforward information theoretical framework to assess the quality of information decomposition for latent representations in the context of style transfer. Experimenting with several state-of-the-art models, we demonstrate that such estimates could be used as a fast and straightforward health check for the models instead of more laborious empirical experiments.
Correcting Coherent Errors by Random Operation on Actual Quantum Hardware
Authors: Cenedese Gabriele , Benenti Giuliano , Bondani Maria
Published: February 2023

Characterizing and mitigating errors in current noisy intermediate-scale devices is important to improve the performance of the next generation of quantum hardware. To investigate the importance of the different noise mechanisms affecting quantum computation, we performed a full quantum process tomography of single qubits in a real quantum processor in which echo experiments are implemented. In addition to the sources of error already included in the standard models, the obtained results show the dominant role of coherent errors, which we practically corrected by inserting random single-qubit unitaries in the quantum circuit, significantly increasing the circuit length over which quantum computations on actual quantum hardware produce reliable results.
Kernel Partial Least Squares Feature Selection Based on Maximum Weight Minimum Redundancy
Authors: Liu Xiling , Zhou Shuisheng
Published: February 2023

Feature selection refers to a vital function in machine learning and data mining. The maximum weight minimum redundancy feature selection method not only considers the importance of features but also reduces the redundancy among features. However, the characteristics of various datasets are not identical, and thus the feature selection method should have different feature evaluation criteria for all datasets. Additionally, high-dimensional data analysis poses a challenge to enhancing the classification performance of the different feature selection methods. This study presents a kernel partial least squares feature selection method on the basis of the enhanced maximum weight minimum redundancy algorithm to simplify the calculation and improve the classification accuracy of high-dimensional datasets. By introducing a weight factor, the correlation between the maximum weight and the minimum redundancy in the evaluation criterion can be adjusted to develop an improved maximum weight minimum redundancy method. In this study, the proposed KPLS feature selection method considers the redundancy between the features and the feature weighting between any feature and a class label in different datasets. Moreover, the feature selection method proposed in this study has been tested regarding its classification accuracy on data containing noise and several datasets. The experimental findings achieved using different datasets explore the feasibility and effectiveness of the proposed method which can select an optimal feature subset and obtain great classification performance based on three different metrics when compared with other feature selection methods.
Kullback–Leibler Divergence of an Open-Queuing Network of a Cell-Signal-Transduction Cascade
Authors: Tsuruyama Tatsuaki
Published: February 2023

Queuing networks (QNs) are essential models in operations research, with applications in cloud computing and healthcare systems. However, few studies have analyzed the cell’s biological signal transduction using QN theory. This study entailed the modeling of signal transduction as an open Jackson’s QN (JQN) to theoretically determine cell signal transduction, under the assumption that the signal mediator queues in the cytoplasm, and the mediator is exchanged from one signaling molecule to another through interactions between the signaling molecules. Each signaling molecule was regarded as a network node in the JQN. The JQN Kullback–Leibler divergence (KLD) was defined using the ratio of the queuing time (λ) to the exchange time (μ), λ/μ. The mitogen-activated protein kinase (MAPK) signal-cascade model was applied, and the KLD rate per signal-transduction-period was shown to be conserved when the KLD was maximized. Our experimental study on MAPK cascade supported this conclusion. This result is similar to the entropy-rate conservation of chemical kinetics and entropy coding reported in our previous studies. Thus, JQN can be used as a novel framework to analyze signal transduction.
An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey
Authors: Aubret Arthur , Matignon Laetitia , Hassas Salima
Published: February 2023

The reinforcement learning (RL) research area is very active, with an important number of new contributions, especially considering the emergent field of deep RL (DRL). However, a number of scientific and technical challenges still need to be resolved, among which we acknowledge the ability to abstract actions or the difficulty to explore the environment in sparse-reward settings which can be addressed by intrinsic motivation (IM). We propose to survey these research works through a new taxonomy based on information theory: we computationally revisit the notions of surprise, novelty, and skill-learning. This allows us to identify advantages and disadvantages of methods and exhibit current outlooks of research. Our analysis suggests that novelty and surprise can assist the building of a hierarchy of transferable skills which abstracts dynamics and makes the exploration process more robust.
Taxonomic Diversity of the Microbial Biofilms Collected along the Thermal Streams on Kunashir Island
Authors: Malygina Alexandra , Balkin Alexander , Polyakova Elena , Stefanov Sergey , Potekhin Alexey , Gogoleva Natalia
Published: February 2023

Hot springs are known as highly adverse extreme environments where thermophilic and hyperthermophilic microorganisms can survive. We describe taxonomic diversity of several microbial biofilms collected along water temperature gradient in hot streams in the aquatic system of the Stolbovskie hot springs on Kunashir Island, Kurils, Russia. The taxonomic composition of the studied microbial communities was assessed by the 16S rRNA gene metabarcoding for bacteria and archaea, and by the 18S rRNA gene metabarcoding for protists. Richness and diversity of bacteria in the geothermal microbial communities decreased with the increase of temperature, while for archaea, the tendency was the opposite. Ciliophora was the most represented taxon of protists. The biofilms of various kinds that we found in a very local area of the geothermal system were different from each other by taxonomic composition, and the level of their taxonomic diversity was significantly influenced by water temperature.
Evaluating Emergency Remote Assessment Adaptations in Higher Education due to COVID-19: Faculty Insights and Challenges
Authors: Papanastasiou Elena C. , Solomonidou Georgia
Published: February 2023

The purpose of this study was to critically examine the feedback obtained from higher education instructors regarding the implementation of the emergency remote assessment practices that took place within a university in the Republic of Cyprus, in order to identify the strengths and weaknesses of the changes that took place. This was essential since the abruptness of the pandemic did not always allow for smooth transitions during the introduction of these changes. Therefore, the results of this survey study that was based on an online questionnaire identified certain aspects of the assessment adaptations that were evaluated as positive (e.g., the use of e-invigilation software), and other aspects that were not as positive (e.g., performing oral examinations after the written test). However, the results also revealed that cheating and plagiarism were issues that concerned the instructors, as were the technological problems that were faced. All these results are discussed holistically at the end of this article in order to guide further research and decision making regarding online assessments.
The Impact of Immersion through Virtual Reality in the Learning Experiences of Art and Design Students: The Mediating Effect of the Flow Experience
Authors: Guerra-Tamez Cristobal Rodolfo
Published: February 2023

This study provides a theoretical model on the effectiveness of learning through virtual reality technology in bachelor art and design students. Surveys were applied to 200 undergraduate art and design students, and the data obtained were analyzed using multivariate partial least squares (PLS) structural equation modeling. Our model results indicate that immersion VR has a positive impact on the flow experience. Moreover, the data demonstrated a mediating effect of the flow experience on the learning experience variable explained through motivation, curiosity, cognitive benefits, reflective thinking, and the perception of value. These results have possibilities for academic art and design institutions to increase learning in the classroom through virtual reality technology, leaving the possibility of replicating the model in other areas of study.
Social Entrepreneurship and Complex Thinking: Validation of SEL4C Methodology for Scaling the Perception of Achieved Competency
Authors: Vázquez-Parra José Carlos , Carlos-Arroyo Martina , Cruz-Sandoval Marco
Published: February 2023

This article aims to show the validated results of implementing a self-created methodology for developing the perceived achievement of social entrepreneurship competency and how this methodology is equally valid in developing the perceived achievement of complex thinking competency. Presenting a multivariate descriptive statistical analysis, this article offers the results of an educational intervention carried out on a sample group of students from a Mexican university before and after implementing the SEL4C (Social Entrepreneurship Learning for Complexity) methodology (developed by the Interdisciplinary Research Group IRG Reasoning for Complexity R4C at the Institute for the Future of Education IFE of the Tecnologico de Monterrey). It corroborates that the proposed methodology impacts the perceived achievement of social entrepreneurship competency and its sub-competencies and also manages to develop the perception of achievement of the complex thinking competency. This article contributes to the studies associated with the formation of competencies and the social entrepreneurship approach as a valid pedagogical tool for developing transversal skills.
Photocurrent Production from Cherries in a Bio-Electrochemical Cell
Authors: Shlosberg Yaniv , Rubino Kimi C. , Nasseri Nathan S. , Carlini Andrea S.
Published: February 2023

In recent years, clean energy technologies that meet ever-increasing energy demands without the risk of environmental contamination has been a major interest. One approach is the utilization of plant leaves, which release redox-active NADPH as a result of photosynthesis, to generate photocurrent. In this work, we show for the first time that photocurrent can be harvested directly from the fruit of a cherry tree when associated with a bio-electrochemical cell. Furthermore, we apply electrochemical and spectroscopic methods to show that NADH in the fruit plays a major role in electric current production.
Convolution Feature Inference-Based Semantic Understanding Method for Remote Sensing Images of Mangrove Forests
Authors: Wu Shulei , Zhao Yuchen , Wang Yaoru , Chen Jinbiao , Zang Tao , Chen Huandong
Published: February 2023

The semantic segmentation and understanding of remote sensing images applying computer technology has become an important component of monitoring mangrove forests’ ecological changes due to the rapid advancement of remote sensing technology. To improve the semantic segmentation capability of various surface features, this paper proposes a semantic understanding method for mangrove remote sensing images based on convolution feature inference. Firstly, the sample data is randomly selected, and next a model of convolution feature extraction is used to obtain the features of the selected sample data and build an initial feature set. Then, the convolution feature space and rule base are generated by establishing the three-dimensional color space distribution map for each class and domain similarity is introduced to construct the feature set and rules for reasoning. Next, a confidence reasoning method based on the convolution feature region growth, which introduces an improved similarity calculation, is put forward to obtain the first-time reasoning results. Finally, this approach adds a correction module, which removes the boundary information and reduces the noise from the results of the first-time reasoning as a new sample to correct the original feature set and rules, and uses the corrected feature set and rules for reasoning and understanding to obtain the final image segmentation results. It uses the corrected feature set and rules for reasoning and understanding to obtain the final image segmentation results. Experiments show that this algorithm has the benefits of a simple process, a short training time, and easy feature acquisition. The effect has been obviously improved compared to a single threshold segmentation method, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and other image segmentation methods.
Malaysia’s Electricity Decarbonisation Pathways: Exploring the Role of Renewable Energy Policies Using Agent-Based Modelling
Authors: Babatunde Kazeem Alasinrin , Mahmoud Moamin A. , Ibrahim Nazrita , Said Fathin Faizah
Published: February 2023

Coal’s rising prominence in the power industry has raised concerns about future CO2 emissions and energy reliability. As of 2017, it is estimated that Malaysia’s existing natural gas production can only be maintained for another 40 years. Consequently, the carbon intensity of electricity production has increased due to the increasing share of coal-fired plants and electricity infrastructure inefficiencies. To summarise, energy industries have been the highest emitters of CO2 emissions, with a 54-percent share. In response to these challenges, the government implemented a series of renewable energy (RE) policy measures. Whether these policies are sufficient in driving Malaysian energy decarbonisation is yet to be seen. In this study, we simulated different scenarios from 2015 to 2050 with an agent-based model to explore the roles of renewable energy policies towards emission reduction in the energy sector. The simulation results reveal that when all renewables initiatives were implemented, the share of RE increased to 16 percent, and emissions intensity fell by 26 percent relative to its level in 2005, albeit with increasing absolute carbon emissions. This milestone is still far below the government’s 45 percent reduction target. The simulation results demonstrate that renewable energy policies are less effective in driving Malaysian electricity towards desired low-carbon pathways. Furthermore, it is evidenced that no single approach can achieve the emission reduction target. Therefore, a combination of energy efficiency and renewable energy policy measures is unavoidable to decarbonise the electricity sector in Malaysia.
Cost Efficiency Analysis of H2 Production from Formic Acid by Molecular Catalysts
Authors: Solakidou Maria , Gemenetzi Aikaterini , Koutsikou Georgia , Theodorakopoulos Marinos , Deligiannakis Yiannis , Louloudi Maria
Published: February 2023

The development of low-carbon technologies that will facilitate the efficient use of hydrogen (H2) as an energy carrier is a critical requirement of contemporary society. To this end, it is anticipated that the cost of H2 production will become a key factor in tandem with production efficiency, process safety, and transport. Much effort has been made to create and develop new, reversible, and sustainable H2 storage systems. Among current techniques, formic acid (FA) has been identified as an efficient energy carrier for H2 storage. Numerous homogeneous catalysts based on transition metals with high activity and selectivity have been reported for selective FA dehydrogenation. In this review, we outline the recent advances in transition-metal molecular catalysts for FA dehydrogenation. Selected catalytic systems that could be implemented on an industrial scale and considered potential materials in fuel cell (FC) technology have been cost-evaluated. We highlight some critical engineering challenges faced during the technology’s scale-up process and explain other factors that are frequently ignored by academic researchers. Finally, we offer a critical assessment and identify several system limitations on an industrial scale that are currently impeding future implementation.
Barriers and Prospects for the Development of Renewable Energy Sources in Poland during the Energy Crisis
Authors: Kryszk Hubert , Kurowska Krystyna , Marks-Bielska Renata , Bielski Stanisław , Eźlakowski Bartłomiej
Published: February 2023

Europe is currently facing the potentially biggest energy crisis in history. There are many reasons for this, and the current geopolitical situation makes it clear that we are being forced to take immediate action to ensure sufficient energy supplies to consumers. Until recently, the European Union was dependent on Russian energy resources (mainly oil and gas). For many years, the EU countries had been shutting down their own production and importing much cheaper raw material from Russia. The threat of a blackout is becoming increasingly possible. European governments are preparing businesses and households for the energy crisis in various ways, but there is also a great deal of mobilization to accelerate the development of renewable energy sources (RES). The aim of this study was to identify the barriers and prospects for RES development in Poland in the current geopolitical conditions. The reasons for insufficient grid HC were analyzed. Additionally, the article aimed to assess the prospects for solar energy development in Poland. Wind power was discussed only in general terms because the development of wind farms is inhibited by the regulatory framework. Particular attention was paid to hosting capacity (HC) and the condition of the power infrastructure as the main determinants of RES development in Poland. Numerous documents developed by power companies and government agencies responsible for implementing and managing energy in Poland were analyzed. Special attention was paid to legal regulations and the need for legislative changes. As a country, Poland has one of the highest growth rates in photovoltaic (PV) installations. The forecasts for increasing HC, as a prerequisite for RES development in Poland, are not promising.
Coupling Mechanism and Synergic Development of Carbon Market and Electricity Market in the Region of Beijing–Tianjin–Hebei
Authors: Lu Yan , Xiang Jing , Geng Pengyun , Zhang Huimin , Liu Lili , Wang Haoran , Kong Jiajie , Cui Mingli , Li Yan , Zhong Cheng , Feng Tiantian
Published: February 2023

The national carbon emission trading mechanism is an important policy tool for the Chinese government to control and reduce greenhouse gas emissions by using the market mechanism. The Beijing–Tianjin–Hebei power market is the focus of energy conservation and consumption reduction in China. Problems have already existed in the synergic development of the Beijing–Tianjin–Hebei power market and carbon trading market. In this article, the development status of the Beijing–Tianjin–Hebei power market is analyzed and the coupling mechanism between the carbon market and power market is combed out to build a synergism model of the carbon market and the Beijing–Tianjin–Hebei power market based on the system dynamics. From the research results, firstly, the Beijing–Tianjin–Hebei power market comes with a high energy consumption intensity and a high proportion of carbon emissions. The coupling of carbon market and power market forces the power industry to reduce carbon emissions through the effective transmission of carbon costs to power prices. Secondly, carbon price shows an upward trend in the context of the current policy scenario, which can give play to the role of price signal in the future. The revenue of thermal power plants, which are the carbon emission right sellers, with new technologies, has increased significantly, while the revenue of carbon emission right buyers, which are the manufacturers of undeveloped units, has increased less. Finally, the technical progress of thermal power plants, the introduction of auction mechanism, the increase in initial carbon price settings and the direct transmission of carbon costs are all factors that promote the effectiveness of carbon trading policy tools in the Beijing–Tianjin–Hebei power market. This study provides theoretical guidance for the synergic development of the “power-carbon” market.
Energy Tariff Policies for Renewable Energy Development: Comparison between Selected European Countries and Sri Lanka
Authors: Enescu Diana , Ciocia Alessandro , Galappaththi Udayanga I. K. , Wickramasinghe Harsha , Alagna Francesco , Amato Angela , Díaz-González Francisco , Spertino Filippo , Cocina Valeria
Published: February 2023

This article is written within the European Project “THREE-Lanka” which has the aim of modernizing the higher education related to Renewable Energy (RE) in Sri Lanka. The paper presents the outcomes of analysing various incentive schemes to stimulate RE development. In Europe, there was substantial growth in RE installation through generous incentives in the first years. Then, to regulate this growth, in recent years, the auction system has been introduced to improve the competition among companies that install RE plants. In Sri Lanka, on the other hand, the main energy tariff policies focus on the spread of PhotoVoltaics (PV) through contributions based on the electricity fed into the grid. This paper provides an updated view of the evolution of the energy tariff policies in the relevant European countries with respect to Sri Lanka, covering some recent policy developments. Within the Sri Lankan framework, four case studies involving residential, commercial, and industrial users are outlined to suggest better mechanisms (in the case of not adequate current incentive tariff) for supporting the deployment of grid-connected PV systems in a wide power range. Such knowledge transfer in the THREE-Lanka project will demonstrate the enormous potential RE capacity in a developing country, still depending on fossil fuels but willing to follow the path towards sustainability.
Spatiotemporal Dynamics and Topological Evolution of the Global Crude Oil Trade Network
Authors: Niu Xiaoyu , Chen Wei , Wang Nyuying
Published: February 2023

The high separation of crude oil supply and demand markets has led to the formation of a global crude oil trading system. This paper constructs global crude oil trade networks, integrates macro, meso, and micro network analysis methods, combines geospatial visualization techniques, and then portrays the spatiotemporal patterns and topological evolution of the global crude oil trade networks. Thus, it attempts to dig deeper into the world crude oil competition and cooperation links and evolution laws and provides a scientific reference for a comprehensive understanding of the global crude oil market dynamics. The results show that: (1) After three fluctuations of increase and decrease since 2000, the global crude oil trade volume is entering the adjustment period, and the scale of the crude oil market is rising slowly. (2) The international crude oil trade has formed trade network patterns with complex structures, clear hierarchy and unbalanced distribution. The “rich club” phenomenon is significant, with large trading countries dominating the trade network. (3) The scale and density of the global crude oil trade network show a trend of increasing and then decreasing, the network agglomeration pattern becoming more obvious, the inter-nodal links continuously strengthening, and the network connectivity improving. (4) The global crude oil trade networks are characterized by core–periphery structures, and the polarization effect is significant. The US, Russia, China, Japan, the Netherlands, and South Korea hold the core positions in the crude oil trade network, and the major importing countries have become the dominant forces in the trade network. In addition, we present policy suggestions for different types of countries for energy transformation and security in the global trade market system, which can be used as a reference for policymakers.
Wireless Chargers for Electric Vehicle: A Systematic Review on Converter Topologies, Environmental Assessment, and Review Policy
Authors: Vishnuram Pradeep , P. Suresh , R. Narayanamoorthi , K. Vijayakumar , Nastasi Benedetto
Published: February 2023

The delivery of electricity employing an electromagnetic field that extends across an intervening region is called a wireless power transfer (WPT). This approach paves the way for electric vehicles (EVs) to use newly available options to reduce their environmental impact. This article is a review that examines the WPT technology for use in electric vehicle applications from both the technical aspect and the environmental impact. This review will attempt to accomplish the following objectives: (1) describe the present state of the technology behind the development and application of a WPT across the transportation industry; (2) substantiate the actual implementation of WPT EV systems; and (3) estimate the functioning of the autonomous system, as well as detect the potential stumbling blocks and openings for enhancement. The most recent advancements and implementation in compensating topologies, power electronics converters, and control techniques are dissected and debated scientifically to improve the system’s performance. To evaluate the performance from a sustainable perspective, energy, environmental, and economic factors are utilized, and at the same time, policy drivers and health and safety problems are researched.
An Insight into Post-Consumer Food Waste Characteristics as the Key to an Organic Recycling Method Selection in a Circular Economy
Authors: Lelicińska-Serafin Krystyna , Manczarski Piotr , Rolewicz-Kalińska Anna
Published: February 2023

Reducing the phenomenon of food waste and effective management of already wasted food in the form of post-consumer waste, included in the source-separated organic fraction of municipal solid waste (SS-OFMSW) from households and catering facilities, are some of the key challenges of the circular economy (CE), in particular in highly urbanized areas. The basis for the effective use of this waste is the knowledge of its physical and chemical properties. The main objective of the paper is to identify the key technological and organizational parameters for selective collection determining the characteristics of the SS-OFMSW and, consequently, the optimal path for its management. This paper presents the results of qualitative research of SS-OFMSW generated in the capital of Poland—Warsaw—coming from three sources: multi- and single-family housing and catering facilities. The collection efficiency of this waste was determined in the form of quality in container rate (QCR = 92–97%) and variability in terms of impurities and admixtures present in it (CV = 56–87%). High variability indicates that the system of selective waste collection in Warsaw is immature, which may hinder undertaking activities in the field of waste management planning. The study confirmed the suitability of the tested SS-OFMSW for organic recycling, especially using anaerobic digestion (AD), to which it is predisposed by water content, C/N, and biomethane potential (BMP). All tested food waste is characterized by a high yield of biogas in the range of 384–426 m3/Mg VS and an average share of methane in biogas at the level of 52–61%. Fertilizer properties, moisture, and its gas potential show little variability (CV ≤ 16%), which means that these data can be treated as stable data. The obtained results indicate the optimal direction for the collection and processing of SS-OFMSW based on post-consumer food waste in urbanized areas.