bluuebunny/crossref_metadata_2025 huggingface dataset:
Modification of Cantor High Entropy Alloy by the Addition of Mo and Nb: Microstructure Evaluation, Nanoindentation-Based Mechanical Properties, and Sliding Wear Response Assessment
Authors: Karantzalis Alexandros E. , Poulia Anthoula , Kamnis Spyros , Sfikas Athanasios , Fotsis Anastasios , Georgatis Emmanuel
Published: May 2022

The classic Cantor (FeCoCrMnNi) isoatomic high entropy alloy was modified by separate additions of Mo and Nb in an effort to optimize its mechanical properties and sliding wear response. It was found that the introduction of Mo and Nb modified the single phase FCC solid solution structure of the original alloy and led to the formation of new phases such as the BCC solid solution, σ-phase, and Laves, along with the possible existence of intermetallic phases. The overall phase formation sequence was approached by parametric model assessment and solidification considerations. Nanoindentation-based mechanical property evaluation showed that due to the introduction of Mo and Nb; the modulus of elasticity and microhardness were increased. Creep nanoindentation assessment revealed the beneficial action of Mo and Nb in increasing the creep resistance based on the stress sensitivity exponent, strain rate sensitivity, and critical volume for the dislocation nucleation considerations. The power law and power law breakdown were identified as the main creep deformation mechanisms. Finally, the sliding wear response was increased by the addition of Mo and Nb with this behavior obeying Archard’s law. A correlation between microstructure, wear track morphologies, and debris characteristics was also attempted.
Cardiopulmonary Effects and Pharmacokinetics of Dexmedetomidine Used as an Adjunctive Analgesic to Regional Anesthesia of the Oral Cavity with Levobupivacaine in Dogs
Authors: Pavlica Matic , Kržan Mojca , Nemec Ana , Kosjek Tina , Baš Anže , Seliškar Alenka
Published: May 2022

This study investigated the cardiopulmonary effects and pharmacokinetics of dexmedetomidine (DEX) used as an adjunctive analgesic for regional anesthesia of the oral cavity with levobupivacaine in anesthetized dogs. Forty dogs were randomly assigned to four groups of 10 dogs. All dogs received levobupivacaine (4 blocks) with DEX IO (infraorbital block, n = 10) or IA (inferior alveolar block, n = 10) or placebo (PLC; n = 10) or DEX (n = 10) was injected intravenously (IV) after administration of levobupivacaine. The dose of DEX was always 0.5 µg/kg. Cardiopulmonary parameters were recorded, and blood was drawn for the quantification of DEX in plasma using LC-MS/MS. Heart rate was lower in all LB + DEX groups, while mean arterial pressure (MAP) was higher in the LB + DEX IV and LB + DEX IA groups compared to the LB + PLC IV group. Compared to DEX IV, IO and IA administration resulted in lower MAP up to 2 min after application. Absorption of DEX was faster at IO administration (Cmax and Tmax were 0.47 ± 0.08 ng/mL and 7.22 ± 1.28 min and 0.76 ± 0.09 ng/mL and 7.50 ± 1.63 min for the IO and IA block, respectively). The IA administration resulted in better bioavailability and faster elimination (t1/2 was 63.44 ± 24.15 min and 23.78 ± 3.78 min for the IO and IA block, respectively). Perineural administration of DEX may be preferable because of the less pronounced cardiovascular response compared to IV administration.
Partial Substitution of Fish Oil with Microalgae (Schizochytrium sp.) Can Improve Growth Performance, Nonspecific Immunity and Disease Resistance in Rainbow Trout, Oncorhynchus mykiss
Authors: Lee Seunghan , Park Cheol-Oh , Choi Wonsuk , Bae Jinho , Kim Jiyoung , Choi Sera , Katya Kumar , Kim Kang-Woong , Bai Sungchul C.
Published: May 2022

The price of fish oil has reached a historical peak due to a consistent downward production trend, and therefore, the search for sustainable alternative sources has received great attention. This research was conducted to evaluate dietary micro-algae, Schizochytrium sp. (SC) as fish oil (FO) replacer in rainbow trout, Oncorhynchus mykiss. In the first trial, apparent digestibility coefficient (ADC) was 92.4% for dry matter, 91.4% for crude protein, and 94.2% for crude lipid in rainbow trout. In the second trial, six diets were formulated to replace FO at 0% (CON), 20% (T20), 40% (T40), 60% (T60), 80% (T80), and 100% (T100) with SC in the rainbow trout (3.0 ± 0.4 g, mean ± SD) diet. After eight weeks’ feeding trial, weight gain (WG), specific growth rate (SGR), and feed efficiency (FE) of fish fed the T20 diet were significantly higher than those of fish fed other diets (p < 0.05). However, there were no significant differences in these parameters among those of fish fed CON, T40, T60, and T80 diets. Lysozyme activity of fish fed the T20 diet was significantly higher than those of fish fed other experimental diets (p < 0.05). After 10 days of disease challenge testing with pathogenic bacteria (Lactococcus garvieae 1 × 108 CFU/mL), the cumulative survival rate of fish fed the T20 diet was significantly higher than those of fish fed the CON, T80, and T100 diets. Therefore, these results suggest dietary microalgae SC is well-digested and could replace up to 80% of fish oil in the diet of rainbow trout without negative effects on growth and immune responses.
Multiobject Tracking of Wildlife in Videos Using Few-Shot Learning
Authors: Feng Jiangfan , Xiao Xinxin
Published: May 2022

Camera trapping and video recording are now ubiquitous in the study of animal ecology. These technologies hold great potential for wildlife tracking, but are limited by current learning approaches, and are hampered by dependence on large samples. Most species of wildlife are rarely captured by camera traps, and thus only a few shot samples are available for processing and subsequent identification. These drawbacks can be overcome in multiobject tracking by combining wildlife detection and tracking with few-shot learning. This work proposes a multiobject-tracking approach based on a tracking-by-detection paradigm for wildlife to improve detection and tracking performance. We used few-shot object detection to localize objects using a camera trap and direct video recordings that could augment the synthetically generated parts of separate images with spatial constraints. In addition, we introduced a trajectory reconstruction module for better association. It could alleviate a few-shot object detector’s missed and false detections; in addition, it could optimize the target identification between consecutive frames. Our approach produced a fully automated pipeline for detecting and tracking wildlife from video records. The experimental results aligned with theoretical anticipation according to various evaluation metrics, and revealed the future potential of camera traps to address wildlife detection and tracking in behavior and conservation.
Dietary Chitosan Supplementation Improved Egg Production and Antioxidative Function in Laying Breeders
Authors: Li Yinhao , Zhang Qingyue , Feng Yonghui , Yan Sumei , Shi Binlin , Guo Xiaoyu , Zhao Yanli , Guo Yongmei
Published: May 2022

This study was conducted to explore the dietary effect of chitosan on the production performance, and antioxidative enzyme activities and corresponding gene expression in the liver and duodenum of laying breeders. A total of 450 laying breeders (92.44% ± 0.030% of hen-day egg production) were randomly assigned to five dietary treatments fed 8 weeks: maize-soybean meal as the basal control diet and the basal diet containing 250, 500, 1000 and 2000 mg/kg of chitosan, respectively. Each treatment was randomly divided into 6 equal replicates, with 15 laying breeders in each replicate. The results showed that dietary chitosan could increase hen-day egg production and feed conversion ratio, especially at the level of 250~500 mg/kg; however, chitosan had no prominent effect on feed intake and average egg weight. Dietary chitosan could dose-dependently promote the antioxidant status in serum, liver and duodenum of layer breeders. It has a better promotion effect at the level of 500 mg/kg; however, the effect was weakened at the level of 2000 mg/kg. Chitosan was likely to enhance the gene expression and activities of Nrf2-mediated phase II detoxification enzyme by up-regulating the expression of Nrf2, thereby improving the antioxidant capacity of laying breeder hens.
Mammalian Display Platform for the Maturation of Bispecific TCR-Based Molecules
Authors: Dilchert Janine , Hofmann Martin , Unverdorben Felix , Kontermann Roland , Bunk Sebastian
Published: May 2022

Bispecific T cell receptor (TCR)-based molecules capable of redirecting and activating T cells towards tumor cells represent a novel and promising class of biotherapeutics for the treatment of cancer. Usage of TCRs allows for targeting of intracellularly expressed and highly selective cancer antigens, but also requires a complex maturation process to increase the naturally low affinity and stability of TCRs. Even though TCR domains can be matured via phage and yeast display, these techniques share the disadvantages of non-human glycosylation patterns and the need for a later reformatting into the final bispecific format. Here, we describe the development and application of a Chinese Hamster Ovary (CHO) display for affinity engineering of TCRs in the context of the final bispecific TCR format. The recombinase-mediated cassette exchange (RCME)-based system allows for stable, single-copy integration of bispecific TCR molecules with high efficiency into a defined genetic locus of CHO cells. We used the system to isolate affinity-increased variants of bispecific T cell engaging receptor (TCER) molecules from a library encoding different CDR variants of a model TCR targeting preferentially expressed antigen in melanoma (PRAME). When expressed as a soluble protein, the selected TCER molecules exhibited strong reactivity against PRAME-positive tumor cells associated with a pronounced cytokine release from activated T cells. The obtained data support the usage of the CHO display-based maturation system for TCR affinity maturation in the context of the final bispecific TCER format.
Understanding the Implementation of Antimicrobial Policies: Lessons from the Hong Kong Strategy and Action Plan
Authors: Song Mingqi , Deng Ziru , Chan Olivia , Grépin Karen Ann
Published: May 2022

In 2017, the Hong Kong Strategy and Action Plan on Antimicrobial Resistance 2017–2022 (HKSAP) was announced with the aim of tackling the growing threat of antimicrobial resistance (AMR) in Hong Kong. However, little is known about how the planned activities have been implemented. In this study, we examine the status of implementation of the HKSAP using the Smith Policy Implementation Process Model. Semi-structured interviews with 17 informants found that important achievements have been made, including launching educational and training activities targeting the public, farmers, and healthcare professionals; upgrading the AMR surveillance system; and strengthening AMR stewardship and infection control. Nevertheless, participants also identified barriers to greater implementation, such as tensions across sectors, ongoing inappropriate drug use and prescription habits, insufficient human and technical resources, as well as a weak accountability framework. Environmental factors such as the COVID-19 pandemic also affected the implementation of HKSAP. Our study indicated that expanding engagement with the public and professionals, creating a collaborative environment for policy implementation, and building a well-functioning monitoring and evaluation system should be areas to focus on in future AMR policies.
Studying Factors Affecting Success of Antimicrobial Resistance Interventions through the Lens of Experience: A Thematic Analysis
Authors: Graells Tiscar , Lambraki Irene A. , Cousins Melanie , Léger Anaïs , Lillepold Kate , Henriksson Patrik J. G. , Troell Max , Carson Carolee A. , Parmley Elizabeth Jane , Majowicz Shannon E. , Wernli Didier , Jørgensen Peter Søgaard
Published: May 2022

Antimicrobial resistance (AMR) affects the environment, and animal and human health. Institutions worldwide have applied various measures, some of which have reduced antimicrobial use and AMR. However, little is known about factors influencing the success of AMR interventions. To address this gap, we engaged health professionals, designers, and implementers of AMR interventions in an exploratory study to learn about their experience and factors that challenged or facilitated interventions and the context in which interventions were implemented. Based on participant input, our thematic analysis identified behaviour; institutional governance and management; and sharing and enhancing information as key factors influencing success. Important sub-themes included: correct behaviour reinforcement, financial resources, training, assessment, and awareness of AMR. Overall, interventions were located in high-income countries, the human sector, and were publicly funded and implemented. In these contexts, behaviour patterns strongly influenced success, yet are often underrated or overlooked when designing AMR interventions. Improving our understanding of what contributes to successful interventions would allow for better designs of policies that are tailored to specific contexts. Exploratory approaches can provide encouraging results in complex challenges, as made evident in our study. Remaining challenges include more engagement in this type of study by professionals and characterisation of themes that influence intervention outcomes by context.
Genetic Programming-Based Feature Construction for System Setting Recognition and Component-Level Prognostics
Authors: Calabrese Francesca , Regattieri Alberto , Piscitelli Raffaele , Bortolini Marco , Galizia Francesco Gabriele
Published: May 2022

Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components’ behavior understanding. The literature proposes various methods, including signal processing in the time, frequency, and time–frequency domains, feature selection, and unsupervised feature learning. An emerging task in data science is Feature Construction (FC), which has the advantages of both feature selection and feature learning. In particular, the constructed features address a specific objective function without requiring a label during the construction process. Genetic Programming (GP) is a powerful tool to perform FC in the PHM context, as it allows to obtain distinct feature sets depending on the analysis goal, i.e., diagnostics and prognostics. This paper adopts GP to extract system-level features for machinery setting recognition and component-level features for prognostics. Three distinct fitness functions are considered for the GP training, which requires a set of statistical time-domain features as input. The methodology is applied to vibration signals extracted from a test rig during run-to-failure tests under different settings. The performances of constructed features are evaluated through the classification accuracy and the Remaining Useful Life (RUL) prediction error. Results demonstrate that GP-based features classify known and novel machinery operating conditions better than feature selection and learning methods.
Computational Imaging for Simultaneous Image Restoration and Super-Resolution Image Reconstruction of Single-Lens Diffractive Optical System
Authors: Liu Kai , Yu Xiao , Xu Yongsen , Xu Yulei , Yao Yuan , Di Nan , Wang Yefei , Wang Hao , Shen Honghai
Published: May 2022

Diffractive optical elements (DOEs) are difficult to apply in natural scenes imaging covering the visible bandwidth-spectral due to their strong chromatic aberration and the decrease in diffraction efficiency. Advances in computational imaging make it possible. In this paper, the image quality degradation model of DOE in bandwidth-spectral imaging is established to quantitatively analyze its degradation process. We design a DDZMR network for a single-lens diffractive lens computational imaging system, which can simultaneously perform image restoration and image super-resolution reconstruction on degraded images. The multimodal loss function was created to evaluate the reconstruction of the diffraction imaging degradation by the DDZMR network. The prototype physical prototype of the single-lens harmonic diffraction computational imaging system (SHDCIS) was built to verify the imaging performance. SHDCIS testing showed that optical chromatic aberration is corrected by computational reconstruction, and the computational imaging module can interpret an image and restore it at 1.4 times the resolution. We also evaluated the performance of the DDZMR model using the B100 and Urban100 datasets. Mean Peak Signal to Noise Ratio (PSNR)/Structural Similarity (SSIM) were, respectively, 32.09/0.8975 and 31.82/0.9247, which indicates that DDZMR performed comparably to the state-of-the-art (SOTA) methods. This work can promote the development and application of diffractive imaging systems in the imaging of natural scenes in the bandwidth-spectrum.
Experimental Investigation of the Concrete Cone Failure of Bonded Anchors at Room and High Temperature
Authors: Robson Miora Nirina , Al-Mansouri Omar , Pinoteau Nicolas , Abate Marco , McBride Kenton , Piccinin Roberto , Rémond Sébastien , Hoxha Dashnor
Published: May 2022

Under fire conditions, bonded anchors often exhibit pull-out failure due to the thermal sensitivity of polymer-based adhesives. However, progress in manufacturing has allowed the development of more thermoresistant mortars, enhancing the probability of observing concrete-related failure modes at high temperature. For concrete cone failure, Annex D (Informative) to the European Standard EN 1992-4 provides a method to determine the characteristic fire resistance. This method is based on ISO 834-1 fire ratings and on limited experimental data without inclusion of bonded anchors. To remedy these shortcomings, the present contribution aims to provide the first experimental analyses on the concrete cone failure of bonded anchors loaded in tension and exposed to ISO 834-1 fire conditions, as well as heating with a relatively slower rate. The recorded ultimate loads show that the loss of capacity depends on the embedment depth, failure mode and heating scenario. Regarding exposure to ISO 834-1 fire, the 125 mm anchors lost 50% to 60% of their capacity at ambient temperature after 30 min to 75 min of fire exposure. The results highlight that the existing method gives a conservative prediction of the concrete cone capacity at high temperature. However, its accuracy can be improved. Moreover, the obtained crack patterns by the concrete cone breakout failure mode show that the rise in temperature did not significantly affect the geometry of the failure with slow-rate heating. In contrast, the ISO 834-1 fire conditions increased the radius of the failure cone at the exposed surface to up to 5.5 times the embedment depth. However, in any case, the initial slope of the failure surface was not significantly different from its value at ambient temperature.
Variability and Complexity of Knee Neuromuscular Control during an Isometric Task in Uninjured Physically Active Adults: A Secondary Analysis Exploring Right/Left and Dominant/Nondominant Asymmetry
Authors: Clark Nicholas C. , Pethick Jamie
Published: May 2022

Work is needed to better understand the control of knee movement and knee health. Specifically, work is needed to further understand knee muscle force control variability and complexity and how it is organized on both sides of the body. The purpose of this study was to explore side-to-side comparisons of magnitude- and complexity-based measures of knee muscle force control to support future interpretations of complexity-based analyses and clinical reasoning in knee injury control. Participants (male/female n = 11/5) performed constant-force isometric efforts at 50% maximal effort. Force variability was quantified during the constant-force efforts using a coefficient of variation (CV%) and force complexity using approximate entropy (ApEn) and detrended fluctuation analysis (DFA) α. Outcomes were right/left and dominant/nondominant group-level and individual-level comparisons. A limb-symmetry index was calculated for each variable and clinically significant absolute asymmetry was defined (>15%). The only significant side-to-side difference was for right/left DFA α (p = 0.00; d = 1.12). Maximum absolute asymmetries were (right/left, dominant/nondominant): CV 18.2%, 18.0%; ApEn 34.5%, 32.3%; DFA α 4.9%, 5.0%. Different side-to-side comparisons yield different findings. Consideration for how side-to-side comparisons are performed (right/left, dominant/nondominant) is required. Because a significant difference existed for complexity but not variability, this indicates that both complexity-based and magnitude-based measures should be used when studying knee muscle force control.
A Numerical Simulation Study of the Impact of Microchannels on Fluid Flow through the Cement–Rock Interface
Authors: Yang Xinxiang , Guo Siqi , Kuru Ergun
Published: May 2022

Microchannels located at the cement–rock interface can form potential pathways for formation fluid leakage in oil and gas wells. The effects of geometric shape, quantity, and the inclination angle of microchannels on the flow through cemented rock samples were explored. Finite element 3D models were established based on modified micro-CT images obtained from physical samples. The volume flow rate through different sections of cemented rock samples was extracted after the fluid flow simulations. The numerical results showed that with the presence of a single microchannel, the total volume flow rate could be higher than that of the base case by as much as 9%. Microchannel contact and cross-sectional areas were found to be the two most important factors affecting the total volume flow rate. The overall volume flow rate increased with the increasing cross-sectional area, contact area, and inclination angle of the microchannel. The total volume flow rate for the cases with microchannels having the same cross-sectional area but different shapes increased with the decreasing number of sides of the shape (from circular to triangular) due to the increased contact area. The simulation results also revealed that the relative magnitude of the rock permeability may influence the volume flow rate through each section.
Calculation of a Climate Change Vulnerability Index for Nakdong Watersheds Considering Non-Point Pollution Sources
Authors: Kim Jungmin , Kwon Heongak
Published: May 2022

As a response to climate change, South Korea has established its third National Climate Change Adaptation Plan (2021–2025) alongside the local governments’ plans. In this study, proxy variables in 22 sub-watersheds of the Nakdong River, Korea were used to investigate climate exposure, sensitivity, adaptive capacity, and non-point pollution in sub-watersheds, a climate change vulnerability index (CCVI) was established, and the vulnerability of each sub-watershed in the Nakdong River was evaluated. Climate exposure was highest in the Nakdong Estuary sub-watershed (75.5–81.7) and lowest in the Geumhogang sub-watershed (21.1–28.1). Sensitivity was highest (55.7) in the Nakdong Miryang sub-watershed and lowest (19.6) in the Habcheon dam sub-watershed. Adaptive capacity and the resulting CCVI were highest in the Geumhogang sub-watershed (96.2 and 66.2–67.9, respectively) and lowest in the Wicheon sub-watershed (2.61 and 18.5–20.4, respectively), indicating low and high vulnerabilities to climate change, respectively. The study revealed that the high CCVI sensitivity was due to adaptive capacity. These findings can help establish rational climate change response plans for regional water resource management. To assess climate change vulnerability more accurately, regional bias can be prevented by considering various human factors, including resources, budget, and facilities.
Parallel Frequent Subtrees Mining Method by an Effective Edge Division Strategy
Authors: Wang Jing , Li Xiongfei
Published: May 2022

Most data with a complicated structure can be represented by a tree structure. Parallel processing is essential to mining frequent subtrees from massive data in a timely manner. However, only a few algorithms could be transplanted to a parallel framework. A new parallel algorithm is proposed to mine frequent subtrees by grouping strategy (GS) and edge division strategy (EDS). The main idea of GS is dividing edges according to different intervals and then dividing subtrees consisting of the edges in different intervals to their corresponding groups. Besides, the compression stage in mining is optimized by avoiding all candidate subtrees of a compression tree, which reduces the mining time on the nodes. Load balancing can improve the performance of parallel computing. An effective EDS is proposed to achieve load balancing. EDS divides the edges with different frequencies into different intervals reasonably, which directly affects the task amount in each computing node. Experiments demonstrate that the proposed algorithm can implement parallel mining, and it outperforms other compared methods on load balancing and speedup.
Research on a mmWave Beam-Prediction Algorithm with Situational Awareness Based on Deep Learning for Intelligent Transportation Systems
Authors: Liang Jia , Li Kaiming , Zhang Qun , Qi Zisen
Published: May 2022

Simply speaking, automatic driving requires the calculation of a large amount of traffic data and, finally, the obtainment of the optimal driving route and speed. However, the key technical difficulty is the obtainment of data; thus, radar has become an indispensable hardware for automatic driving. Compared to the optical and infrared radar, millimeter-wave radar is not affected by the shape and color of the target, and it is not affected by the atmospheric turbulence, compared to ultrasonic, and so it has a stable detection performance and good environmental adaptability. It is little affected by changes in the weather, and the external environment, rain, snow, dust, and sunshine have no interference in it. The Doppler frequency shift is large, and the accuracy of the relative velocity measurement is improved. However, one challenge for vehicles in fast environments is millimeter-wave-based communication. Because of the short wavelength of the millimeter wave and the high path and penetration losses, the beamforming technology of a large-scale antenna array plays a key role in the construction and maintenance of millimeter-wave communication links. Millimeter waves have wide channel bandwidths, unique channel characteristics, and hardware limitations, and so there are many challenges in the direct use of beamforming technology in millimeter-wave communication. Traditional beam training cannot meet the requirements of low overhead and low delay. This paper, in order to obtain beam information, introduces the context-awareness module to the deep-learning net, which is derived from past observation data. This paper establishes a model that contains the receiver and the surrounding vehicles to perceive the environment. Then, a long short-term memory (LSTM) neural network is used to foresee the acquired power, which is quantized by several beam powers. According to the conclusion, the prediction accuracy is greatly increased, and the model could yield throughput with almost zero overhead and little performance loss.
Development and Opportunities of Clean Energy in China
Authors: Han Jin , Chang Hongmei
Published: May 2022

In the context of the energy crisis and global climate deterioration, the sustainable development of clean energy will become a new direction for future energy development. Based on the development process of clean energy in China in the past ten years, this paper expounds on China’s clean energy policy and development plan. The development of hydropower, wind power, and solar power in China in recent years is analyzed. On this basis, the Grey Forecasting Model is used to forecast the development and structure of China’s clean energy in the next 10 years, point out the direction and market opportunities of China’s clean energy development in the future, and put forward the implementation methods for the sustainable development of China’s clean energy. It provides a reference for the policy decision-making of China’s clean energy development.
Development of Charging/Discharging Scheduling Algorithm for Economical and Energy-Efficient Operation of Multi-EV Charging Station
Authors: Jin Hojun , Lee Sangkeum , Nengroo Sarvar Hussain , Har Dongsoo
Published: May 2022

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy.
Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
Authors: Zhang Tianyi , Peng Song , Jia Yang , Sun Junkai , Tian He , Yan Chuliang
Published: May 2022

Monitoring the rover slip is important; however, a certain level of estimation uncertainty is inevitable. In this paper, we establish slip estimation models for China’s Mars rover, Zhurong, using Gaussian process regression (GPR). The model was able to predict not only the average value of the longitudinal (slip_x) and lateral slip (slip_y), but also the maximum possible value that slip_x and slip_y could reach. The training data were collected on two simulated soils, TYII-2 and JLU Mars-2, and the GA-BP algorithm was applied as a comparison. The analysis results demonstrated that the soil type and dataset source had a direct impact on the applicability of the slip model on Mars conditions. The properties of the Martian soil near the Zhurong landing site were closer to the JLU Mars-2 simulated soil. The proposed GPR model had high estimation accuracy and estimation potential in slip value, and a 95% confidence interval that the rover could reach during motion. This work was part of a research effort aimed at ensuring the safety of Zhurong. The slip value may be used in subsequent path tracking research, and the slip confidence interval will be able to help guide path planning.
Forecasting the Global Battery Material Flow: Analyzing the Break-Even Points at Which Secondary Battery Raw Materials Can Substitute Primary Materials in the Battery Production
Authors: Neidhardt Michael , Mas-Peiro Jordi , Schulz-Moenninghoff Magnus , Pou Josep O. , Gonzalez-Olmos Rafael , Kwade Arno , Schmuelling Benedikt
Published: May 2022

Growing numbers of electric vehicles (EVs) as well as controversial discussions on cost, scarcity and the environmental and social sustainability of primary raw materials that are needed for battery production together emphasize the necessity for battery recycling in the future. Nonetheless, the market for battery recycling is not fully understood and captured in data today. The underlying reasons are found in both market size and various parameters such as the battery-technology mix, the resulting material demand and expected battery lifetime. In consequence, the question of when secondary-material availability from battery recycling is sufficient to satisfy the global cobalt demand for EV applications has not yet been clarified. To address this question, this study estimates the global battery raw-material demand together with the expected amount of the recycled materials by 2035, taking into account a number of parameters affecting future battery material flows. While focusing on cobalt, nickel, lithium, and manganese, the results indicate that the global cobalt demand can be satisfied from secondary sources by the early 2030s in three out of four different technology forecast scenarios. Furthermore, a sensitivity analysis highlights the amount of waste occurring during battery production and battery lifetime as the main drivers for secondary-material availability by 2035.
PAS: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
Authors: Deng Xinyang , Gao Tianhan , Guo Nan , Qi Jiayu , Zhao Cong
Published: May 2022

Privacy disclosure has become a key challenge in vehicular ad hoc networks (VANETs). Although IEEE, ERSI, etc. suggest that a pseudonym-based scheme is a solution, how to support pseudonym management and vehicle authentication is still an open issue. In this paper, a secure VANETs authentication scheme (PAS) is proposed, where software-defined network (SDN) is integrated as a suitable infrastructure to support anonymous authentication and pseudonym management, while removing the requirement for pseudonym certification in the dynamic VANETs environment. The security and performance analysis indicate that PAS is able protect the privacy of vehicles and has a high efficiency.
First Report on the Phenotypic and Genotypic Susceptibility Profiles to Silver Nitrate in Bacterial Strains Isolated from Infected Leg Ulcers in Romanian Patients
Authors: Georgescu Mihaela , Vrancianu Corneliu Ovidiu , Popa Marcela , Gheorghe Irina , Chifiriuc Mariana Carmen
Published: May 2022

Silver-ion-based antiseptics are widely used in treating chronic leg ulcers and, given the emergence of resistance to such compounds, the investigation of silver susceptibility and resistance profiles of pathogenic strains isolated from this type of wound is a topic of great interest. Therefore, in this study, 125 bacterial strains isolated from 103 patients with venous ulcers were investigated to elucidate their susceptibility to silver-nitrate solutions in planktonic and biofilm growth states, and the associated genetic determinants. The isolated strains, both in the planktonic and biofilm growth phases, showed high sensitivity to the standard concentration of 1/6000 silver-nitrate solution. It was noticed that even at concentrations lower than the clinical one (the first 2–3 binary dilutions in the case of planktonic cultures and the first 6–7 binary dilutions in the case of biofilms), the antiseptic solution proved to maintain its antibacterial activity. The phenotypic results were correlated with the genetic analysis, highlighting the presence of silver-resistance genes (sil operon) in only a few of the tested Staphylococcus sp. (especially in S. aureus) strains, Escherichia coli and Pseudomonas aeruginosa strains. These results demonstrate that despite its large use, this antiseptic remains a viable treatment alternative for the management of chronic leg wounds.
Application of Noise Detection Using Confidence Learning in Lightweight Expression Recognition System
Authors: Zhao Yu , Song Aiguo , Qin Chaolong
Published: May 2022

Facial expression is an important carrier to reflect psychological emotion, and the lightweight expression recognition system with small-scale and high transportability is the basis of emotional interaction technology of intelligent robots. With the rapid development of deep learning, fine-grained expression classification based on the convolutional neural network has strong data-driven properties, and the quality of data has an important impact on the performance of the model. To solve the problem that the model has a strong dependence on the training dataset and weak generalization performance in real environments in a lightweight expression recognition system, an application method of confidence learning is proposed. The method modifies self-confidence and introduces two hyper-parameters to adjust the noise of the facial expression datasets. A lightweight model structure combining a deep separation convolution network and attention mechanism is adopted for noise detection and expression recognition. The effectiveness of dynamic noise detection is verified on datasets with different noise ratios. Optimization and model training is carried out on four public expression datasets, and the accuracy is improved by 4.41% on average in multiple test sample sets. A lightweight expression recognition system is developed, and the accuracy is significantly improved, which verifies the effectiveness of the application method.
A Study on Correggio Wall Paintings: Characterization of Technique and Materials of Abbey Church of S. Giovanni Evangelista in Parma, Italy
Authors: Casoli Antonella , Lottici Pier Paolo , Bersani Danilo
Published: May 2022

This study deals with the materials of the sub-arch painting of the Del Bono Chapel of the Abbey Church of S. Giovanni Evangelista in Parma, Italy, datable to around 1523. The artist is Antonio Allegri, known as Correggio (1489–1534), who is considered to be one of the greatest painters of the 16th century. Micro-Raman spectroscopy, micro-Fourier transform infrared spectroscopy and gas chromatography coupled with mass spectrometry were used as the main techniques to identify the pigments and binding media. The analysis enabled us to identify the pigments which were characteristic of the epoch. Correggio’s palette was composed by mineral pigments—sometimes expensive ones such as lapis lazuli, azurite and cinnabar—together with a wide range of earths, or by synthetic pigments like smalt blue. From the amino acid content determination, it was shown that, in the samples containing lazurite, smalt, hematite, green earth and goethite, the protein fraction was attributable to the presence of a mixture of egg and animal glue, from which the use of the a secco technique could be assumed, with pigments that did not need organic binding media on the wall. For the gilding sample, the study found that Au foil had been applied on a brown background (oil-based missione).
Digital Twin for Human–Robot Collaboration in Manufacturing: Review and Outlook
Authors: Ramasubramanian Aswin K. , Mathew Robins , Kelly Matthew , Hargaden Vincent , Papakostas Nikolaos
Published: May 2022

Industry 4.0, as an enabler of smart factories, focuses on flexible automation and customization of products by utilizing technologies such as the Internet of Things and cyber–physical systems. These technologies can also support the creation of virtual replicas which exhibit real-time characteristics of a physical system. These virtual replicas are commonly referred to as digital twins. With the increased adoption of digitized products, processes and services across manufacturing sectors, digital twins will play an important role throughout the entire product lifecycle. At the same time, collaborative robots have begun to make their way onto the shop floor to aid operators in completing tasks through human–robot collaboration. Therefore, the focus of this paper is to provide insights into approaches used to create digital twins of human–robot collaboration and the challenges in developing these digital twins. A review of different approaches for the creation of digital twins is presented, and the function and importance of digital twins in human–robot collaboration scenarios are described. Finally, the paper discusses the challenges of creating a digital twin, in particular the complexities of modelling the digital twin of human–robot collaboration and the exactness of the digital twin with respect to the physical system.
Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
Authors: Li Suli , Lee Hyo Jong
Published: May 2022

Deep-learning-based, cross-age face recognition has improved significantly in recent years. However, when using the discriminative method, it is still challenging to extract robust age-invariant features that can reduce the interference caused by age. In this paper, we propose a novel, effective, attention-based feature decomposition model, the age-invariant features extraction network, which can learn more discriminative feature representations and reduce the disturbance caused by aging. Our method uses an efficient channel attention block-based feature decomposition module to extract age-independent identity features from facial representations. Our end-to-end framework learns the age-invariant features directly, which is more convenient and can greatly reduce training complexity compared with existing multi-stage training methods. In addition, we propose a direct sum loss function to reduce the interference of age-related features. Our method achieves a comparable and stable performance. Experimental results demonstrate superior performance on four benchmarked datasets over the state-of-the-art. We obtain the relative improvements of 0.06%, 0.2%, and 2.2% on the cross-age datasets CACD-VS, AgeDB, and CALFW, respectively, and a relative 0.03% improvement on a general dataset LFW.
Numerical Study and Geometric Investigation of the Influence of Rectangular Baffles over the Mixture of Turbulent Flows into Stirred Tanks
Authors: Soares Laísa Luiz , Biserni Cesare , da Rosa Costa Roger , Oliveira Júnior João Américo Aguirre , dos Santos Elizaldo Domingues , Galarça Marcelo Moraes
Published: May 2022

The present work aims to define strategies for numerical simulation of the mixture of turbulent flows in a stirred tank with a low computational effort, and to investigate the influence of the geometry of four rectangular baffles on the problem of performance. Two computational models based on momentum source and sliding mesh are validated by comparison with experimental results from the literature. For both models, the time-averaged conservation equations of mass, momentum and transport of the mixture are solved using the finite volume method (FVM) (FLUENT® v.14.5). The standard k–ε model is used for closure of turbulence. Concerning the geometrical investigation, constructal design is employed to define the search space, degrees of freedom and performance indicators of the problem. More precisely, seven configurations with different width/length (L/B) ratios for the rectangular baffles are studied and compared with an unbaffled case. The momentum source model leads to valid results and significantly reduces the computational effort in comparison with the sliding mesh model. Concerning the design, the results indicate that the case without baffles creates the highest magnitude of turbulence kinetic energy, but poorly distributes it along the domain. The best configuration, (L/B)o = 1.0, leads to a mixture performance nearly two times superior than the case without baffles.
The Research of 30 mm Detecting Distance of Testing Device for Wire Rope Based on Open Magnetizer
Authors: Liu Mengqi , Zhang Chi , Jiang Xiaoyuan , Sun Yanhua , Jiang Xiaotian , Li Ran , He Lingsong
Published: May 2022

Wire rope will have defects such as local faults (LF) and loss of metal area (LMA) during the long-term using process. The nondestructive testing method of magnetic flux leakage (MFL) has been widely used in wire rope defect detection. Currently, the detecting distance between magnetic sensors and wire rope with the MFL method is relatively small (2–5 mm). Considering the inner surface of the sensor head is close to the wire rope, it quickly leads to the sensor head scraping off the surface oil of the wire rope or being stuck by a cut wire in the course of MFL detection. Therefore, it is challenging to realize the sensor with MFL detection of wire rope obtaining the weak signal at a large lift-off (>30 mm). This study used finite element analysis to verify if the MFL signal exists at the large lift-off (>30 mm). Meanwhile, the sensor head was improved using an open magnetizer to make the wire rope reach saturation and excite enough magnetic leakage field. By combining magnetic sensing and coupling and a weak analog signal processing method, not only was the signal effectively detected, but also the signal-to-noise ratio (SNR) was improved. Finally, experiments verify the feasibility of detecting defects at a large distance. The method also has been applied in the high-speed detection of wire rope, which can detect broken wire of 1 mm diameter.
Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe
Authors: Chrit Mounir , Majdi Marwa
Published: May 2022

We used the objective analysis method in conjunction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into the Chimère model in order to improve the modeling of fine particulate matter (PM2.5) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such a data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM2.5 concentrations and the AOD field could be reduced from −34 to −15% and from −45 to −27%. The assimilation, however, leads to false alarms because of the difficulty in distributing AOD550 over different particle sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using the Chimère model and can be further developed to improve modeled PM2.5 and ozone concentrations.
Characterizing Real-World Particle-Bound Polycyclic Aromatic Hydrocarbon Emissions from Diesel-Fueled Construction Machines
Authors: Dhital Narayan Babu , Wang Lin-Chi , Yang Hsi-Hsien , Cheruiyot Nicholas Kiprotich , Lee Che-Hsuan
Published: May 2022

This study employed an onboard emission measurement system to measure the real-world emission factors of particulate matter (PM), particle-bound polycyclic aromatic hydrocarbons (PAHs), and gaseous air pollutants for different types of diesel-fueled non-road construction machines operated inside confined spaces within a brick manufacturing factory located in Taiwan. To the best knowledge of the authors, this is the first study that reports real-world PM, PAH, and gaseous pollutant emission factors for non-road engines in Taiwan. The mean real-world fuel-specific emission factors of PM, carbon monoxide, total hydrocarbons, and nitric oxide were 0.712–1.17, 8.27–17.9, 3.04–5.77, and 38.1–96.8 g/kg-fuel, respectively, for the test machines. Likewise, mean ΣPAHs emission factors ranged from 157 to 230 μg/kg-fuel for three types of test machines. Further, the average emission of particle-bound PAH per unit PM emission ranged from 213 to 384 μg-PAH/g-PM. Among the analyzed PAHs, the medium-molecular weight (3- and 4-ring) compounds contributed to the largest share of particle-bound PAH emissions. However, in terms of Benzo[a]pyrene equivalent (BaPeq) toxicity, the high-molecular weight (5- and 6-ring) PAHs were more important, as they had the highest BaPeq toxic emission factors. This study provides detailed composition and emission factors of particle-bound PAHs in non-road diesel construction machine emissions, which may be useful as a chemical fingerprint for source apportionment studies.