bluuebunny/crossref_metadata_2025_split huggingface dataset:
E-Moticon's Effective Curriculum Revolutionizes Technical Education
Authors: Ramadhani Mohammad Afif , Cahyaning Astuti Cindy 
Published: May 2024

This research focuses on developing an E-Moticon E-Module as a digital learning tool for classroom use in the field of basic computer network and telecommunications. Utilizing the ADDIE method, the study includes expert validation and student trials to assess the media's effectiveness. Results show an 84.31% approval rate from experts and an 82.14% acceptance rate from students, indicating high feasibility and positive reception. This E-Module addresses a knowledge gap in tailored technical learning resources and highlights the potential impact of digital media integration in enhancing classroom learning experiences. Further research is recommended to validate its broader applicability and effectiveness in larger-scale classroom settings.

Highlight:

Tailored Technical Resource: E-Moticon fills gaps in technical learning for classrooms.
High Feasibility: Expert and student approval rates exceed 80%, validating effectiveness.
Structured Development: ADDIE method ensures systematic and effective media creation.

Keyword: E-Moticon, E-Module, Digital Learning, Classroom Integration, ADDIE Method
The Influence Of The Project-Based Learning Model On The Scientific Creativity Of Physics Education Undergraduate Students At Madura Islamic University
Authors: Sumo Maimon , Jatmiko Budi , Supardi Zainul Arifin Imam , Suprianto Suprianto 
Published: May 2024

Scientific creativity is a skill needed in the 21st century. 21st-century skills are known as 4C skills, one of which is being creative to produce innovative work to answer the challenges of the Industrial Revolution era 5.0. The Project-based learning model is a learning model that can guide students to increase students' scientific creativity through a series of questions that direct and explore students' scientific knowledge and creativity. This research used a quasi-experimental research design with 36 students divided into control classes and experimental classes. This research aims to see how much influence the PjBL learning model has in increasing the scientific creativity of students in physics subjects in terms of the implementation of the model and the influence of the PjBL model. The instruments used to obtain data were essay test questions for pretest and posttest, and observation sheets to observe the implementation of learning in class. All test questions have been validated by experts and tried out to test the reliability of the questions, level of difficulty, and distinguishing power of the questions using the ANATES Essay Questions application. Concept mastery data (posttest) was analyzed by hypothesis testing (t-test) using the SPSS application. The results of the research show that: (1) There is a significant influence of using the Project-based learning model on students' mastery of physics concepts. A significance value of 0.031 (Sig < 0.05) was obtained at the 95% confidence level. (2) The implementation of the project-based learning model accompanied by probing prompting techniques is included in the good category

Keywords: Scientific Creativity, Project Based Learning
Pressure Dependent Nonlinearities of a Compact Hydro-Pneumatic Strut with Integrated Gas-Oil Chamber
Authors: Seifi Abolfazl , Rakheja Subhash , Yin Yuming 
Published: May 2024

Abstract

Hydro-Pneumatic Struts (HPS) are widely implemented in automobile, aerospace, and construction industries, mainly for the purpose of vibration and shock absorption. The HPS design with integrated gas-oil chamber is relatively more compact and robust, while mixing gas and oil inside the HPS generates gas-oil emulsion and more nonlinearities. This study formulated a nonlinear analytical model of the compact HPS with gas-oil emulsion, considering the real gas law and pressure-dependent LuGre friction model. The polytropic version of the van der Waals (vdW) method for real gas is applied to represent the thermodynamic behavior of nitrogen. The experimental data were collected at a near constant temperature of 30°C with three charging pressures under excitations in the frequency range of 0.5-6Hz, considering two flow connection configurations between chambers as one- and two-bleed orifice. The nonlinear behavior of the gas volume fraction of the emulsion was identified based on peak strut velocity and charge pressure. Discharge coefficients of bleed and check valves were determined as a function of the instantaneous pressure difference between chambers. The parameters of the pressure-dependent LuGre model, such as the Stiction force and Coulomb force apart from stiffness and damping coefficient of bristle deflection, were also investigated considering the effect of pressure variation. Compared to the generally used ideal gas and Coulomb friction models, the proposed model considerably improved the prediction accuracy of the total force and pressures of HPS, with normalized root-mean-square deviations of about 8%.
Linking affected community and academic knowledge: community-based research for the 2024 Noto Peninsula Earthquake
Authors: Seto Shuji , Okuyama Junko , Iwasaki Toshiki , Fukuda Yu , Matsuzawa Toru , Ito Kiyoshi , Takakura Hiroki , Terada Kenjiro , Imamura Fumihiko 
Published: May 2024

Abstract

On January 1, 2024, Japan was hit by the Noto Peninsula Earthquake. While the victims are receiving assistance, Japan must also begin to recover from the disaster. To facilitate recovery from the Noto Peninsula earthquake, we introduce a community-based participatory research program, implemented in cooperation between universities and local communities after the 2011 Great East Japan Earthquake. In this project, the university and the town of Shichigahama, one of the affected areas, collaborated to hold annual workshops in the target area, which evolved into a climate monitoring survey. Even in Japan, where disaster prevention planning is common, various problems arise in the process of emergency response, recovery and reconstruction, and building back better when disasters beyond expectations occur. It is difficult for residents and local governments to solve these problems alone, and it is desirable for experts to participate in the response process. In this paper, we interviewed town hall official and university officials as representatives of local residents regarding this project and discussed their mutual concerns. The community-based participatory research framework developed in the Shichigahama project could be used effectively in recovering from the Noto Peninsula Earthquake as well as in future reconstruction and disaster management projects.
A novel many-objective route optimization algorithm for multimodal transportation under uncertain demand
Authors: Cui Tianxu , Shi Ying , Wang Jingkun , Ding Rijia , Li Jinze , Li Kai 
Published: May 2024

Abstract

Over the past decades, multimodal transportation has played a crucial role in modern logistics and transportation systems due to its high capacity and low cost. However, multimodal transportation, which is mainly driven by fossil fuels, potentially contributes to significant carbon emissions. In the context of global sustainable development, reducing carbon emissions from the transportation sector has far-reaching implications for supporting society-wide carbon neutrality. In this paper, we have developed for the first time a many-objective multimodal transportation route optimization (MTRO) model that simultaneously considers economic cost, carbon emission cost, time cost, and customer satisfaction, and solve it using the Non-dominated Sorting Genetic Algorithm Version III (NSGAIII). Second, to further improve the convergence performance, we introduce a fuzzy decision variable framework to improve the NSGAIII algorithm. This framework can reduce the search range of optimization algorithm in the decision space and make it converge better. Finally, we conducted a large number of simulation experiments on test problems to verify the applicability and superiority of the improved algorithm, and applied it to MTRO problems under uncertain demand. This work fills the research gap for MTRO problems and provides guidance for relevant departments to develop transportation and decarbonization plans.
Inhibition of PKC-δ retards kidney fibrosis via inhibiting cGAS-STING signaling pathway in mice
Authors: Shi Yonghong , Wang Dongyun , Li Yue , Li Guiying , Liu Mengyu , Zhou Zihui , Wu Ming , Song Shan , Bian Yawei , Dong Jiajia , Li Xinran , Du Yunxia , Zhang Tao 
Published: May 2024

Abstract

Kidney fibrosis is considered to be the ultimate aggregation pathway of chronic kidney disease (CKD), but its underlying mechanism remains elusive. Protein kinase C-delta (PKC-δ) plays critical roles in the control of growth, differentiation, and apoptosis. In this study, we found that PKC-δ was highly upregulated in human biopsy samples and mouse kidneys with fibrosis. Rottlerin, a PKC-δ inhibitor, alleviated unilateral ureteral ligation (UUO)-induced kidney fibrosis, inflammation, VDAC1 expression, and cGAS-STING signaling pathway activation. Adeno-associated virus 9 (AAV9)-mediated VDAC1 silencing or VBIT-12, a VDAC1 inhibitor, attenuated renal injury, inflammation, and activation of cGAS-STING signaling pathway in UUO mouse model. Genetic and pharmacologic inhibition of STING relieved renal fibrosis and inflammation in UUO mice. In vitro, hypoxia resulted in PKC-δ phosphorylation, VDAC1 oligomerization, and activation of cGAS-STING signaling pathway in HK-2 cells. Inhibition of PKC-δ, VDAC1 or STING alleviated hypoxia-induced fibrotic and inflammatory responses in HK-2 cells, respectively. Mechanistically, PKC-δ activation induced mitochondrial membrane VDAC1 oligomerization via direct binding VDAC1, followed by the mitochondrial DNA (mtDNA) release into the cytoplasm, and subsequent activated cGAS-STING signaling pathway, which contributed to the inflammation leading to fibrosis. In conclusion, this study has indicated for the first time that PKC-δ is an important regulator in kidney fibrosis by promoting cGAS-STING signaling pathway which mediated by VDAC1. PKC-δ may be useful for treating renal fibrosis and subsequent CKD.
GmCOL4-GmZTL1 interaction co-regulates GmSBH1 to improve seed deterioration under high temperature and humidity stress and affect leaf growth and development
Authors: Mu Kebin , Shu Yingjie , Chen Ming , Chen Keke , Peng Yuxin , Hu Huimin , Shen Yingzi , Zhang Xi , Zhuang Lifang , Ma Hao 
Published: May 2024

Abstract

BBX transcription factors have a transcriptional regulatory role in response to light, circadian cues, and brassinosteroid-light crosstalk signaling. However, the functions of BBX in soybean resistance to seed deterioration have not been shown. In our previous study, a soybean gene GmSBH1 and a HSE cis-element of GmSBH1 promoter were found in response to high temperature and humidity (HTH) stress, respectively. GmCOL4 was a candidate protein, which bound to HSE cis-element. In the present study, GmCOL4 was isolated and characterized. Subcellular localization and transcriptional activation assays showed that GmCOL4 was a nuclear protein with transcriptional activation function. The BBOX2 domain was found to play an obvious role in transcriptional activation activity of GmCOL4. Furthermore, GmCOL4 interacted with GmZTL1 was confirmed in vivo and in vitro. GmCOL4 and GmZTL1 presented different expression patterns among diverse soybean tissues and were synergistically involved in response to HTH stress in developing seeds, respectively. Overexpression of GmCOL4 and GmZTL1 could alter tobacco phenotypes and enhance developing seed tolerance to seed deterioration under HTH stress, respectively. Based on these results, a regulation network was conjectured, GmCOL4 interacts with GmZTL1 to co-regulate the GmSBH1 via directly binding to the HSE cis-element, thereby enhancing the soybean resistance to seed deterioration under HTH stress and affecting leaf growth and development.
DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property
Authors: Ramezani Maryam , Ahadinia Aryan , Farhadi Erfan , Rabiee Hamid R. 
Published: May 2024

Abstract

The fast growth of social networks and their data access limitations in recent years have led to increasing difficulty in obtaining the complete topology of these networks. However, diffusion information over these networks is available, and many algorithms have been proposed to infer the underlying networks using this information. The previously proposed algorithms only focus on inferring more links and ignore preserving the critical topological characteristics of the underlying social networks. In this paper, we propose a novel method called DANI to infer the underlying network while preserving its structural properties. It is based on the Markov transition matrix derived from time series cascades, as well as the node-node similarity that can be observed in the cascade behavior from a structural point of view. In addition, the presented method has linear time complexity (increases linearly with the number of nodes, number of cascades, and square of the average length of cascades), and its distributed version in the MapReduce framework is also scalable. We applied the proposed approach to both real and synthetic networks. The experimental results showed that DANI has higher accuracy and lower run time while maintaining structural properties, including modular structure, degree distribution, connected components, density, and clustering coefficients, than well-known network inference methods.
Probabilistic estimation of stop words from document-term matrix for topic analysis application.
Authors: Harada Nami , Kano Yutaka 
Published: May 2024

Abstract

In topic analysis, it is important to remove stop words to make inference about topics. Prior knowledge about the data is necessary to remove stop words. Stop words imply no contribution and interference of topic estimation. There is a suggestion of probabilistic inference of stop words. Probabilistic inference of stop words without prior knowledge about the data will assist in topic analysis application to unfamiliar languages and other various data fields.
Harmonizing Innovation: Chorus Work Creation and Innovation through Deep Learning and Generative Models
Authors: Zhang Hao 
Published: May 2024

Abstract

A precise characterization of the chorus's timbre is essential for accurate identification. It is imperative to provide a detailed representation of the timbre for analyzing temporal frequency domain characteristics, inverted spectrum features, sparse attributes, and ultimately, for chorus identification. Utilizing both temporal and frequency domain attributes enables effective differentiation of the chorus. This is exemplified through the examination of probability features, inverted spectrum characteristics, temporal frequency domain attributes, and inverted spectrum features. To enhance the temporal-frequency representation of the chorus's timbre and facilitate chorus identification, deep learning techniques are applied. The objective of this research is to analyze the structural elements of an intelligent music chorus detection and evaluation system employing deep learning. In response to significant classification errors, this study introduces a noise-reducing instrument identification model that mimics human hearing using a cochlear model with harmonious decomposition of music, incorporating time and frequency information in the auditory spectrum. This model utilizes probability characteristics, time-domain attributes, congestion features, and sparse attributes to evaluate the adverse impacts of instrumental interference. A deep hybrid network, integrating generative deep learning and an LSTM framework, establishes a correlation between the abstract feature extraction capability of the generative deep network and the feature expression capability of the serial-level noise cancellation encoder. The proposed method demonstrates remarkable accuracy on datasets and significantly improves the overall evaluation effectiveness.
Normative Data on Measures of Cardiovascular Autonomic Neuropathy and the Effect of Pretest Conditions in a Large Danish Non-Diabetic CVD-Free Population
Authors: Hansen Christian Stevns , Christensen Marie Mathilde Bjerg , Vistisen Dorte , Jepsen Randi , Ellervik Christina , Jørgensen Marit Eika , Fleischer Jesper 
Published: May 2024

Abstract

Background
Cardiovascular autonomic neuropathy (CAN) is a common diabetic complication associated with excess morbidity and mortality. Normative reference data for cardiovascular autonomic function are used to stratify individuals into those with and without CAN. However, reference thresholds for both cardiovascular autonomic reflex tests (CARTs) and heart rate variability (HRV) are scarce and based on small sample sizes. The aim of the study was to establish contemporary normative reference thresholds based on a large non-diabetic population free of cardiovascular disease (CVD).
Methods
Cardiovascular autonomic function, CARTs and 5-minute HRV indices were assessed in individuals without diabetes and CVD from the Lolland-Falster Health Study (2018-2020) by applying the point-of-care device Vagus™. Age-specific normative reference thresholds were estimated by using log-transformed quantile regression models at the 5th and 10th percentile, with adjustments made for sex. Models assessing the association between age and HRV indices were further adjusted for heart rate.
Results
We present age-specific normative reference thresholds for cardiovascular autonomic function including CARTs and HRV for 875 individuals (48% females) aged 15-85 years. The reference thresholds are presented for both the 5th and 10th lower percentile. Higher age was inversely associated with all outcomes. Females tended to have a higher parasympathetic drive compared to males. Pre-test conditions did not affect CARTs significantly.
Conclusions
The presented age-related normative reference thresholds for both CARTs and HRV indices based on a large Danish cohort may facilitate improved quality of research and treatment.
Survival, Adhesion and Migration of Metastatic Breast Tumor Cells: Roles of Parvins (PARVA and PARVB)
Authors: Dylami Sadegh , Farahani Mohammad Kamalabadi , Kia Vahid , Atashi Amir 
Published: May 2024

Abstract

Changes in cell-ECM interaction can lead to the anchorage-independent proliferation, resistance to apoptosis, and improved motility that characterize breast metastatic cells. The parvin, alpha (Parva) and parvin, beta (Parvb) localizes to focal adhesions and play a part in cell attachment, migration, and persistence. The assumption of our work is that parvins contribute to the capacity of tumor cells to spread. Following the extraction of primary (4T1T) and metastatic (4T1B and 4T1L) breast tumor cells from mouse metastatic breast cancer model, MTT assay and scratch test were utilized to determine the vitality and motility of the cells. qPCR was performed to examine the expression of parvins. Tumor cells did not show any difference in cellular viability in 2- or 3-dimensional cell culture. Our research illustrated that metastatic tumor cells have a higher capacity of migration. Molecular analysis revealed that parvins expression was significantly increased in metastatic breast carcinoma cells. These findings revealed new information about a major change in parvins expression in metastatic breast carcinoma cells. A targeted therapeutic approach against breast cancer metastasis could be developed using examination of the genetic characteristics of metastatic tumor cells.
Model prediction of spatial non-cooperative target measurement information based on the Koopman operator
Authors: Zhang Xiaohong , Zhou Haiyin , Hou Bowen , Wang Jiongqi , Peng Jian , Zhou Xuanying 
Published: May 2024

Abstract

This paper proposes a prediction method for spatial non-cooperative measurement information based on the Koopman operator, aiming at the problem of incomplete or missing measurement information in optical relative navigation. Combining the global linear representation theory of the Koopman operator, the paper first projects the nonlinear measurement model into a Hilbert space using a dimensionality expansion function, and constructs a global linear optical measurement model based on the Koopman operator. Secondly, by using a linear combination of Koopman modes, eigenvalues, and feature functions, the paper achieves global linearization of the measurement model, reducing the loss of nonlinear information. Furthermore, a model algorithm based on the Dynamic Mode Decomposition (DMD) method is designed to approximate the Koopman operator, and the main modes of the operator are used to obtain the phase space topology structure of the model, realizing high-precision prediction of measurement information. Finally, Monte Carlo simulation results show that compared with existing methods, the proposed method improves the accuracy of measurement information prediction by 52.07%, providing an effective solution for high-precision prediction of measurement information for subsequent optical relative navigation targets.
A Novel and Cost-Friendly Expression Method of Taq DNA Polymerase Using Milk Powder and Effective Utilization of the Enzyme in Diagnostic Kits
Authors: Hekim Suleyman , Kaçıran Arife , Akmehmet Ayşe Nur , Şakalar Çağrı , ÇANAKÇI Sabriye , Belduz Ali Osman 
Published: May 2024

Abstract

Taq DNA polymerase has been used in PCR-based pathogen detection kits and the demand increased during the COVID-19 epidemic. In this study, it was aimed to produce recombinant Taq DNA polymerase in a low-cost and optimized setting and test its activity in various qPCR kits. Taq DNA polymerase gene was amplified by PCR and cloned into expression vectors. Recombinant protein expression was induced using IPTG and purification was performed using heat incubation and ammonium sulfate precipitation and dialysis. Protein expression was analyzed by SDS-polyacrylamide gel electrophoresis. Enzyme activity was tested by PCR and qPCR in different buffers. Recombinant Taq DNA polymerase was purified, and purified protein and commercial Taq DNA polymerase were compared by SDS-PAGE. The PCR activity of the recombinant enzyme was shown in different commercial and in-house buffers. Enzyme was stable for at least 12 months and kept at -20ºC. Recombinant enzyme and commercial enzyme had PCR activity at dilutions up to 1/32. Milk powder, which is an economical alternative for IPTG, was tested and, use of milk powder equivalent to 3 mM lactose was as efficient as IPTG for the induction of the recombinant protein. Recombinant Taq DNA polymerase was tested in two DNA-based qPCR kits that were developed in our laboratory and was shown to perform as well as commercial enzymes. Finally, our enzyme was tested in our commercial SARS-CoV-2 diagnosis RT-qPCR kit using in-house produced primer-probe oligonucleotides and was shown to have equivalent efficacy to the commercial enzyme.