bluuebunny/crossref_metadata_2025 huggingface dataset:
The Shear Bond Strength between Milled Denture Base Materials and Artificial Teeth: A Systematic Review
Authors: Prpic Vladimir , Catic Amir , Kraljevic Simunkovic Sonja , Bergman Lana , Cimic Samir
Published: March 2023

The data about bond strength between digitally produced denture base resins and artificial teeth are scarce. Several studies investigated shear bond strength values of milled denture base resins and different types of artificial teeth. The purpose of the present study was to compare and evaluate the available evidence through a systematic review. A bibliographic search was conducted in PubMed, Scopus, and Web of Science to assess adequate studies published up to 1 June 2022. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The appropriate studies that determined the shear bond strength values between milled denture base resins and artificial teeth were selected. The initial search identified 103 studies, which were included in the PRISMA 2020 flow diagram for new systematic reviews. Three studies met the inclusion criteria, and all of them present a moderate risk of bias (score 6). Two studies found no statistical differences between heat-polymerized and CAD/CAM (milled) denture base materials when attached with different types of artificial teeth, while one study showed higher values of CAD/CAM (milled) denture base materials. Bonding agents ensure bonding strength at least similar to the conventional methods. In order to improve the quality of future studies, it would be advantageous to use a larger number of specimens with standardized dimensions and a blinded testing machine operator to decrease the risk of bias.
Comparative Study of Transmission of 2940 nm Wavelength in Six Different Aesthetic Orthodontic Brackets
Authors: Zamzam Mohammad Khare , Hamadah Omar , Espana-Tost Toni , Arnabat-Dominguez Josep
Published: March 2023

Background: Previous studies have confirmed the superiority of using erbium lasers (2940, 2780 nm) over other lasers in the debonding of ceramic brackets due to their safety and effectiveness. The most important factor in the debonding of aesthetic brackets is the transmission of the erbium laser through the aesthetic bracket to the adhesive resin. Objective: To identify the transmission of the 2940 nm wavelength through different types of aesthetic brackets. Materials and methods: A total of 60 aesthetic brackets were divided into six equal groups (10 monocrystalline sapphire brackets—Radiance, AO; 10 monocrystalline sapphire brackets—Absolute, Star Dentech; 10 polycrystalline brackets—20/40, AO; 10 polycrystalline brackets—3M Unitek Gemini Clear Ceramic; 10 silicon brackets—Silkon Plus, AO; 10 composite brackets—Orthoflex, OrthoTech). The aesthetic brackets were mounted in a Fourier transform infrared spectrophotometer (FTIR IRPrestige-21, SHIMADZU) following the typical spectroscopy lab procedure for such samples. The transmission ratio for the 2940 nm wavelength was obtained using IRsolution software. The mean transmission values of the tested groups were compared using a one-way analysis of variance (ANOVA) test followed by a Bonferroni test (post-hoc test). Results: The highest transmission ratio was observed for the Radiance sapphire brackets (64.75%) and the lowest was observed for the 3M polycrystalline brackets (40.48%). The differences among the Aesthetic brackets were significant (p < 0.05). Conclusions: The thick polycrystalline and composite brackets have the lowest transmissibility, whereas the monocrystalline sapphire brackets have the highest transmissibility for the 2940 nm wavelength, meaning that there is a higher possibility of debonding them with a hard tissue laser through thermal ablation.
HDP-TSRRT*: A Time–Space Cooperative Path Planning Algorithm for Multiple UAVs
Authors: Guo Yicong , Liu Xiaoxiong , Jiang Wei , Zhang Weiguo
Published: March 2023

This paper proposes a fast cooperative path planning algorithm for multiple UAVs that satisfies the time–space cooperative constraints, namely, the RRT* algorithm based on heuristic decentralized prioritized planning (HDP-TSRRT*), which takes into account the simultaneous arrival time variables of each UAV as well as the avoidance of conflicts and threats. HDP-TSRRT* is a hierarchical decoupling algorithm. First, all UAV pre-paths are planned simultaneously at the synchronous decentralized planning level. Second, at the coordination path level, the heuristic decentralized prioritized planning algorithm (HDP) is proposed to quickly complete the coordination process of the path planning sequence. This strategy assigns reasonable and robust priority to all UAVs based on the performance evaluation function composed of the number of potential collisions and the violation of collaboration time of the pre-planned path. Third, the time–space cooperative constraints-based RRT* algorithm (TSRRT*) is proposed at the single-machine cooperative path planning level. Based on this, the algorithm uses multiple sampling and cost evaluation strategies to guide the expansion of new nodes, and then optimizes neighborhood nodes based on the time coordination cost function so as to improve the efficiency of coordination path planning. Simulation and comparison show that HDP-TSRRT* has certain advantages in algorithm performance.
Enhancing Bayesian Approaches in the Cognitive and Neural Sciences via Complex Dynamical Systems Theory
Authors: Favela Luis H. , Amon Mary Jean
Published: March 2023

In the cognitive and neural sciences, Bayesianism refers to a collection of concepts and methods stemming from various implementations of Bayes’ theorem, which is a formal way to calculate the conditional probability of a hypothesis being true based on prior expectations and updating priors in the face of errors. Bayes’ theorem has been fruitfully applied to describe and explain a wide range of cognitive and neural phenomena (e.g., visual perception and neural population activity) and is at the core of various theories (e.g., predictive processing). Despite these successes, we claim that Bayesianism has two interrelated shortcomings: its calculations and models are predominantly linear and noise is assumed to be random and unstructured versus deterministic. We outline ways that Bayesianism can address those shortcomings: first, by making more central the nonlinearities characteristic of biological cognitive systems, and second, by treating noise not as random and unstructured dynamics, but as the kind of structured nonlinearities of complex dynamical systems (e.g., chaos and fractals). We provide bistable visual percepts as an example of a real-world phenomenon that demonstrates the fruitfulness of integrating complex dynamical systems theory in Bayesian treatments of perception. Doing so facilitates a Bayesianism that is more capable of explaining a number of currently out-of-reach natural phenomena on their own, biologically realistic terms.
Effect of the Radial Velocity Distribution on the Loss Generation of a Contra-Rotating Fan in a Ventilation System
Authors: Jia Xingyu , Zhang Xi , Guo Kui , Li Xuehui
Published: March 2023

Quantification of the loss generation of ducted contra-rotating fan (CRF) blades is difficult to achieve, since there are no guide vanes between rotors. A blade design program was established to investigate the relationship between radial velocity distribution and incurred loss. Numerical and experimental techniques were used to confirm the optimal configuration’s overall performance. The relationship between loss and velocity distribution under the impact of spanwise load distribution was confirmed by the entropy contour from various perspectives. The appropriate radial velocity distribution can improve the operating efficiency of a CRF by reducing the entropy around the annulus under design and near-stall conditions. This regularity could provide some strategies in the design of contra-rotating blades.
Far from Asymptopia: Unbiased High-Dimensional Inference Cannot Assume Unlimited Data
Authors: Abbott Michael C. , Machta Benjamin B.
Published: March 2023

Inference from limited data requires a notion of measure on parameter space, which is most explicit in the Bayesian framework as a prior distribution. Jeffreys prior is the best-known uninformative choice, the invariant volume element from information geometry, but we demonstrate here that this leads to enormous bias in typical high-dimensional models. This is because models found in science typically have an effective dimensionality of accessible behaviors much smaller than the number of microscopic parameters. Any measure which treats all of these parameters equally is far from uniform when projected onto the sub-space of relevant parameters, due to variations in the local co-volume of irrelevant directions. We present results on a principled choice of measure which avoids this issue and leads to unbiased posteriors by focusing on relevant parameters. This optimal prior depends on the quantity of data to be gathered, and approaches Jeffreys prior in the asymptotic limit. However, for typical models, this limit cannot be justified without an impossibly large increase in the quantity of data, exponential in the number of microscopic parameters.
A Quantum-Inspired Ant Colony Optimization Approach for Exploring Routing Gateways in Mobile Ad Hoc Networks
Authors: Khudair Madhloom Jamal , Abd Ali Hussein Najm , Hasan Haifaa Ahmed , Hassen Oday Ali , Darwish Saad Mohamed
Published: March 2023

Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and complex. MANET is used to build a broad range of applications, both commercial and non-commercial, with the majority of these apps obtaining access to internet resources. Since the gateways (GWs) are the central nodes in a MANET’s ability to connect to the internet, it is common practice to deploy numerous GWs to increase the capabilities of a MANET. Current routing methods have been adapted and optimized for use with MANET through the use of both conventional routing techniques and tree-based network architectures. Exploring new or tacking-failure GWs also increases network overhead but is essential given that MANET is a dynamic and complicated network. To handle these issues, the work presented in this paper presents a modified gateway discovery approach inspired by the quantum swarm intelligence technique. The suggested approach follows the non-root tree-based GW discovery category to reduce broadcasting in the process of exploring GWs and uses quantum-inspired ant colony optimization (QACO) for constructing new paths. Due to the sequential method of execution of the algorithms, the complexity of ACO grows dramatically with the rise in the number of paths explored and the number of iterations required to obtain better performance. The exploration of a huge optimization problem’s solution space may be made much more efficient with the help of quantum parallelization and entanglement of quantum states. Compared to other broad evolutionary algorithms, QACO s have more promise for tackling large-scale issues due to their ability to prevent premature convergence with a simple implementation. The experimental results using benchmarked datasets reveal the feasibility of the suggested approach of improving the processes of exploring new GWs, testing and maintaining existing paths to GWs, exploring different paths to existing GWs, detecting any connection failure in any route, and attempting to fix that failure by discovering an alternative optimal path. Furthermore, the comparative study demonstrates that the utilized QACO is valid and outperforms the discrete binary ACO algorithm (AntHocNet Protocol) in terms of time to discover new GWs (27% improvement on average), time that the recently inserted node takes to discover all GWs (on average, 70% improvement), routing overhead (53% improvement on average), and gateway’s overhead (on average, 60% improvement).
Use of a Realistic Ray-Based Model for the Evaluation of Indoor RF Coverage Solutions Using Reconfigurable Intelligent Surfaces
Authors: Vitucci Enrico M. , Fabiani Mattia , Degli-Esposti Vittorio
Published: March 2023

A previously developed Huygens-based, “antenna-array-like” macroscopic model for scattering from metasurfaces is embedded into a ray-tracing tool and used to carry out realistic RF-coverage evaluations in indoor environment. Using the reciprocity of the link, we extend prediction to multiple-bounce paths that include metasurface scattering at the beginning or at the end of the interaction chain. The proposed model allows us to carry out coverage evaluations for any realistic RIS design by modifying a few simple parameters. In this work, reference environments such as T–shaped and L–shaped corridor cases are considered with different deployment solutions of anomalous and focusing reflectors. The results show that a gain of about 15–20 dB can be obtained in blind-spot locations with proper RIS placement and configuration, without the use of any additional active radio head, even when using simple designs such as pre-configured lossy phase-gradient metasurfaces.
A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)
Authors: Abdullah Saad , Hafid Abdelakram , Folke Mia , Lindén Maria , Kristoffersson Annica
Published: March 2023

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.
A Compositional Transformer Based Autoencoder for Image Style Transfer
Authors: Feng Jianxin , Zhang Geng , Li Xinhui , Ding Yuanming , Liu Zhiguo , Pan Chengsheng , Deng Siyuan , Fang Hui
Published: March 2023

Image style transfer has become a key technique in modern photo-editing applications. Although significant progress has been made to blend content from one image with style from another image, the synthesized image may have a hallucinatory effect when the texture from the style image is rich when processing high-resolution image style transfer tasks. In this paper, we propose a novel attention mechanism, named compositional attention, to design a compositional transformer-based autoencoder (CTA) to solve this above-mentioned issue. With the support from this module, our model is capable of generating high-quality images when transferring from texture-riched style images to content images with semantics. Additionally, we embed region-based consistency terms in our loss function for ensuring internal structure semantic preservation in our synthesized image. Moreover, information theory-based CTA is discussed and Kullback–Leibler divergence loss is introduced to preserve more brightness information for photo-realistic style transfer. Extensive experimental results based on three benchmark datasets, namely Churches, Flickr Landscapes, and Flickr Faces HQ, confirmed excellent performance when compared to several state-of-the-art methods. Based on a user study assessment, the majority number of users, ranging from 61% to 66%, gave high scores on the transfer effects of our method compared to 9% users who supported the second best method. Further, for the questions of realism and style transfer quality, we achieved the best score, i.e., an average of 4.5 out of 5 compared to other style transfer methods.
Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model
Authors: Hwang Jeonghee , Park Soyoung , Chi Jeonghee
Published: March 2023

Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems. MI tasks are performed by imagining doing a specific task and classifying MI through EEG signal processing. However, it is a challenging task to classify EEG signals accurately. In this study, we propose a LSTM-based classification framework to enhance classification accuracy of four-class MI signals. To obtain time-varying data of EEG signals, a sliding window technique is used, and an overlapping-band-based FBCSP is applied to extract the subject-specific spatial features. Experimental results on BCI competition IV dataset 2a showed an average accuracy of 97% and kappa value of 0.95 in all subjects. It is demonstrated that the proposed method outperforms the existing algorithms for classifying the four-class MI EEG, and it also illustrates the robustness on the variability of inter-trial and inter-session of MI data. Furthermore, the extended experimental results for channel selection showed the best performance of classification accuracy when using all twenty-two channels by the proposed method, but an average kappa value of 0.93 was achieved with only seven channels.
Thermohydraulic and Economic Evaluation of a New Design for Printed Circuit Heat Exchangers in Supercritical CO2 Brayton Cycle
Authors: Villada-Castillo Dora , Valencia-Ochoa Guillermo , Duarte-Forero Jorge
Published: March 2023

The present study focused on the analysis of a new geometrical modification of the conventional zig-zag channel for Printed Circuit Heat Exchangers. The research was carried out using OpenFOAM and Salome software, which were used for the CFD analysis and the construction of the computational domain. For the development of the study, three types of channel geometries were defined: a modified zig-zag channel, a conventional zig-zag channel, and a straight channel. The results show that the modified zig-zag channel achieves better thermal hydraulic performance compared to that of the conventional zig-zag channel, evidenced by a 7.6% increase in the thermal performance factor. The modified zig-zag channel proposed in the research caused a 1.5% reduction of the power consumption of supercritical Brayton cycle compressors. Additionally, the modified zig-zag channel achieves a maximum efficiency of 49.1%, which is 1.5% higher compared to that of the conventional zig-zag channel. The above results caused a 20.9% reduction of the operating costs of the supercritical Brayton cycle. This leads to a 5.9% decrease in the cost associated with using the PCHE compared to that of the conventional zig-zag channel. In general, the new geometric characteristics proposed for the conventional zig-zag channel minimize the high loss of the hydraulic performance without significantly compromising its heat transfer capacity. The geometric analysis of the proposed new zig-zag channel geometry was limited to evaluating the influence of the bend angle of 20–30°. Therefore, a more detailed geometric optimization process involving other geometric parameters of the channel is still needed. Future research will be focused on addressing this approach.
Numerical Simulation and Optimization of Inorganic Lead-Free Cs3Bi2I9-Based Perovskite Photovoltaic Cell: Impact of Various Design Parameters
Authors: Das Arnob , Peu Susmita Datta , Akanda Md Abdul Mannan , Salah Mostafa M. , Hossain Md. Sejan , Das Barun Kumar
Published: March 2023

The lead halide-based perovskite solar cells have attracted much attention in the photovoltaic industry due to their high efficiency, easy manufacturing, lightweight, and low cost. However, these lead halide-based perovskite solar cells are not manufactured commercially due to lead-based toxicity. To investigate lead-free inorganic perovskite solar cells (PSCs), we investigated a novel Cs3Bi2I9-based perovskite configuration in SCAPS-1D software using different hole transport layers (HTLs). At the same time, WS2 is applied as an electron transport layer (ETL). Comparative analysis of the various design configurations reveals that ITO/WS2/Cs3Bi2I9/PEDOT:PSS/Au offers the best performance with 20.12% of power conversion efficiency (PCE). After optimizing the thickness, bandgap, defect density, and carrier density, the efficiency of the configuration is increased from 20.12 to 24.91%. Improvement in other performance parameters such as short circuit current (17.325 mA/cm2), open circuit voltage (1.5683 V), and fill factor (91.66%) are also observed after tuning different attributes. This investigation indicates the potential application of Cs3Bi2I9 as a lead-free and stable perovskite material that can contribute to improving the renewable energy sector.
Enhancing Biomass-Exopolysaccharides Production of Lignosus rhinocerus in a High-Scale Stirred-Tank Bioreactor and Its Potential Lipid as Bioenergy
Authors: Usuldin Siti Rokhiyah Ahmad , Ilham Zul , Jamaludin Adi Ainurzaman , Ahmad Rahayu , Wan-Mohtar Wan Abd Al Qadr Imad
Published: March 2023

The depletion of fossil fuels and the emission of greenhouse gases have increased the demand for new and sustainable energy sources, leading to growing interest in using fast-growing filamentous fungi as a source of bioenergy. This study aimed to optimize the production of exopolysaccharides (EPS) and mycelial biomass (MB) from the native medicinal mushroom, Lignosus rhinocerus, through submerged liquid fermentation. Using response surface methodology (RSM), it was found that the glucose concentration and speed of agitation significantly influenced the production of MB and EPS (p < 0.05), while the initial pH medium had an insignificant effect. The validated optimized parameters of 50.0 g/L glucose, initial pH 4.0, and 128 rpm for speed of agitation were tested in 500 mL shake flasks, 5 L, and 13 L stirred-tank (STR) bioreactors. The production of MB and EPS increased significantly by ~1.2-fold in the 5 L STR and further increased to ~1.7-fold (MB) and ~2.4-fold (EPS) in the 13 L STR bioreactor compared to the shake flask. The lipid content of MB was also determined, with a result of 2.07% w/w using the Soxhlet extraction method. To conclude, this study emphasizes the ability of L. rhinocerus as a new source of bioenergy through large-scale production, with optimized parameters serving as a reference for future research and practical applications.
Assessment of Energy Consumption Characteristics of Ultra-Heavy-Duty Vehicles under Real Driving Conditions
Authors: Jo Seongin , Kim Hyung Jun , Kwon Sang Il , Lee Jong Tae , Park Suhan
Published: March 2023

Passenger cars account for the largest share of GHG emissions in the road sector. However, given that the number of heavy-duty vehicles registered is lower but accounts for about a quarter of GHG emissions in the road sector, it is necessary to reduce carbon dioxide (CO2) emissions by improving the fuel efficiency of heavy-duty vehicles. However, experiments using dynamometers during the vehicle development process consume a lot of time and cost. Conversely, simulations can quantitatively analyze the sensitivity of parameters and accelerate optimization. Therefore, in this study, we modeled a heavy-duty vehicle using an AVL Cruise simulation and analyzed the effects of payload, air drag coefficient, and rolling resistance on fuel economy, CO2 emission, and the valid window ratio among the moving average window (MAW) for three driving routes. When the average vehicle speed was higher, the effect of the air drag coefficient on fuel economy was high. Additionally, when the average vehicle speed was lowered, the effect of the reduced rolling resistance on improving fuel efficiency was higher than that of the reducing air drag. Thus, the fuel efficiency improvement rate according to each 10% decrease in rolling resistance was higher by 2.2%, on average, in the low average speed route. Additionally, it was confirmed that the valid window ratio was high when driving in a section with a high vehicle speed first. Thus, the valid window ratio was almost 100% in the test of the route conditions starting from the highway section.
Corporate Social Responsibility and Renewable Energy Development for the Green Brand within SDGs: A Meta-Analytic Review
Authors: Us Yana , Pimonenko Tetyana , Lyulyov Oleksii
Published: March 2023

This study aimed to systemize global scholarly publications on corporate social responsibility and renewable energy to detect their influence on green brand development within the SDGs. The initial data are retrieved from the Scopus database. To operate with the most relevant publications, several limitations were applied. The research sample consists of the 2000 most cited articles in the subject areas of (1) Social Sciences, (2) Business, Management, and Accounting, and (3) Economics, Econometrics, and Finance. This study is carried out in the following logical sequence. The first stage involves searching, collecting, and preprocessing articles representing the investigated topic. Then, several bibliometric techniques were employed to analyze and map the findings. The third stage integrated the obtained results and discussed future research directions. This paper offers some theoretical implications by analyzing and visualizing the investigated scientific output, particularly publication dynamics, the main trends in the investigated research field, the most productive scholars and their collaborations, and the contributions of the journals, affiliations, and countries analyzed. In addition, this study makes some practical contributions and could be used by scholars as detailed instructions for conducting bibliometric analyses in different contexts. Furthermore, researchers could exploit and expand their current study directions using the findings of this paper. However, the findings of this study are limited since they consider only articles from the Scopus database. Therefore, it is appropriate that further studies involve a broader data range.
RETRACTED: Investigation and Evaluation of the Hybrid System of Energy Storage for Renewable Energies
Authors: Anazi Abeer , Barboza-Arenas Luis , Romero-Parra Rosario , Sivaraman Ramaswamy , Qasim Maytham , Al-Khafaji Sara , Gatea Maher , Alayi Reza , Farooq Waqas , Jasiński Michał , Leonowicz Zbigniew , Novak Filip , Gono Radomir
Published: March 2023

The system presented in this paper can change the energy storage landscape by having the advantages of a compressed air storage system and pump storage, as well as minimizing the disadvantages of these two systems. One of the advantages of this system compared to similar systems is the lack of combustion of natural gas. Correspondingly, for construction, it does not require specific specifications for the executive site, and control of the energy and heat of the system (due to the use of water as an operational fluid) is easier than similar systems. In addition, this system is very scalable and can be designed in low capacities to high capacities, energy analysis of this research to identify the basic and effective parameters of the system and determine the limitations and relationships between them. The amount of energy saved in the current research system compared to previous research is significant, and 92% efficiency can be achieved. The energy analysis of this research determined the effect of the parameters on each other and their limitations so that the path of its feasibility design was paved.
Conventional Natural Gas Project Investment and Decision Making under Multiple Uncertainties
Authors: Yong Chi , Tong Mu , Yang Zhongyi , Zhou Jixian
Published: March 2023

Similar to many energy projects, the evaluation of investments in natural gas projects is influenced by technical and economic uncertainties. These uncertainties include natural resource characteristics, production, decline laws, prices, taxes, benchmark yield, and so on. In China, conventional natural gas is still the dominant energy source. The investors are mainly large state-owned energy companies. Therefore, it is necessary to include the technical and economic uncertainties, as well as the investment decision and optimization problems of the enterprises in a unified analytical framework. To this end, this paper innovatively constructs such a framework. Using numerical simulations of approaches, the process of investment decision optimization by companies based on technology assessment and price forecasting is visualized in detail. The results suggest that the investment decision of the enterprise needs to consider technical and economic uncertainties in an integrated manner. It also needs to combine the business strategy and social responsibility of the enterprise in order to construct the objective function. With the availability of data, the framework and its algorithms can be used for practical evaluation of investment plans and decision supports for conventional natural gas projects. The framework can also integrate the analytical perspective of the macroeconomic and political environment to bring in a more comprehensive range of uncertainties.
A Review of Physics-Informed Machine Learning in Fluid Mechanics
Authors: Sharma Pushan , Chung Wai Tong , Akoush Bassem , Ihme Matthias
Published: March 2023

Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.
Second-Life of Lithium-Ion Batteries from Electric Vehicles: Concept, Aging, Testing, and Applications
Authors: Nováková Kateřina , Pražanová Anna , Stroe Daniel-Ioan , Knap Vaclav
Published: March 2023

The last decade has seen a significant increase in electromobility. With this trend, it will be necessary to start dealing with the subsequent recycling and disposal of electric vehicles, including the batteries. Currently, the battery is one of the most expensive components of an electric vehicle, which in part hinders their sufficient competitiveness with the internal combustion engine. Furthermore, the lifetime of a battery for use in an electric vehicle is assumed to be 8–10 years/160,000 km, after which the battery capacity drops to 80% of the initial capacity. However, it transpires that a battery at the end of its life in an electric vehicle does not need to be disposed of immediately, but can be used in other applications wherein the emphasis is not so strictly on an excellent power and capacity capability related to its volume or weight. Thus, reusing batteries can help reduce their cost for use in electric vehicles, increase their utility value, and reduce the environmental impact of batteries. This paper discusses methods for researching battery aging in electric vehicles, testing methods for batteries during the transition from first life to second life, and prospective battery second-life use and its specifics. The main contribution of this perspective article is to provide a comprehensive view of the current state of second-life batteries and an overview of the challenges that need to be overcome in order to use them on a large industrial scale.
Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model
Authors: Thanh Hung Vo , Ebrahimnia Taremsari Sajad , Ranjbar Benyamin , Mashhadimoslem Hossein , Rahimi Ehsan , Rahimi Mohammad , Elkamel Ali
Published: March 2023

Porous carbons as solid adsorbent materials possess effective porosity characteristics that are the most important factors for gas storage. The chemical activating routes facilitate hydrogen storage by adsorbing on the high surface area and microporous features of porous carbon-based adsorbents. The present research proposed to predict H2 storage using four nature-inspired algorithms applied in the random forest (RF) model. Various carbon-based adsorbents, chemical activating agents, ratios, micro-structural features, and operational parameters as input variables are applied in the ML model to predict H2 uptake (wt%). Particle swarm and gray wolf optimizations (PSO and GWO) in the RF model display accuracy in the train and test phases, with an R2 of ~0.98 and 0.91, respectively. Sensitivity analysis demonstrated the ranks for temperature, total pore volume, specific surface area, and micropore volume in first to fourth, with relevancy scores of 1 and 0.48. The feasibility of algorithms in training sizes 80 to 60% evaluated that RMSE and MAE achieved 0.6 to 1, and 0.38 to 0.52. This study contributes to the development of sustainable energy sources by providing a predictive model and insights into the design of porous carbon adsorbents for hydrogen storage. The use of nature-inspired algorithms in the model development process is also a novel approach that could be applied to other areas of materials science and engineering.
Progress in CO2 Conversion Using Renewable Energy Sources
Authors: Milanese Marco , Colangelo Gianpiero , de Risi Arturo
Published: March 2023

The latest Intergovernmental Panel on Climate Change special report, on the impacts of global warming of 1 [...]
Analysis of the Combustion Process in a Hydrogen-Fueled CFR Engine
Authors: Beccari Stefano , Pipitone Emiliano , Caltabellotta Salvatore
Published: March 2023

Green hydrogen, produced using renewable energy, is nowadays one of the most promising alternatives to fossil fuels for reducing pollutant emissions and in turn global warming. In particular, the use of hydrogen as fuel for internal combustion engines has been widely analyzed over the past few years. In this paper, the authors show the results of some experimental tests performed on a hydrogen-fueled CFR (Cooperative Fuel Research) engine, with particular reference to the combustion. Both the air/fuel (A/F) ratio and the engine compression ratio (CR) were varied in order to evaluate the influence of the two parameters on the combustion process. The combustion duration was divided in two parts: the flame front development (characterized by laminar flame speed) and the rapid combustion phase (characterized by turbulent flame speed). The results of the hydrogen-fueled engine have been compared with results obtained with gasoline in a reference operating condition. The increase in engine CR reduces the combustion duration whereas the opposite effect is observed with an increase in the A/F ratio. It is interesting to observe how the two parameters, CR and A/F ratio, have a different influence on the laminar and turbulent combustion phases. The influence of both A/F ratio and engine CR on heat transfer to the combustion chamber wall was also evaluated and compared with the gasoline operation. The heat transfer resulting from hydrogen combustion was found to be higher than the heat transfer resulting from gasoline combustion, and this is probably due to the different quenching distance of the two fuels.
On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data
Authors: Dao Phong B.
Published: March 2023

Cointegration theory has been recently proposed for condition monitoring and fault detection of wind turbines. However, the existing cointegration-based methods and results presented in the literature are limited and not encouraging enough for the broader deployment of the technique. To close this research gap, this paper presents a new investigation on cointegration for wind turbine monitoring using a four-year SCADA data set acquired from a commercial wind turbine. A gearbox fault is used as a testing case to validate the analysis. A cointegration-based wind turbine monitoring model is established using five process parameters, including the wind speed, generator speed, generator temperature, gearbox temperature, and generated power. Two different sets of SCADA data were used to train the cointegration-based model and calculate the normalized cointegrating vectors. The first training data set involves 12,000 samples recorded before the occurrence of the gearbox fault, whereas the second one includes 6000 samples acquired after the fault occurrence. Cointegration residuals—obtained from projecting the testing data (2000 samples including the gearbox fault event) on the normalized cointegrating vectors—are used in control charts for operational state monitoring and automated fault detection. The results demonstrate that regardless of which training data set was used, the cointegration residuals can effectively monitor the wind turbine and reliably detect the fault at the early stage. Interestingly, despite using different training data sets, the cointegration analysis creates two residuals which are almost identical in their shapes and trends. In addition, the gearbox fault can be detected by these two residuals at the same moment. These interesting findings have never been reported in the literature.
The Complexity of Energy Price Fluctuations
Authors: Failler Pierre , Liu Yue
Published: March 2023

As a topic of widespread concern in the international community, the issue of energy prices has “increased in popularity” in recent years due to its prominent strategic position and the impact of the development of alternative renewable resources [...]
A Review of Techniques to Enhance an Amplifier’s Performance Using Resistive Local Common Mode Feedback
Authors: Ramirez-Angulo Jaime , Lopez-Martin Antonio J. , Carvajal Ramón G. , Torralba Antonio , Huerta-Chua Jesus
Published: March 2023

A review of some of the most common applications of the resistive local common mode feedback technique to enhance amplifier’s performance is presented. It is shown that this simple technique offers essential improvement in open loop gain, gain-bandwidth product, slew rate, common mode rejection ratio, power supply rejection ratio, etc. This is achieved without increasing power dissipation or supply voltage requirements and with small additional silicon area and circuit complexity. It is also shown that it is especially appropriate to improve amplifiers’ performance in current fine-line submicrometer CMOS technology. Some of the applications discussed are GB enhanced, class AB and super class AB operational amplifiers, gain boosted op-amps, bulk-driven circuits, sample and hold circuits and power management circuits, among others.
A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks
Authors: Ntafloukas Konstantinos , Pasquale Liliana , Martinez-Pastor Beatriz , McCrum Daniel P.
Published: March 2023

Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system.
Fish Nutrition and Feed Technology
Authors: Paolucci Marina
Published: March 2023

This Special Issue was designed to address advances in feeding and feed technology and challenges in aquaculture in order to achieve a greater understanding of its management and improve the sector [...]
Improving Homogeneity of 3D-Printed Cementitious Material Distribution for Radial Toolpath
Authors: Li Mingyang , Liu Zhixin , Ho Jin Yao , Wong Teck Neng
Published: March 2023

The 3D cementitious material printing method is an extrusion-based additive manufacturing strategy in which cementitious materials are extruded through a dynamic nozzle system to form filaments. Despite its ability to fabricate structures with high complexity and efficiency, the uneven material distribution during the extrusion and deposition process is often encountered when a radial toolpath is introduced. This limits the design freedom and printing parameters that can be utilized during radial toolpath printing. Here, we report a facile strategy to overcome the existing challenges of cementitious material non-homogeneity by rationally developing new nozzle geometries that passively compensate the differential deposition rate encountered in conventional rectangular nozzles. Using two-phase numerical study, we showed that our strategy has the potential of achieving a homogeneous mass distribution even when the nozzle travel speed is unfavorably high, while filament from a rectangular nozzle remains highly non-homogenous. The material distribution unevenness can be reduced from 1.35 to 1.23 and to 0.98 after adopting trapezoid and gaussian nozzles, indicating improvements of 34.3% and 94.2%, respectively. This work not only outlines the methodology for improving the quality of corner/curved features in 3DCMP, but also introduces a new strategy which can be adopted for other extrusion-based fabrication techniques with high material inertia.
Quantification of 363 Pesticides in Leafy Vegetables (Dill, Rocket and Parsley) in the Turkey Market by Using QuEChERS with LC-MS/MS and GC-MS/MS
Authors: Deveci Bilge , Golge Ozgur , Kabak Bulent
Published: March 2023

Contamination of agricultural products with pesticide residues is a growing concern due to their adverse health effects and increasing worldwide usage of pesticides. In 2021 a total of 200 samples of green leafy vegetables, including 80 dill, 80 rocket and 40 parsley, purchased from greengrocer shops, markets and bazaars in Corum Province, Turkey, were monitored for pesticide residues. In green leafy vegetables, 363 pesticides were analyzed using a quick, easy, cheap, effective, rugged, and safe (QuEChERS) sample preparation, followed by liquid chromatography coupled to mass spectrometry (LC-MS/MS) for 311 residues and gas chromatography coupled to mass spectrometry (GC-MS/MS) for 52 residues. The method was in-house validated at two fortification levels, and satisfactory recoveries and precisions were achieved for all residues. No quantifiable residues were found in 35% of the samples, whereas 43 residues belonging to 24 different chemical classes were detected in 130 green leafy vegetables. Among the green leafy vegetables, the highest occurrence frequency was recorded in the rocket, followed by dill and parsley. In 46% of the green leafy vegetables, the residue levels exceeded European Union Maximum Residue Levels (EU MRLs). The most frequently detected pesticides were pendimethalin (22.5%), diuron (38.7%) and pymetrozine (52.5%) in dill, rocket and parsley, respectively.
Emerging Perspectives of Blockchains in Food Supply Chain Traceability Based on Patent Analysis
Authors: Mastilović Jasna , Kukolj Dragan , Kevrešan Žarko , Ostojić Gordana , Kovač Renata , Đerić Marina , Samek Dragana Ubiparip
Published: March 2023

In the field of blockchain (BC) technology application in the food supply chain (FSC), a patent portfolio is collected, described, and analyzed using Latent Dirichlet Allocation (LDA) modeling, with the aim of obtaining insight into technology trends in this emerging and promising field. A patent portfolio consisting of 82 documents was extracted from patent databases using PatSnap software. The analysis of latent topics using LDA indicates that inventions related to the application of BCs in FSCs are patented in four key areas: (A) BC-supported tracing and tracking in FSCs; (B) devices and methods supporting application of BCs in FSCs; (C) combining BCs and other ICT technologies in FSC; and (D) BC-supported trading in FSCs. Patenting of BC technology applications in FSCs started during the second decade of the 21st century. Consequently, patent forward citation has been relatively low, while the family size confirms that application of BCs in FSCs is not yet widely accepted. A significant increase in the number of patent applications was registered after 2019, indicating that the number of potential users in FSCs is expected to grow over time. The largest numbers of patents originate from China, India, and the US.