bluuebunny/crossref_metadata_2025 huggingface dataset:
Diversity of Phytochemical and Antioxidant Characteristics of Black Mulberry (Morus nigra L.) Fruits from Turkey
Authors: Skrovankova Sona , Ercisli Sezai , Ozkan Gursel , Ilhan Gulce , Sagbas Halil Ibrahim , Karatas Neva , Jurikova Tunde , Mlcek Jiri
Published: July 2022

Black mulberry (Morus nigra L.) fruits are known due to their delicious, sweet and slightly acid flavor and high anthocyanin content. In the present study, the diversity of phytochemical, sensory, and antioxidant characteristics of the fruits of 20 black mulberry genotypes, from the Artvin region of Turkey, were evaluated. As important phytochemical assessments in fruits, we chromatographically (HPLC/DAD) determined glucose (7.22 to 11.10 g/100 g fresh weight (fw)) and fructose content (6.32 and 9.94 g/100 g fw), as well as predominant organic acid in black mulberry genotypes fruits—malic acid (6.02–11.44 g/100 g fw), followed by citric acid. Titrative determination was used for ascorbic acid, finding contents of 17.41–28.33 mg/100 g fw. There was found a great diversity of sensory (taste, juiciness, and aroma) characteristics, indicating a richness of the fruit germplasm. Antioxidant parameters, such as total phenolic (TP) and anthocyanin (TA) content, were assessed spectrometrically; antioxidant activity (AA) was assessed by DPPH and FRAP assays; individual flavonoids and phenolic acids were determined chromatographically (HPLC/DAD). Antioxidant characteristics of the fruits, defined by TP and TA content, ranged from 1951 to 2733 μg GAE/g fw and 508–712 μg C3GE/g fw, respectively. The most abundant compounds of flavonoids and phenolic acids groups were determined to be rutin (47.10–97.20 mg/100 g fw) and chlorogenic acid (51.3–90.8 mg/100 g fw). AA results, measured by the DPPH method as EC50 value, ranged between 16.10 and 25.45 μg/mL; a FRAP assay revealed values of AA between 9.80 and 13.22 μmol TE/g fw. Significant differences in phytochemical and antioxidant qualities were observed among the analyzed M. nigra genotypes. Regarding the best values of phytochemical and antioxidant characteristics, three genotypes of M. nigra were selected to be recommended for fruit production. The results thus highlight the potential for the exploitation of local black mulberry genotypes through crop selection and breeding program.
Effects of Dietary Yeast β-Glucan Supplementation on Meat Quality, Antioxidant Capacity and Gut Microbiota of Finishing Pigs
Authors: He Linjuan , Guo Jianxin , Wang Yubo , Wang Lu , Xu Doudou , Yan Enfa , Zhang Xin , Yin Jingdong
Published: July 2022

Yeast β-glucan is a natural antioxidant and has been reported to improve growth performance of piglets, but its application in improving pork quality is limited. This study investigated the effects of dietary yeast β-glucan supplementation on meat quality, antioxidant capacity and gut microbiota of finishing pigs. In a 40-day experiment, ninety finishing pigs (Duroc × Landrace × Yorkshire, 70.47 ± 0.04 kg) were randomly allocated into five treatments including a basal diet supplemented with 0, 50, 100, 200 and 400 mg/kg yeast β-glucan. Results showed that yeast β-glucan significantly increased pH45 min (linear and quadratic, p < 0.01) and a*45 min (linear, p < 0.05), and reduced cooking loss (linear, p < 0.05) and drip loss (quadratic, p < 0.05) of meat in finishing pigs. Importantly, the 200 mg/kg group exhibited the highest values of pH45 min (p < 0.01) and the lowest values of drip loss (p < 0.05), accompanied by a decreased lactate content (p < 0.05) and glycolytic potential (p < 0.05). Dietary supplementation of 200 mg/kg yeast β-glucan markedly increased catalase (CAT) (p < 0.05), superoxide dismutase (SOD) (p < 0.05) and total antioxidant capacity (T-AOC) (p < 0.01) activities in skeletal muscle. Moreover, WPS-2 abundance was decreased significantly in colonic digesta by 200 mg/kg yeast β-glucan and exhibited a positive association with muscle lactate content and drip loss. Together, dietary 200 mg/kg yeast β-glucan supplementation effectively improved pH value and the water-holding capacity of fresh meat through reducing muscle postmortem glycolysis, increasing antioxidant capacity and altering the gut microbiota composition of finishing pigs.
Anti-Inflammatory and Antioxidant Capacity of a Fruit and Vegetable-Based Nutraceutical Measured by Urinary Oxylipin Concentration in a Healthy Population: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial
Authors: Arcusa Raúl , Carrillo Juan Ángel , Cerdá Begoña , Durand Thierry , Gil-Izquierdo Ángel , Medina Sonia , Galano Jean-Marie , Villaño Valencia Débora , Marhuenda Javier , Zafrilla Pilar
Published: July 2022

Oxylipins, lipid biomarkers of inflammation are considered the gold standard method to evaluate the inflammatory and antioxidant status. The aim of the present study was to investigate whether the administration of a polyphenolic extract shot in the form of a nutraceutical was able to reduce inflammation, measured in urine markers. Ninety-two participants (45 males, 47 females, age 34 ± 11 years, weight 73.10 ± 14.29 kg, height 1.72 ± 9 cm, BMI 24.40 ± 3.43 kg/m2) completed the study after an intervention of two 16-week periods consuming extract or placebo separated by a 4-week washout period. The results showed significant differences in terms of reduction of different pro-inflammatory oxylipins (15-keto-PGF2α (from 0.90 ± 0.25 ng/mL to 0.74 ± 0.19 ng/mL p < 0.05), ent-PGF2α (from 1.59 ± 0.37 ng/mL to 1.44 ± 0.32 ng/mL p < 0.05), 2,3-dinor-15-F2t-Isop) (from 1.17 ± 0.35 ng/mL to 1.02 ± 0.27 ng/mL p < 0.05), in total oxylipins count (from 8.03 ± 1.86 ng/mL to 7.25 ± 1.23 ng/mL p < 0.05), and increase in PGE2 (from 1.02 ± 0.38 ng/mL to 1.26 ± 0.38 ng/mL p < 0.05) which has an anti-inflammatory character, after extract consumption compared to placebo. The available data seem to indicate that long-term consumption of a nutraceutical with high polyphenol content improves inflammation and oxidation parameters measured in urine, through UHPLC-QqQ-ESI-MS/MS.
Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location Recommendations
Authors: Chen Xiang , Chen Junxin , Lian Xiaoqin , Mai Weimin
Published: July 2022

Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a small part of total places in a city. This problem directly leads to the difficulty in learning latent representations of users and locations. In order to tackle the data sparsity problem and make better recommendations, users’ app usage records in different locations are introduced to compensated for both users’ interests and locations’ characteristics in this paper. An attributed graph-based representation model is proposed to dig out user–app–location associations with high-order features aggregated. Extensive experiments prove that better representations of users and locations are obtained by our proposed model, thus it greatly improves location recommendation performances compared with the state-of-art methods. For example, our model achieves 13.20%, 10.1%, and 9.44% higher performance than the state-of-art (SOTA) models in Top3Hitrate, Top3Accuracy, and nDCG3, respectively, in the Telecom dataset. In the TalkingData dataset, our model achieves 9.34%, 13.35%, and 8.56% better performance than the SOTA models in Top2Hitrate, Top2Accuracy, and nDCG2, respectively. Furthermore, numerical results demonstrate that our model can effectively alleviate the data sparsity problem in recommendation systems.
Integration of Hydrological Model and Time Series Model for Improving the Runoff Simulation: A Case Study on BTOP Model in Zhou River Basin, China
Authors: Xiao Qintai , Zhou Li , Xiang Xin , Liu Lingxue , Liu Xing , Li Xiaodong , Ao Tianqi
Published: July 2022

Improving the accuracy of runoff simulations is a significant focus of hydrological science for multiple purposes such as water resources management, flood and drought prediction, and water environment protection. However, the simulated runoff has limitations that cannot be eliminated. This paper proposes a method that integrates the hydrological and time series models to improve the reliability and accuracy of simulated runoffs. Specifically, the block-wise use of TOPMODEL (BTOP) is integrated with three time series models to improve the simulated runoff from a hydrological model of the Zhou River Basin, China. Unlike most previous research that has not addressed the influence of runoff patterns while correcting the runoff, this study manually adds the hydrologic cycle to the machine learning-based time series model. This also incorporates scenario-specific knowledge from the researcher’s area of expertise into the prediction model. The results show that the improved Prophet model proposed in this study, that is, by adjusting its holiday function to a flow function, significantly improved the Nash–Sutcliffe efficiency (NSE) of the simulated runoff by 53.47% (highest) and 23.93% (average). The autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) improved the runoff but performed less well than the improved Prophet model. This paper presents an effective method to improve the runoff simulation by integrating the hydrological and time series models.
Assessment of Cracking in Masonry Structures Based on the Breakage of Ordinary Silica-Core Silica-Clad Optical Fibers
Authors: Khotiaintsev Sergei , Timofeyev Volodymyr
Published: July 2022

This paper presents a study on the suitability and accuracy of detecting structural cracks in brick masonry by exploiting the breakage of ordinary silica optical fibers bonded to its surface with an epoxy adhesive. The deformations and cracking of the masonry specimen, and the behavior of pilot optical signals transmitted through the fibers upon loading of the test specimen were observed. For the first time, reliable detection of structural cracks with a given minimum value was achieved, despite the random nature of the ultimate strength of the optical fibers. This was achieved using arrays of several optical fibers placed on the structural element. The detection of such cracks allows the degree of structural danger of buildings affected by earthquake or other destructive phenomena to be determined. The implementation of this technique is simple and cost effective. For this reason, it may have a broad application in permanent damage-detection systems in buildings in seismic zones. It may also find application in automatic systems for the detection of structural damage to the load-bearing elements of land vehicles, aircraft, and ships.
Design, Fabrication, and Control Algorithm of Self-Reconfigurable Modular Intelligent Vehicles
Authors: Zhou Jing , Wang Jiacheng , He Jiazhong , Gao Jian , Yang Aixi , Hu Sideng , Li Qiang
Published: July 2022

Self-reconfigurable vehicles are able to transform into new formations on the basis of mutual communication and positioning so as to adapt to a new environment and new tasks. Compared with fixed-formation vehicles, reconfigurable vehicles feature multifunction and high reliability. First, a modular vehicle with a Mecanum wheel is designed, and a motion control method of a modular vehicle is derived through the establishment and analysis of its kinematics model. Afterwards, a visual positioning system based on AprilTag is designed, and the reconfiguration experiment of modular vehicles is completed in order to validate the feasibility of the proposed control method. A hardware control platform is built, which is able to realize the omnidirectional movement of the vehicle in the plane. The proposed modular vehicles with the corresponding control method successfully completes centralized perception, trajectory planning, and self-reconfiguration, which can be potentially applied in various scenarios, such as orientation guidance and logistics handling.
Sustainable Microalgae and Cyanobacteria Biotechnology
Authors: López-Hernández Jenny-Fabiola , Kean-Meng Tan , Asencio-Alcudia Gloria-Gertrudys , Asyraf-Kassim Mohd , Alvarez-González Carlos-Alfonso , Márquez-Rocha Facundo-Joaquín
Published: July 2022

Marine organisms are a valuable source of new compounds, many of which have remarkable biotechnological properties, such as microalgae and cyanobacteria, which have attracted special attention to develop new industrial production routes. These organisms are a source of many biologically active molecules in nature, including antioxidants, immunostimulants, antivirals, antibiotics, hemagglutinates, polyunsaturated fatty acids, peptides, proteins, biofuels, and pigments. The use of several technologies to improve biomass production, in the first step, industrial processes schemes have been addressed with different accomplishments. It is critical to consider all steps involved in producing a bioactive valuable compound, such as species and strain selection, nutrient supply required to support productivity, type of photobioreactor, downstream processes, namely extraction, recovery, and purification. In general, two product production schemes can be mentioned; one for large amounts of product, such as biodiesel or any other biofuel and the biomass for feeding purposes; the other for when the product will be used in the human health domain, such as antivirals, antibiotics, antioxidants, etc. Several applications for microalgae have been documented. In general, the usefulness of an application for each species of microalgae is determined by growth and product production. Furthermore, the use of OMICS technologies enabled the development of a new design for human therapeutic recombinant proteins, including strain selection based on previous proteomic profiles, gene cloning, and the development of expression networks. Microalgal expression systems have an advantage over traditional microbial, plant, and mammalian expression systems for new and sustainable microalga applications, for responsible production and consumption.
Simulation and Experiment Research on the Surface Deformation and Residual Stress of Fractal Rough Surface Single-Shot Peening
Authors: Zhang Shengfang , Leng Zhiyi , Gu Hongtao , Yin Jian , Wang Ziguang , Liu Yu
Published: July 2022

In order to study the influence of workpiece surface topography on deformation and residual stress during shot peening, the paper built a rough surface single-shot peening simulation model and designed a rough surface workpieces shot peening experiment to verify the correctness of the built simulation model, which differed from the smooth surface simulation model and was closer to the actual processing situation. Based on the W-M fractal dimension theory, the shot peening model of the target with a rough surface is established, and the simulation analysis is carried out by finite element software. The effect of different impact positions of the pellet on surface residual stress and surface displacement were also simulated. The simulation found that with the increase of surface complexity, the surface deformation and residual stress would both increase after shot peening. Through shot peening tests on workpieces with different surface roughness, it is found that the error between simulation and experiment results on surface deformation and residual stress are both less than 20%, which verifies the validity of the rough surface single-shot peening model and that the established model could provide a new method on shot peening simulation analysis.
A Many-Objective Joint Parallel Simulation Method for Acoustic Optimization Design of Sound-Absorbing Structures
Authors: Zhao Xinyang , Zhu Yi , Mei Zhiyuan
Published: July 2022

This paper establishes a many-objective MATLAB with a COMSOL joint parallel simulation optimization method in order to solve the current situation of low efficiency, single objective, and poor effect in acoustic optimization design research for a sound-absorbing structure. Our proposed method combines the means for population partitioning, monitoring, and adaptive normalization, within the framework of the NSGA-III algorithm, which takes the hyperplane deployment scheme into account in its entirety. Compared to the traditional genetic algorithm toolbox of the joint COMSOL optimization scheme, it is shown that the joint parallel simulation optimization method that is constructed in this paper achieves a higher optimization efficiency and a better experimental performance, thereby aiding in the identification of the optimal solution to multiple objectives. The optimization efficiency can increase linearly as the number of available cores on the computer increases. This method is then used to construct a parallel, low-frequency, broadband, highly-sound-absorbing structure. Without any constraints on the optimization objective, the diversity of the optimization results is evident within the parameter optimization range of this paper. The optimization results are stable and substantial, with constrained optimization objectives that have some reference value. In addition, the proposed method can solve acoustic vibration optimization problems and can be applied to other finite element optimization problems.
Preliminary Study on the Generating Mechanism of the Atmospheric Vertical Electric Field before Earthquakes
Authors: Han Ruilong , Cai Minghui , Chen Tao , Yang Tao , Xu Liangliang , Xia Qing , Jia Xinyu , Han Jianwei
Published: July 2022

Precursor signals for earthquakes, such as radon anomalies, thermal anomalies, and water level changes, have been studied in earthquake prediction over several centuries. The atmospheric vertical electric field anomaly has been observed in recent years as a new and valuable signal for short-term earthquake prediction. In this paper, a physical mechanism of the atmospheric vertical electric field anomaly before the earthquake was proposed, based on which the Wenchuan earthquake verified the correctness of the model. Using Monte Carlo simulations, the variation of the radon concentration with height before the earthquake was used to simulate and calculate the ionization rates of radioactive radon decay products at different heights. We derived the atmospheric vertical electric field from −593 to −285 V/m from the surface to 10 m before the earthquake by solving the system of convection-diffusion partial equations for positive and negative particles. Moreover, negative atmospheric electric field anomalies were observed in both Wenjiang and Pixian before the Wenchuan earthquake on 12 May, with peaks of −600 V/m in Pixian and −200 V/m in Wenjiang. The atmospheric electric field data obtained from the simulation were shown to be in excellent concordance with the observed data of the Wenchuan earthquake. The physical mechanism can provide theoretical support for the atmospheric electric field anomaly as an earthquake precursor.
Sliding Mode Control of Electro-Hydraulic Position Servo System Based on Adaptive Reaching Law
Authors: Sun Chungeng , Dong Xiangxiang , Wang Mingjin , Li Jipeng
Published: July 2022

For the problem of the system state variable taking a long time to reach the sliding mode surface and the chattering frequency being high in the sliding mode surface, a sliding mode control method based on the adaptive reaching law is proposed, the system state variable is introduced based on the subreaching law, and an improved variable-speed reaching law is added with reference to the characteristics of the hyperbolic tangent function. The sliding mode control method is divided into two stages, namely, the initial state to the critical value s = ±1 and the system state variable reaching the equilibrium point of the sliding mode surface, and the total time obtained is less than the sum of these two stages. Secondly, this method is adopted in the electro-hydraulic position servo system, and a sliding mode controller is established. Through an AMESim/Simulink co-simulation, it is compared with the sliding mode controller based on the traditional exponential reaching law. The results show that this method can effectively reduce the jitter of the system, reduce the time for the system to reach the sliding surface, and improve the robustness of the system.
A Methodological Framework to Predict Future Market Needs for Sustainable Skills Management Using AI and Big Data Technologies
Authors: Aljohani Naif Radi , Aslam Muhammad Ahtisham , Khadidos Alaa O. , Hassan Saeed-Ul
Published: July 2022

Analysing big data job posts in Saudi cyberspace to describe the future market need for sustainable skills, this study used the power of artificial intelligence, deep learning, and big data technologies. The study targeted three main stakeholders: students, universities, and job providers. It provides analytical insights to improve student satisfaction, retention, and employability, investigating recent trends in the essential skills pinpointed as enhancing the social effect of learning, and identifying and developing the competencies and talents required for the Kingdom of Saudi Arabia’s (KSA’s) digital transformation into a regional and global leader in technology-driven innovation. The methodological framework comprises smart data processing, word embedding, and case-based reasoning to identify the skills required for job positions. The study’s outcomes may promote the alignment of KSA’s business and industry to academia, highlighting where to build competencies and skills. They may facilitate the parameterisation of the learning process, boost universities’ ability to promote learning efficiency, and foster the labour market’s sustainable evolution towards technology-driven innovation. We believe that this study is crucial to Vision 2030’s realisation through a long-term, inclusive approach to KSA’s transformation of knowledge and research into new employment, innovation, and capacity.
Numerical Simulation on Backfilling of Buried Pipes Using Controlled Low Strength Materials
Authors: Liu Hao , Xiao Yiqi , Liu Kaixin , Zhu Youzeng , Zhang Peng
Published: July 2022

The backfill quality of a pipeline has an important influence on pipeline operation. When loose backfill is used, the pipeline may be damaged after short term operation. In this study, the numerical simulation analysis of buried pipes was carried out under three conditions: loose backfill around the pipe, dense backfill, and controlled low strength materials (CLSM) backfill. The effects of narrow trench backfilling using CLSM on the force and deformation of pipelines were studied. The results showed that When CLSM was used for buried pipe backfilling, the pressure on the top of the pipe and on the side of the pipe was significantly reduced. When the surface pressure was 200 kPa, the radial displacement at the top of the pipe was only 0.6 mm. Compared with the dense backfill of the pipe, the radial displacement of the pipe top was reduced by 82.9%, which greatly reduced the deformation of the pipe. CLSM backfilling is a good way to protect the pipeline. The pressure is uniformly applied around the pipe, and the circumferential strain around the pipe is greatly reduced. Pipelines backfilling with CLSM for buried flexible pipes has good mechanical properties and it is expected to be applied to engineering practice.
A Method for Evaluating the Influence of the Field Wind Environment on Cross-Country Skiing Performance
Authors: He Jiawei , Yan Li , Zhu Ming , Xin Dabo , Zhao Yagebai , Zhang Hongfu
Published: July 2022

Aerodynamic drag plays an important role in high-speed skiing. The wind-induced thrust or resistance of athletes, the sliding speed, and the work to overcome the aerodynamic drag are greatly affected by wind; therefore, reducing wind-induced drag is a focus of sport science. This paper proposes a method for evaluating the influence of wind on cross-country skiing performance, which is based on the athlete’s aerodynamic-drag-work relative to the environmental wind field and the establishment of a racetrack wind field model. Aiming at an athlete’s typical sport posture in the Yabuli Ski Field, the impact of field wind on the skier’s speed, the work done by the athlete to overcome aerodynamic drag, and the ratio of the field wind-induced work to the athlete’s total work are analyzed. Through the analysis of the athlete’s work to overcome aerodynamic drag and the wind resistance energy dissipation ratio in three training cases, it is shown that the field wind has a great influence on the athlete’s performance during sliding, which verified the effectiveness of the method. This method will provide coaches and sport researchers with accurate wind resistance energy dissipation data and provide a scientific basis for routine athletic training.
Mobile Device-Based Train Ride Comfort Measuring System
Authors: Hu Yuwei , Xu Lanxin , Wang Shuangbu , Gu Zhen , Tang Zhao
Published: July 2022

As an important train performance quality, comfort depends on vibration and noise data measured on a running train. Traditional vibration and noise measurement tools are facing challenges in terms of collecting big data, portability, and cost. With the continuous upgrade of mobile terminal hardware, the built-in sensors of mobile phones have the ability to undertake relatively complex data measurement and processing tasks. In this study, a new type of train comfort measurement system based on a mobile device is developed by using a built-in sensor to measure the vibration and noise. The functions of the developed system include the real-time display of three-way vibration acceleration, lateral and vertical Sperling indicators, sound pressure level, and train comfort-related data storage and processing. To verify the accuracy of the mobile device-based train ride comfort measuring system (DTRCMS), a comparison of test results from this system and from the traditional measuring system is conducted. The comparison results show that the DTRCMS is in good agreement with the traditional measuring system. The relative error in three-direction acceleration and Sperling values is 2~10%. The fluctuation range of the noise measured by DTRCMS is slightly lower than that of the professional noise meter, and the relative error is mainly between 1.5% and 4.5%. Overall, the study shows that using mobile devices to measure train comfort is feasible and practical and has great potential for big data-based train comfort evaluation in the future.
Human-Related Hazardous Events Assessment for Suffocation on Ships by Integrating Bayesian Network and Complex Network
Authors: Qiao Weiliang , Guo Hongtongyang , Huang Enze , Deng Wanyi , Lian Chuanping , Chen Haiquan
Published: July 2022

To investigate the human-related factors associated with suffocation on ships during docking repair, a comprehensive analysis model composed of a Bayesian network (BN) and a complex network (CN) is proposed in the present study. The principle of event tree analysis (ETA) is firstly applied to identify the hazardous events involved in the accident according to the accident report, based on which the CN would then be developed with the logic relationships among the hazardous events. The improved K-shell decomposition algorithm is utilized to determine the criticality of nodes in the CN, the results of which are then used to develop the BN model within the framework of a human factor analysis classification system (HFACS). Then, the developed BN model can be simulated with the probability distribution of all the nodes within the BN, which are obtained on the basis of node criticality. Finally, the results of the BN simulation are interpreted from the perspectives of a brief analysis, backward analysis and sensitivity analysis. The results are verified with existing studies and the accident investigation report issued by authority, which are presented as evidence to verify the effectiveness of the proposed methodology to evaluate the human-related risk involved in the suffocation on ships. The methodology proposed in this study integrates the advantages of BN and CN to investigate the human-related hazardous events involved in maritime accidents, which can be seen as the main innovation of this work.
Numerical Analysis and Optimization of the Front Window Visor for Vehicle Wind Buffeting Noise Reduction Based on Zonal SAS k-ε Method
Authors: Yang Zhendong , Liu Longgui , Gu Zhengqi
Published: July 2022

Numerical investigations were conducted to determine the effectiveness of the front window visor for wind buffeting noise reduction. An unsteady flow simulation was carried out using a zonal Scale Adaptive Simulation (SAS) k-ε turbulence model. Firstly, the accuracy of the simulation method was validated based on a benchmark problem. The benchmark results, frequency, and sound pressure levels of feedback and resonance modes all matched well with the experimental data. The effect of the front window on the buffeting noise reduction was numerically investigated based on three different front side window openings. The analysis focused on the suppression effect of the front window visor. The results show that the front window visor changed the A-pillar vortex shedding trajectory and thus reduced the driver’s ear pressure fluctuation. On this basis, an optimization algorithm was employed to optimize the shape of the front window visor. The main design goal was to decrease the sound pressure level (SPL) values of the driver’s left ear. Simulation results showed that the monitoring point’s SPL of buffeting noise after the visor optimization was reduced by 12.6%, compared with that of the original visor.
Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm
Authors: Zhang Lingling , Fu Yinjun , Wei Yan , Chen Huiling , Xia Chunyu , Cai Zhennao
Published: July 2022

College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.
Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
Authors: Nengroo Sarvar Hussain , Jin Hojun , Lee Sangkeum
Published: July 2022

Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.
Research on Deformation Analysis and Rehabilitation for a Beam–Arch Combination Bridge Suffering an Extreme Temperature Field
Authors: Fu Meizhen , Liang Yuxiong , Wu Bitao , Zhang Ling , Tang Guoxi
Published: July 2022

In situ monitoring was conducted throughout the construction period to investigate the abnormal deformation of a bridge under construction subjected to sudden cooling by Typhoon Lekima. An FE model considering the temperature field based on measurement data was also established to reveal the exact causes of the bridge’s abnormal deformation and provide theoretical guidance for rehabilitation measures. The FE model simulation and measurement results showed that (1) the exact cause of the abnormal deformation of the bridge was the inconsistency of the temperature field between the top and bottom plates, and the sudden approach of the typhoon aggravated the inconsistency; (2) the abnormal deformation of the construction bridge caused by the typhoon could be addressed with rehabilitation before forming the bridge; (3) an extreme temperature field should be considered in the design of a beam–arch combination bridge. These results can provide a reference for the design and construction of similar bridges.
A Simple and Compact Laser Scattering Setup for Characterization of a Pulsed Low-Current Discharge
Authors: Szulc Michał , Forster Günter , Marques-Lopez Jose-Luis , Schein Jochen
Published: July 2022

Recent research trends show an increasing interest in non-equilibrium plasmas operated at atmospheric pressure, which are often used to tackle several environmental and health issues. Nevertheless, due to the complexity of the applications, these trends also show the need for a comprehensive characterization of such plasmas for a deeper understanding of the observed effects. One of the diagnostic methods for experimental determination of key parameters which affect the reactivity of a plasma, i.e., electron temperature, electron density and heavy particle temperature, is laser scattering. In this work, an approach based on a simple and compact laser scattering setup is proposed, which allows an estimation of the above parameters without any additional changes in the acquisition settings. Thus, the experimental effort and possible sources of error can be reduced. The proposed setup is tested experimentally with a commercially available pulsed plasma system, and the results are compared to available data. From this comparison, it is found that the plasma parameters estimated with the proposed scattering setup are plausible.
2D CFD Simulation of Dynamic Heat Transfer in an Open-Type Refrigerated Display Cabinet
Authors: Vengalis Tadas , Mokšin Vadim
Published: July 2022

This study uses a simplified two-dimensional time-dependent computational fluid dynamic (CFD) model to study the influence of wall shelf configuration and outtake honeycomb inclination angle on the performance of the air curtain in an open-type semi-vertical refrigerated display cabinet. Numerical simulation results show that changing the angle, along with changing the shelf configuration, can reduce the temperature of the intake air grille and improve the warm/cold air ratio. The average temperature of the simulated food products decreased by 1.2 °C compared to the unmodified display cabinet and began to meet the requirements of the temperature category 3M0. The proposed CFD model was validated by the experimental results. The simulation results obtained are important for cabinet design to improve product storage quality and reduce energy consumption.
Phytochemical, Antimicrobial and Cytotoxic Activities of Gaultheria Trichophylla Royle
Authors: Zaman Shah , Al-Joufi Fakhria A. , Zafar Muhammad , Zahoor Muhammad
Published: July 2022

Gaultheria trichophylla fruit is used by the indigenous people to treat asthma, headache, and as an appetizer in the alpine and sub-alpine regions of Western Himalaya. No studies exist on the antimicrobial significance of this species. The current study describes the phytochemical composition, in vitro cytotoxic, and antimicrobial effects of different extracts of Gaultheria trichophylla. In antimicrobial assay, four different bacterial strains (Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa, and Staphylococcus aureus) as well as four fungal strains (Helminthosporium solani, Fusarium solani, Aspergillus flavus, and Aspergillus fumigates) were used. Qualitative phytochemical screening showed the existence of different active compounds. Quantitative phytochemical screening showed the existence of phenolic contents in the range from 3.27 ± 0.44 mg GE/g to 14.17 ± 088 mg GE/g, whereas flavonoids were from 8.08 ± 0.48 mg QE/g to 26.9 ± 0.23 mg QE/g. The elemental analysis quantified essential minerals of life importance such as Na (3.24 ± 0.05 µgg−1), Mg (1.93 ± 0.08 µgg−1), and Ca (1.83 ± 0.056 µgg−1), while none of the heavy metal levels were high from the permissible limit of WHO. Cytotoxic assay showed moderate activity in terms of LC50 of (50 μg/mL) for methanolic extracts. Antifungal assay of methanolic and other extracts against different tested fungal strains showed a zone of inhibitions from 29 ± 1.154% to 86.66 ± 0.09%. As an antibacterial, the MIC values were from 7.5 mg/mL to 15 mg/mL for the tested extracts. The observed biological potentials were at the expense of its phytochemical composition, however, further confirmation in animal models and responsible phytochemical isolations in pure form is needed.
Effectiveness of a Serious Game Design and Game Mechanic Factors for Attention and Executive Function Improvement in the Elderly: A Pretest-Posttest Study
Authors: Wong Ying-Kit , Wu Chih-Fu , Tu Yung-Hsiang
Published: July 2022

Attention allows us to focus and process information from our environment, and executive function enables us to plan, work, and manage our daily lives. As individuals become older, both of these cognitive abilities decline. It is essential for the elderly to perform more cognitive exercises. Previous studies have shown that arithmetic calculations require attention span and that playing video games requires executive function. Therefore, we developed a serious game involving mental arithmetic calculations specifically for improving attention span and executive functions. Our objective was to analyze the effectiveness of the game and the efficacy of the game’s mechanic factors affecting attention span and executive function in the elderly. Forty elderly volunteers who are over 60 years of age were invited to join an eight-week cognitive training program through an elderly social welfare center. Four assessment tests were used in pre-test and post-test before and after the training period. D-CAT and SAT are used for screening attention span; TMT-A and TMT-B are used for screening executive function. They were instructed to play the game for at least 15 min per day, 5 days per week, for a total of 8 weeks. There were three independent variables (difficulty, pressure, and competition) with two parameters that could be selected. A paired-sample t-test showed the effective results by comparing the pre-test scores and post-test scores of the cognitive training. There were significant improvements in attention span and executive functions. The mixed repeated-measure ANOVA and MANCOVA results showed that two game mechanic factors (difficulty and pressure) had a significant effect and an interaction effect, but the other factor (competition) had a non-significant effect. In conclusion, the game showed a significant enhancement in both attention span and executive functions after training, and the difficulty factor and the pressure factor were shown to have an effect, but the competition factor was shown to have no effect.
Genetic Algorithm-Based Intelligent Selection Method of Universal Shield Segment Assembly Points
Authors: Liu Rui , Hu Jinlong , Zhang Dailin , Peng Dandan , Zhu Guoli
Published: July 2022

The proportion of universal segment in tunnel construction is constantly increasing. A key factor affecting the quality of tunnel construction is the selection of the shield segment assembly points. Nevertheless, the quality and efficiency of the current manual selection method cannot be guaranteed. To realize a high correct rate, high efficiency and intelligence of universal segment assembly points selection, an intelligent selection method of assembly points is proposed. First, the objective function is established by considering the thrust cylinder stroke and shield tail gap differences. Second, to adaptively optimize the weights of the objective function, the working conditions are divided into 81 intervals, and a genetic algorithm is proposed to optimize weights in each interval. Third, a Monte-Carlo-based method is proposed to generate an example dataset, which is used for the genetic algorithm to optimize the weights. Finally, the proposed method was applied to the segment assembly points selection for Line 8 of the Zhengzhou rail transit in China. The results show that the method of assembly segment selection can reach a 90.6% correct rate in the field. The research results of this paper can be used for the selection of the universal shield segment assembly points.
Simultaneous DC Railway Power System Analysis Method Using Model-Based TPS
Authors: Cho Hwanhee , Kim Jaewon , Jung Hosung , Kim Hyungchul
Published: July 2022

This paper focuses on the development of a model-based train performance simulation with the practical train operation data using MATLAB Simulink. The developed program uses input operation data of a DC 1500 V metro line. And the simultaneous and multi-rate simulation for DC metro was performed to interface train schedule data with actual railway electrification system and vehicle. In the simulation, the voltage and power measured at each substation are provided during the traction, coasting and breaking mode. From this study, railroad vehicle load can be virtually modeled in the system internally and contribute to verifying new parts of the overall railway electrification systems.
Joint Maintenance Strategy Optimization for Railway Bogie Wheelset
Authors: Zhang Huixian , Wei Xiukun , Guan Qingluan , Zhang Wei
Published: July 2022

A wheelset is one of the most severely worn components of railway bogies. Its health condition has a significant impact on the safety and comfort of railway trains. Moreover, wheelset maintenance costs account for a sizeable part of the railway operating company. Therefore, it is essential to investigate executable maintenance strategies for wheelsets. In this paper, a maintenance strategy that combines periodic inspection and preventive maintenance is proposed. The wheel flange and wheel diameter deterioration models are established by the compound Poisson process based on the cumulative shock from the failure mechanism, and the Weibull distribution is adopted for modeling wheel tread failure probability. The age reduction factor is introduced to describe the maintenance effect. Then, the joint maintenance optimization model is constructed to determine the periodic inspection interval and the reprofiling strategy, with the objective of achieving a minimum maintenance cost rate and with the wheel flange thickness failure as the failure risk constraint. Lastly, a case study is provided, and the results show that, compared with the two conventional maintenance strategies with fixed inspection periods, the maintenance strategy proposed in this paper can reduce the maintenance cost rate by 27.06% and 12.0%, respectively. Moreover, the life span is prolonged by 11.51% and 11.98%, respectively.
Development of Low-Fidelity Virtual Replicas of Products for Usability Testing
Authors: Joyner Janell S. , Kong Aaron , Angelo Julius , He William , Vaughn-Cooke Monifa
Published: July 2022

Designers perform early-stage formative usability tests with low-fidelity prototypes to improve the design of new products. This low-tech prototype style reduces the manufacturing resources but limits the functions that can be assessed. Recent advances in technology enable designers to create low-fidelity 3D models for users to engage in a virtual environment. Three-dimensional models communicate design concepts and are not often used in formative usability testing. The proposed method discusses how to create a virtual replica of a product by assessing key human interaction steps and addresses the limitations of translating those steps into a virtual environment. In addition, the paper will provide a framework to evaluate the usability of a product in a virtual setting, with a specific emphasis on low-resource online testing in the user population. A study was performed to pilot the subject’s experience with the proposed approach and determine how the virtual online simulation impacted the performance. The study outcomes demonstrated that subjects were able to successfully interact with the virtual replica and found the simulation realistic. This method can be followed to perform formative usability tests earlier and incorporate subject feedback into future iterations of their design, which can improve safety and product efficacy.
Multi-Level Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
Authors: Feng Lei , Xie Yuxuan , Liu Bing , Wang Shuyan
Published: July 2022

Multi-agent reinforcement learning (MARL) has become more and more popular over recent decades, and the need for high-level cooperation is increasing every day because of the complexity of the real-world environment. However, the multi-agent credit assignment problem that serves as the main obstacle to high-level coordination is still not addressed properly. Though lots of methods have been proposed, none of them have thought to perform credit assignments across multi-levels. In this paper, we aim to propose an approach to learning a better credit assignment scheme by credit assignment across multi-levels. First, we propose a hierarchical model that consists of the manager level and the worker level. The manager level incorporates the dilated Gated Recurrent Unit (GRU) to focus on high-level plans and the worker level uses GRU to execute primitive actions conditioned on high-level plans. Then, one centralized critic is designed for each level to learn each level’s credit assignment scheme. To this end, we construct a novel hierarchical MARL algorithm, named MLCA, which can achieve multi-level credit assignment. We also conduct experiments on three classical and challenging tasks to demonstrate the performance of the proposed algorithm against three baseline methods. The results show that our method gains great performance improvement across all maps that require high-level cooperation.