bluuebunny/crossref_metadata_2025 huggingface dataset:
The Unmet Needs for Studying Chronic Pelvic/Visceral Pain Using Animal Models
Authors: Neto Ana Catarina , Santos-Pereira Mariana , Abreu-Mendes Pedro , Neves Delminda , Almeida Henrique , Cruz Francisco , Charrua Ana
Published: February 2023

The different definitions of chronic pelvic/visceral pain used by international societies have changed over the years. These differences have a great impact on the way researchers study chronic pelvic/visceral pain. Recently, the role of systemic changes, including the role of the central nervous system, in the perpetuation and chronification of pelvic/visceral pain has gained weight. Consequently, researchers are using animal models that resemble those systemic changes rather than using models that are organ- or tissue-specific. In this review, we discuss the advantages and disadvantages of using bladder-centric and systemic models, enumerating some of the central nervous system changes and pain-related behaviors occurring in each model. We also present some drawbacks when using animal models and pain-related behavior tests and raise questions about possible, yet to be demonstrated, investigator-related bias. We also suggest new approaches to study chronic pelvic/visceral pain by refining existing animal models or using new ones.
Effects of a New Soft Skills Metacognition Training Program on Self-Efficacy and Adaptive Performance
Authors: Joie-La Marle Chantal , Parmentier François , Weiss Pierre-Louis , Storme Martin , Lubart Todd , Borteyrou Xavier
Published: February 2023

Although soft skills training is called for by many scholars and managers, empirical studies on concrete training programs are scarce and do not always have the methodological rigor that is necessary to draw meaningful conclusions about their impact. In the present research, we investigate the effects of a new soft skills metacognition training program on self-efficacy and adaptive performance. To test these effects, we conducted an experiment with a sample of employees of a large firm (n = 180). The experiment included pre- and post-measurements and a control condition. The results suggested that participating in the training led to an increase in soft skills metacognition, self-efficacy, and four dimensions of adaptive performance, compared to a control condition. Mediation analyses suggested that an increase in soft skills metacognition led to an increase in self-efficacy, which led, in turn, to an increase in adaptive performance. Theoretical and practical implications are discussed, as well as limitations.
Effect of Soundscape on Place Attachment for Historical Blocks: A Case Study of Harbin, China
Authors: Zhao Wei , Rui Qingxuan , Zhu Xun , Xu Hongpeng
Published: February 2023

Sound is a memory carrier in places with rich history and culture, which can invoke place memories and images. The purpose of this paper is to explore the effect of soundscape on place attachment in different types of historical blocks. Six historical blocks in Harbin, China, were selected, and a questionnaire survey was conducted to evaluate place attachment and soundscape. The soundscape evaluations of harmony, quietness and nature were significantly higher in residential historical blocks than in tourism historical blocks. The mechanical sound source preference of residential historical blocks was lower than that of tourism historical blocks. The main components of place attachment were place bonding and identity, and place dependence, but in different orders in different types of historical blocks. In tourism historical blocks, the preference of sound source helps to enhance place attachment, especially the sounds of activities and equipment related to the history and culture of the blocks. Soundscapes promote place attachment in tourism historical blocks and are positively correlated with place satisfaction. There was no significant correlation between soundscape and place attachment in the residential historical blocks surveyed.
Modeling and Optimizing the Effect of Palm Oil Fuel Ash on the Properties of Engineered Cementitious Composite
Authors: Hong Wong Chi , Mohammed Bashar S. , Abdulkadir Isyaka , Liew M. S.
Published: February 2023

Supplementary cementitious materials (SCMs) are strongly advised as an alternative to cement to reduce its adverse environmental effects. One such SCMs is palm oil fuel ash (POFA), a waste material generated in large quantities in Southeast Asian countries, and there is insufficient data on its use in engineered cementitious composite (ECC). This study aims to optimize the properties of ECC using POFA as a cement replacement, by using 13 mixes developed by response surface methodology (RSM) with the POFA (at 20, 30, and 40% cement replacement levels) and PVA fiber (at 1, 1.5, and 2% volume fractions) as the input factors. The compressive, tensile, and flexural strengths, and tensile capacity (CS, TS, FS, and TC) were assessed. The microstructural properties were determined using Field-Emission Scanning Electron Microscopy (FESEM) and Mercury Intrusion Porosimetry (MIP). Results indicated that while the ductility and strain capacity increased with POFA, the strengths decreased by up to 51.5%. However, a structural POFA-ECC could be made with up to 30% POFA and 1–5% PVA fiber. The RSM optimization revealed 27.68% POFA and 2% PVA fiber as the optimal levels of the input factors, with the experimental validation correlating with the predicted values at less than 10% error.
Damage Effect of Thermal Shock on the Heated Granite at Different Cooling Rates
Authors: Li Xiang , Fan Xiaodong , Tao Ming , Yin Tubing , Huang Si
Published: February 2023

This study investigates the affecting pattern of cooling rates on the physical and mechanical properties of granite at high temperature. The different cooling rates are realized by cooling the heated granite specimens in the air at room temperature, −60 °C, and −100 °C. Slow cooling in the unplugged furnace is also performed on an additional set of specimens as a reference group. Physical and mechanical tests are performed on the granite specimens after thermal shock treatments. The results indicate a decreasing trend of the dry density, P-wave velocity, strength, and fracture toughness, and an increasing trend of the porosity, as the heating level or the cooling rate ascends. The microscopic observation on the fracture surface of the tested specimens manifests the deteriorating effect of thermal shock with a higher cooling rate, where the transition from trans-granular and intra-granular fracturing to intergranular fracturing serves to explain the variation pattern of the properties obtained in the lab tests.
Enhanced Photocatalytic Performance of Visible-Light-Driven BiVO4 Nanoparticles through W and Mo Substituting
Authors: Tsay Chien-Yie , Chung Ching-Yu , Chen Chin-Yi , Chang Yu-Cheng , Chang Chi-Jung , Wu Jerry J.
Published: February 2023

Bismuth vanadate (BiVO4), W-doped BiVO4 (BiVO4:W), and Mo-doped BiVO4 (BiVO4:Mo) nanoparticles were synthesized at pH = 4 using a green hydrothermal method. The effects of 2 at% W or Mo doping on the microstructural and optical characteristics of as-prepared BiVO4 nanoparticles and the effect of combining particle morphology modification and impurity dopant incorporation on the visible-light-derived photocatalytic degradation of dilute Rhodamine B (RhB) solution are studied. XRD examination revealed that these obtained BiVO4-based nanoparticles had a highly crystalline and single monoclinic phase. SEM and TEM observations showed that impurity doping could modify the surface morphology, change the particle shape, and reduce the particle diameter to enlarge their specific surface area, increasing the reactive sites of the photocatalytic process. XPS and FL measurements indicated that W- and Mo-doped nanoparticles possessed higher concentrations of oxygen vacancies, which could promote the n-type semiconductor property. It was found that the BiVO4:W and BiVO4:Mo powder samples exhibited better photocatalytic activity for efficient RhB removal than that shown by pristine BiVO4 powder samples under visible light illumination. That feature can be ascribed to the larger surface area and improved concentration of photogenerated charge carriers of the former.
A Thermodynamic and Kinetic Study on Electrochemical Esterification in Aroma-Enhanced Distilled Liquor (Baijiu)
Authors: Xu Haiyue , Li Qu , Yu Yougui , Zheng Qing
Published: February 2023

The development of low-alcohol Baijiu is consistent with demand for the industry’s sustainable development. However, the ester aroma of low-alcohol Baijiu is insipid and unstable—mainly due to the hydrolysis of esters during shelf life—thus reducing the industry scale of low-alcohol Baijiu to a significantly small range. An electrochemical method for improving low-alcohol Baijiu’s ester concentration and stability was investigated from the aspects of thermodynamics and kinetics. The key finding is that the new Baijiu’s ester content obtained through distillation is relatively high, exceeding its content in the thermodynamic equilibrium state. Thus, the ester will be hydrolyzed during shelf life. The idea of applying electrochemical catalytic esterification technology to the production of low-alcohol Baijiu in this study is directly derived from the production practice of Baijiu factories; it provides a direction for the further optimization of low-alcohol Baijiu to facilitate the production of an alternative product that will contribute to public health.
Rational Design Copper Nanocluster-Based Fluorescent Sensors towards Heavy Metal Ions: A Review
Authors: Yuan Lili , Liang Mengna , Hummel Matthew , Shao Congying , Lu Shun
Published: February 2023

Recently, copper nanoclusters (CuNCs) have attracted great research interest for their low synthesis cost, wide application, and easy functionalization. Until now, CuNCs have been developed and applied in multi-fields such as sensing, catalysis, light-emitting diode manufacturing, and cell imaging. Furthermore, the application of heavy metal ions (HMIs) detection is also regarded as a major part of fluorescence sensing and the necessity of detecting the makeup of HMIs (Ag+, Te3+, Co2+, Se6+, Hg2+, Mn2+, etc.) in organisms and the environment. This has promoted the development of CuNCs in fluorescence sensing. This paper reviews the research progress of CuNCs detection in HMIs, which can be divided into four parts. The synthesis and characterization of CuNCs are first described. Then, the synthesis methods making the types of CuNCs more varied are also summarized. Furthermore, mechanisms of fluorescence changes induced by HMIs are explained. After that, the relevant reports of CuNCs in several typical HMI detection are further listed. In addition, combined with the above content, the challenges and prospects of CuNCs in HMIs detection are also proposed.
Indulgent Parenting and the Psychological Well-Being of Adolescents and Their Parents
Authors: Feng Qinglan , Cui Ming
Published: February 2023

Adolescence is a time for identity development and exploration. Indulgent parenting during adolescence could be developmentally inappropriate and could be associated with adolescent psychological well-being problems. Little research on indulgent parenting, however, has included and investigated both adolescent and parental well-being problems. To extend the literature, the current study used both adolescent and parental reports in a dyadic context to investigate the association between indulgent parenting and the psychological well-being problems of both adolescents and their parents. This study used a sample of 128 adolescent–parent dyads. The findings from the actor–partner interdependence model (APIM) suggested that (1) the adolescent perceptions of behavioral indulgent parenting were significantly related to their own well-being problems; (2) the parents’ perceptions of relational and behavioral indulgent parenting were significantly related to their own well-being problems; and (3) no effects were found between adolescents and their parents. The findings from this study have implications for prevention and intervention programs to improve parenting practices and reduce parents’ well-being problems.
Relationship between Skeletal Muscle Thickness and Physical Activity in 4- to 6-Year-Olds in Japan
Authors: Deng Pengyu , Ozaki Hayao , Natsume Toshiharu , Ishihara Yoshihiko , Ke Dandan , Suzuki Koya , Naito Hisashi
Published: February 2023

Purpose: Physical activity (PA) is widely recognized as a key factor in promoting skeletal muscle growth, though little is known about the specific impact of PA on the skeletal muscle development of preschool children. The purpose of this study is to investigate whether there is a relationship between PA levels and skeletal muscle thickness in preschoolers. By exploring this relationship, we hope to gain a better understanding of how PA can be used to promote healthy skeletal muscle development in preschoolers. Methods: In this study, a total of 275 healthy Japanese preschoolers, aged 4–6 years, from seven nursery schools in the town of Togo were recruited. Participants were asked to wear an accelerometer for four consecutive days to record their daily steps and the amount of time spent in moderate-to-vigorous PA and t total physical activity. Muscle thickness (MTs) was measured using B-mode ultrasonography at four sites: the anterior and posterior thigh (AT and PT, respectively) and the anterior and posterior lower leg (AL and PL, respectively). Results: On weekdays, boys were found to be more physically active and engaged in significantly higher levels of total physical activity and moderate-to-vigorous PA than girls. Both boys and girls recorded more physical activity, daily steps, and higher levels of total physical activity and MVPA on weekdays compared to weekends. After adjusting for daylight duration, multivariable regression analyses revealed that increased total physical activity and moderate-to-vigorous PA were positively associated with greater muscle thickness size in the anterior tibialis (AT) and posterior lower leg (PL) muscles (β = 1.11 and β = 1.37 for AT, β = 1.18 and β = 0.94 for PL, p < 0.05) in Japanese preschoolers. Conclusions: The time spent involved in most of the different categories of moderate-to-vigorous PA was significantly higher for boys than for girls on the weekdays and weekends. Additionally, there was a positive correlation between time spent in moderate-to-vigorous PA and greater development of skeletal muscle in the lower body.
Constructing a Shariah Document Screening Prototype Based on Serverless Architecture
Authors: Che Mohd Salleh Marhanum , Nor Rizal Mohd , Yusof Faizal , Amiruzzaman Md
Published: February 2023

The aim of this research is to discuss the groundwork of building an Islamic Banking Document Screening Prototype based on a serverless architecture framework. This research first forms an algorithm for document matching based Vector Space Model (VCM) and adopts Levenshtein Distance for similarity setting. Product proposals will become a query, and policy documents by the central bank will be a corpus or database for document matching. Both the query and corpus went through preprocessing stage prior to similarity analysis. One set of queries with two sets of corpora is tested in this research to compare similarity values. Finally, a prototype of Shariah Document Screening is built based on a serverless architecture framework and ReactJS interface. This research is the first attempt to introduce a Shariah document screening prototype based on a serverless architecture technology that would be useful to the Islamic financial industry towards achieving a Shariah-compliant business. Given the development of Fintech, the output of this research study would be a complement to the existing Fintech applications, which focus on ensuring the Islamic nature of the businesses.
Eco-Friendly, Low-Cost, and Flexible Cotton Fabric for Capacitive Touchscreen Devices Based on Graphite
Authors: Alhashmi Alamer Fahad , Aqiely Wedad
Published: February 2023

Cotton fabrics with high electrical conductivity were prepared using graphite dispersed in ethanol as the conductive material. The graphite particles were drop-cast onto the cotton fabrics at room temperature. The samples were characterized by SEM, EDX, XPS, and XRD. In addition, the electrical properties of the cotton samples were investigated using a four-probe technique. The concentration of the dispersed graphite was increased to a saturation concentration of 74.48 wt% to investigate the relation between the sheet resistance of the conductive cotton and the graphite concentration. With increasing graphite concentration, the sheet resistance decreased and reached the minimum value of 7.97 Ω/□ at a saturation concentration of 74.48 wt%. Samples with low, medium, and high graphite concentration showed semiconducting metallic behavior at a transition temperature of 90 °C. Based on their individual electrical properties, a smart glove was fabricated for touchscreen devices such as cell phones and self-service devices by dropping a small amount of dispersed graphite into one of the fingertips of the glove. The smart glove showed high efficiency and durability up to 10 wash cycles.
Diagnosis of Dengue Virus Infections Imported to Hungary and Phylogenetic Analysis of Virus Isolates
Authors: Nagy Orsolya , Nagy Anna , Koroknai Anita , Csonka Nikolett , Takács Mária
Published: February 2023

Background: Dengue virus is one of the most important arbovirus infections of public health concern. Between 2017 and June 2022, 75 imported dengue infections were confirmed by laboratory diagnostic methods in Hungary. Our study aimed to isolate the imported Dengue strains and characterize them by whole-genome sequencing. Methods: Laboratory diagnosis of imported infections was carried out using both serological and molecular methods. Virus isolation was attempted on Vero E6 cell lines. An in-house amplicon-based whole-genome sequencing method was applied for the detailed molecular characterization of the isolated virus strains. Results: From 75 confirmed Dengue infected patients, 68 samples were used for virus isolation. Isolation and whole-genome sequencing were successful in the case of eleven specimens. Isolated strains belonged to Dengue-1,-2,-3 serotypes. Discussion: The isolated strains corresponded to the circulating genotypes of the visited geographic area, and some of the genotypes were linked with more severe DENV cases in the literature. We found that multiple factors, including viral load, specimen type, and patient antibody status, influence the isolation efficacy. Conclusions: Analysis of imported DENV strains can help estimate the outcomes of a possible local DENV transmission in Hungary, a threat from the near future.
The Adaptive Force as a Potential Biomechanical Parameter in the Recovery Process of Patients with Long COVID
Authors: Schaefer Laura V. , Bittmann Frank N.
Published: February 2023

Long COVID patients show symptoms, such as fatigue, muscle weakness and pain. Adequate diagnostics are still lacking. Investigating muscle function might be a beneficial approach. The holding capacity (maximal isometric Adaptive Force; AFisomax) was previously suggested to be especially sensitive for impairments. This longitudinal, non-clinical study aimed to investigate the AF in long COVID patients and their recovery process. AF parameters of elbow and hip flexors were assessed in 17 patients at three time points (pre: long COVID state, post: immediately after first treatment, end: recovery) by an objectified manual muscle test. The tester applied an increasing force on the limb of the patient, who had to resist isometrically for as long as possible. The intensity of 13 common symptoms were queried. At pre, patients started to lengthen their muscles at ~50% of the maximal AF (AFmax), which was then reached during eccentric motion, indicating unstable adaptation. At post and end, AFisomax increased significantly to ~99% and 100% of AFmax, respectively, reflecting stable adaptation. AFmax was statistically similar for all three time points. Symptom intensity decreased significantly from pre to end. The findings revealed a substantially impaired maximal holding capacity in long COVID patients, which returned to normal function with substantial health improvement. AFisomax might be a suitable sensitive functional parameter to assess long COVID patients and to support therapy process.
Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification
Authors: Anjum Mohd , Shahab Sana , Yu Yang
Published: February 2023

Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively.
Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology
Authors: von Ende Elizabeth , Ryan Sean , Crain Matthew A. , Makary Mina S.
Published: February 2023

Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is capable of using neural networks to extract more complex, high-level data, even from unlabeled data sets, and better emulate, or even exceed, the human brain. Advances in AI have and will continue to revolutionize medicine, especially the field of radiology. Compared to the field of interventional radiology, AI innovations in the field of diagnostic radiology are more widely understood and used, although still with significant potential and growth on the horizon. Additionally, AI is closely related and often incorporated into the technology and programming of augmented reality, virtual reality, and radiogenomic innovations which have the potential to enhance the efficiency and accuracy of radiological diagnoses and treatment planning. There are many barriers that limit the applications of artificial intelligence applications into the clinical practice and dynamic procedures of interventional radiology. Despite these barriers to implementation, artificial intelligence in IR continues to advance and the continued development of machine learning and deep learning places interventional radiology in a unique position for exponential growth. This review describes the current and possible future applications of artificial intelligence, radiogenomics, and augmented and virtual reality in interventional radiology while also describing the challenges and limitations that must be addressed before these applications can be fully implemented into common clinical practice.
Gene Mutation Spectrum among Alpha-Thalassaemia Patients in Northeast Peninsular Malaysia
Authors: Vijian Divashini , Wan Ab Rahman Wan Suriana , Ponnuraj Kannan Thirumulu , Zulkafli Zefarina , Bahar Rosnah , Yasin Norafiza , Hassan Syahzuwan , Esa Ezalia
Published: February 2023

(1) Background: Alpha (α)-thalassaemia is a genetic disorder that affects 5% of the world population. Deletional or nondeletional mutations of one or both HBA1 and HBA2 on chromosome 16 will result in reduced production of α-globin chains, a component of haemoglobin (Hb) that is required for the formation of red blood cells (RBCs). This study aimed to determine the prevalence, haematological and molecular characterisations of α-thalassaemia. (2) Method: The parameters were based on full blood count, high-performance liquid chromatography and capillary electrophoresis. The molecular analysis involved gap-polymerase chain reaction (PCR), multiplex amplification refractory mutation system-PCR, multiplex ligation-dependent probe amplification and Sanger sequencing. (3) Results: With a total cohort of 131 patients, the prevalence of α-thalassaemia was 48.9%, leaving the remaining 51.1% with potentially undetected α gene mutations. The following genotypes were detected: -α3.7/αα (15.4%), -α4.2/αα (3.7%), --SEA/αα (7.4%), αCSα/αα (10.3%), αAdanaα/αα (0.7%), αQuong Szeα/αα (1.5%), -α3.7/-α3.7 (0.7%), αCSα/αCSα (0.7%), -α4.2/αCSα (0.7%), –SEA/αCSα (1.5%), –SEA/αQuong Szeα (0.7%), -α3.7/αAdanaα (0.7%), --SEA/-α3.7 (2.2%) and αCSα/αAdanaα (0.7%). Indicators such as Hb (p = 0.022), mean corpuscular volume (p = 0.009), mean corpuscular haemoglobin (p = 0.017), RBC (p = 0.038) and haematocrit (p = 0.058) showed significant changes among patients with deletional mutations, but not between patients with nondeletional mutations. (4) Conclusions: A wide range of haematological parameters was observed among patients, including those with the same genotype. Thus, a combination of molecular technologies and haematological parameters is necessary for the accurate detection of α-globin chain mutations.
Sound Recognition Method of Coal Mine Gas and Coal Dust Explosion Based on GoogLeNet
Authors: Yu Xingchen , Li Xiaowei
Published: February 2023

To solve the problems of backward means of coal mine gas and coal dust explosion monitoring, late reporting, and low leakage rate, a sound recognition method of coal mine gas and coal dust explosion based on GoogLeNet was proposed. After installing mining pickups in key monitoring areas of coal mines to collect the sounds of the working equipment and the environment, the collected sound was analyzed by continuous wavelet to obtain its scale coefficient map. This was then imported into GoogLeNet to obtain the recognition model of coal mine gas and coal dust explosions. The test sound was obtained by continuous wavelet analysis to obtain the scale coefficient map, brought into the completed training recognition model to obtain the sound signal class, and verified by experiment. Firstly, the scale coefficient map extracted from the sound signal by continuous wavelet analysis showed that the similarity between the subjective and objective indicators of the wavelet coefficient maps of the gas explosion sound and coal dust explosion sound was higher, but the difference between these and the rest of the coal mine sounds was clearer, helping to effectively distinguish gas and coal dust explosion sounds from other sounds. Secondly, the experimental results of GoogLeNet parameters can be obtained. When the dropout parameter is 0.5 and the initial learning rate is 0.001, the recognition effect of the model established by GoogLeNet was optimal. According to the selected parameters, the training loss, testing loss, training recognition rate, and testing recognition rate of the model are all in line with expectations. Finally, the experimental recognition results show that the recognition rate of the proposed method is 97.38%, the recall rate is 86.1%, and the accuracy rate is 100% for the case of a 9:1 ratio of test data to training data, and the overall recognition effect of the proposed GoogLeNet is significantly better than that of vgg and Alexnet, which can effectively solve the problem of under-sampling of coal mine gas and coal dust explosion sounds and can meet the need for the intelligent recognition of coal mine gas and dust explosions.
Primary School Teachers’ Perspectives on Emergency Remote Teaching of Mathematics: Challenges and Opportunities for the Post-COVID-19 Era
Authors: Meletiou-Mavrotheris Maria , Konstantinou Panagiota , Katzis Konstantinos , Stylianidou Nayia , Sofianidis Angelos
Published: February 2023

In this study, we aimed to determine Cypriot primary mathematics teachers’ perspectives and lived experiences during the transition to emergency remote teaching (ERT) in the early stage of the COVID-19 pandemic. An in-depth online survey combining closed-ended and open-ended questions was administered to sixty-two (n = 62) educators teaching mathematics in public primary schools during the first lockdown in spring, 2020. The data from closed-ended questions were analyzed using descriptive and inferential statistics, whereas, for the open-ended questions, a thematic analysis approach was employed. Our findings provide useful insights regarding teachers’ self-reported technology backgrounds and levels of instruction regarding the use of technology in mathematics prior to the pandemic, as well their level of preparedness for ERT and the main challenges they faced in implementing ERT of mathematics. Our findings also indicate teachers’ levels of satisfaction with their ERT practices and their beliefs concerning the extent of achievement of the curriculum learning objectives through ERT, and how these varied based on teachers’ self-reported levels of familiarity with technology, their self-reported levels of preparedness for teaching at a distance, and their engagement (or non-engagement) in synchronous instruction during ERT. Teachers’ suggestions, based on their experiences from the lockdown period, regarding how to transform mathematics teaching and learning in the post-COVID-19 era are also presented.
Toward a Framework of Integrating Ability: Conceptualization and Design of an Integrated Physics and Mathematics Test
Authors: De Loof Haydée , Ceuppens Stijn , De Meester Jolien , Goovaerts Leen , Thibaut Lieve , De Cock Mieke , Dehaene Wim , Depaepe Fien , Knipprath Heidi , Boeve-de Pauw Jelle , Van Petegem Peter
Published: February 2023

The awareness that many problems in our society are interdisciplinary in nature and require the integration of multiple STEM (Science, Technology, Engineering, Mathematics) concepts to solve them has given rise to a new instructional approach, called “integrated STEM education”. Integrated STEM education aims to remove the barriers from the STEM fields and has the potential to increase students’ interest and motivation for learning, as well as to lead to improved achievement. It is important to assess the effectiveness of educational STEM initiatives in terms of students’ integrating ability, but to date, no such instruments are available. This study provides a definition of “integrating ability” and establishes a framework for understanding its components. Based on this definition and framework, a multiple-choice instrument for testing integrated physics and mathematics in the ninth grade (IPM9) was developed and validated. The definition and framework for integrating ability and the construction guidelines for an integrated test, can be used by researchers to assess students’ ability to integrate STEM subjects.
Grid-Forming Converter Overcurrent Limiting Strategy Based on Additional Current Loop
Authors: Liu Chongru , Xi Jiahui , Hao Qi , Li Jufeng , Wang Jinyuan , Dong Haoyun , Su Chenbo
Published: February 2023

Compared with current source converters, voltage source converters (grid-forming converters) have better frequency support capabilities, voltage support capabilities, and regulation performance, thus they have broad application prospects. However, the grid-forming (GFM) converter has insufficient current control ability, and it easily causes problems such as overcurrent issues when a fault occurs. Thus, this insufficiency is one of the most important challenges the GFM converter is faced with. Aiming to solve the problems mentioned above, this paper proposes a control method of a GFM converter achieved with a low-pass filter structure and an additional current loop. The additional current loop controls the dq-axis current components by acting on the outer loop to generate appropriate phase and voltage amplitude reference. The low-pass filter structure is used to solve the system frequency stability problem caused by the inclusion of the additional current loop. On the premise of ensuring that the system frequency meets the grid-connection requirements, the proposed strategy rapidly limits the output current within the allowable range and guarantees expected voltage source characteristics of the converter during the fault period. Finally, the effectiveness and superiority of the proposed control strategy are verified by MATLAB/Simulink simulations.
Deep-Learning-Based Sequence Causal Long-Term Recurrent Convolutional Network for Data Fusion Using Video Data
Authors: Jeon DaeHyeon , Kim Min-Suk
Published: February 2023

The purpose of AI-Based schemes in intelligent systems is to advance and optimize system performance. Most intelligent systems adopt sequential data types derived from such systems. Realtime video data, for example, are continuously updated as a sequence to make necessary predictions for efficient system performance. The majority of deep-learning-based network architectures such as long short-term memory (LSTM), data fusion, two streams, and temporal convolutional network (TCN) for sequence data fusion are generally used to enhance robust system efficiency. In this paper, we propose a deep-learning-based neural network architecture for non-fix data that uses both a causal convolutional neural network (CNN) and a long-term recurrent convolutional network (LRCN). Causal CNNs and LRCNs use incorporated convolutional layers for feature extraction, so both architectures are capable of processing sequential data such as time series or video data that can be used in a variety of applications. Both architectures also have extracted features from the input sequence data to reduce the dimensionality of the data and capture the important information, and learn hierarchical representations for effective sequence processing tasks. We have also adopted a concept of series compact convolutional recurrent neural network (SCCRNN), which is a type of neural network architecture designed for processing sequential data combined by both convolutional and recurrent layers compactly, reducing the number of parameters and memory usage to maintain high accuracy. The architecture is challenge-able and suitable for continuously incoming sequence video data, and doing so allowed us to bring advantages to both LSTM-based networks and CNNbased networks. To verify this method, we evaluated it through a sequence learning model with network parameters and memory that are required in real environments based on the UCF-101 dataset, which is an action recognition data set of realistic action videos, collected from YouTube with 101 action categories. The results show that the proposed model in a sequence causal long-term recurrent convolutional network (SCLRCN) provides a performance improvement of at least 12% approximately or more to be compared with the existing models (LRCN and TCN).
A Misalignment-Insensitive Hybrid IPT System with Constant Current Output Based on Parameter Optimization
Authors: Gong Zhaowei , Li Jingang , Tong Xiangqian
Published: February 2023

High misalignment-insensitive capacity is of great importance for inductive power transfer (IPT) systems. A hybrid topology based on a parameter optimization design method is presented to obtain constant current output regardless of the coupling and load. DDQ coils are employed to protect against system key parameter changes, and a particle swarm optimization (PSO) method has been presented to achieve a nearly constant current output without using complex control schemes. A hybrid IPT system using DDQ coils has been built. The experimental results verify that the current fluctuation is within 5% when the load varies from 5 Ω to 10 Ω within 50% misalignment of the coupling pads. Moreover, the maximum system DC–DC efficiency is up to 91%.
Modelling the Operation Process of Light Utility Vehicles in Transport Systems Using Monte Carlo Simulation and Semi-Markov Approach
Authors: Oszczypała Mateusz , Ziółkowski Jarosław , Małachowski Jerzy
Published: February 2023

This research paper presents studies on the operation process of the Honker 2000 light utility vehicles that are part of the Polish Armed Forces transport system. The phase space of the process was identified based on the assumption that at any given moment the vehicle remains in one of four states, namely, task execution, awaiting a transport task, periodic maintenance, or repair. Vehicle functional readiness and technical suitability indices were adopted as performance measures for the technical system. A simulation model based on Monte Carlo methods was developed to determine the changes in the operational states. The occurrence of the periodic maintenance state is strictly determined by a planned and preventive strategy of operation applied within the analysed system. Other states are implementations of stochastic processes. The original source code was developed in the MATLAB environment to implement the model. Based on estimated probabilistic characteristics, the authors validated 16 simulation models resulting from all possible cumulative distribution functions (CDFs) that satisfied the condition of a proper match to empirical data. Based on the simulated operation process for a sample of 19 vehicles over the assumed 20-year forecast horizon, it was possible to determine the functional readiness and technical suitability indices. The relative differences between the results of all simulation models and the results obtained through the semi-Markov model did not exceed 6%. The best-fit model was subjected to sensitivity analysis in terms of the dependence between functional readiness and technical suitability indices on vehicle operation intensity. As a result, the proposed simulation system based on Monte Carlo methods turned out to be a useful tool in analysing the current operation process of means of transport in terms of forecasts related to a current environment, as well as when attempting its extrapolation.
Hybrid Propulsion Efficiency Increment through Exhaust Energy Recovery—Part 2: Numerical Simulation Results
Authors: Pipitone Emiliano , Caltabellotta Salvatore , Sferlazza Antonino , Cirrincione Maurizio
Published: February 2023

The efficiency of hybrid electric vehicles may be substantially increased if the energy of exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered using a properly designed turbo-generator and employed for vehicle propulsion. Previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% employing a 20 kW turbine on a 100 HP-rated power thermal unit. The innovative thermal unit proposed here is composed of a supercharged engine endowed with a properly designed turbo-generator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In this two-part work, the realistic efficiency of the innovative thermal unit for hybrid vehicles is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors presented a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, thanks to some assumptions and approximations, allows fast determination of the right turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine here considered is quite different from the ones commonly employed for turbocharging applications; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In this paper, Part 2, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantages that could be achieved by the implementation of the turbo-generator on a hybrid propulsion system are evaluated through the performance prediction model under different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbo-compound system could gain vehicle efficiency improvement between 3.1% and 17.9%, according to the output power delivered, with an average efficiency increment of 10.9% evaluated on the whole operating range.
The Impact of Battery Storage on Power Flow and Economy in an Automated Transactive Energy Market
Authors: Zhang Shida , Musilek Petr
Published: February 2023

This article explores the use of battery energy storage in a transactive energy approach for a heavily solar-penetrated community. We hypothesize that the efficient market interactions between independently acting, fully automated agents (some equipped with battery energy storage) can result in both bill savings and improvements in power flow, without explicitly optimizing for power flow ahead of time. A test setting of nine typical residential prosumers and one heavily loaded prosumer is used. The heavily loaded prosumer initially experiences multiple undervoltage violations, and a 13.5 kWh battery is installed to alleviate the problem. Two profile-shaping strategies are compared. The first scenario uses greedy control that maximizes self-sufficiency, while the second scenario uses a local market to enable energy trading between participants and a rule-based trading and management agent for control. The results show that the first scenario made minimal improvements to power flow, but the second scenario eliminated all occurrences of under-voltage violations. Furthermore, the total amount of energy from the grid is reduced by 24.3%, and the amount of energy injected into the grid is reduced by 39.2%. This results in lower bills for every participant and a bill reduction of 16.7% for the community as a whole.
Clustering Method for Load Demand to Shorten the Time of Annual Simulation
Authors: Tanigawa Yuya , Krishnan Narayanan , Oomine Eitaro , Yona Atushi , Takahashi Hiroshi , Senjyu Tomonobu
Published: February 2023

UC (unit commitment) for grid operation has been attracting increasing attention due to the growing interest in global warming. Compared to other methods, MILP, which is one of the calculation methods for UC, has the disadvantage of a long calculation time, although it is more accurate in considering constraints and in finding solutions. However, RLCs (representative load curves) require a more accurate clustering method to select representative dates because the calculation results vary greatly depending on the clustering method. DBSCAN, one of the clustering methods, has the feature that the clustering accuracy varies depending on two parameters. Therefore, this paper proposes two algorithms to automatically determine the two parameters of DBSCAN to perform RLCs using DBSCAN. In addition, since DBSCAN has the feature of being able to represent different data as two-dimensional elements, a survey of the data to be used as clustering was conducted. As a result, the proposed algorithms enabled a more accurate clustering than the conventional method. It was also proved that clustering including temperature and load demand as clustering classification factors enables clustering with higher accuracy. The simulation with shorter time was also possible for the system including storage batteries as a demand response.
Rapid Prototyping of Three-Phase AC Machine Drive System with Subtractive and Additive Manufacturing
Authors: Kang Ye Gu , Lee Kang Min , Choi Gilsu
Published: February 2023

We propose a method for rapid prototyping of a three-phase AC drive system for educational purposes. The proposed method allows college students to design and manufacture a drive system comprising three-phase inverters, a permanent-magnet (PM) machine, a controller, and sensors within a semester. The rapid prototyping process, which requires less than a day, enables efficient iteration and testing during the development process. In addition to addressing the electrical design considerations, this study also addresses the mechanical aspects of the drive system, including the use of coreless PM machines fabricated, using additive manufacturing technology, and the inverter manufacturing process, utilizing an auto-milling machine. Finally, we provide details of the rapid prototyping of closed-loop control, based on sensor feedback to regulate the rotating magnetic fields and output torque.
A Comparison Study of Hydro-Compact Generators with Horizontal Spiral Turbines (HSTs) and a Three-Blade Turbine Used in Irrigation Canals
Authors: Monatrakul Wiroon , Senawong Kritsadang , Sritram Piyawat , Suntivarakorn Ratchaphon
Published: February 2023

This study aimed to present the experimental results of two types of turbines and attachments used in a hydro-compact generator. Two Horizontal Spiral Turbines (HSTs) with blade angles of eighteen and twenty-one degrees, respectively, and a three-blade turbine were tested and experimented in a laboratory at five levels of water flow rate ranging from 1–2 m/s. After the efficiency and torque values of each turbine were identified, they were installed in two 200 W power generator systems: (1) with a “diffuser” attachment; and (2) with an “in-line+diffuser+nozzle chamber” attachment, and tested in a local irrigation canal with 1.2 m/s. The results from the laboratory indicated that the HST with a twenty-one degree blade angle had 38.10% efficiency at the water flow rate of 2 m/s. It could reach 120.0 rpm and produced 212 Nm of torque. The results from the field experiment revealed that the combination of the power generator with the twenty-one degree blade angle HST and the in-line + diffuser + nozzle chamber attachment was the most efficient, with 284 Nm of torque at 108 rpm and could generate 67.63 W of electrical power. When the water flow rate of the irrigation canal reached 1.5 m/s, it could reach 114 rpm and generate 129.2 W. This hydro-compact generator set is suitable for irrigation canals with a water flow rate ranging from 1–1.5 m/s.
Towards Renewable Energy: Opportunities and Challenges
Authors: Tvaronavičienė Manuela
Published: February 2023

In current times, when the rising demand for energy causes the deterioration of our planet, the transition towards renewable sources seems inevitable [...]