bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preparing undergraduates for the post-pandemic workplace: Teams of education and engineering students teach engineering virtually
Authors: Gutierrez Kristie S. , Kidd Jennifer J. , Lee Min J. , Pazos Pilar , Kaipa Krishnanand , Ayala Orlando 
Published: November 2023

AbstractWhen schools and universities across the world transitioned online due to the COVID-19 pandemic, Ed+gineering, a National Science Foundation (NSF) project that partners engineering and education undergraduates to design and deliver engineering lessons to elementary students, also had to shift its hands-on lessons to a virtual format. Through the lens of social cognitive theory (SCT), this study investigates engineering and education students’ experiences during the shift to online instruction to understand how they perceived its influence on their learning. As a result of modifying their lessons for online delivery, students reported learning professional skills, including skills for teaching online and educational technology skills, as well as Science, Technology, Engineering, and Mathematics (STEM) content. Some also lamented missed learning opportunities, like practice presenting face-to-face. Students’ affective responses were often associated with preparing and delivering their lessons. SCT sheds light on how the mid-semester change in their environment, caused by the shift in designing and teaching from face-to-face to online, affected the undergraduate engineering and education students’ personal experiences and affect. Overall, the transition to fully online was effective for students’ perceived learning and teaching of engineering. Though students experienced many challenges developing multimedia content for delivering hands-on lessons online, they reported learning new skills and knowledge and expressed positive affective responses. From the gains reported by undergraduates, we believe that this cross-disciplinary virtual team assignment was a successful strategy for helping undergraduates build competencies in virtual skills. We posit that similar assignment structures and opportunities post-pandemic will also continue to prepare future students for the post-pandemic workplace.
Construction of vaccination network and influencing factors: a case study of Chongqing, China
Authors: Li Jianing , Fan Jie , Zhu Ling , Wu Xiaohua , Luo Chunyu , Wang Wei 
Published: November 2023

AbstractVaccination is a fundamental tool in preventing infectious diseases. However, due to the wide array of vaccines available, comprehending the entire vaccine landscape can be a daunting task. To tackle this complexity, this study employs advanced network analysis methods capable of capturing the intricate relationships within multivariate datasets. The objective is to investigate how the vaccination landscape has evolved both before and after the COVID-19 pandemic. This study examined vaccination data in the Nanan District of Chongqing, China, spanning from 2016 to 2022. Additionally, the network topological characteristics were computed and scrutinized across 2326 sliding windows. The investigation focused on assessing alterations in the topological structure of the vaccination network before and after the COVID-19 pandemic, encompassing analyses at both macro and mesoscale levels. Furthermore, at the micro level, this study delved into the correlation degrees of selected vaccine nodes within the vaccination network. The analysis unveiled that the correlation and activity within the vaccination network showed a noticeable enhancement in strength in the wake of the COVID-19 pandemic, especially during the spring and winter months. However, the community structure and the average interactions between vaccines displayed a diminishing trend post-pandemic. Among the analyzed vaccines, the HepA vaccine emerged as the one with the highest average node-degree centrality rank. A closer examination of the node-degree centrality ranking chart within the vaccination network disclosed fluctuations in the rankings of various vaccine types across different time periods and seasons. In contrast, vaccines incorporated into NIP exhibited a remarkable degree of consistency, which is attributed to children adhering to a fixed vaccination schedule, rendering NIP vaccines less susceptible to disruptions and enhancing their stability within the vaccination network. This study offers valuable insights into the dynamics of the vaccination network, shedding light on the impact of the COVID-19 pandemic, seasonal variations, and the ever-shifting correlation patterns among different vaccine types. These discoveries enrich our comprehension of vaccination trends and have the potential to guide forthcoming endeavors aimed at refining vaccination strategies and enhancing public health outcomes, not only in the Nanan District but also in analogous settings.
A novel method for the measurement of superconducting transmission lines at terahertz frequencies
Authors: Peng Zhaohang , Fan Bowen , Miao Wei , Wang Zheng , Ren Yuan , Li Jing , Shi Shengcai 
Published: November 2023

Characterizing the properties (e.g., effective dielectric constant εeff, attenuation constant α, and characteristic impedance Z0) of terahertz (THz) superconducting transmission lines is of particular interest in designing on-chip integrated THz bandpass filters, which are a critical component for THz astronomical instruments, such as multi-color camera and broadband imaging spectrometers. Here, we propose a novel method for the characterization of three parameters (εeff, α, and Z0) of THz superconducting transmission lines. This method measures the ratio of the THz signal powers through two different-length branches of the superconducting transmission line to be measured. In addition, only one measurement is required for an all-in-one device chip, including an antenna, a half-power divider, the superconducting transmission line to be measured, and two detectors. The key point is that the superconducting transmission line to be measured is impedance-mismatched with the two integrated detectors. The method is validated through simulation and measurement for superconducting coplanar waveguide transmission lines around 400 GHz.
The Bojo Algouinsan Ecotourism Initiative: Crisis Mitigation Through Community Tourism Development
Authors: Ibrahim Zainub , Gemmell Paul 
Published: November 2023

Summary
Faced with many environmental threats to the ecologically sensitive Tañon Strait in the Filipino province of Cebu, the town of Aloguinsan and the village of Bojo turn to tourism to protect their community from oil drilling and cement manufacturing. Guided by Mr Joselito “Boboi” Costas, the Bojo community has created an award-winning ecotourism destination. The aim of this case study is to highlight how a community successfully employed tourism as a tool to mitigate various risks and to provide a much-needed extra source of income to the community.

Information
© The Authors 2023
Displacement of PAMAM-Au via acoustic streaming on an electrochemical immunosensing platform
Authors: Syamila Noor , Hamzah Amir Syahir Amir , Laurell Thomas , Sulaiman Yusran , Ikeno Shinya , Siang Tan Wen , Ahmad Tajudin Asilah 
Published: November 2023

Abstract
The displacement of an electroactive monitoring agent, i.e., polyamidoamine dendrimers encapsulated gold nanoparticles (PAMAM-Au) upon the presence of a target antibody via acoustic streaming has been studied. Acoustic streaming has been used to improve the mass transfer and reduce the sample incubation rate, thus this study investigated its ability in enhancing the PAMAM-Au displacement efficiency of our immunosensor. For this purpose, the bio-nanogate components of maltose-binding protein carrying the antigenic determinant (MBP-aD) of hepatitis B surface antigen (HBsAg) as a bioreceptor was functionalized, followed by the monitoring agent i.e. PAMAM-Au on the electrode prior to the incubation with targeted anti-hepatitis B surface antigen (anti-HBsAg) antibody. The modified electrode was then coupled with a piezotransducer and connected to the signal transducer to induce acoustic streaming upon sample incubation. Under optimal acoustic actuation, the sample incubation time has been reduced from 20 min to 8 min via the enhancement of PAMAM-Au displacement induced by acoustic streaming. The result also demonstrated that the specificity and selectivity of the sensing platform under acoustic actuation are comparable to the static incubation in detecting the targeted antibody.
A newly predicted stable calcium argon compound by ab initio calculations under high pressure
Authors: Gao Xinlei , Wei Shuli , Guo Yanhui , Yin Guowei , Meng Yue , Ju Xiaoshi , Chang Qiang , Sun Yuping 
Published: November 2023

Abstract
High pressure can change the valence electron arrangement of the elements, and it can be as a new method for the emergence of unexpected new compounds. In this paper, the Ca–Ar compounds at 0–200 GPa are systematically investigated by using CALYPSO structure prediction methods combined with first principles calculations. The study of the Ca–Ar system can provide theoretical guidance for the exploration of new structures of inert elemental Ar compounds under high pressure. A stable structure: P63/mmc-CaAr and six metastable structures: Rm-CaAr2, P4/mmm-CaAr2, Pm1-CaAr3, P4/mmm-CaAr3, P21/m-CaAr4 and Pm1-CaAr5 were obtained. Our calculations show that the only stable phase P63/mmc-CaAr can be synthesized at high pressure of 90 GPa. All the structures are ionic compounds of metallic nature, and surprisingly all Ar atoms attract electrons and act as an oxidant under high pressure conditions. The calculation results of ab initio molecular dynamics show that P63/mmc-CaAr compound maintains significant thermodynamic stability at high temperatures up to 1000 K. The high-pressure structures and electronic behaviors of the Ca–Ar system are expected to expand the understanding of the high-pressure chemical reactivity of compounds containing inert elements, and provide important theoretical support for the search of novel anomalous alkaline-earth metal inert element compounds.
Advancing occupational health in mining: investigating low-cost sensors suitability for improved coal dust exposure monitoring
Authors: Zaid Mirza Muhammad , Amoah Nana , Kakoria Ashish , Wang Yang , Xu Guang 
Published: November 2023

Abstract
Exposure to coal dust in underground coal mines poses significant health risks to workers, including the development of diseases such as coal workers’ pneumoconiosis and silicosis. Current available methods for monitoring coal dust exposure are expensive and time-consuming, necessitating the exploration of alternative approaches. Low-cost light scattering particulate matter sensors offer a promising solution, and its development in recent years has demonstrated some success in air quality monitoring However, its application in sensing coal particles is limited partially due to that the operating condition in a mine is different than the atmosphere. Thus, the objective of this paper is to evaluate the impact of common factors encountered in a mining environment on these sensors. The findings revealed that the Air trek and Gaslab sensors were unsuitable, showing poor correlation with reference monitors. SPS30 was promising for low concentrations (0–1.0 mg m−3), while PMS5003 effectively monitored up to 3.0 mg m−3. Changing sensor orientation reduced accuracy. Higher wind speeds (3 m s−1) improved results. Low-cost sensors performed well with coal dust but poorly with Arizona road dust. This study underscores the imperative for enhancing these sensors, thereby facilitating their potential application to enhance the occupational health of miners.
Multisource information fusion considering the weight of focal element’s beliefs: a Gaussian kernel similarity approach
Authors: Yang Rui-Shi , Li Hai-Bin , Huang Hong-Zhong 
Published: November 2023

Abstract
Similarity has been extensively utilized to measure the degree of conflicts between evidences in multisource information fusion. The existent works, however, assumed that the contribution of each focal element’s belief to the similarity measure is the same, and the influence of the weights of focal element’s belief is not considered, which is unreasonable. This article proposes a new Gaussian kernel similarity approach to measure the similarity between evidences. The proposed Gaussian kernel similarity coefficient can effectively take account of the weights of focal element’s beliefs. In addition, it possesses some preferable properties, such as, bounded, consistent, and symmetrical. A multisource information fusion method based on the Gaussian kernel similarity coefficient is, therefore, investigated. The developed method mainly contains three steps: (1) the Gaussian kernel similarity coefficient, as a connection, is leveraged to calculate the weight of evidences based on the weight of focal element’s beliefs; (2) the initial evidences are, thereby, modified based on the weight of evidence via the weight-average method; and (3) the final multisource information fusion can be achieved by the Dempster’s combination rule using the modified evidences. An illustrative example with single-element subset and an application with multi-element subset are presented, and it is verified that the proposed method is effective in dealing with conflicting evidences.
A time–frequency ridge extraction diagnostic method for composite faults of bearing gears in wind turbine gearboxes
Authors: Zhiyu Zhang , Xiangfeng Zhang , Hong Jiang 
Published: November 2023

Abstract
The bearing and gear faults in the gearbox interact with each other, and the weak fault characteristics are often masked by the strong fault characteristics, making it difficult to accurately extract complete fault information. To solve this problem, a time–frequency ridge extraction diagnosis method based on multisynchrosqueezing transform (MSST) is proposed. This method utilizes MSST to enhance the compound fault features, especially the gear fault amplitude modulation components. It also utilizes the time–frequency ridge extraction method to separate the gear fault amplitude modulation components and the bearing fault impact pulse components. Additionally, it uses time shifting to substitute data and verifies the independence of the harmonic zero hypothesis to determine the accuracy of the fault components. This method provides a favorable basis for the extraction and identification of compound faults, especially weak faults, in complex dynamic signals of the gearbox. The effectiveness of this method is validated through simulation examples and practical applications.
Anomaly detection method based on temporal spatial information enhancement
Authors: Wang Lin , Wang Xiangjun , Li Mingyang , Liu Feng 
Published: November 2023

Abstract
The proposed Anomaly Detection Method Based on Temporal Spatial Information Enhancement addresses the limitations of unsupervised techniques in detecting abnormal events beyond boundaries and limited samples. It incorporates a Serial Depth Separable Residual Block (Serial Block) as the backbone for predicting future frame. Additionally, a DenseReserve Subsample Module (DRSM) facilitates feature scale scaling, and a U-shaped Pyramid Attention Module (UPAM) guides feature fusion and enhances spatial details. During the prediction stage, reconstructed optical flow information aids in distinguishing abnormal and normal event features, with the abnormal score determined by a weighted fusion of optical flow reconstruction error and prediction error for future frames. Experimental results demonstrate the method’s outstanding performance, achieving area under the curve metrics of 99.7%, 92.1%, and 78.3% on UCSD Ped2, CUHK Avenue, and ShahanghaiTech datasets, respectively. This method offers significant advancements in detecting anomalous events in complex and multi-scene surveillance videos, with improved operational speed and reduced parameters.
A thin film resistive humidity sensor based on polymer and carbon black nanoparticle composites
Authors: Zhang Wei , Huang Yubo , Lin Sijian , Xiao Hongyang , Huang Chun , Yu Wei , Wang Zhuqing , Xia Cao 
Published: November 2023

Abstract
This paper proposes a resistive humidity sensor that uses a carbon-black and polyvinyl alcohol composites thin film, fabricated with a unique film coating method for thinner thickness and higher sensitivity. Improving the sensitivity of sensing films is still of great importance in the research field of gas sensors. The humidity sensor devices with thin composite film and microelectrode structure are fabricated on the glass substrate for a low cost and a simple fabrication process. The sensor gives a rapid response for humidity levels from 10.9% relative humidity (RH) to 73.7% RH, and the response time is about 5.77 s. Experimental results reveal that the sensor has good sensitivity, reproducibility, fast reaction time, and wide range. In addition to humidity, the sensor also responds well to gases such as ethanol. The proposed gas sensor in this paper can be applied to the other combinations of polymers and nanoparticles to form new gas sensors, which have the potential to be used as a gas sensor array for detecting the composition of complex gases such as volatile organic components.
Torque estimation in three-phase induction motors under broken bars and voltage variations using airgap flux measurement and a machine learning model
Authors: Dias Cleber Gustavo , Custódio da Costa José Garcia , Fontenele Jhone 
Published: November 2023

Abstract
This paper proposes a new approach for load torque estimation in squirrel cage induction motors using airgap flux measurement, by means of a Hall effect sensor installed between two stator slots of the machine. The rotor speed was estimated from the Hall sensor signal using a previous method designed by the authors, and the root mean square and mean value were also computed from the Hall signal to serve as inputs to a multi-layer perceptron model.The positioning of the Hall sensor inside the machine was also investigated during different experiments for torque prediction and considered as an additional input for the regression task. The present methodology does not require motor parameters for torque prediction. This research has been validated using some simulated and experimental results for different motor operational conditions. The performance metrics disclosed a good torque prediction for the machine fed not only by a rated voltage, but also by overvoltage/undervoltage power supply and with broken rotor bars, even for low loads. Both experimental and simulation results showed a mean absolute percentage error lower than 4% for a 7.5 kW induction motor.
A novel time-frequency analysis method for fault diagnosis based on generalized S-transform and synchroextracting transform
Authors: Wang Hongwei , Fang Zhiwen , Wang Hongli , Li Yong’an , Geng Yide , Chen Long , Chang Xin 
Published: November 2023

Abstract
Rotating machinery usually operates under variable-speed conditions, and how to effectively handle nonstationary signal in fault diagnosis is a critical task. The time-frequency analysis (TFA) method is widely used in the processing of nonstationary signal. To improve the time-frequency resolution and clearly identify instantaneous frequency (IF) characteristics, the adaptive generalized S-synchroextracting transform (AGSSET), which is a novel TFA method proposed in this paper. Firstly, a new transform named adaptive generalized S-transform (AGST) is put forward by optimizing the window function of generalized S transform. In this paper, an adaptive window function optimization method based on the frequency spectrum of the vibration signal is introduced, and the energy concentration measure is used to determine the window function’s parameters in AGST. Simultaneously, the synchrony extraction idea is incorporated into the AGST, then the AGSSET is derived. To address more complex IF characteristics, the synchronous extraction operator (SEO) is reconstructed. In the simulation experiment, the GMLC signal model is selected to represent nonstationary signal and to verify the effectiveness of the proposed method. In addition, bearing fault data is also used for fault diagnosis experiments. The results of both numerical simulation and experimental analysis indicate that AGSSET performs well in identifying the time-varying IF characteristic in nonstationary signals. It can also efficiently detect faults with high accuracy and strong stability.
Intelligent Levenberg–Marquardt neural network solution to flow of carbon nanotubes in a nozzle of liquid rocket engine
Authors: Muhammad Noor , Ahmed Naveed 
Published: November 2023

Abstract
In this research, we utilized artificial neural networks along with the Levenberg–Marquardt algorithm (ANN-LMA) to interpret numerical computations related to the efficiency of heat transfer in a regenerative cooling channel of a rocket engine. We used a mixture of Kerosene and carbon nanotubes (CNTs) for this purpose, examining both single-wall carbon nanotubes and multi-wall carbon nanotubes. The primary equations were converted into a dimensionless form using a similarity transformation technique. To establish a reference dataset for ANN- LMA and to analyze the movement and heat transfer properties of CNTs, we employed a numerical computation method called bvp4c, which is a solver for boundary value problems in ordinary differential equations using finite difference schemes combined with the Lobatto IIIA algorithm in MATLAB mathematical software. The ANN- LMA method was trained, tested and validated using these reference datasets to approximate the solutions of the flow model under different scenarios involving various significant physical parameters. We evaluated the accuracy of the proposed ANN- LMA model by comparing its results with the reference outcomes. We validated the performance of ANN- LMA in solving the Kerosene-based flow with CNTs in a rocket engine through regression analysis, histogram studies, and the calculation of the mean square error. The comprehensive examination of parameters undertaken in this research endeavor is poised to provide invaluable support to aerospace engineers as they endeavor to craft regenerative equipment with optimal efficiency. The pragmatic implications of our study are wide-ranging, encompassing domains as diverse as aerospace technology, materials science, and artificial intelligence. This research holds the potential to catalyze progress across multiple sectors and foster the evolution of increasingly efficient and sustainable systems.