bluuebunny/crossref_metadata_2025 huggingface dataset:
Combining Non-Thermal Processing Techniques with Edible Coating Materials: An Innovative Approach to Food Preservation
Authors: Khezerlou Arezou , Zolfaghari Hajar , Forghani Samira , Abedi-Firoozjah Reza , Alizadeh Sani Mahmood , Negahdari Babak , Jalalvand Masumeh , Ehsani Ali , McClements David Julian
Published: April 2023

Innovative processing and packaging technologies are required to create the next generation of high-quality, healthy, safe, and sustainable food products. In this review, we overview the potential of combining edible coating materials with non-thermal processing technologies to improve the quality, increase the safety, extend the shelf life, and reduce the waste of foods and plastics. Edible coatings are typically assembled from food-grade structuring ingredients that can provide the required mechanical and barrier properties, such as proteins, polysaccharides, and/or lipids. These materials can be fortified with functional additives to further improve the quality, safety, and shelf life of coated foods by reducing ripening, gas exchange, and decay caused by bacteria and fungi. Non-thermal processing techniques include high hydrostatic pressure, pulsed light, ultrasound, and radiation technologies. These technologies can be used to inhibit the growth of pathogenic or spoilage microorganisms on packaged foods. Examples of the application of this combined approach to a range of highly perishable foods are given. In addition, the impact of these combined methods on the quality attributes of these food products is discussed.
The Tribological Behavior of TiN/TiC CVD Coatings under Dry Sliding Conditions against Zirconia and Steel Counterparts
Authors: Jakab Miklós , Ali Osamah , Gyurika István , Korim Tamás , Telegdi Judit
Published: April 2023

During this research work, the wear and friction properties of TiN/TiC coatings produced with different manufacturing parameters were investigated against steel and zirconium counterparts. The coatings, which were deposited on WC–Co substrate, were characterized based on their microstructure, phase composition, and microhardness. The tribological behavior of the coatings was examined in accordance with ASTM G99 and ASTM G133 standards using pin-on-disc tests with steel and zirconium counterfaces, while the adhesion of the coatings was investigated with a modified scratch test. According to the results obtained from the experiments, the use of zirconium counterpart is advantageous even for coatings produced with different microhardness and layer thickness using the CVD method. The results show that the change in the friction coefficient can be well traced with properly chosen parameters, thereby determining the wear resistance of the coating.
Study on the Construction of Dynamic Modulus Master Curve of Polyurethane Mixture with Dense Gradation
Authors: Zhao Haisheng , Gao Wenbin , Cui Shiping , Zhang Wensheng , Ma Shijie , Miao Baoji , Su Chunhua , Wei Jincheng , Liu Shan , Liu Fuxiu
Published: April 2023

The PU mixture considered here is a new kind of pavement material with excellent road performance, which lacks study into its dynamic mechanical and viscoelastic properties. In this study, the dynamic modulus of the polyurethane (PU) mixture was fitted by using five master curve models, five shift factor equations, and four error minimization methods. According to test results, the log–log plot form was able to more effectively display the differences between master curves. The solver method, the sum of square error minimization (≤0.02), proved to be more appropriate and accurate with higher fitting parameter results. The line of equality statistic and Pearson linear correlation analysis results demonstrated that WLF and Kaelble equations were appropriate for five master curve models with trend line R2 values higher than 0.98. The GLS and SCM model with the WLF equation had the most accurate master curve fitting results. The dynamic modulus master curve shape of the PU mixture did not follow the traditional smooth “S” shape and did not show the ultimate dynamic modulus at extreme frequency. The viscoelasticity of the PU mixture is quite different from that of the asphalt mixture. This study recommended the most accurate error minimization method, the master curve model, and shift factor equations for characterizing the dynamic properties of the PU mixture.
Preparation of Polyurethane-Modified Silicone Rubber Insulating Coating and Its Application in 10 kV Overhead Bare Wire Wrapping
Authors: Wang Wei , Yang Fan , Zhang Pan , Luo Zhi , Li Fangya , Jiang Jingjing , Zhang Jianbing , Li Wei , Liu Aimei , Qin Caiqin
Published: April 2023

Room-temperature vulcanized rubber is an excellent polymer material with excellent electrical insulation and mechanical properties, which can be used for field insulation of high-voltage bare wire. In this paper, a room-temperature cured coating material was prepared from α,ω-dihydroxy polydimethylsiloxane (PDMS), polyurethane-modified silicone material (PU-Si), and Methyltris(methylethylketoxime) silane (MOS). The study showed that the coating made by adding PDMS (70 g), PU-Si (30 g), CaCO3 (90 g), MOS (7 g), KH792 (5 g), and Mg(OH)2 (30 g) had a surface drying time of 19.1 min, tensile strength of 3.2 Mpa, volume resistivity greater than 4 × 1014 Ω·cm, breakdown voltage greater than 20 kV/mm, and flame retardant performance of the FV-1 level. The comprehensive performance of the insulation material meets the national standard of “Aerial insulated cables for rated voltage of 10 kV” (GB/T14049-2008), while the insulation material has been successfully applied to the overhead line renovation project of the State Grid Hubei Electric Power Company (Wuhan, China).
Artificial Spray Modeling of Ships for Personnel Training and Its Case Study
Authors: Xu Hanzhong , Wu Dianliang , Yu Wenjuan , Zhao Yue , Yu Qihang
Published: April 2023

Ship painting personnel usually employ physical, hands-on training to enhance their proficiency and improve the quality of their painting. However, this method poses environmental pollution problems and has high costs. Therefore, this paper develops a ship artificial spraying simulation training system based on a Virtual Reality (VR) environment. Firstly, a artificial spraying model for a ship for personnel training is established by the relationship between the gun parameters, gun position, spraying direction, and coating thickness. A method for calculating the coating thickness and coating loss by using a frame cycle in a VR environment is proposed. And the artificial spraying model is verified from these two perspectives by a spraying test. Finally, based on the artificial spraying model, a ship spraying simulation training system is developed by combining C# and Unity3D, and a corresponding scoring mechanism is established to evaluate the spraying results. By recruiting personnel with different spraying experiences to wear VR helmets for spraying training trials, the results show that the method of using VR for spraying training can effectively improve the spraying level of operators with different spraying experiences.
Mapping the Accouterment Effects of Plasma Nitriding on AISI 316L in Biomedical Applications
Authors: Mali Amol Shivaji , Vagge Shashikant Tukaram , Rathod Manoj Jagannath
Published: April 2023

The present study aimed to critique the corrosion resistance of plasma-nitrided films of AISI 316L stainless steel with regards to their biomedical applications. The plasma nitriding process improves austenitic stainless steel’s micro-hardness and corrosion resistance. Austenitic stainless steel was treated at a temperature of 470 °C for 12, 24, and 36 h, to observe the outcomes of plasma nitriding. The corresponding microstructure, microhardness, depth of the nitrided layer, and electrochemical parameters were systematically characterized. The corrosion resistance of the plasma-nitrided specimens was gauged using the weight loss method in simulated body fluids (Phosphate-buffered saline (PBS), saline water, and ringer solution) by static immersion for 9, 18, and 27 days. Optimization was catalogued using the Taguchi method L27 orthogonal array to determine the optimum combination of plasma nitriding time and immersion time in simulated body fluid. The material characterization showed that the corrosion resistance of the plasma-nitrided specimens improved with longer nitriding times by Tafel polarization curves. Microhardness was observed at 12, 24, and 36 h as 1060, 1150, and 1220 HV0.1. SEM, with an energy-dispersive X-ray analysis (EDS) used to characterize the surface before and after plasma nitriding testing. It was concluded that CrN, which precipitates during processing and contributes to the loss of chromium from the surrounding matrix and the onset of a corrosive environment, is the primary cause of this behaviour.
A New, Precise Constitutive Model and Thermal Processing Map Based on the Hot Deformation Behavior of 2219 Aluminum Alloy
Authors: Wang Jing , Xiao Guiqian , Zhang Jiansheng
Published: April 2023

To study the hot deformation behavior of and obtain the optimal hot processing parameters for 2219 aluminum alloy, a new, precise constitutive model based on the partial derivative of flow data was constructed and hot processing maps were constructed based on the new model. First, isothermal compression experiments were conducted at strain rates of 0.01–10 s−1 and temperatures of 573–773 K, and the high-order differences of the logarithmic stress with respect to the temperature and logarithmic strain rate were calculated. Second, a new, precise constitutive model based on the high-order differences was constructed, and the predictive accuracies of the new model and the Arrhenius model were compared. Finally, the hot processing maps of 2219 aluminum alloy were constructed using the new model, and its optimal hot processing parameters were validated with metallographic experiments. The results showed that a first-order approximation between logarithmic stress and temperature and a third-order approximation between logarithmic stress and the logarithmic strain rate need to be considered to construct a high-precision constitutive model without significantly increasing material parameters. The new model exhibited a significantly higher prediction accuracy than the Arrhenius model at a high strain rate and low temperature levels. With an increase in temperature, the energy dissipation increased at a constant strain rate, and with an increase in the strain rate, the energy dissipation first increased and then decreased at constant temperature. The best region for hot processing was located in the temperature range of 673–773 K and the strain rate range of 0.1–1 s−1. The results of microstructure analysis were in good agreement with the prediction results of hot processing maps. Hot processing maps can be used to guide the hot working process formulation of 2219 aluminum alloy.
Research on the Morphology, Electro-Optical Properties and Mechanical Properties of Electrochromic Polymer-Dispersed Liquid Crystalline Films Doped with Anthraquinone Dyes
Authors: Zhao Chenghu , Hu Yongchuan , Xu Jianjun , Yu Meina , Zou Cheng , Wang Qian , Gao Yanzi , Yang Huai
Published: April 2023

In this paper, dye-doped polymer-dispersed liquid crystalline (DDPDLC) films were prepared with high mechanical properties and low driving voltage by doping different dichroic anthraquinone dyes. The effects of various dye and doping concentrations on microscopic morphology, electro-optical characteristics, and mechanical characteristics were investigated. The optimal doping concentrations of different dyes were also explored. The results show that the addition of all dyes decreased the contrast ratio (CR) and the transmittance and mechanical properties of the polymer-dispersed liquid crystalline (PDLC) films. Similar mechanisms underlie the effects of solvent red 111 and solvent blue 104, which lower the driving voltages of the PDLC films. With the increasing concentration of the dye, the haze of the films first decreased and then increased after the content of the dye reached a certain level. For PDLC films doped with solvent green 28, the driving voltage and haze increased with the increasing content of the dye. According to different influencing factors, the dye content corresponding to the best performance of solvent red 111, solvent green 28 and solvent blue 104 is 0.8 wt%, 2.0 wt% and 0.3 wt%. Electrochromic PDLC films have been prepared based on the research results of dye content. The mechanical properties, electro-optical properties and microstructures of the films have been studied. The results show that the DDPDLC films could change color by tuning the applied voltages. The research provides a theoretical basis for obtaining PDLC films with a wider color gamut and supports the practical application of visible light camouflage technology in the military.
Ecosystem Service Relationships, Drivers, and Regulation Strategies in a Degraded Alpine Shrub Meadow on the Northeastern Qinghai-Tibetan Plateau
Authors: Qian Dawen , Fan Bo , Lan Yuting , Si Mengke , Li Qian , Guo Xiaowei
Published: April 2023

One of the challenges of managing grasslands sustainably is the conflict between the different ecosystem services they provide. This is especially evident in the Qinghai-Tibetan Plateau (QTP) region, where fragile alpine ecosystems make balancing the needs of grassland conservation and development difficult. However, our current understanding of the relationships and drivers of ecosystem services in degraded alpine shrub meadows on the QTP is insufficient. To address this, we studied forage provisioning and water retention services in a degraded alpine shrub meadow in the northeastern QTP. We analyzed the changes and relationships between these services at different levels of degradation and identified those factors that influenced ecosystem service relationships. The results showed that the forage supply service and the water retention service of the alpine shrub meadow increased and decreased by 23.6% and 27.07%, respectively, due to degradation. The trade-offs between these two services varied depending on the degree of degradation, with light and moderate degradation showing a preference for water retention service, and heavy and extreme degradation showing a preference for forage supply. Water retention was constrained by forage supply and both services showed an exponential function form of decay. The physical and chemical properties of the soil in the alpine shrub meadow remained relatively stable during the degradation process, with only soil organic carbon (SOC), total potassium (TK), and total nitrogen (TN) decreasing significantly. SOC may have indirectly influenced the relationship between the two services by affecting water retention. This study provides insights into alpine shrub meadow management and conservation on the QTP.
Drought Influences Annual Survival of Painted Turtles in Western Nebraska
Authors: Beard Allyson N. , Powell Larkin A.
Published: April 2023

Aquatic habitats in the Great Plains of North America have increased risk of droughts under climate forecasts. Droughts have the potential to influence the population dynamics of pond turtles, and long-term studies are useful to assess the impact of climatic variation on turtles. We compiled twelve years of mark-recapture data for painted turtles (Chrysemys picta) captured in a pond in Keith County, Nebraska during 2005–2016 that included two periods of drought. We used a robust design analysis to investigate influences on population size, annual survival, temporary immigration, and capture probability. Estimates of the annual population size ranged from 92 (CI: 90–94) to 180 (CI: 175–186) but did not vary with drought conditions. Despite a relatively stable depth of water in our study pond, the probability of annual survival was reduced by 0.07 in females and 0.10 in males during drought years. Approximately one-fifth (temporary emigration probability: 0.19, CI = 0.16–0.23) of the population was outside the study pond at any given time. Our long-term research provides insights into the potential challenges to turtles in aquatic habitats undergoing prolonged changes in long-term climate conditions.
Obesity-Related Pitfalls of Virtual versus True Non-Contrast Imaging—An Intraindividual Comparison in 253 Oncologic Patients
Authors: Huflage Henner , Kunz Andreas Steven , Hendel Robin , Kraft Johannes , Weick Stefan , Razinskas Gary , Sauer Stephanie Tina , Pennig Lenhard , Bley Thorsten Alexander , Grunz Jan-Peter
Published: April 2023

Objectives: Dual-source dual-energy CT (DECT) facilitates reconstruction of virtual non-contrast images from contrast-enhanced scans within a limited field of view. This study evaluates the replacement of true non-contrast acquisition with virtual non-contrast reconstructions and investigates the limitations of dual-source DECT in obese patients. Materials and Methods: A total of 253 oncologic patients (153 women; age 64.5 ± 16.2 years; BMI 26.6 ± 5.1 kg/m2) received both multi-phase single-energy CT (SECT) and DECT in sequential staging examinations with a third-generation dual-source scanner. Patients were allocated to one of three BMI clusters: non-obese: <25 kg/m2 (n = 110), pre-obese: 25–29.9 kg/m2 (n = 73), and obese: >30 kg/m2 (n = 70). Radiation dose and image quality were compared for each scan. DECT examinations were evaluated regarding liver coverage within the dual-energy field of view. Results: While arterial contrast phases in DECT were associated with a higher CTDIvol than in SECT (11.1 vs. 8.1 mGy; p < 0.001), replacement of true with virtual non-contrast imaging resulted in a considerably lower overall dose-length product (312.6 vs. 475.3 mGy·cm; p < 0.001). The proportion of DLP variance predictable from patient BMI was substantial in DECT (R2 = 0.738) and SECT (R2 = 0.620); however, DLP of SECT showed a stronger increase in obese patients (p < 0.001). Incomplete coverage of the liver within the dual-energy field of view was most common in the obese subgroup (17.1%) compared with non-obese (0%) and pre-obese patients (4.1%). Conclusion: DECT facilitates a 30.8% dose reduction over SECT in abdominal oncologic staging examinations. Employing dual-source scanner architecture, the risk for incomplete liver coverage increases in obese patients.
CT Perfusion in Lacunar Stroke: A Systematic Review
Authors: Zedde Marialuisa , Napoli Manuela , Grisendi Ilaria , Assenza Federica , Moratti Claudio , Valzania Franco , Pascarella Rosario
Published: April 2023

Background. The main theory underlying the use of perfusion imaging in acute ischemic stroke is the presence of a hypoperfused volume of the brain downstream of an occluded artery. Indeed, the main purpose of perfusion imaging is to select patients for endovascular treatment. Computed Tomography Perfusion (CTP) is the more used technique because of its wide availability but lacunar infarcts are theoretically outside the purpose of CTP, and limited data are available about CTP performance in acute stroke patients with lacunar stroke. Methods. We performed a systematic review searching in PubMed and EMBASE for CTP and lacunar stroke with a final selection of 14 papers, which were examined for data extraction and, in particular, CTP technical issues and sensitivity, specificity, PPV, and NPV values. Results. A global cohort of 583 patients with lacunar stroke was identified, with a mean age ranging from 59.8 to 72 years and a female percentage ranging from 32 to 53.1%.CTP was performed with different technologies (16 to 320 rows), different post-processing software, and different maps. Sensitivity ranges from 0 to 62.5%, and specificity from 20 to 100%. Conclusions. CTP does not allow to reasonable exclude lacunar infarct if no perfusion deficit is found, but the pathophysiology of lacunar infarct is more complex than previously thought.
Development and Comparison of Conventional and 3D-Printed Laboratory Models of Maxillary Defects
Authors: Alanezi Ahmad , Aljanahi May , Moharamzadeh Keyvan , Ghoneima Ahmed , Tawfik Abdel Rahman , Khamis Amar Hassan , Abuzayeda Moosa
Published: April 2023

Background: Recording accurate impressions from maxillary defects is a critical and challenging stage in the prosthetic rehabilitation of patients following maxillectomy surgery. The aim of this study was to develop and optimize conventional and 3D-printed laboratory models of maxillary defects and to compare conventional and digital impression techniques using these models. Methods: Six different types of maxillary defect models were fabricated. A central palatal defect model was used to compare conventional silicon impressions with digital intra-oral scanning in terms of dimensional accuracy and total time taken to record the defect and produce a laboratory analogue. Results: Digital workflow produced different results than the conventional technique in terms of defect size measurements which were statistically significant (p < 0.05). The time taken to record the arch and the defect using an intra-oral scanner was significantly less compared with the traditional impression method. However, there was no statistically significant difference between the two techniques in terms of the total time taken to fabricate a maxillary central defect model (p > 0.05). Conclusions: The laboratory models of different maxillary defects developed in this study have the potential to be used to compare conventional and digital workflow in prosthetic treatment procedures.
A Lightweight Traffic Lights Detection and Recognition Method for Mobile Platform
Authors: Wang Xiaoyuan , Han Junyan , Xiang Hui , Wang Bin , Wang Gang , Shi Huili , Chen Longfei , Wang Quanzheng
Published: April 2023

Traffic lights detection and recognition (TLDR) is one of the necessary abilities of multi-type intelligent mobile platforms such as drones. Although previous TLDR methods have strong robustness in their recognition results, the feasibility of deployment of these methods is limited by their large model size and high requirements of computing power. In this paper, a novel lightweight TLDR method is proposed to improve its feasibility to be deployed on mobile platforms. The proposed method is a two-stage approach. In the detection stage, a novel lightweight YOLOv5s model is constructed to locate and extract the region of interest (ROI). In the recognition stage, the HSV color space is employed along with an extended twin support vector machines (TWSVMs) model to achieve the recognition of multi-type traffic lights including the arrow shapes. The dataset, collected in naturalistic driving experiments with an instrument vehicle, is utilized to train, verify, and evaluate the proposed method. The results suggest that compared with the previous YOLOv5s-based TLDR methods, the model size of the proposed lightweight TLDR method is reduced by 73.3%, and the computing power consumption of it is reduced by 79.21%. Meanwhile, the satisfied reasoning speed and recognition robustness are also achieved. The feasibility of the proposed method to be deployed on mobile platforms is verified with the Nvidia Jetson NANO platform.
Constitutive Modeling with Single and Dual Internal Variables
Authors: Berezovski Arkadi
Published: April 2023

Phenomenological constitutive models with internal variables have been applied for a wide range of material behavior. The developed models can be classified as related to the single internal variable formalism based on the thermodynamic approach by Coleman and Gurtin. The extension of this theory to so-called dual internal variables opens up new avenues for the constitutive modeling of macroscopic material behavior. This paper reveals the distinction between constitutive modeling with single and dual internal variables using examples of heat conduction in rigid solids, linear thermoelasticity, and viscous fluids. A thermodynamically consistent framework for treating internal variables with as little a priori knowledge as possible is presented. This framework is based on the exploitation of the Clausius–Duhem inequality. Since the considered internal variables are “observable but not controllable”, only the Onsagerian procedure with the use of the extra entropy flux is appropriate for the derivation of evolution equations for internal variables. The key distinctions between single and dual internal variables are that the evolution equations are parabolic in the case of a single internal variable and hyperbolic if dual internal variables are employed.
Quantum Image Processing Algorithm Using Line Detection Mask Based on NEQR
Authors: Li Tao , Zhao Pengpeng , Zhou Yadong , Zhang Yidai
Published: May 2023

Line detection is a fundamental technique in image processing. It can extract the required information, while the information that does not need attention can be ignored, thus reducing the amount of data. At the same time, line detection is also the basis of image segmentation and plays an important role in this process. In this paper, we implement a quantum algorithm based on a line detection mask for novel enhanced quantum representation (NEQR). We build a quantum algorithm for line detection in different directions and design a quantum circuit for line detection. The detailed module designed is also provided. On a classical computer, we simulate the quantum method, and the simulation results prove the feasibility of the quantum method. By analyzing the complexity of quantum line detection, we find that the computation complexity of the proposed method is improved compared to some similar edge detection algorithms.
Position-Wise Gated Res2Net-Based Convolutional Network with Selective Fusing for Sentiment Analysis
Authors: Zhou Jinfeng , Zeng Xiaoqin , Zou Yang , Zhu Haoran
Published: May 2023

Sentiment analysis (SA) is an important task in natural language processing in which convolutional neural networks (CNNs) have been successfully applied. However, most existing CNNs can only extract predefined, fixed-scale sentiment features and cannot synthesize flexible, multi-scale sentiment features. Moreover, these models’ convolutional and pooling layers gradually lose local detailed information. In this study, a new CNN model based on residual network technology and attention mechanisms is proposed. This model exploits more abundant multi-scale sentiment features and addresses the loss of locally detailed information to enhance the accuracy of sentiment classification. It is primarily composed of a position-wise gated Res2Net (PG-Res2Net) module and a selective fusing module. The PG-Res2Net module can adaptively learn multi-scale sentiment features over a large range using multi-way convolution, residual-like connections, and position-wise gates. The selective fusing module is developed to fully reuse and selectively fuse these features for prediction. The proposed model was evaluated using five baseline datasets. The experimental results demonstrate that the proposed model surpassed the other models in performance. In the best case, the model outperforms the other models by up to 1.2%. Ablation studies and visualizations further revealed the model’s ability to extract and fuse multi-scale sentiment features.
Students’ Perceptions towards the Role of Online Teaching Platforms in Enhancing Online Engagement and Academic Performance Levels in Palestinian Higher Education Institutions
Authors: Tarazi Ayat , Ruiz-Cecilia Raúl
Published: April 2023

The present research aimed to determine the role of online teaching platforms in enhancing learning and teaching as perceived by bachelor students of English specialization. This study also sought to examine the association between students’ engagement and their academic performance during online learning. In doing so, a quantitative approach was used to collect data, and 423 bachelor students from three Palestinian higher education institutions (Al Quds Open University, An Najah National University, and Arab American University) completed a closed-ended questionnaire. The study’s outcomes demonstrated that the students’ attitudes toward the role of online teaching platforms in enhancing their learning can be classified as positive and negative, and these attitudes varied among the respondents due to problems and challenges during online learning and previous experiences, skills, and learning style. Moreover, about 58.6% of students were dissatisfied with their online learning and had negative attitudes toward online teaching platforms. Therefore, more future studies relating to the design of online courses, resources that are available on the platform, and online teaching strategies that are considered fundamental components for fostering students’ engagement at higher education institutions should be taken into account. Moreover, further studies involving more universities with samples from different specializations will confirm or contrast the findings of the current study.
Deep Learning for Visual SLAM: The State-of-the-Art and Future Trends
Authors: Favorskaya Margarita N.
Published: April 2023

Visual Simultaneous Localization and Mapping (VSLAM) has been a hot topic of research since the 1990s, first based on traditional computer vision and recognition techniques and later on deep learning models. Although the implementation of VSLAM methods is far from perfect and complete, recent research in deep learning has yielded promising results for applications such as autonomous driving and navigation, service robots, virtual and augmented reality, and pose estimation. The pipeline of traditional VSLAM methods based on classical image processing algorithms consists of six main steps, including initialization (data acquisition), feature extraction, feature matching, pose estimation, map construction, and loop closure. Since 2017, deep learning has changed this approach from individual steps to implementation as a whole. Currently, three ways are developing with varying degrees of integration of deep learning into traditional VSLAM systems: (1) adding auxiliary modules based on deep learning, (2) replacing the original modules of traditional VSLAM with deep learning modules, and (3) replacing the traditional VSLAM system with end-to-end deep neural networks. The first way is the most elaborate and includes multiple algorithms. The other two are in the early stages of development due to complex requirements and criteria. The available datasets with multi-modal data are also of interest. The discussed challenges, advantages, and disadvantages underlie future VSLAM trends, guiding subsequent directions of research.
Digital Finite Impulse Response Equalizer for Nonlinear Frequency Response Compensation in Wireless Communication
Authors: Zhang Zhenyu , Li Yanan , Nima Bassam
Published: April 2023

Signal distortion can occur when the gain or attenuation of a component changes non-linearly with frequency, which is referred to as nonlinear frequency response. Common communications components such as filters, amplifiers, and mixers can lead to nonlinear frequency responses, which can cause errors in transmitting and receiving. This article outlines the design and demonstration of a static and dynamic finite impulse response (FIR) digital equalizer circuit. Using predistortion topology with a coupled feedback loop, the adaptive Least-Mean Square (LMS) algorithm was implemented. The FIR filter was simulated in MATLAB and Vivado and then implemented onto an Eclypse Z7 Field Programmable Gate Array (FPGA) evaluation board. Simulations showed that the custom RTL module gave the same frequency response that was produced in MATLAB calculations. The filter was able to dynamically equalize the frequency responses of different nonlinear boards that were used as the devices under test (DUT). Measurements showed that the equalizer was able to compensate for system distortion from 0.2 to 0.8 Nyquist frequency. The phase response remained relatively linear across the band of interest, with a group delay flatness less than 10 ns.
Classification of Engine Type of Vehicle Based on Audio Signal as a Source of Identification
Authors: Materlak Mateusz , Majda-Zdancewicz Ewelina
Published: April 2023

In this work, a combination of signal processing and machine learning techniques is applied for petrol and diesel engine identification based on engine sound. The research utilized real recordings acquired in car dealerships within Poland. The sound database recorded by the authors contains 80 various audio signals, equally divided. The study was conducted using feature engineering techniques based on frequency analysis for the generation of sound signal features. The discriminatory ability of feature vectors was evaluated using different machine learning techniques. In order to test the robustness of the proposed solution, the authors executed a number of system experimental tests, including different work conditions for the proposed system. The results show that the proposed approach produces a good accuracy at a level of 91.7%. The proposed system can support intelligent transportation systems through employing a sound signal as a medium carrying information on the type of car moving along a road. Such solutions can be implemented in the so-called ‘clean transport zones’, where only petrol-powered vehicles can freely move. Another potential application is to prevent misfuelling diesel to a petrol engine or petrol to a diesel engine. This kind of system can be implemented in petrol stations to recognize the vehicle based on the sound of the engine.
Hybrid Forecasting Methods—A Systematic Review
Authors: Sina Lennart B. , Secco Cristian A. , Blazevic Midhad , Nazemi Kawa
Published: April 2023

Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization.
Impact of Waste Fry Biofuel on Diesel Engine Performance and Emissions
Authors: Mandal Adhirath , Cha Dowan , Cho HaengMuk
Published: April 2023

Energy is primarily obtained from fossil fuels and with the use of fossil fuels, we are increasing the emissions and greenhouse gases. It takes constant effort to meet the energy need from environmentally acceptable and renewable fuels. In order to find a replacement for depleting fossil fuel energy, a range of oxygenated fuels was investigated based on their accessibility and geographic areas. This work assessed the transesterification process’s feasibility of turning used fry oil into biodiesel fuel and its physiochemical characteristics. The performances of a diesel engine operating on biodiesel and diesel fuel were assessed and compared. Four different types of fry oils were utilized for the research on a diesel agricultural engine with indirect injection. The first fry, second fry, third fry, and restaurant fry were the various sorts of fry oil. Five different types of biodiesels and their blends were investigated for their engine efficiency and emission metrics. B40 (biodiesel 40% and diesel 60%) and B80 (biodiesel 80% and diesel 20%) biodiesel blends were tested in different engine speed conditions under 50% and 100% engine loads. While the brake thermal efficiency (BTE) decreased as the engine rpm increased, it was found that the brake-specific fuel consumption (BSFC) increased. Due to the poor air–fuel ratio at higher engine speeds, the BTE decreased. NOx (nitrogen oxides) emissions were higher for all the biodiesel blends because of the higher oxygen content in the biodiesel blends. The smoke opacity in both blends decreased with rising rpm under both load situations and was lower than in pure diesel. Because of the larger cetane number and lower heating value, the exhaust gas temperature (EGT) dropped. It was determined that prolonging the fry time altered the engine performance and emission metrics. The use of sustainable fuel is essential; waste fry cooking oil as a substitute for fossil diesel could be a prospective replacement in the agricultural engine and transportation sector.
An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm
Authors: Li Fan , Su Jingxi , Sun Bo
Published: April 2023

This study proposes an optimal scheduling method for complex integrated energy systems. The proposed method employs a heuristic algorithm to maximize its energy, economy, and environment indices and optimize the system operation plan. It uses the k-means combined with box plots (Imk-means) to improve the convergence speed of the heuristic algorithm by forming its initial conditions. Thus, the optimization scheduling speed is enhanced. First of all, considering the system source and load factors, the Imk-means is presented to find the typical and extreme days in a historical optimization dataset. The output results for these typical and extreme days can represent common and abnormal optimization results, respectively. Thus, based on the representative historical data, a traditional heuristic algorithm with an initial solution set, such as the genetic algorithm, can be accelerated greatly. Secondly, the initial populations of the genetic algorithm are dispersed at the historical outputs of the typical and extreme days, and many random populations are supplemented simultaneously. Finally, the improved genetic algorithm performs the solution process faster to find optimal results and can possibly prevent the results from falling into local optima. A case study was conducted to verify the effectiveness of the proposed method. The results show that the proposed method can decrease the running time by up to 89.29% at the most, and 72.68% on average, compared with the traditional genetic algorithm. Meanwhile, the proposed method has a slightly increased optimization index, indicating no loss of optimization accuracy during acceleration. It can also indicate that the proposed method does not fall into local optima, as it has fewer iterations.
Analysis of Specific Greenhouse Gas Emissions Savings from Biogas Production Based on Agricultural Residues and Industrial By-Products
Authors: Kodba Ana , Pukšec Tomislav , Duić Neven
Published: April 2023

The aim of this study was to analyse specific greenhouse gas emissions savings for a variety of agricultural residues, industrial by-products, and municipal biowaste. One of the most viable alternatives to fossil fuels is bioenergy, particularly biogas produced by the anaerobic digestion of renewable feedstocks. The revised Renewable Energy Directive (D 2018/2001) recognizes that biogas production from agricultural residues, livestock production, and industrial by-products is an acknowledged greenhouse gas mitigation technology in cases where their use results in a certain level of specific greenhouse gas savings. This study delivered values for the maximum transport distance of agricultural residues and industrial by-products to achieve the greenhouse gas (GHG) emissions-saving requirement defined by Directive 2018/2001. It analysed the greenhouse gas emissions reduction for numerous feedstocks for which Directive 2018/2001 has not defined the default and typical values but which could be used as sustainable substitutes for currently dominantly used maize silage in biogas production. The results obtained in this work define the maximum transport and distribution distance for which biogas produced from considered feedstocks achieved required specific greenhouse gas emissions savings (80%), compared with fossil fuel comparator. The obtained results can be used as the constraints in the optimisation of the biomass supply chains for the feedstocks considered in this work.
An Offshore Solar Irradiance Calculator (OSIC) Applied to Photovoltaic Tracking Systems
Authors: Bugeja Ryan , Mule’ Stagno Luciano , Dexarcis Lucas
Published: April 2023

Offshore photovoltaic installations are the future technology in solar energy since they enable the use of the large amount of maritime space, which is especially important when land space is not available. Various research groups are working to create viable installations. However, there are currently no tools available that an offshore system designer can use to quantify the effect of wave response motion on offshore photovoltaic installations. This research presents a new simulation tool termed the Offshore Solar Irradiance Calculator (OSIC) that is able to quantify this effect. Furthermore, a yearly parametric analysis is presented to show the effects of a characteristic wave equation on different offshore tracking systems; namely, horizontal single-axis tracking, vertical single-axis tracking and dual-axis tracking. Finally, another parametric analysis is presented to show the effects of varying wave amplitudes of oscillations on the incident irradiance received by these tracking systems.
Can Hydrogen Production Be Economically Viable on the Existing Gas-Fired Power Plant Location? New Empirical Evidence
Authors: Dumančić Andrea , Vlahinić Lenz Nela , Majstrović Goran
Published: April 2023

The paper provides an economic model for the assessment of hydrogen production at the site of an existing thermal power plant, which is then integrated into the existing gas grid. The model uses projections of electricity prices, natural gas prices, and CO2 prices, as well as estimates of the cost of building a power-to-gas system for a 25-year period. The objective of this research is to calculate the yellow hydrogen production price for each lifetime year of the Power-to-gas system to evaluate yellow hydrogen competitiveness compared to the fossil alternatives. We test if an incentive scheme is needed to make this technology economically viable. The research also provides several sensitivity scenarios of electricity, natural gas, and CO2 price changes. Our research results clearly prove that yellow hydrogen is not yet competitive with fossil alternatives and needs incentive mechanisms for the time being. At given natural gas and CO2 prices, the incentive for hydrogen production needs to be 52.90 EUR/MWh in 2025 and 36.18 EUR/MWh in 2050. However, the role of hydrogen in the green transition could be very important as it provides ancillary services and balances energy sources in the power system.
CFD Analysis of the Forced Airflow and Temperature Distribution in the Air-Conditioned Operator’s Cabin of the Stationary Rock Breaker in Underground Mine under Increasing Heat Flux
Authors: Wróblewski Adam , Macek Arkadiusz , Banasiewicz Aleksandra , Gola Sebastian , Zawiślak Maciej , Janicka Anna
Published: May 2023

The exploitation of natural resources is associated with many natural hazards. Currently, the copper ore deposits exploited in Polish mines are located at a depth of about 1200 m below the surface. The primary temperature of the rocks in the exploited areas reaches 48 ∘C, which constitutes a major source of heat flux to the mine air. However, another important source of heat is the machine plant, which mainly consists of machines powered by diesel engines. Following the results of in situ measurements, boundary conditions for a simulation were determined and a geometric model of the cabin was created. Furthermore, an average human model was created, whose radiative heat transfer was included in the analysis. Three cases were studied: the first covering the current state of thermal conditions, based on the measurement results, and two cases of forecast conditions. In the second case, the temperature of the conditioned air was determined, and in the third, the flow velocity required to ensure thermal comfort was found. The results of the simulation indicated that for the microclimatic conditions established based on the measurements (ambient air temperature in the excavation 35.0 ∘C, air-conditioned airflow 2.4 × 10−2 m3/s, and temperature 10.0 ∘C), the temperature of the air inside the air-conditioned operator’s cabin would be 20.4 ∘C. Based on the personal mean vote (PMV) index, it was concluded that the thermal sensation would range from neutral to slightly cool, which confirmed the legitimacy of the actions taken to reduce the adverse impact of the microclimatic conditions on workers in the workplace. However, for the case of predicted conditions of enhanced heat flux from strata and machinery, resulting in an average ambient temperature increased to 38.0 ∘C, it would be necessary to lower the temperature of air from the air conditioner to 8.00 ∘C or increase the flow rate to 3.14 × 10−2 m3/s to maintain thermal comfort at the same level of PMV index.
State-of-Health Prediction of Lithium-Ion Batteries Based on Diffusion Model with Transfer Learning
Authors: Luo Chenqiang , Zhang Zhendong , Zhu Shunliang , Li Yongying
Published: May 2023

An accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) is crucial to their safe and reliable. Although recently the data-driven methods have drawn great attention, owe to its efficient deep learning, it is worthwhile to continue devoting many efforts to prediction performance. In practice, fast charging mode has been widely applied in battery replenishing, which poses challenges for SOH prediction due to the diversity of charging conditions and electrochemical properties of LIBs; although, the process is stable and detectable. Furthermore, most previous data-driven prediction methods based discriminative model cannot describe the whole picture of the problem though sample data, affecting robustness of model in real-life applications. In this study, it is presented a SOH prediction model based on diffusion model, as an efficient new family of deep generative model, with time series information tackled through Bi-LSTM and the features derived from the voltage profiles in multi-stage charging process, which can identify distribution characteristics of training data accurately. The model is further refined by means of transfer learning, by adding a featured transformation from the base model for SOH prediction of different type LIBs. Two different types of LIBs datasets are used to evaluate the proposed model and the verified results revealed its better performance than those of other methods, reducing efforts required to collect data cycles of new battery types with the generality and robustness.