bluuebunny/crossref_metadata_2025 huggingface dataset:
The Non-Equilibrium Thermodynamics of Natural Selection: From Molecules to the Biosphere
Authors: Michaelian Karo
Published: July 2023

Evolutionary theory suggests that the origin, persistence, and evolution of biology is driven by the “natural selection” of characteristics improving the differential reproductive success of the organism in the given environment. The theory, however, lacks physical foundation, and, therefore, at best, can only be considered a heuristic narrative, of some utility for assimilating the biological and paleontological data at the level of the organism. On deeper analysis, it becomes apparent that this narrative is plagued with problems and paradoxes. Alternatively, non-equilibrium thermodynamic theory, derived from physical law, provides a physical foundation for describing material interaction with its environment at all scales. Here we describe a “natural thermodynamic selection” of characteristics of structures (or processes), based stochastically on increases in the global rate of dissipation of the prevailing solar spectrum. Different mechanisms of thermodynamic selection are delineated for the different biotic-abiotic levels, from the molecular level at the origin of life, up to the level of the present biosphere with non-linear coupling of biotic and abiotic processes. At the levels of the organism and the biosphere, the non-equilibrium thermodynamic description of evolution resembles, respectively, the Darwinian and Gaia descriptions, although the underlying mechanisms and the objective function of selection are fundamentally very different.
Several Basic Elements of Entropic Statistics
Authors: Zhang Zhiyi
Published: July 2023

Inspired by the development in modern data science, a shift is increasingly visible in the foundation of statistical inference, away from a real space, where random variables reside, toward a nonmetrized and nonordinal alphabet, where more general random elements reside. While statistical inferences based on random variables are theoretically well supported in the rich literature of probability and statistics, inferences on alphabets, mostly by way of various entropies and their estimation, are less systematically supported in theory. Without the familiar notions of neighborhood, real or complex moments, tails, et cetera, associated with random variables, probability and statistics based on random elements on alphabets need more attention to foster a sound framework for rigorous development of entropy-based statistical exercises. In this article, several basic elements of entropic statistics are introduced and discussed, including notions of general entropies, entropic sample spaces, entropic distributions, entropic statistics, entropic multinomial distributions, entropic moments, and entropic basis, among other entropic objects. In particular, an entropic-moment-generating function is defined and it is shown to uniquely characterize the underlying distribution in entropic perspective, and, hence, all entropies. An entropic version of the Glivenko–Cantelli convergence theorem is also established.
Quantum Bohmian-Inspired Potential to Model Non–Gaussian Time Series and Its Application in Financial Markets
Authors: Hosseini Reza , Tajik Samin , Koohi Lai Zahra , Jamali Tayeb , Haven Emmanuel , Jafari Reza
Published: July 2023

We have implemented quantum modeling mainly based on Bohmian mechanics to study time series that contain strong coupling between their events. Compared to time series with normal densities, such time series are associated with rare events. Hence, employing Gaussian statistics drastically underestimates the occurrence of their rare events. The central objective of this study was to investigate the effects of rare events in the probability densities of time series from the point of view of quantum measurements. For this purpose, we first model the non-Gaussian behavior of time series using the multifractal random walk (MRW) approach. Then, we examine the role of the key parameter of MRW, λ, which controls the degree of non-Gaussianity, in quantum potentials derived for time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for rare events. Thus, rare events can generate a potential barrier in the high-frequency region of the quantum potential, and the effect of such a barrier becomes prominent when the system transverses it. Finally, as an example of applying the quantum potential beyond the microscopic world, we compute quantum potentials for the S&P financial market time series to verify the presence of rare events in the non-Gaussian densities and demonstrate deviation from the Gaussian case.
On Neural Networks Fitting, Compression, and Generalization Behavior via Information-Bottleneck-like Approaches
Authors: Lyu Zhaoyan , Aminian Gholamali , Rodrigues Miguel R. D.
Published: July 2023

It is well-known that a neural network learning process—along with its connections to fitting, compression, and generalization—is not yet well understood. In this paper, we propose a novel approach to capturing such neural network dynamics using information-bottleneck-type techniques, involving the replacement of mutual information measures (which are notoriously difficult to estimate in high-dimensional spaces) by other more tractable ones, including (1) the minimum mean-squared error associated with the reconstruction of the network input data from some intermediate network representation and (2) the cross-entropy associated with a certain class label given some network representation. We then conducted an empirical study in order to ascertain how different network models, network learning algorithms, and datasets may affect the learning dynamics. Our experiments show that our proposed approach appears to be more reliable in comparison with classical information bottleneck ones in capturing network dynamics during both the training and testing phases. Our experiments also reveal that the fitting and compression phases exist regardless of the choice of activation function. Additionally, our findings suggest that model architectures, training algorithms, and datasets that lead to better generalization tend to exhibit more pronounced fitting and compression phases.
Moderating Effect of Business Environmental Dynamism in the Innovativeness—Company Performance Relationship of Congolese Manufacturing Companies
Authors: Ruba Remo Metalor , Chiloane-Tsoka Germinah E. , Van der Westhuizen Thea
Published: July 2023

Research has shown that innovativeness is a key factor in business performance. However, the link between innovativeness and organisational performance and the role of environmental dynamism are still being debated. This study thus sought to analyse the moderating effect of the dynamism of the business environment in the relationship between innovativeness and performance of manufacturing companies. In order to do this, a questionnaire was distributed to 344 owners and managers of manufacturing companies operating in the north-eastern Democratic Republic of Congo. One hundred and seventy-eight of these questionnaires were returned and used to test the study’s hypotheses. The study used an ordinary least squares (OLS) regression under the hierarchical regression analysis approach. The results confirm the positive and significant effect of innovativeness on company performance. On the other hand, results indicate that business environmental dynamism has a direct positive and significant effect on company performance. However, the dynamism of the business environment negatively influences the relationship between innovativeness and company performance. Based on these results, recommendations and further research perspectives are suggested.
Flexible Use of the Large-Scale Short-Time Work Scheme in Germany during the Pandemic: Dynamic Labour Demand Models Estimation with High-Frequency Establishment Data
Authors: Bellmann Lisa , Bellmann Lutz , Kölling Arnd
Published: July 2023

Our study uses 24 waves of the survey Establishments in the COVID-19 crisis (BeCOVID), a high-frequency dataset collected at monthly intervals by the Institute for Employment Research during the COVID-19 pandemic, to investigate the behaviour of establishments with respect to the dynamics of their employment, in particular their use of short-time work. Due to the high-frequency intervals, the present data are considerably better suited than annual panel surveys to investigate adjustment processes. This is especially true for the role of short-time work, which is seen as a particularly fast adjustment option and thus reduces adjustment costs rapidly. Our estimations reveal a much faster overall workforce adjustment process compared with previous studies, which rely on annual panel surveys. In addition, our empirical results show that the employment adjustment in establishments using short-time work during the COVID-19 crisis occurred almost immediately within one month.
Investigating the Principle of Relativity and the Principle of Equivalence in Classical Mechanics: Design and Evaluation of a Teaching–Learning Sequence Based on Experiments and Simulations
Authors: Marzari Alessio , Di Mauro Marco , Rosi Tommaso , Onorato Pasquale , Malgieri Massimiliano
Published: July 2023

We designed a teaching–learning sequence on relative motion in classical mechanics, based on the model of educational reconstruction and on the fundamental design principle of highlighting those conceptual elements which could be valuable in the future learning of special and general relativity. Thus, we propose an educational reconstruction strongly focused on the principle of relativity and the principle of equivalence. To highlight selected key concepts and motivate students in their exploration, we used a series of experiments based on video analysis and interactive simulations, which can be modified on the fly by the students. These tools are useful to stimulate autonomous investigation and to support the modelling of different physical situations. The sequence of activities was designed for students in introductory physics courses and was tested with a group of 24 undergraduate students in an online lab course, in which some distance learning techniques were also studied.
Intersectional Program Evaluation: Considering Race, Class, Sex, and Language in Gifted Program Effectiveness
Authors: Kuykendall Tristta M.
Published: July 2023

Gifted education is an effective intervention for high-ability students who need more academic challenges. However, the relationship between program effectiveness and demographic categories has been scantly evaluated. Research focused on the effectiveness of gifted education infrequently considers the intersections of ability, race, sex, socioeconomic status, and language. To fill this gap, I used an ex post facto quasi-experimental design to conduct a cross-sectional evaluation of gifted service models at the intersections of cultural identity groups in Ohio. Findings underscore the relationship between the type of gifted service model and achievement on standardized math test scores varying across demographic groups.
Examining the Influence of Exploration and Parental Education Attainment on Students’ Acceptance of Collectivist Values
Authors: Jin Ruining , Le Tam-Tri , Nguyen Minh-Hoang , Vuong Quan-Hoang
Published: July 2023

Exploration can help students access a wider range of information and make connections among values within the natural and social world. This study investigated the relationship between students’ previous exploration of their surroundings and their acceptance of collectivist values in the context of China. A sample of 343 college students was analyzed based on the Bayesian Mindsponge Framework to explore this relationship. The results revealed a positive association between students’ prior exploration of surroundings and their degree of collectivist orientation. Furthermore, parental education attainment was found to negatively moderate this association, albeit with a small effect size. These findings contribute to the understanding of how information acquisition influences students’ acceptance of collectivist values and highlight the potential role of the family infosphere in shaping this relationship.
A Study of Optimization in Deep Neural Networks for Regression
Authors: Chen Chieh-Huang , Lai Jung-Pin , Chang Yu-Ming , Lai Chi-Ju , Pai Ping-Feng
Published: July 2023

Due to rapid development in information technology in both hardware and software, deep neural networks for regression have become widely used in many fields. The optimization of deep neural networks for regression (DNNR), including selections of data preprocessing, network architectures, optimizers, and hyperparameters, greatly influence the performance of regression tasks. Thus, this study aimed to collect and analyze the recent literature surrounding DNNR from the aspect of optimization. In addition, various platforms used for conducting DNNR models were investigated. This study has a number of contributions. First, it provides sections for the optimization of DNNR models. Then, elements of the optimization of each section are listed and analyzed. Furthermore, this study delivers insights and critical issues related to DNNR optimization. Optimizing elements of sections simultaneously instead of individually or sequentially could improve the performance of DNNR models. Finally, possible and potential directions for future study are provided.
A Novel Adversarial Learning Framework for Passive Bistatic Radar Signal Enhancement
Authors: Che Jibin , Wang Li , Wang Changlong , Zhou Feng
Published: July 2023

Passive Bistatic Radar (PBR) has significant civilian and military applications due to its ability to detect low-altitude targets. However, the uncontrollable characteristics of the transmitter often lead to subpar target detection performance, primarily due to a low signal-to-noise ratio (SNR). Coherent accumulation typically has limited ability to improve SNR in the presence of strong noise and clutter. In this paper, we propose an adversarial learning-based radar signal enhancement method, called radar signal enhancement generative adversarial network (RSEGAN), to overcome this challenge. On one hand, an encoder-decoder structure is designed to map noisy signals to clean ones without intervention in the adversarial training stage. On the other hand, a hybrid loss constrained by L1 regularization, L2 regularization, and gradient penalty is proposed to ensure effective training of RSEGAN. Experimental results demonstrate that RSEGAN can reliably remove noise from target information, providing an SNR gain higher than 5 dB for the basic coherent integration method even under low SNR conditions.
Traffic Sign Recognition Based on Bayesian Angular Margin Loss for an Autonomous Vehicle
Authors: Kim Taehyeon , Park Seho , Lee Kyoungtaek
Published: July 2023

Traffic sign recognition is a pivotal technology in the advancement of autonomous vehicles as it is critical for adhering to country- or region-specific traffic regulations. Defined as an image classification problem in computer vision, traffic sign recognition is a technique that determines the class of a given traffic sign from input data processed by a neural network. Although image classification has been considered a relatively manageable task with the advent of neural networks, traffic sign classification presents its own unique set of challenges due to the similar visual features inherent in traffic signs. This can make designing a softmax-based classifier problematic. To address this challenge, this paper presents a novel traffic sign recognition model that employs angular margin loss. This model optimizes the necessary hyperparameters for the angular margin loss via Bayesian optimization, thereby maximizing the effectiveness of the loss and achieving a high level of classification performance. This paper showcases the impressive performance of the proposed method through experimental results on benchmark datasets for traffic sign classification.
A Multi-Feature Fusion-Based Automatic Detection Method for High-Severity Defects
Authors: Liu Jie , Liang Cangming , Feng Jintao , Xiao Anhong , Zeng Hui , Wu Qujin , Yu Tonglan
Published: July 2023

It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label method cannot achieve high-precision defect confirmation results for high-severity defects. Therefore, a multi-feature fusion defect automatic confirmation method is proposed. The label generation method solves the dimensionality disaster problem caused by multi-feature fusion by fusing features with strong correlations, improving the classifier’s performance. This method extracts node features and basic path features from the program dependency graph and designs high-severity contextual defect confirmation labels combined with contextual features. Finally, an optimized Support Vector Machine is used to train the automatic detection model for high-severity defects. This study uses open-source programs to manually implant defects for high-severity defect confirmation verification. The experimental results show that compared with existing methods, this model significantly improves the efficiency of confirming high-severity defects.
Prediction Model for Transient NOx Emission of Diesel Engine Based on CNN-LSTM Network
Authors: Shen Qianqiao , Wang Guiyong , Wang Yuhua , Zeng Boshun , Yu Xuan , He Shuchao
Published: July 2023

In order to address the challenge of accurately predicting nitrogen oxide (NOx) emission from diesel engines in transient operation using traditional neural network models, this study proposes a NOx emission forecasting model based on a hybrid neural network architecture combining the convolutional neural network (CNN) and long short-term memory (LSTM) neural network. The objective is to enhance calibration efficiency and reduce diesel engine emissions. The proposed model utilizes data collected under the thermal cycle according to the world harmonized transient cycle (WHTC) emission test standard for training and verifying the prediction model. The CNN is employed to extract features from the training data, while LSTM networks are used to fit the data, resulting in the precise prediction of training NOx emissions from diesel engines. Experimental verification was conducted and the results demonstrate that the fitting coefficient (R2) of the CNN-LSTM network model in predicting transient NOx emissions from diesel engines is 0.977 with a root mean square error of 33.495. Compared to predictions made by a single LSTM neural network, CNN neural network predictions, and back-propagation (BP) neural network predictions, the root mean square error (RMSE) decreases by 35.6%, 50.8%, and 62.9%, respectively, while the fitting degree R2 increases by 2.5%, 4.4%, and 6.6%. These results demonstrate that the CNN-LSTM network prediction model has higher accuracy, good convergence, and robustness.
Biomimetic Copper Forest Structural Modification Enhances the Capillary Flow Characteristics of the Copper Mesh Wick
Authors: Luo Jia-Li , Zhao Fan-Bin , Xu Mou , Mo Dong-Chuan , Lyu Shu-Shen
Published: July 2023

In a two-phase heat transfer device, achieving a high capillarity of the wick while reducing flow resistance within a limited space becomes the key to improving the heat dissipation performance. As a commonly used wick structure, mesh is widely employed because of its high permeability. However, achieving the desired capillary performance often requires multiple layers to be superimposed to ensure an adequate capillary, resulting in an increased thickness of the wick. In this study, an ultra-thin biomimetic copper forest structural modification of copper mesh was performed using an electrochemical deposition to solve the contradiction between the permeability and the capillary. The experiments were conducted on a copper mesh to investigate the effects of various conditions on their morphology and capillary performance. The results indicate that the capillary performance of the modified copper mesh improves with a longer deposition time. The capillary pressure drops can reach up to 1400 Pa when using ethanol as the working fluid. Furthermore, the modified copper mesh demonstrates a capillary performance value (ΔPc·K) of 8.44 × 10−8 N, which is 1.7 times higher than that of the unmodified samples. Notably, this enhanced performance is achieved with a thickness of only 142 μm. The capillary limit can reach up to 45 W when the modified copper mesh is only 180 μm. Microscopic flow analysis reveals that the copper forest modified structure maintains the original high permeability of the copper mesh while providing a greater capillary force, thereby enhancing the overall flow characteristics.
Comprehensive Assessment of Biomass Properties for Energy Usage Using Near-Infrared Spectroscopy and Spectral Multi-Preprocessing Techniques
Authors: Shrestha Bijendra , Posom Jetsada , Sirisomboon Panmanas , Shrestha Bim Prasad
Published: July 2023

In this study, partial least squares regression (PLSR) models were developed using no preprocessing, traditional preprocessing, multi-preprocessing 5-range, multi-preprocessing 3-range, a genetic algorithm (GA), and a successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage by employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis were compared, and the best model was selected based on the coefficient of determination of the validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on the model performance results, the proposed HHV model from GA-PLSR and the N models from the multi-preprocessing PLSR 5-range could be used for most applications, including research, whereas the C and H models from GA-PLSR and the O model from the multi-preprocessing PLSR 5 range method 5-range air performance and are applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving these models by 4.1839%, 8.1842%, 3.7587%, and 4.0085%, respectively. Therefore, this method can be considered a reliable and non-destructive alternative method for rapidly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for robust application.
Lattice Boltzmann Modelling of Fluid Flow through Porous Media: A Comparison between Pore-Structure and Representative Elementary Volume Methods
Authors: D’Orazio Annunziata , Karimipour Arash , Ranjbarzadeh Ramin
Published: July 2023

In this study, we present a novel comparison between pore-structure (PS) and representative elementary volume (REV) methods for modelling fluid flow through porous media using a second-order lattice Boltzmann method (LBM). We employ the LBM to demonstrate the importance of the configuration of square obstacles in the PS method and compare the PS and the REV methods. This research provides new insights into fluid flow through porous media as a novel study. The behaviour of fluid flow through porous media has important applications in various engineering structures. The aim of this study is to compare two methods for simulating porous media: the PS method, which resolves the details of the solid matrix, and the REV method, which treats the porous medium as a continuum. Our research methodology involves using different arrangements of square obstacles in a channel including in-line, staggered and random for the PS method and a porosity factor and permeability value for the REV method. We found that the porosity and obstacle arrangement have significant effects on the pressure drop, permeability and flow patterns in the porous region. While the REV method cannot simulate the details of fluid flow through pore structures compared to the PS method, it is able to provide a better understanding of the flow field details around obstacles (Tortuosity). This study has important applications in improving our understanding of transport phenomena in porous media. Our results can be useful for designing and optimizing various engineering systems involving porous media.
Improving the Efficiency of Spark-Ignition Internal Combustion Engine Using a Novel Electromagnetic Actuator and Adapting Increased Compression
Authors: Mamala Jarosław , Tomczuk Bronisław , Waindok Andrzej , Graba Mariusz , Hennek Krystian
Published: July 2023

This paper presents an empirical study of a spark-ignition internal combustion engine with modifications made to increase its effectiveness. The modification was implemented bi-directionally in terms of changes to the compression ratio and changes to the engine’s valve train. The compression ratio was increased by 2.3 units by design and a hybrid intake valve opening control was used in the engine’s valve train. The hybrid control involved autonomous control of one of the inlet valves with a dedicated electromagnetic actuator. The designed electromagnetic actuator was mounted downstream of the single-cylinder engine’s intake system’s modified camshaft to control the effective compression pressure build-up. Field calculations were carried out for the electromagnetic actuator’s design variants and its current characteristics were determined. The multivariate calculations were carried out in order to find the quasi-optimal geometry of the actuator. The width and height of magnetic field coils and the dimensions of the stator poles were changed, while maintaining the same external dimensions of the actuator to enable its mounting in the cylinder head system. In the next step, the prototype of the actuator was made and placed on the combustion engine in order to conduct the experimental investigations. The work was aimed at improving the internal combustion engine’s efficiency at the low load range, as this is load range in which it has low efficiency despite it being the most often used during normal vehicle operation. The original measurement stand was prepared, and many tests were carried out in order to investigate the influence of the electromagnetic valve on the combustion engine characteristic. This improved the internal combustion engine’s efficiency at its low-load range by up to 25%. Both calculation and measurement results are presented in form of graphs.
Performance Analysis of a Millimeter Wave Communication System in Urban Micro, Urban Macro, and Rural Macro Environments
Authors: Sudhamani Chilakala , Roslee Mardeni , Chuan Lee Loo , Waseem Athar , Osman Anwar Faizd , Jusoh Mohamad Huzaimy
Published: July 2023

The signal power in wireless communication systems is influenced by various factors, including the environment. These factors include path differences, operational frequency, and environmental conditions. Consequently, designing a communication system that generates a stronger signal is highly challenging. To address this, large-scale path-loss models are employed to estimate the path loss and signal power across different frequencies, distances, and environments. In this paper, we focused on the urban micro, urban macro, and rural macro environments to estimate path loss and signal power at millimeter wave frequencies. We compared the path loss and received power among different path-loss models developed by standard organizations. Simulation results indicate that the fifth-generation channel model provides enhanced path loss and signal power in urban micro environments, while the third-generation partnership project model performs well in urban macro and rural macro environments when compared to other path-loss models.
Microstructure and First Hydrogenation Properties of Ti16V60Cr24−xFex + 4 wt.% Zr Alloy for x = 0, 4, 8, 12, 16, 20, 24
Authors: Ravalison Francia , Huot Jacques
Published: July 2023

In body-centered cubic (BCC) alloys of transition elements, elemental addition or substitution in the vanadium-based alloys can be beneficial for improving the hydrogen storage properties and for reducing the production cost. In this context, the current study focused on the effect of the substitution of Cr by Fe in Ti16V60Cr24−xFex + 4 wt.% Zr alloys where x = 0, 4, 8, 12, 16, 20, 24. The microstructure of each alloy was composed of a matrix having a chemical composition close to the nominal one and a Zr-rich region. From X-ray diffraction patterns, it was found that the matrix has a BCC structure, and the Zr-rich regions present the C14 Laves phase structure. The lattice parameter of BCC phases decreased linearly with x, in accordance with Vegard’s law. The measurement of the first hydrogenation at 298 K under 3 MPa of hydrogen revealed a decrease in the maximum hydrogen capacity: 3.8 wt.% for x = 0, 3.1 wt.% for x = 4 and around 2 wt.% for x = 8 to 24. The XRD patterns after hydrogenation showed a BCT phase for all alloys, with a C14 phase for x = 4, 8, 12 and with C14 and C15 for x = 16, 20 and 24.
Comparative Study on the Performance and Mechanism of Adsorption–Oriented Phosphorus–Modified High–Efficiency and Durable Activated Biochar from Fast Pyrolysis
Authors: Sun Chenhao , Luo Zhongyang , Yu Peng , Wang Qinhui
Published: July 2023

Extensive research has demonstrated the advantageous utilization of medium–low temperature fast pyrolysis (FP) for biomass, yielding high–grade liquid–phase chemicals or fuels. However, the field of FP–based high–performance solid biochar research still presents several gaps. Herein, a one–step versus two–step method for biomass H3PO4 activation under FP was comparatively analyzed for the first time, and efficiently activated carbons (ACs) for dye removal were successfully synthesized at a low temperature (723 K). Investigation of methylene blue (MB) adsorption revealed that the one–step sample P–H–0.5, possessing a specific surface area of 1004 m2·g−1, exhibited a remarkable adsorption capacity of 695.54 mg·g−1 with an ultra–high removal rate (99.94%, C0 = 150 mg·L−1). The two–step sample P–2–H–2, a modified byproduct of FP, achieved efficient dye adsorption in the shortest time (2 min, 383.91 mg·g−1). This originated from the well–developed surface macropores and elevated group content derived from phosphorus (P)—modification. Both adsorption data were well–fitted with pseudo–second–order kinetics and the Langmuir model, revealing the presence of chemical effects and the dominance of monolayer adsorption. A more detailed kinetic study suggested intrapore transport primarily governed the adsorption process on P–H–0.5, whereas P–2–H–2 relied on surface diffusion. FTIR and XPS revealed notable differences in the active sites between the two methods. Aside from –OH, –COOH with C–O–P, the P elements of P–H–0.5 were classified as C–P–O3 and C2–O–P2, demonstrating the ability of one–step FP to introduce heteroatoms into carbon defects. The basic interactions of ACs with MB were π–π stacking and hydrogen bonding established by –OH–containing groups. At a suitable pH (>5), most H+ was removed from the surface, and the electrostatic attraction became the strongest linking force. Both ACs exhibited exceptional reusability, with removal rates surpassing 90% of the initial rate after four cycles of regeneration.
Exploring the Commercialization of Smart Rural Energy in Times of Energy Supply Chain Disruptions
Authors: Min Hokey
Published: July 2023

The lingering COVID-19 pandemic and ongoing war between Russia and Ukraine have wreaked havoc on the global oil supply chain. The current disruption of the oil supply chain and the rapidly growing energy demand created unprecedented oil shortages and raised the oil price beyond the affordable level. As worldwide oil price hikes continue, there is an urgent need for developing alternative energy sources, such as smart rural energy. Despite its enormous potential as a viable alternative to traditional fossil fuel-based energy sources, smart rural energy has never been fully utilized in society. The limited use of smart rural energy may be related to its lack of commercialization, which could have created more eco-friendly and cost-efficient alternative energy sources. This paper assesses the eco-friendliness and cost-efficiency of smart rural energy sources such as solar, wind, biomass, and hydropower for the first time. This paper is also one of the first studies that intends to develop viable strategic plans for commercializing smart rural energy using strategy maps, which subsequently helps increase public awareness of renewable energy by creating visual communication tools that convey the benefits of smart rural energy commercialization to multiple stakeholders, including government entities, business communities, and energy consumers.
Dynamics of Gas Generation in Porous Electrode Alkaline Electrolysis Cells: An Investigation and Optimization Using Machine Learning
Authors: Babay Mohamed-Amine , Adar Mustapha , Chebak Ahmed , Mabrouki Mustapha
Published: July 2023

This paper presents a systematic and comprehensive mathematical model for alkaline water electrolyzer cells, which can be used for simulation and analysis. The model accounts for factors such as gas evolution reactions, dissolution of gases in the electrolyte, bubble formation, and charge transport. It is based on a numerical two-phase model using the Euler-Euler approach, which has been validated against experimental data for various current densities. The study compares the impact of varying potassium hydroxide (KOH) concentration, separator porosity, and electrolyte flow rates on two-phase flow and bubble coverage. Therefore, the electrolyte in the cell consists of a solution of potassium hydroxide in water. The formation of gas bubbles at the electrodes decreases the electrolyte’s ionic conductivity. Additionally, the presence of these bubbles on the electrode surfaces reduces the available surface area for electrochemical reactions, leading to an increase in the overpotential at a given current density. Furthermore, this paper demonstrates how a neural network and ensembled tree model can predict hydrogen production rates in an alkaline water electrolysis process. The trained neural network accurately predicted the hydrogen production rates, indicating the potential of using neural networks for optimization and control of alkaline water electrolysis processes. The model has an average R-squared value of 0.98, indicating a good fit to the data. A new method of describing bubble transfer, “bubble diffusion,” is introduced to improve performance and reduce costs. The model is solved using COMSOL Multi physics 6.0. The machine learning models in this study were built, trained, and tested using MATLAB software R2020a.
Model-Free Approach to DC Microgrid Optimal Operation under System Uncertainty Based on Reinforcement Learning
Authors: Irnawan Roni , Rizqi Ahmad Ataka Awwalur , Yasirroni Muhammad , Putranto Lesnanto Multa , Ali Husni Rois , Firmansyah Eka , Sarjiya
Published: July 2023

There has been tremendous interest in the development of DC microgrid systems which consist of interconnected DC renewable energy sources. However, operating a DC microgrid system optimally by minimizing operational cost and ensures stability remains a problem when the system’s model is not available. In this paper, a novel model-free approach to perform operation control of DC microgrids based on reinforcement learning algorithms, specifically Q-learning and Q-network, has been proposed. This approach circumvents the need to know the accurate model of a DC grid by exploiting an interaction with the DC microgrids to learn the best policy, which leads to more optimal operation. The proposed approach has been compared with with mixed-integer quadratic programming (MIQP) as the baseline deterministic model that requires an accurate system model. The result shows that, in a system of three nodes, both Q-learning (74.2707) and Q-network (74.4254) are able to learn to make a control decision that is close to the MIQP (75.0489) solution. With the introduction of both model uncertainty and noisy sensor measurements, the Q-network performs better (72.3714) compared to MIQP (72.1596), whereas Q-learn fails to learn.
Comparison of Different Coupling Modes between the Power System and the Hydrogen System Based on a Power–Hydrogen Coordinated Planning Optimization Model
Authors: Zhang Siyu , Zhang Ning , Dai Hongcai , Liu Lin , Zhou Zhuan , Shi Qing , Lu Jing
Published: July 2023

Hydrogen is receiving unprecedented momentum and is projected to meet a sizable share of the final energy demand in the future. The coupling between the hydrogen and power systems can help integrate volatile renewable energy, reduce curtailment, and realize long-term energy storage. Different coupling modes are being proposed, yet quantitative comparisons are absent. To fill this gap, this paper firstly summarizes the system composition of potential power–hydrogen coupling modes and analyzes their advantages and disadvantages. Then, a model for power–hydrogen coordinated planning optimization is proposed for quantitative analysis. Southern Xinjiang is chosen as a representative of the northwestern area in China, which has plentiful renewable resources and a relatively small local load at present, for a case study. Through result analysis, it is found that the mode of power–hydrogen coupling at the source side, either for in situ utilization or for long-distance transport via pipelines, shows better economic competitiveness. The discussion provides suggestions and a reference to policymakers for formulating infrastructure or industry plans in advance to better accommodate the rapidly developing renewable energy.
Hydraulic Performance and Energy Dissipation Mechanism Analysis of the Tesla-Shaped Emitter
Authors: Du Peisen , Li Zhiqin , Hao Ruixia , Ma Juanjuan , Yan Da
Published: July 2023

In this paper, a tesla-shaped emitter is proposed based on the structure of the “tesla valve” as the source of inspiration, so that the water flow in the channel would produce a variety of energy dissipation phenomena, such as diversion, hedging, and mixing, to explore the hydraulic performance and energy dissipation mechanism of the tesla-shaped emitter. The channel structure parameters were taken as factors, and 16 groups of orthogonal tests were arranged. Based on CFD technology, the pressure–flow relationship curve slope, flow ratio between the main channel and secondary channel, flow field, and head loss of the emitter were calculated and analyzed for different combinations of structural parameters. Based on a significance level α = 0.05 test, the main channel inlet section length (L3) had a significant impact on the curve slope, and the secondary channel length (L1) and main channel inlet section length (L3) had a significant impact on the flow. The multiple linear regression mathematical models between the channel structure parameters and the curve slope and the flow were constructed. The larger the ratio between the main channel and the secondary channel flow, the better the hydraulic performance of the emitter. The channel unit loss coefficient increased linearly with the increase of the emitter inlet pressure, and its value ranged from 4.5769 to 8.1716, with an excellent energy dissipation effect. The hedge mixing of the water flow was the core of the energy dissipation of the tesla-shaped emitter. By appropriately increasing the inlet size of the main channel and other elements to increase the main channel flow and optimize the flow ratio between the main channel and the secondary channel, the mixing was improved, which consequently improved the hydraulic performance of the emitter.
Indicators of Engine Performance Powered by a Biofuel Blend Produced from Microalgal Biomass: A Step towards the Decarbonization of Transport
Authors: Ratomski Patryk , Hawrot-Paw Małgorzata , Koniuszy Adam , Golimowski Wojciech , Kwaśnica Andrzej , Marcinkowski Damian
Published: July 2023

According to the EU Directive, the so-called RED II, there is increasing significance for biofuels produced from biomass with low indirect land use change (ILUC) risk. Such an alternative and sustainable feedstock could be microalgae, among others, used for biodiesel production. This is due to the high lipid content of their cells and their potential ability to accumulate significant amounts of carbon dioxide in their biomass, which has a positive effect on the carbon footprint of the product. The aim of this study was to determine the effect of adding algal biodiesel to conventional diesel fuel on selected performance parameters of a diesel engine, taking into account the composition of the emitted exhaust gas. Energy-related engine performance parameters such as power, hourly and specific fuel consumption, engine thermal efficiency, and indicated efficiency were determined. No significant differences were found in the energy parameters of engine operation with the fuels tested. In terms of carbon monoxide and NOx emissions, at the highest engine torque, more favorable parameters were obtained for fuel with biodiesel produced from rapeseed oil (B/RME). Under the same conditions, carbon dioxide emissions for the fuel with the addition of biodiesel from microalgae (B/Algae) were 8.1% lower.
The European DEMO Helium Cooled Pebble Bed Breeding Blanket: Design Status at the Conclusion of the Pre-Concept Design Phase
Authors: Zhou Guangming , Hernández Francisco A. , Pereslavtsev Pavel , Kiss Béla , Retheesh Anoop , Maqueda Luis , Park Jin Hun
Published: July 2023

Significant design efforts were undertaken during the Pre-Concept Design (PCD) phase of the European DEMO program to optimize the helium cooled pebble bed (HCPB) breeding blanket. A gate review was conducted for the entire European DEMO program at the conclusion of the PCD phase. This article presents a summary of the design evolution and the rationale behind the HCPB breeding blanket concept for the European DEMO. The main performance metrics, including nuclear, thermal hydraulics, thermal mechanical, and tritium permeation behaviors, are reported. These figures demonstrate that the HCPB breeding blanket is a highly effective tritium-breeding and robust driver blanket concept for the European DEMO. In addition, three alternative concepts of interest were explored. Furthermore, this article outlines the upcoming design and R&D activities for the HCPB breeding blanket during the Concept Design phase (2021–2027).
A Critical Review of Growth Media Recycling to Enhance the Economics and Sustainability of Algae Cultivation
Authors: Arora Neha , Lo Enlin , Legall Noah , Philippidis George P.
Published: July 2023

Microalgae hold promise as a sustainable source of biofuels and bioproducts but their commercial development is impeded by high cultivation costs, primarily for growth nutrients, and concerns about the water-intensive nature of algae cultivation. As a result, minimizing water and nutrient input is imperative to reducing algal operating costs, while enhancing the sustainability of future algal biorefineries. However, spent media recycling often results in the accumulation of growth inhibitors, such as free fatty acids, polysaccharides, polyunsaturated aldehydes, and humic acid, which negatively affect algal growth and productivity. In this review, we critically assess media recycling research findings to assess the advantages and disadvantages of spent media reuse for a wide range of algae strains. Particular emphasis is placed on strategies to overcome growth inhibition through spent media treatment processes, such as ultraviolet oxidation, activated carbon, ultrasonication, microfiltration, crop rotation, and nutrient replenishment.
Employment in the 21st Century: Pre- and Post-COVID-19 Changes
Authors: Malousis Antonios Th. , Zefkilis Panagiotis N. , Daglis Theodoros
Published: July 2023

In the 21st century, prior to the outbreak of the COVID-19 pandemic, numerous transformations were already underway in the field of employment. However, this unprecedented global health crisis has had a profound influence on employment worldwide, yielding both positive and negative outcomes across various labor aspects. Consequently, while certain effects are anticipated to be temporary, others are likely to instigate enduring changes in employment practices.
Cellulase and Xylanase Production by a Newly Isolated Penicillium crustosum Strain under Solid-State Fermentation, Using Water Hyacinth Biomass as Support, Substrate, and Inducer
Authors: Espinoza-Abundis César , Soltero-Sánchez Carlos , Romero-Borbón Evelyn , Córdova Jesús
Published: July 2023

Cellulase and xylanase have been widely studied for bioconversion processes and applied in various industries. The high cost of these enzymes remains to be the major bottleneck for large-scale commercial application of lignocellulosic biorefinery. The use of agroindustrial residues and weeds as fermentation substrates is an important strategy to increase cellulolytic enzymes production and reduce costs. Penicillium crustosum was newly isolated and selected to study its enzyme production during solid-state fermentation (SSF). Natural and pretreated water hyacinth (WH) biomass was used as support, substrate and inducer of cellulases and xylanases. Thermochemical pretreatments of WH biomass at 121 °C and sulfuric acid at three concentrations (0.2, 0.6 and 1 M) were assayed. The pretreatments of WH biomass released mono- and oligo-saccharides that favored fungal growth and enzymes production on SSF. WH is a cost-effective substrate-support and inducer, which to be used as a solid medium, was impregnated with a saline solution, containing only (NH4)2SO4, KH2PO4 y MgCl2. Maximum cellulases (carboxymethylcellulase (CMCase)) and xylanases productions of P. crustosum cultured on SSF were reached using the WH pretreated biomass with H2SO4 0.6 M and 121 °C. The simultaneous CMCase and xylanases production reached (647.51 and 4257.35 U/g dry WH, respectively) are among the highest values ever reported.
Alternative Utilization of Pennisetum purpureum × Pennisetum americanum: Press Cake Conversion to Biobutanol
Authors: Suaisom Pitchaya , Pholchan Patiroop , Chaiyaso Thanongsak , Tippayawong Nakorn
Published: July 2023

Conversion of Pennisetum purpureum × Pennisetum americanum (Napier Pak Chong1) press cake into biobutanol using Clostridium beijerinckii TISTR 1461 was proposed as an alternative to combustion in this study. The optimum conditions for biobutanol fermentation were determined using a full factorial design and a central composite design of experiment. The studied factors were initial pHs (5.50–6.50) and sugar concentrations (40–60 g/L), while butanol yield (g/g reducing sugar utilized) was specified as the optimization response. The results showed that the suitable enzyme loading of alkali-pretreated press cake (at 3% w/w NaOH, 10% substrate loading, boiling at 90 °C, with a reaction time of 1 h) was 10 FPU/g biomass, which provided a glucose yield of 345 mg/g pretreated press cake. The optimized pH and reducing sugar concentration were 6.08 and 43 g/L, respectively. At these conditions, the maximum butanol yield from the hydrolysate of NaOH-pretreated press cake was 0.135 g/g reducing sugar utilized (0.30 g/g glucose utilized). Apart from the possibility of generating much less pollution, it was estimated that using the same amount of press cake, butanol production could possibly have a value comparable to that obtained from combustion for electricity production. A new concept for overall Napier Pak Chong1 grass utilization was also presented.