bluuebunny/crossref_metadata_2025 huggingface dataset:
MazeGen: A Low-Code Framework for Bootstrapping Robotic Navigation Scenarios for Smart Manufacturing Contexts
Authors: Guevara Ivan Hugo , Margaria Tiziana
Published: May 2023

In this research, we describe the MazeGen framework (as a maze generator), which generates navigation scenarios using Grammatical Evolution for robots or drones to navigate. The maze generator uses evolutionary algorithms to create robotic navigation scenarios with different semantic levels along a scenario profile. Grammatical Evolution is a Machine Learning technique from the Evolutionary Computing branch that uses a BNF grammar to describe the language of the possible scenario universe and a numerical encoding of individual scenarios along that grammar. Through a mapping process, it converts new numerical individuals obtained by operations on the parents’ encodings to a new solution by means of grammar. In this context, the grammar describes the scenario elements and some composition rules. We also analyze associated concepts of complexity, understanding complexity as the cost of production of the scenario and skill levels needed to move around the maze. Preliminary results and statistics evidence a low correlation between complexity and the number of obstacles placed, as configurations with more difficult obstacle dispositions were found in the early stages of the evolution process and also when analyzing mazes taking into account their semantic meaning, earlier versions of the experiment not only resulted as too simplistic for the Smart Manufacturing domain, but also lacked correlation with possible real-world scenarios, as was evidenced in our experiments, where the most semantic meaning results had the lowest fitness score. They also show the emerging technology status of this approach, as we still need to find out how to reliably find solvable scenarios and characterize those belonging to the same class of equivalence. Despite being an emerging technology, MazeGen allows users to simplify the process of building configurations for smart manufacturing environments, by making it faster, more efficient, and reproducible, and it also puts the non-expert programmer in the center of the development process, as little boilerplate code is needed.
Motivations and Tools Relevant to Personalized Workspaces in VR Environments
Authors: Horváth Ildikó , Csapó Ádám B.
Published: May 2023

In this paper, we propose a new virtual reality (VR) concept referred to as ‘context control’, which we use to describe VR workspaces that are dynamically reconfigurable based on the task at hand and the user’s individual learning and working style. To demonstrate the viability of the concept as well as how it could be applied in practical applications, we present an implementation framework that, at its foundations, relies on Kolb’s learning styles taxonomy, consisting of Assimilators, Accommodators, Convergers and Divergers. We propose a layout schema for each of these categories of learning style, and validate them based on an experiment involving 52 university students, showing that the test subjects preferred content layouts that represent cognitive profiles matching their own to a greater extent. We also propose a hierarchical schema template language with which the schemas can be formalized and made amenable to further dynamic customization.
Scalable Inline Network-Intrusion Detection System with Minimized Memory Requirement
Authors: Kim Taehoon , Pak Wooguil
Published: May 2023

Currently used network-intrusion detection systems (NIDSs) using deep learning have limitations in processing large amounts of data in real time. This is because collecting flow information and creating features are time consuming and require considerable memory. To solve this problem, a novel NIDS with θ(1) memory complexity for processing a flow is proposed in this study. Owing to its small memory requirement, the proposed model can handle numerous concurrent flows. In addition, it uses raw packet data as input features for the deep learning models, resulting in a lightweight feature-creation process. For fast detection, the proposed NIDS classifies a flow using a received packet, though it is prone to false detection. This weakness is solved through the validation model proposed in this research, resulting in high detection accuracy. Furthermore, real-time detection is possible since intrusion detection can be performed for every received packet using the Inception model. A performance comparison with existing methods confirmed an effectively improved detection time and lower memory requirement by 73% and 77% on average while maintaining high detection accuracy. Thus, the proposed model can effectively overcome the problems with modern deep-learning-based NIDSs.
Deterministic Construction of Compressed Sensing Measurement Matrix with Arbitrary Sizes via QC-LDPC and Arithmetic Sequence Sets
Authors: Wang Yue , Qin Yali , Ren Hongliang
Published: May 2023

It is of great significance to construct deterministic measurement matrices with good practical characteristics in Compressed Sensing (CS), including good reconstruction performance, low memory cost and low computing resources. Low-density-parity check (LDPC) codes and CS codes can be closely related. This paper presents a method of constructing compressed sensing measurement matrices based on quasi-cyclic (QC) LDPC codes and arithmetic sequence sets. The cyclic shift factor in each submatrix of QC-LDPC is determined by arithmetic sequence sets. Compared with random matrices, the proposed method has great advantages because it is generated based on a cyclic shift matrix, which requires less storage memory and lower computing resources. Because the restricted isometric property (RIP) is difficult to verify, mutual coherence and girth are used as computationally tractable indicators to evaluate the measurement matrix reconstruction performance. Compared with several typical matrices, the proposed measurement matrix has the minimum mutual coherence and superior reconstruction capability of CS signal according to one-dimensional (1D) signals and two-dimensional (2D) image simulation results. When the sampling rate is 0.2, the maximum (minimum) gain of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) is up to 2.89 dB (0.33 dB) and 0.031 (0.016) while using 10 test images. Meanwhile, the reconstruction time is reduced by nearly half.
An Improved Genetic Algorithm with Swarm Intelligence for Security-Aware Task Scheduling in Hybrid Clouds
Authors: Huang Yinfeng , Zhang Shizheng , Wang Bo
Published: May 2023

The hybrid cloud has attracted more and more attention from various fields by combining the benefits of both private and public clouds. Task scheduling is still a challenging open issue to optimize user satisfaction and resource efficiency for providing services by a hybrid cloud. Thus, in this paper, we focus on the task scheduling problem with deadline and security constraints in hybrid clouds. We formulate the problem into mixed-integer non-linear programming, and propose a polynomial time algorithm by integrating swarm intelligence into the genetic algorithm, which is named SPGA. Specifically, SPGA uses the self and social cognition exploited by particle swarm optimization in the population evolution of GA. In each evolutionary iteration, SPGA performs the mutation operator on an individual with not only another individual, as in GA, but also the individual’s personal best code and the global best code. Extensive experiments are conducted for evaluating the performance of SPGA, and the results show that SPGA achieves up to a 53.2% higher accepted ratio and 37.2% higher resource utilization, on average, compared with 12 other scheduling algorithms.
Are Commercial EV Chargers Ready to Aid with Household Power Consumption?
Authors: Shams Ashkezari Leila , Succetti Marco , Jabœuf Rémi , Tosco Paolo , Brenna Morris
Published: May 2023

The transportation industry now accounts for approximately a quarter of worldwide energy-related direct CO2 emissions, and governments all around the globe have committed to converting their fossil-fuel vehicles to zero-emission ones by adopting electric vehicles. Current electric vehicles (EV) can store approximately 18 to 100 kWh of energy, which may be employed not only for commuting but also for other purposes such as delivering energy to households (V2H) or buildings (V2B), as well as offering ancillary services to the power grid (V2G). In this study, a real test setting including a trending bidirectional charger, an EV, a PV simulator, and household appliances are utilized to evaluate the performance of various V2H components and to learn about the concerns that may arise during V2H operation. The results of the tests on the bidirectional EV charger are presented in this paper. Although the results of the tests on the charger installed in the house are not satisfactory and consistent to the project’s goal, they are released in order to aid future studies in better understanding the true challenges of commercial bidirectional chargers.
Convergence of Software-Defined Vehicular Cloud and 5G Enabling Technologies: A Survey
Authors: Nkenyereye Lionel , Nkenyereye Lewis , Jang Jong-Wook
Published: May 2023

Vehicular cloud computing (VCC) and connected vehicles have prompted the intensive investigation of communication and computing solutions. As an important enabler, software-defined network (SDN) broadly changes the design of vehicle services, from resource allocation to ambitious autonomous cars. However, current VCC architectures face challenges that hinder the vision of providing reliable services to connected vehicles. As a result, deploying VC services using SDN network has emerged as a viable option. Therefore, software-defined VC architecture (SDVC) dynamically manages the control and resource utilization of VC by centralizing the overall knowledge. In addition, SDN stands as the representative technique of virtual resources and network function virtualization (NFV). NFV is integrated into SDVC frameworks to design extended SDVC (ESDVC) for dynamic, adaptive VC maintenance, VC network slicing management, and to meet constraint requirements such as network latency and reliable connectivity. This paper presents and discusses: (1) the architecture scenario of both SDVC and ESDVC; (2) the effective deployment methods enabling NFV and network slicing (NS) frameworks to customize VC frameworks; (3) challenges and future concepts of more VC services based on ESDVC architecture. From this survey, we believe readers would find relevant methods for realigning information dispersed across the SDVC, fifth generation (5G)-based VC, and NS domains and comprehending the relationships between these technologies while encouraging further debate on the fusion of 5G enabling technologies over SDVC to enable VC network slicing.
Antenna Booster Versus a Spiral Monopole Antenna for Single-Band Operation at 900 MHz
Authors: Oller Bernat , Andújar Aurora , Anguera Jaume
Published: May 2023

The number of devices connected to the internet has grown exponentially through the last decade, making IoT and connection worldwide more possible every year, enabling an incredible number of applications. This calls for better and more efficient methods for designing wireless devices. An efficient and small antenna is needed to ensure connectivity, range, and battery life. Under these circumstances, antenna booster technology is proposed, which uses a tiny component called an antenna booster to excite currents in the ground plane of the IoT device. This allows the antenna booster to be as small as 12 mm × 3 mm × 2.4 mm, representing only ~λ/30 at 863 MHz. The antenna booster is matched across the frequency range using a matching network. The paper compares an antenna booster and a monopole antenna regarding bandwidth for a design using a 120 mm × 60 mm ground plane in the 863 MHz to 928 MHz frequency range. Afterwards, the same designs are analysed when the ground plane size changes from 20 mm × 30 mm to 200 mm × 200 mm using steps of 10 mm to determine which approach can be reused across 53.8% of the ground planes with S11 < −6 dB without making any changes to the antenna system; for contrast, the monopole antenna can be reused only 4.6%. In addition, the antenna booster features better total efficiencies of up to 2.3 dB. A physical prototype with the antenna booster validates the numerical analysis.
Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection
Authors: Bian Genqing , Qu Wenjing , Shao Bilin
Published: May 2023

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.
Naturalistic Driving Data-Based Anomalous Driving Behavior Detection Using Hypertuned Deep Autoencoders
Authors: Abbas Shafqat , Malik Muhammad Ozair , Javed Abdul Rehman , Hong Seng-Phil
Published: May 2023

Autonomous driving is predicted to play a large part in future transportation systems, providing benefits such as enhanced road usage and mobility schemes. However, self-driving cars must be perceived as safe drivers by other road users and contribute to traffic safety in addition to being operationally safe. Despite efforts to develop machine learning algorithms and solutions for the safety of automated vehicles, researchers have yet to agree upon a single approach to categorizing and accurately detecting safe and unsafe driving behaviors. This paper proposes a modified Z-score method-based autoencoder for anomalous behavior detection using multiple driving indicators. The experiments are performed on the benchmark Next Generation Simulation (NGSIM) vehicle trajectories and supporting datasets to discover anomalous driving behavior to assess our proposed approach’s performance. The experiments reveal that the proposed approach detected 81 anomalous driving behaviors out of 1031 naturalistic driving behavior instances (7.86%) with an accuracy of 96.31% without early stopping. With early stopping, our method successfully detected 147 anomalous driving behaviors (14.26%) with an accuracy of 95.25%. Overall, the proposed approach provides promising results for detecting anomalous driving behavior in automated vehicles using multiple driving indicators.
Synthesized Improvement of Die Fly and Die Shift Concerning the Wafer Molding Process for Ultrafine SAW Filter FOWLP
Authors: Li Wei , Yu Daquan
Published: May 2023

As the surface acoustic wave (SAW) filters incline to ultrafine, the failures resulting from the wafer molding process have become increasingly prominent. A methodology for coupling the mechanisms of die fly and die shift for the SAW filter miniatured with 737 μm × 517 μm × 200 μm is developed for the trade-off between reliability and yields. In terms of die fly and die shift, the former occurs before the epoxy molding compound (EMC) is cured in the temperature rise period, while the latter occurs in the cooling stage after being cured. The die fly is induced by the fluid flow force in the high-temperature stage of heat compression, which is fatal for the scrap. Followed by the cooling stage, the CTE (coefficient of thermal expansion) misalignment between the die and epoxy molding compound (EMC) seriously affects the die shift and the following lithography process yields. The debonding critical energy in the mixed mode is employed to avert the die fly. Then, the die fly can be shunned by fine-tuning the die thickness, die layout, and EMC layout. A methodology to measure die shift was conducted, by which a total of 47,568 dies were embedded using compression molding. The mechanical error of the mounter and the die shift law are comprehensively leveraged, indicating that the die shift can be controlled within 50 μm for 8-inch wafer-level packaging.
An AI-Based Power Reserve Control Strategy for Photovoltaic Power Generation Systems Participating in Frequency Regulation of Microgrids
Authors: Zhou Sihan , Qin Liang , Ruan Jiangjun , Wang Jing , Liu Haofeng , Tang Xu , Wang Xiaole , Liu Kaipei
Published: May 2023

In this paper, a novel AI-based power reserve control strategy is proposed for photovoltaic (PV) power generation systems participating in the frequency regulation (FR) of microgrids. The proposed strategy uses a frequency response module to determine the target power reserve ratio of the PV system based on microgrid frequency deviation, as well as a power reserve control module to obtain the target duty cycle, which is input to the BOOST converter. The use of artificial neural networks (ANN) in the power reserve control module enables the PV system to work at a specified power reserve ratio, producing appropriate power and mitigating frequency fluctuations in the microgrid. Additionally, a deep reinforcement learning (DRL) algorithm is employed as the decision maker for variable step-size control and initial power reserve ratio determination. Simulations were performed to validate the effectiveness of the proposed method, demonstrating a significant reduction in average frequency deviation by 72.36% when subjected to random variations in irradiance intensity and load conditions. Overall, the proposed AI-based power reserve control strategy has good potential for practical applications in real-world microgrids, promoting the absorption of new energy led by PV and reducing the phenomenon of light abandonment.
Barycentric Kernel for Bayesian Optimization of Chemical Mixture
Authors: Kim San , Kim Jaekwang
Published: May 2023

Chemical-reaction optimization not only increases the yield of chemical processes but also reduces impurities and improves the performance of the resulting products, contributing to important innovations in various industries. This paper presents a novel barycentric kernel for chemical-reaction optimization using Bayesian optimization (BO), a powerful machine-learning method designed to optimize costly black-box functions. The barycentric kernel is specifically tailored as a positive definite kernel for Gaussian-process surrogate models in BO, ensuring stability in logarithmic and differential operations while effectively mapping concentration space for solving optimization problems. We conducted comprehensive experiments comparing the proposed barycentric kernel with other widely used kernels, such as the radial basis function (RBF) kernel, across six benchmark functions in concentration space and three Hartmann functions in Euclidean space. The results demonstrated the barycentric kernel’s stable convergence and superior performance in these optimization scenarios. Furthermore, the paper highlights the importance of accurately parameterizing chemical concentrations to prevent BO from searching for infeasible solutions. Initially designed for chemical reactions, the versatile barycentric kernel shows promising potential for a wide range of optimization problems, including those requiring a meaningful distance metric between mixtures.
Visual Perception Based Intra Coding Algorithm for H.266/VVC
Authors: Tsai Yu-Hsiang , Lu Chen-Rung , Chen Mei-Juan , Hsieh Meng-Chun , Yang Chieh-Ming , Yeh Chia-Hung
Published: May 2023

The latest international video coding standard, H.266/Versatile Video Coding (VVC), supports high-definition videos, with resolutions from 4 K to 8 K or even larger. It offers a higher compression ratio than its predecessor, H.265/High Efficiency Video Coding (HEVC). In addition to the quadtree partition structure of H.265/HEVC, the nested multi-type tree (MTT) structure of H.266/VVC provides more diverse splits through binary and ternary trees. It also includes many new coding tools, which tremendously increases the encoding complexity. This paper proposes a fast intra coding algorithm for H.266/VVC based on visual perception analysis. The algorithm applies the factor of average background luminance for just-noticeable-distortion to identify the visually distinguishable (VD) pixels within a coding unit (CU). We propose calculating the variances of the numbers of VD pixels in various MTT splits of a CU. Intra sub-partitions and matrix weighted intra prediction are turned off conditionally based on the variance of the four variances for MTT splits and a thresholding criterion. The fast horizontal/vertical splitting decisions for binary and ternary trees are proposed by utilizing random forest classifiers of machine learning techniques, which use the information of VD pixels and the quantization parameter. Experimental results show that the proposed algorithm achieves around 47.26% encoding time reduction with a Bjøntegaard Delta Bitrate (BDBR) of 1.535% on average under the All Intra configuration. Overall, this algorithm can significantly speed up H.266/VVC intra coding and outperform previous studies.
Layerwise Adversarial Learning for Image Steganography
Authors: Chen Bin , Shi Lei , Cao Zhiyi , Niu Shaozhang
Published: May 2023

Image steganography is a subfield of pattern recognition. It involves hiding secret data in a cover image and extracting the secret data from the stego image (described as a container image) when needed. Existing image steganography methods based on Deep Neural Networks (DNN) usually have a strong embedding capacity, but the appearance of container images is easily altered by visual watermarks of secret data. One of the reasons for this is that, during the end-to-end training process of their Hiding Network, the location information of the visual watermarks has changed. In this paper, we proposed a layerwise adversarial training method to solve the constraint. Specifically, unlike other methods, we added a single-layer subnetwork and a discriminator behind each layer to capture their representational power. The representational power serves two purposes: first, it can update the weights of each layer which alleviates memory requirements; second, it can update the weights of the same discriminator which guarantees that the location information of the visual watermarks remains unchanged. Experiments on two datasets show that the proposed method significantly outperforms the most advanced methods.
Generation of Synthetic CPTs with Access to Limited Geotechnical Data for Offshore Sites
Authors: Shoukat Gohar , Michel Guillaume , Coughlan Mark , Malekjafarian Abdollah , Thusyanthan Indrasenan , Desmond Cian , Pakrashi Vikram
Published: May 2023

The initial design phase for offshore wind farms does not require complete geotechnical mapping and individual cone penetration testing (CPT) for each expected turbine location. Instead, background information from open source studies and previous historic records for geology and seismic data are typically used at this early stage to develop a preliminary ground model. This study focuses specifically on the interpolation and extrapolation of cone penetration test (CPT) data. A detailed methodology is presented for the process of using a limited number of CPTs to characterise the geotechnical behavior of an offshore site using artificial neural networks. In the presented study, the optimised neural network achieved a predictive error of 0.067. Accuracy is greatest at depths of less than 10 m. The pitfalls of using machine learning for geospatial interpolation are explained and discussed.
Characterization of the Hydropower Inlet Effect on the Electric Network Frequency
Authors: Álvarez-Higueruela Jesús , Gutiérrez-Villalba Francisco-José , Sierra-Fernández José-María , González-de-la-Rosa Juan-José , Florencias-Oliveros Olivia , Palomares-Salas José-Carlos , Agüera-Pérez Agustín , Espinosa-Gavira Manuel-Jesús
Published: May 2023

One of the challenges posed by renewable energies is the stabilization of parameters related to the quality of electrical energy. This study demonstrates the existence of a relationship between input blocks of hydropower and the variation of the fundamental frequency of the electricity grid. By mapping production data provided by the Spanish Electric Network with frequency measured in the laboratory, it is shown that gradients of hydropower are correlated with frequency fluctuations for certain characteristic times. Considering hourly instances of energy input, the study compares two methods for calculating hydropower gradients (linear regression and pseudo-linear regression) and two methods for calculating local frequency extrema (the “specular inertia” method and analysis by comparison with the moving average) in order to corroborate the results.
Investigation into Dynamic Pressure Pulsation Characteristics in a Centrifugal Pump with Staggered Impeller
Authors: Ni Dan , Chen Jinbo , Wang Feifan , Zheng Yanjuan , Zhang Yang , Gao Bo
Published: May 2023

For the centrifugal pump, the rotor–stator interaction (RSI) induces high-energy pressure pulsation, which directly affects the stability of systems and equipment. Therefore, this work proposes a new staggered impeller structure to suppress high-energy pressure pulsation in centrifugal pumps. The original impeller blade is divided into two layers and is staggered at 10°, 20° and 30° to form a staggered impeller. The dynamic pressure pulsation characteristics of both the original impeller and the staggered impeller are predicted using large eddy simulation (LES). The results indicate that the uniform staggered arrangement of blades can significantly reduce the pressure pulsation energy in the pump by 54.69% under the design conditions, while also achieving the best performance. Even under off-design conditions, the pressure pulsation energy can still be effectively suppressed by the staggered blades. The study of the time–frequency domain of the monitoring points near the tongue found that the phase difference in the pressure fluctuation caused by the RSI between the staggered impeller and the tongue prevents the superposition of pressure pulsation energy and efficiently suppresses it in the pump. The results can provide a reference for optimizing low-vibration-noise pump impellers in engineering applications.
The European Dilemma—Energy Security or Green Transition
Authors: Joița Diana , Panait Mirela , Dobrotă Carmen-Elena , Diniță Alin , Neacșa Adrian , Naghi Laura Elly
Published: May 2023

The energy transition is a subject of significant interest in the countries of the European Union, and the involvement of public authorities is remarkable. Unlike previous energy transitions, this process is politically driven, the efforts of public authorities being secondary to the actions of companies whose guidelines are based on stability at the European level through strategies and directives. This paper aims to provide an overview of the current evolution of energy security status in the European Union and in Romania, with reference to global efforts to achieve climate neutrality through the energy transition. The historical method was used in the elaboration of the material, in order to outline the current energy security policies in terms of sustainability, both environmental and social. The logical method was used in order to emphasize the cause–effect relationship between economic processes in the energy sector. The use of bibliometric analysis demonstrated the importance of the topic of energy transition in scientific literature, but it also demonstrated the increasing interest of researchers in subjects related to energy security and energy poverty in the context of this new transition. By using the graphical method, statistic data and their evolution were highlighted in detail for the present study. The results of the articles are embodied in an approach that aims to focus on economic and social factors that determine the capacity of states to support the EU’s independence from a single supplier in the current context of conflict. The authors conclude by anticipating massive challenges in achieving the environmental objectives of the European Green Deal, as well as by highlighting the issue of energy poverty as a social factor influencing global strategic decisions.
Comparison of Space Cooling Systems from Energy and Economic Perspectives for a Future City District in Sweden
Authors: Sayadi Sana , Akander Jan , Hayati Abolfazl , Gustafsson Mattias , Cehlin Mathias
Published: May 2023

In this study, the performance of different cooling technologies from energy and economic perspectives were evaluated for six different prototype residential Nearly Zero Energy Buildings (NZEBs) within a planned future city district in central Sweden. This was carried out by assessing the primary energy number and life cycle cost analysis (LCCA) for each building model and cooling technology. Projected future climate file representing the 2050s (mid-term future) was employed. Three cooling technologies (district cooling, compression chillers coupled/uncoupled with photovoltaic (PV) systems, and absorption chillers) were evaluated. Based on the results obtained from primary energy number and LCCA, compression chillers with PV systems appeared to be favorable as this technology depicted the least value for primary energy use and LCCA. Compared to compression chillers alone, the primary energy number and the life cycle cost were reduced by 13%, on average. Moreover, the district cooling system was found to be an agreeable choice for buildings with large floor areas from an economic perspective. Apart from these, absorption chillers, utilizing environmentally sustainable district heating, displayed the highest primary energy use and life cycle cost which made them the least favorable choice. However, the reoccurring operational cost from the LCCA was about 60 and 50% of the total life cycle cost for district cooling and absorption chillers, respectively, while this value corresponds to 80% for the compression chillers, showing the high net present value for this technology but sensitive to future electricity prices.
Identification and Mitigation of Shortcomings in Direct and Indirect Liquid Cooling-Based Battery Thermal Management System
Authors: Anisha  , Kumar Anil
Published: May 2023

Electric vehicles (EVs) have become a viable solution to the emerging global climate crisis. Rechargeable battery packs are the basic unit of the energy storage system of these vehicles. The battery thermal management system (BTMS) is the primary control unit of the energy source of the vehicles. EV performance is governed by specific power, charging/discharging rate, specific energy, and cycle life of the battery packs. Nevertheless, these parameters are affected by temperature, making thermal management the most significant factor for the performance of a battery pack in an EV. Although the BTMS has acquired plenty of attention, research on the efficiency of the liquid cooling-based BTMS for actual drive cycles has been minimal. Liquid cooling, with appropriate configuration, can provide up to 3500 times more efficient cooling than air cooling. Direct/immersive and indirect liquid cooling are the main types of liquid cooling systems. Immersive/direct cooling utilizes the technique of direct contact between coolant and battery surface, which could provide larger heat transfer across the pack; however, parameters such as leakage, configuration, efficiency, etc., are needed to be considered. Indirect cooling techniques include cold plates, liquid jackets, discrete tubes, etc. It could result in complex configuration or thermal non-uniformity inside the pack. The paper intends to contribute to the alleviation of these gaps by studying various techniques, including different configurations, coolant flow, nanoparticles, varying discharging rates, different coolants, etc. This paper provides a comprehensive perspective of various techniques employed in liquid cooling battery packs, identifying the shortcomings in direct/immersive and indirect liquid cooling systems and discussing their mitigation strategies.
Ice-Templated Method to Promote Electrochemical Energy Storage and Conversion: A Review
Authors: Wang Yucheng , Wu Yanan , Zheng Xingqun , Lu Shun
Published: May 2023

The ice-templated method (ITM) has drawn significant attention to the improvement of the electrochemical properties of various materials. The ITM approach is relatively straightforward and can produce hierarchically porous structures that exhibit superior performance in mass transfer, and the unique morphology has been shown to significantly enhance electrochemical performance, making it a promising method for energy storage and conversion applications. In this review, we aim to present an overview of the ITM and its applications in the electrochemical energy storage and conversion field. The fundamental principles underlying the ITM will be discussed, as well as the factors that influence the morphology and properties of the resulting structures. We will then proceed to comprehensively explore the applications of ITM in the fabrication of high-performance electrodes for supercapacitors, batteries, and fuel cells. We intend to find the key advances in the use of ITM and evaluate its potential to overcome the existing challenges in the development of efficient energy storage and conversion systems.
Improved Antioxidant Capacity of Akebia trifoliata Fruit Inoculated Fermentation by Plantilactobacillus plantarum, Mechanism of Anti-Oxidative Stress through Network Pharmacology, Molecular Docking and Experiment Validation by HepG2 Cells
Authors: Sun Yuhao , Wang Zhenzhen , Dai Jing , Sha Ruyi , Mao Jianwei , Mao Yangchen , Cui Yanli
Published: May 2023

In this work, spontaneously fermented and inoculation-fermented Akebia trifoliata fruit Jiaosu (SFAJ/IFAJ) were compared. The key metabolites and antioxidant activities of SFAJ and IFAJ were tracked and tested during fermentation. The antioxidant effect of fermented Akebia trifoliata fruit and the underlying mechanisms were explored using network pharmacology for the prediction and verification of the molecular targets and pathways of the Akebia trifoliata fruit’s action against oxidative stress. Furthermore, the results were verified by molecular docking and then investigated, based on a HepG2 cell model. The results of correlation analysis and principal component analysis (PCA) showed that there were significant positive correlations between the phenols, flavonoids, and terpenoids in SFAJ and IFAJ and their antioxidant activities. Network pharmacology and molecular docking analysis disclosed the antioxidation mechanism at the molecular level. In addition, both SFAJ and IFAJ were effective at alleviating oxidative stress in HepG2 cells. In particular, IFAJ performed better than SFAJ in protecting cells with an intracellular reactive oxygen species (ROS) level of 99.96 ± 4.07 U/mg prot, superoxide dismutase (SOD) activity of 41.56 ± 0.06 U/mg prot, catalase (CAT) activity of 91.78 ± 3.85 U/mg prot, and glutathione peroxidase (GSH-Px) activity of 39.32 ± 2.75 mU/mg prot in the IFAJ group. Collectively, this study revealed the changes in bioactive metabolite contents and the in vitro antioxidant activity during fermentation and investigated the protectiveness of SFAJ and IFAJ against oxidative stress within HepG2 cells, promoting the study of the antioxidant efficacy of IFAJ, thereby providing valuable reference data for the optimization of its preparation and the development of relevant products with health benefits.
The Complete Mitogenome of Amazonian Hyphessobrycon heterorhabdus (Characiformes: Characidae) as a Valuable Resource for Phylogenetic Analyses of Characidae
Authors: Montag Luciano Fogaça de Assis , Koroiva Ricardo , Ribeiro-dos-Santos Ândrea , Magalhães Leandro , Cavalcante Giovanna C. , Silva Caio S. , Guerreiro Sávio , Gomes Daniel H. F. , Souza Jorge E. S. de , Souza Sandro J. de , Seabra Lidia Brasil , Lucena Maria Dayanne Lima de , Prata Erival Gonçalves , Penha Izabella Cristina da Silva , Michelan Thaisa Sala , Ligeiro Raphael , Juen Leandro
Published: May 2023

Hyphessobrycon heterorhabdus (Ulrey, 1894), popularly known as ‘Flag Tetra’ in English speaking countries, belongs to the genus Hyphessobrycon of the family Characidae, and is widely present in the eastern Amazon basin. Here, using Illumina sequencing, we report the complete mitogenome sequence of H. heterorhabdus. Overall, the mitogenome has 17,021 bp, containing 13 protein-coding, 22 tRNA, and 2 rRNA genes. Non-ambiguous nucleotide compositions of the H. heterorhabdus mitogenome are A: 29.2%, T: 29.4%, G: 15.6%, and C: 25.8%. As recently indicated, the phylogenetic analyses did not support four separate genera (Hemigrammus, Hyphessobrycon, Moenkhausia, and Psalidodon) of Characidae. Understanding the H. heterorhabdus mitogenome is important for taxonomic purposes as well as for the molecular characterization of environmental pollutants. Thus, the mitogenome described here will be a valuable resource for studies on environmental changes, evolutionary genetics, species delimitation, and phylogenetic analyses in Characidae.
Economic Valuation of Fish Provision, Wastewater Treatment, and Coastal Protection in the Israeli Mediterranean Sea
Authors: Zemah-Shamir Shiri , Peled Yoav , Shechter Mordechai , Israel Álvaro , Ofir Eyal , Gal Gideon
Published: May 2023

While many current and potential uses of the Israeli Mediterranean Sea have clearly defined the economic value and apparent benefits to various stakeholders (e.g., energy and raw materials extraction and maritime traffic), the benefits of these local marine ecosystems are still severely underexplored and are not manifested in economic terms. Coupled with ongoing environmental deterioration such as overfishing, climate change, and biological invasion, the need for performing monetary valuations of the benefits derived from this ecosystem is clearly evident. In this study, we evaluated three marine and coastal ecosystem services, namely, food provisioning, wastewater treatment, and coastal protection, in order to better quantify and map their importance to society. Food provisioning was inspected through the fishing sector, and its benefits were analyzed using the bioeconomic model. The results recommend a reduction in fishing efforts to increase overall biomass levels of both local and invasive fish species. However, this may lead to an economic loss in fishery profits due to reduced catch levels. The economic valuation of wastewater treatment as an ecosystem service hint at possible thresholds governed by effluent volumes and environmental conditions, whereby exceedance of Good Environmental Status (GES) standards may lead to a reduction of ~25% in the potential benefit of this ecosystem service. Finally, this study proposes an engineering restoration solution for compromised intertidal abrasion platforms, with estimated costs and potential benefits for the conservation of at-risk areas. The annual economic value of this ecosystem service is NIS 65–209 million (EUR 16.2–52.2 million).
Numerical Investigation of Conjugate Heat Transfer and Natural Convection Using the Lattice-Boltzmann Method for Realistic Thermophysical Properties
Authors: Landl Michael , Prieler René , Monaco Ernesto , Hochenauer Christoph
Published: May 2023

To enable the lattice-Boltzmann method (LBM) to account for temporally constant but spatially varying thermophysical properties, modifications must be made. Recently, many methods have emerged that can account for conjugate heat transfer (CHT). However, there still is a lack of information on the possible physical property range regarding realistic properties. Therefore, two test cases were investigated to gain further insight. First, a differentially heated cavity filled with blocks was used to investigate the influence of CHT on the error and stability of the LBM simulations. Reference finite volume method (FVM) simulations were carried out to estimate the error. It was found that a range between 0.5 to 1.5 is recommended for the fluid relaxation time to balance computational effort, stability, and accuracy. In addition, realistic thermophysical properties of fluids and solids were selected to test whether the lattice-Boltzmann method is suitable for simulating relevant industry-related applications. For a stable simulation, a mesh with 64 times more lattices was needed for the most extreme test case. The second test case was an insulated cavity with a heating pad as the local heat source, which was investigated in terms of the accuracy of a transient simulation and compared to a FVM simulation. It was found that the fluid-phase relaxation time mainly determines the error and that large thermal relaxation times for the solid improve accuracy. Observed deviations from the FVM reference simulations ranged from approximately 20% to below 1%, depending on collision operator and combination of relaxation times. For processes with a large temperature spread, the temporally constant thermophysical properties of the LBM are the primary constraint.
Advances in the Potential Application of 3D Food Printing to Enhance Elderly Nutritional Dietary Intake
Authors: Xie Yisha , Liu Qingqing , Zhang Wenwen , Yang Feng , Zhao Kangyu , Dong Xiuping , Prakash Sangeeta , Yuan Yongjun
Published: May 2023

The contradiction between the growing demand from consumers for “nutrition & personalized” food and traditional industrialized food production has consistently been a problem in the elderly diet that researchers face and discuss. Three-dimensional (3D) food printing could potentially offer a solution to this problem. This article reviews the recent research on 3D food printing, mainly including the use of different sources of protein to improve the performance of food ink printing, high internal phase emulsion or oleogels as a fat replacement and nutrition delivery system, and functional active ingredients and the nutrition delivery system. In our opinion, 3D food printing is crucial for improving the appetite and dietary intake of the elderly. The critical obstacles of 3D-printed food for the elderly regarding energy supplements, nutrition balance, and even the customization of the recipe in a meal are discussed in this paper. By combining big data and artificial intelligence technology with 3D food printing, comprehensive, personalized, and customized geriatric foods, according to the individual traits of each elderly consumer, will be realized via food raw materials-appearance-processing methods. This article provides a theoretical basis and development direction for future 3D food printing for the elderly.
Exploring the Sensory Properties and Preferences of Fruit Wines Based on an Online Survey and Partial Projective Mapping
Authors: Zhu Yuxuan , Su Qingyu , Jiao Jingfang , Kelanne Niina , Kortesniemi Maaria , Xu Xiaoqing , Zhu Baoqing , Laaksonen Oskar
Published: May 2023

Non-grapefruits with unique sensory properties and potential health benefits provide added value to fruit wine production. This study aimed to explore consumers’ fruit wine preferences and descriptors for the varied fruit wines. First, 234 consumers participated in an online survey concerning their preferences for different wines (grape, blueberry, hawthorn, goji, Rosa roxburghii, and apricot). In addition, their attitudes towards general health interests, food neophobia, alcoholic drinks, and sweetness were collected. Grape wine and blueberry wine were the most favored wines, and goji wine was the least liked fruit wine sample. Moreover, 89 consumers were invited to evaluate 10 commercial fruit wines by using partial projective mapping based on appearance, aroma, and flavor (including taste and mouthfeel) to obtain a comprehensive sensory characterization. Multifactor analysis results showed that consumers could differentiate the fruit wines. Participants preferred fruit wines with “sweet”, “sour”, and “balanced fragrance”, whereas “bitter”, “astringent”, “deep appearance”, and “medicinal fragrance” were not preferred. Attitudes toward health, food neophobia, alcohol, and sweetness had less influence than taste and aroma (sensory attributes) on the preferences for fruit wine products. More frequent self-reported wine usage resulted in higher consumption frequency and liking ratings compared to non-users. Overall, the main factors influencing consumer preference for fruit wines were the sensory characteristics of the products, especially the taste.
Complexation with Polysaccharides Enhances the Stability of Isolated Anthocyanins
Authors: Fu Wenyi , Li Shiyu , Helmick Harrison , Hamaker Bruce R. , Kokini Jozef L. , Reddivari Lavanya
Published: May 2023

Isolated anthocyanins have limited colonic bioavailability due to their instability as free forms. Thus, many methods have been fabricated to increase the stability of anthocyanins. Complexation, encapsulation, and co-pigmentation with other pigments, proteins, metal ions, and carbohydrates have been reported to improve the stability and bioavailability of anthocyanins. In this study, anthocyanins extracted from purple potatoes were complexed with four different polysaccharides and their mixture. The anthocyanin–polysaccharide complexes were characterized using a zeta potential analyzer, particle size analyzer, scanning electron microscopy, and Fourier-transform infrared spectroscopy. Complexes were subjected to simulated digestion for assessing the stability of anthocyanins. Furthermore, complexes were subjected to different pH conditions and incubated at high temperatures to monitor color changes. A Caco-2 cell monolayer was used to evaluate the colonic concentrations of anthocyanins. In addition, the bioactivity of complexes was assessed using LPS-treated Caco-2 cell monolayer. Results show that pectin had the best complexation capacity with anthocyanins. The surface morphology of the anthocyanin–pectin complex (APC) was changed after complexation. APC was more resistant to the simulated upper gastrointestinal digestion, and high pH and temperature conditions for a longer duration. Furthermore, APC restored the lipopolysaccharide (LPS)-induced high cell permeability compared to isolated anthocyanins. In conclusion, complexation with pectin increased the stability and colonic bioavailability and the activity of anthocyanins.