Search results for: Jing Peng

Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

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Authors: Yunshi Peng, Shanshan Gao, Sang Qin

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The sense of humor is an integration of cognition, emotion and behavioral tendencies in the process of expressing humor. Previous studies evidenced the positive impact of sense of humor on promoting mental health. However, very few studies investigated this with Chinese populations. The absence of a validated questionnaire limits empirical research on sense of humor in China. This study aimed to develop a Chinese instrument to examine the sense of humor among college students in China. A pool of 72 items was developed by conducting a series of qualitative methods including open-ended questionnaire, individual interviews and literature analysis, followed by an expert rating. A total of 500 college students were recruited from 7 provinces in China to complete all 72 items. The factor structure of sense of humor was established and 25 items were eventually formed by utilizing the exploratory factor analyses (EFA). The questionnaire composed 4 subscales: humor comprehension, humor creativity, attitudes towards humor and optimism level. Confirmatory factor analyses (CFA) from a follow-up study with a different sample of 1200 colleges students showed good model fit. All subscales and the overall questionnaire display satisfying internal consistency. Correlations with criterion variables demonstrated good convergent and discriminant validity. The sense of humor questionnaire is a psychometrically-sound instrument for the population of college students in China. This is applicable for future studies to identify the structure of sense of humor and evaluate the levels of humor for individuals.

Keywords: college students, EFA and CFA, questionnaire, sense of humor

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Authors: Jing Li Hong

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This study compared Michel Foucault’s thoughts and the Chan School’s thoughts on the idea of healing. Healing is not an unfamiliar idea in Buddhist thoughts. The paired concepts of illness and medicine are often used as a metaphor to describe the relationship between people and truth. Foucault investigated the topic of care of self in his later studies and dedicated a large portion of his final semester course at the Collège de France in 1984 to discuss the meaning of Socrates’s offering of a sacrifice to the god of medicine in Phaedo. Foucault indicated a key preposition in ancient philosophy, namely healing. His idea of healing also addressed the relationship between subject and truth. From this relationship, Foucault unraveled his novel study on truth, namely the technologies of the self, with an emphasis on the care of self. Whereas numerous philosophers ask obvious questions such as ‘what is truth’ and ‘how to learn about truth,’ Foucault proposed distinct questions such as ‘what is our relationship to truth’ and ‘how does our relationship with truth turn us into who we are now?’ Thus, healing in both Buddhist and Foucault’s thoughts is related to the relationship between being and truth. This study first reviews Buddhist and Foucault’s ideas of healing to explicate what is illness and what is medicine. Because Buddhist thoughts cover an extensive scope, this study focuses on the thoughts of the Chan School. The second part is a discussion on medicine (treatment), specifically what is used as the medicine for the illness in both thoughts, and how can this medicine treat the illness. This part includes a description and comparison of the use of concepts of negation in these two thought groups. Finally, the subjects that practice the technologies of the self in both groups are compared from the idea of care of self; in other words, the differences between the subjects formed by the different relationships between being and truth are analyzed.

Keywords: Chan, heterogeneous, living style, language of paradox, Michel Foucault, negation, parrhesia, the care of self

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Authors: Qing Zhang, Jiachen Nie, Yujia Wen, Guanyuan Kou, Peng Yu, Kun Xia, Qin Yang, Li Ding

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BACKGROUND: A facility layout problem (FLP) is an NP-complete (non-deterministic polynomial) problem, which is hard to obtain an exact optimal solution. FLP has been widely studied in various limited spaces and workflows. For example, cafeterias with many types of equipment for troops cause chaotic processes when dining. OBJECTIVE: This article tried to optimize the layout of troops’ cafeteria and to improve the overall efficiency of the dining process. METHODS: First, the original cafeteria layout design scheme was analyzed from an ergonomic perspective and two new design schemes were generated. Next, three facility layout models were designed, and further simulation was applied to compare the total time and density of troops between each scheme. Last, an experiment of the dining process with video observation and analysis verified the simulation results. RESULTS: In a simulation, the dining time under the second new layout is shortened by 2.25% and 1.89% (p<0.0001, p=0.0001) compared with the other two layouts, while troops-flow density and interference both greatly reduced in the two new layouts. In the experiment, process completing time and the number of interference reduced as well, which verified corresponding simulation results. CONCLUSIONS: Our two new layout schemes are tested to be optimal by a series of simulation and space experiments. In future research, similar approaches could be applied when taking layout-design algorithm calculation into consideration.

Keywords: layout optimization, dining efficiency, troops’ cafeteria, anylogic simulation, field experiment

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Authors: Peng-Shao Chen, Yen-Jong Chen

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In recent years, global warming, the dramatic change in energy prices and the exhaustion of natural resources illustrated that energy-related topic cannot be ignored. Despite the relationship between the cities and CO₂ emissions has been extensively studied in recent years, little attention has been paid to differences in the geographical location of the city. However, the geographical climate has a great impact on lifestyle from city to city, such as the type of buildings, the major industry of the city, etc. Therefore, the paper instigates empirically the effects of kinds of urban factors and CO₂ emissions with consideration of the different geographic, climatic zones which cities are located. Using the regression model and a dataset of urban agglomeration in East Asia cities with over one million population, including 2005, 2010, and 2015 three years, the findings suggest that the impact of urban factors on CO₂ emissions vary with the latitude of the cities. Surprisingly, all kinds of urban factors, including the urban population, the share of GDP in service industry, per capita income, and others, have different level of impact on the cities locate in the tropical climate zone and temperate climate zone. The results of the study analyze the impact of different urban factors on CO₂ emissions in urban area with different geographical climate zones. These findings will be helpful for the formulation of relevant policies for urban planners and policy makers in different regions.

Keywords: carbon emissions, urban agglomeration, urban factor, urban geography

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Authors: Lin Peng, Jin Zhang, Yuanyuan Miao, Quanquan Zheng

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Group discussion and group decision-making have long been a topic of research interest. Traditional research on group decision making typically focuses on the strategies or functional models of combining members’ preferences to reach an optimal consensus. In this research, we want to explore natural process group decision making through discussion and examine relevant, influential factors--common superordinate identity shared by group and size of the groups. We manipulated the social identity of the groups into either a shared superordinate identity or different subgroup identities. We also manipulated the size to make it either a big (6-8 person) group or small group (3-person group). Using experimental methods, we found members of a superordinate identity group tend to modify more of their own opinions through the discussion, compared to those only identifying with their subgroups. Besides, members of superordinate identity groups also formed stronger identification with group decision--the results of group discussion than their subgroup peers. We also found higher member modification in bigger groups compared to smaller groups. Evaluations of decisions before and after discussion as well as group decisions are strongly linked to group identity, as members of superordinate group feel more confident and satisfied with both the results and decision-making process. Members’ opinions are more similar and homogeneous in smaller groups compared to bigger groups. This research have many implications for further research and applied behaviors in organizations.

Keywords: group decision making, group size, identification, modification, superordinate identity

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Authors: Xingxing Peng

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With the development of society, there are more and more online communication scenarios such as teleconference and online education. In the process of conference communication, the quality of voice communication is a very important part, and noise may cause the communication effect of participants to be greatly reduced. Therefore, voice noise reduction has an important impact on scenarios such as voice calls. This research focuses on the key technologies of the sound transmission process. The purpose is to maintain the audio quality to the maximum so that the listener can hear clearer and smoother sound. Firstly, to solve the problem that the traditional speech enhancement algorithm is not ideal when dealing with non-stationary noise, an adaptive speech noise reduction algorithm is studied in this paper. Traditional noise estimation methods are mainly used to deal with stationary noise. In this chapter, we study the spectral characteristics of different noise types, especially the characteristics of non-stationary Burst noise, and design a noise estimator module to deal with non-stationary noise. Noise features are extracted from non-speech segments, and the noise estimation module is adjusted in real time according to different noise characteristics. This adaptive algorithm can enhance speech according to different noise characteristics, improve the performance of traditional algorithms to deal with non-stationary noise, so as to achieve better enhancement effect. The experimental results show that the algorithm proposed in this chapter is effective and can better adapt to different types of noise, so as to obtain better speech enhancement effect.

Keywords: speech noise reduction, speech enhancement, self-adaptation, Wiener filter algorithm

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Authors: Indarti, Li-Hsun Peng

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Batik is one of the most prominent expressions of cultural identity in Indonesia. On October 2009, UNESCO designated Indonesian batik as “a masterpiece of oral and intangible heritage of humanity”. The center of batik Indonesia is in middle of Java where there is center of Javanese royalty in the 17th century (known as classic batik). The development of batik spread to wide range of area in Indonesia, including in Sidoarjo city, East Java - Indonesia. Sidoarjo batik actually has long been around long time and is known as batik using bright colors, very different from classic batik. The aims of this study is to determine the kinds of ornament of Sidoarjo batik currently as one of the locally-based creative industry and to figure out how the attempt to sustainability of Sidoarjo batik from owner and government. The research is conducted in Jetis Village in Sidoarjo city known as village of batik. There is some batik business and batik maker in there. The research method used in-depth interview to search information about ornament of Sidoarjo batik and to analyze the attempt of business owner and government to sustainability of Sidoarjo Batik. In-depth interviews to batik business owners are used to determine the kinds of ornament of Sidoarjo Batik currently and how their attempt to maintain the existence of Sidoarjo Batik. In-depth interviews are also conducted to government to figure out how the support the sustainability of Sidoarjo batik. The result or research will introduce local products of Sidoarjo community to global market and increase knowledge about traditional textile design in one part of the world.

Keywords: Sidoarjo Batik, Traditional Textile, Ornament, Creative Design.

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Authors: Yake Zhang, Peng Xu

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The development of building energy models (BEM) required for architectural design and analysis is a time-consuming and complex process, demanding a deep understanding and proficient use of simulation software. To streamline the generation of complex building energy models, this study proposes an automated method for generating building energy models using a large language model and the BEM library aimed at improving the efficiency of model generation. This method leverages a large language model to parse user-specified requirements for target building models, extracting key features such as building location, window-to-wall ratio, and thermal performance of the building envelope. The BEM library is utilized to retrieve energy models that match the target building’s characteristics, serving as reference information for the large language model to enhance the accuracy and relevance of the generated model, allowing for the creation of a building energy model that adapts to the user’s modeling requirements. This study enables the automatic creation of building energy models based on natural language inputs, reducing the professional expertise required for model development while significantly decreasing the time and complexity of manual configuration. In summary, this study provides an efficient and intelligent solution for building energy analysis and simulation, demonstrating the potential of a large language model in the field of building simulation and performance modeling.

Keywords: artificial intelligence, building energy modelling, building simulation, large language model

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Authors: Jing Wu, Wei Lv, Yibing Li, Yuanfan You

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The separation of speech signals has become a research hotspot in the field of signal processing in recent years. It has many applications and influences in teleconferencing, hearing aids, speech recognition of machines and so on. The sounds received are usually noisy. The issue of identifying the sounds of interest and obtaining clear sounds in such an environment becomes a problem worth exploring, that is, the problem of blind source separation. This paper focuses on the under-determined blind source separation (UBSS). Sparse component analysis is generally used for the problem of under-determined blind source separation. The method is mainly divided into two parts. Firstly, the clustering algorithm is used to estimate the mixing matrix according to the observed signals. Then the signal is separated based on the known mixing matrix. In this paper, the problem of mixing matrix estimation is studied. This paper proposes an improved algorithm to estimate the mixing matrix for speech signals in the UBSS model. The traditional potential algorithm is not accurate for the mixing matrix estimation, especially for low signal-to noise ratio (SNR).In response to this problem, this paper considers the idea of an improved potential function method to estimate the mixing matrix. The algorithm not only avoids the inuence of insufficient prior information in traditional clustering algorithm, but also improves the estimation accuracy of mixing matrix. This paper takes the mixing of four speech signals into two channels as an example. The results of simulations show that the approach in this paper not only improves the accuracy of estimation, but also applies to any mixing matrix.

Keywords: DBSCAN, potential function, speech signal, the UBSS model

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Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Jen-Taut Yeh, Yuan-jing Hou, Li Cheng, Ya Zhou Wang, Zhi Yu Zhang

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Tensile retention properties of bacterial cellulose (BC) reinforced thermoplastic starch (TPS) resins were successfully improved by reacting with glutaraldehyde (GA) in their gelatinization processes. Small amounts of poly (lactic acid) (PLA) were blended with GA modified TPS resins to improve their processability. As evidenced by the newly developed ether (-C-O-C-) stretching bands on FT-IR spectra of TPS100BC0.02GAx series specimens, hydroxyl groups of TPS100BC0.02 resins were successfully reacted with the aldehyde groups of GA molecules during their modification processes. The retention values of tensile strengths (σf) of TPS100BC0.02GAx and (TPS100BC0.02GAx)75PLA25 specimens improved significantly and reached a maximal value as GA contents approached an optimal value at 0.5 part per hundred parts of TPS resin (PHR). By addition of 0.5 PHR GA in biocomposite specimens, the initial tensile strength and elongation at break values of (TPS100BC0.02GA0.5)75PLA25 specimen improved to 24.6 MPa and 5.6%, respectively, which were slightly improved than those of (TPS100BC0.02)75PLA25 specimen. However, the retention values of tensile strengths of (TPS100BC0.02GA0.5)75PLA25 specimen reached around 82.5%, after placing the specimen under 20oC/50% relative humidity for 56 days, which were significantly better than those of the (TPS100BC0.02)75PLA25 specimen. In order to understand these interesting tensile retention properties found for (TPS100BC0.02GAx)75PLA25 specimens. Thermal analyses of initial and aged TPS100BC0.02, TPS100BC0.02GAx and (TPS100BC0.02GAx)75PLA25 specimens were also performed in this investigation. Possible reasons accounting for the significantly improved tensile retention properties of TPS100BC0.02GAx and (TPS100BC0.02GAx)75PLA25 specimens are proposed.

Keywords: biocomposite, strength retention, thermoplastic starch, tensile retention

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Authors: Zhipeng Chen, Peng Zhang, Qingyun Liu, Li Guo

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With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.

Keywords: bipartite graph, one-mode projection, clustering, web proxy detection

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Authors: Jing Yuan, Hongwei Yang

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This paper aims to understand U.S. middle school students’ mathematics achievements by examining relevant student and school-level predictors. Through a variance component analysis, the study first identifies evidence supporting the use of multilevel modeling. Then, a multilevel analysis is performed under Bayesian statistical inference where prior information is incorporated into the modeling process. During the analysis, independent variables are entered sequentially in the order of theoretical importance to create a hierarchy of models. By evaluating each model using Bayesian fit indices, a best-fit and most parsimonious model is selected where Bayesian statistical inference is performed for the purpose of result interpretation and discussion. The primary dataset for Bayesian modeling is derived from the Program for International Student Assessment (PISA) in 2012 with a secondary PISA dataset from 2003 analyzed under the traditional ordinary least squares method to provide the information needed to specify informative priors for a subset of the model parameters. The dependent variable is a composite measure of mathematics literacy, calculated from an exploratory factor analysis of all five PISA 2012 mathematics achievement plausible values for which multiple evidences are found supporting data unidimensionality. The independent variables include demographics variables and content-specific variables: mathematics efficacy, teacher-student ratio, proportion of girls in the school, etc. Finally, the entire analysis is performed using the MCMCpack and MCMCglmm packages in R.

Keywords: Bayesian multilevel modeling, mathematics education, PISA, multilevel

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Authors: Jing Ma, Guotong Qin

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We reported electro-confinement desalination (ECMD), a desalination method combining electric field effects and confinement effects using nanoporous carbon membranes as electrode. A carbon membrane with average pore size of 8.3 nm was prepared by organic sol-gel method. The precursor of support was prepared by curing porous phenol resin tube. Resorcinol-formaldehyde sol was coated on porous tubular resin support. The membrane was obtained by carbonisation of coated support. A well-combined top layer with the thickness of 35 μm was supported by macroporous support. Measurements of molecular weight cut-off using polyethylene glycol showed the average pore size of 8.3 nm. High salt rejection can be achieved because the water molecules need not overcome high energy barriers in confined space, while huge inherent dehydration energy was required for hydrated ions to enter the nanochannels. Additionally, carbon membrane with additional electric field can be used as an integrated membrane electrode combining the effects of confinement and electric potential gradient. Such membrane electrode can repel co-ions and attract counter-ions using pressure as the driving force for mass transport. When the carbon membrane was set as cathode, the rejection of SO₄²⁻ was 94.89%, while the removal of Na⁺ was less than 20%. We set carbon membrane as anode chamber to treat the effluent water from the cathode chamber. The rejection of SO₄²⁻ and Na⁺ reached to 100% and 88.86%, respectively. ECMD will be a promising energy efficient method for salt rejection.

Keywords: nanoporous carbon membrane, confined effect, electric field, desalination, membrane reactor

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Authors: Lijuan Wang, Junsheng Huo, Jing Sun, Wenxian Li, Jian Huang, Lin Ling, Yiping Zhou, Chengyu Huang, Jifang Hu

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Backgrounds and Objective: This study aimed to evaluate the impact of highly nutrient-dense complementary food supplement-Yingyangbao, at the time of 3 months after Wenchuan earthquake, on the anemia of young children in a county in Sichuan province. Methods: The young children aged 6-23 months in the county were fed one sachet Yingyangbao per day. Yingyangbao were distributed for 15 months for free. The children entering 6 months age would be included. The length, weight and hemoglobin of the children aged 6-29 months were assessed at baseline (n=257) and Yingyangbao intervention for 6 (n=218) and 15 months (n=253) by cluster sampling. Growth status has not been described in the paper. The analysis was conducted based on 6-11, 12-17, 18-23 and 24-29 months. Results: It showed that the hemoglobin concentration in each group among the 4 groups increased by 4.9, 6.4, 8.0, 9.5 g/L after 6 months and 12.7, 11.4, 16.7, 15.7 g/L after 15 months compared to the baseline, respectively. The total anemia prevalence in each group was significantly lower after 6 and 15 months than the baseline (P<0.001), except the 6-11 months group after 6 months because of fewer Yingyangbao consumption. Total moderate anemia rate decreased from 18.3% to 5.5% after 6 months, and kept decreasing to 0.8% after another 9 months. The hemoglobin concentration was significantly correlated with the amount of Yingyangbao consumption(P<0.001) The anemia rate was significantly different based on the Yingyangbao compliance (P<0.001). Conclusion: It was concluded that Yingyangbao which contains quality protein, vitamins and micronutrients intervened 15 months could be effective for the improvement of anemia of young children. The study provides the support that the application of the complementary food supplements to reduce the anemia of young children in the emergency of natural disaster.

Keywords: young children, anemia, nutrition intervention, complementary food supplements, Yingyangbao

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Authors: Chuan Yang, Jing Bie, Panagiotis Psimoulis, Zhong Wang

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Modelling and understanding vehicle volume distribution over the urban network are essential for urban design and transport planning. The space syntax approach was widely applied as the main conceptual and methodological framework for contemporary vehicle volume models with the help of the statistical method of multiple regression analysis (MRA). However, the MRA model with space syntax variables shows a limitation in vehicle volume predicting in accounting for the crossed effect of the urban configurational characters and socio-economic factors. The aim of this paper is to construct models by interacting with the combined impact of the street network structure and socio-economic factors. In this paper, we present a multilevel linear (ML) and an agent-based (AB) vehicle volume model at an urban scale interacting with space syntax theoretical framework. The ML model allowed random effects of urban configurational characteristics in different urban contexts. And the AB model was developed with the incorporation of transformed space syntax components of the MRA models into the agents’ spatial behaviour. Three models were implemented in the same urban environment. The ML model exhibit superiority over the original MRA model in identifying the relative impacts of the configurational characters and macro-scale socio-economic factors that shape vehicle movement distribution over the city. Compared with the ML model, the suggested AB model represented the ability to estimate vehicle volume in the urban network considering the combined effects of configurational characters and land-use patterns at the street segment level.

Keywords: space syntax, vehicle volume modeling, multilevel model, agent-based model

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Authors: Da LI, Peng Xu

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Recent extreme weather events, such as the 2021 European floods and North American heatwaves, have exposed the vulnerability of energy systems to both extreme demand scenarios and potential physical damage. Current integrated energy system designs often overlook performance under these challenging conditions. This research, focusing on a regional integrated energy system in Ningbo, China, proposes a distinct design method to optimize system reliability during extreme events. A multi-scenario model was developed, encompassing various extreme load conditions and potential system damages caused by severe weather. Based on this model, a comprehensive reliability improvement scheme was designed, incorporating a gradient approach to address different levels of disaster severity through the integration of advanced technologies like distributed energy storage. The scheme's effectiveness was validated through Monte Carlo simulations. Results demonstrate significant enhancements in energy supply reliability and peak load reduction capability under extreme scenarios. The findings provide several insights for improving energy system adaptability in the face of climate-induced challenges, offering valuable references for building reliable energy infrastructure capable of withstanding both extreme demands and physical threats across a spectrum of disaster intensities.

Keywords: extreme weather events, integrated energy systems, reliability improvement, climate change adaptation

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Authors: Juan Carlos Baena, Zhongxiao Peng

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Ultra-high molecular weight polyethylene (UHMWPE) is extensively used in industrial and biomedical fields. The slippery nature of UHMWPE makes this material suitable for surface bearing applications, however, the operational conditions limit the lubrication efficiency, inducing boundary and mixed lubrication in the tribological system. The lack of lubrication in a tribological system intensifies friction, contact stress and consequently, operating temperature. With temperature increase, the material’s mechanical properties are affected, and the lifespan of the component is reduced. The understanding of how mechanical properties and wear performance of UHMWPE change when the temperature is increased has not been clearly identified. The understanding of the wear and mechanical performance of UHMWPE at different temperature is important to predict and further improve the lifespan of these components. This study evaluates the effects of temperature variation in a range of 20 °C to 60 °C on the hardness and the wear resistance of UHMWPE. A reduction of the hardness and wear resistance was observed with the increase in temperature. The variation of the wear rate increased 94.8% when the temperature changed from 20 °C to 50 °C. Although hardness is regarded to be an indicator of the material wear resistance, this study found that wear resistance decreased more rapidly than hardness with the temperature increase, evidencing a low material stability of this component in a short temperature interval. The reduction of the hardness was reflected by the plastic deformation and abrasion intensity, resulting in a significant wear rate increase.

Keywords: hardness, surface bearing, tribological system, UHMWPE, wear

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Authors: Jing Li, Li Jin, Fulin Wang, Jie Dong, Wenjiang Ding

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Mg-Li dual-phase alloy exhibits better combination of yield strength and elongation than the Mg single-phase alloy. To exploit its deformation behavior, the deformation mechanisms of Mg-2Gd alloy with or without Li addition, i.e., Mg-6Li-2Gd and Mg-2Gd alloy, have been studied at both microscale and mesoscale. EBSD-assisted slip trace, twin trace, and texture evolution analysis show that the α-Mg phase of Mg-6Li-2Gd alloy exhibits different microscopic deformation mechanisms with the Mg-2Gd alloy, i.e., mainly prismatic slip in the former one, while basal slip, prismatic slip and extension twin in the latter one. Further Schmid factor analysis results attribute this different intra-phase deformation mechanisms to the higher critical resolved shear stress (CRSS) value of extension twin and lower ratio of CRSSprismatic /CRSSbasal in the α-Mg phase of Mg-6Li-2Gd alloy. Additionally, Li addition can induce dual-phase microstructure in the Mg-6Li-2Gd alloy, leading to the formation of hetero-deformation induced (HDI) stress at the mesoscale. This can be evidenced by the hysteresis loops appearing during the loading-unloading-reloading (LUR) tensile tests and the activation of multiple slip activity in the α-Mg phase neighboring β-Li phase. The Mg-6Li-2Gd alloy shows higher yield strength is due to the harder α-Mg phase arising from solid solution hardening of Li addition, as well asthe strengthening of soft β-Li phase by the HDI stress during yield stage. Since the strain hardening rate of Mg-6Li-2Gd alloy is lower than that of Mg-2Gd alloy after ~2% strain, which is partly due to the weak contribution of HDI stress, Mg-6Li-2Gd alloy shows no obvious increase of uniform elongation than the Mg-2Gd alloy.But since the β-Li phase is effective in blunting the crack tips, the Mg-6Li-2Gd alloy shows ununiform elongation, which, thus, leads to the higher total elongation than the Mg-2Gd alloy.

Keywords: Mg-Li-Gd dual-phase alloy, phase boundary, HDI stress, dislocation slip activity, mechanical properties

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Authors: Jiandong Ren, Lijun Zhao, Peng Chen

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Nafamostat (NA), a synthetic broad-spectrum serine protease inhibitor, has been routinely employed for the treatment of acute pancreatitis (AP) and other inflammatory-associated diseases in some East Asia countries. Although the potent inhibitory activity against inflammation-related proteases such as thrombin, trypsin, kallikrein, plasmin, coagulation factors and complement factors is generally considered to be responsible for the anti-inflammatory effects of NA, precise target and molecular mechanism underlying the anti-inflammatory activity in the treatment of AP remain largely unknown yet. As an intracellular inflammatory signaling platform, the NOD-like receptor protein 3 (NLRP3) inflammasome is recently identified to be involved in the development of AP. In present study, we have revealed that NA alleviated pancreatic injury in a caerulein-induced AP model by inhibiting the NLRP3 inflammasome activation in pancreas. Mechanistically, NA interacted with HDAC6, a cytoplasmic deacetylase implicated in the NLRP3 inflammasome pathway, and efficiently abrogated the function of HDAC6. This property enabled NA to influence HDAC6 dependent NF-κB transcriptional activity and thus block NF-κB-driven transcriptional priming of NLRP3 inflammasome. Moreover, NA exerted the potential to interfere HDAC6-mediated intracellular transport of NLRP3, thereby leading to the failure of NLRP3 inflammasome activation. Our current work has provided valuable insight into the molecular mechanism underlying the immunomodulatory effect of NA in treatment of AP, highlighting its promising application in prevention of NLRP3 inflammasome-associated inflammatory pathological damage.

Keywords: acute pancreatitis, HDAC6, nafamostat, NLRP3 inflammasome

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Authors: Chuan Yang, Jing Bie, Zhong Wang, Panagiotis Psimoulis

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Contemporary studies investigating the association between the spatial configuration of the urban network and economic activities at the street level were mostly conducted within space syntax conceptual framework. These findings supported the theory of 'movement economy' and demonstrated the impact of street configuration on the distribution of pedestrian movement and land-use shaping, especially retail activities. However, the effects varied between different urban contexts. In this paper, the relationship between economic activity distribution and the urban configurational characters was examined at the segment level. In the study area, three kinds of neighbourhood types, urban, suburban, and rural neighbourhood, were included. And among all neighbourhoods, three kinds of urban network form, 'tree-like', grid, and organic pattern, were recognised. To investigate the nested effects of urban configuration measured by space syntax approach and urban context, multilevel zero-inflated negative binomial (ZINB) regression models were constructed. Additionally, considering the spatial autocorrelation, spatial lag was also concluded in the model as an independent variable. The random effect ZINB model shows superiority over the ZINB model or multilevel linear (ML) model in the explanation of economic activities pattern shaping over the urban environment. And after adjusting for the neighbourhood type and network form effects, connectivity and syntax centrality significantly affect economic activities clustering. The comparison between accumulative and new established economic activities illustrated the different preferences for economic activity location choice.

Keywords: space syntax, economic activities, multilevel model, Chinese city

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Authors: Peng Hao Wang, Garam Kim, Ronald Sterkenburg

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In composite manufacturing, the fabrication of tooling and tooling maintenance contributes to a large portion of the total cost. However, as the applications of composite materials continue to increase, there is also a growing demand for more tooling. The demand for more tooling places heavy emphasis on the industry’s ability to fabricate high quality tools while maintaining the tool’s cost effectiveness. One of the popular techniques of tool fabrication currently being developed utilizes additive manufacturing technology known as 3D printing. The popularity of 3D printing is due to 3D printing’s ability to maintain low material waste, low cost, and quick fabrication time. In this study, a team of Purdue University School of Aviation and Transportation Technology (SATT) faculty and students investigated the effectiveness of a 3D printed composite mold. A steel valve cover from an aircraft reciprocating engine was modeled utilizing 3D scanning and computer-aided design (CAD) to create a 3D printed composite mold. The mold was used to fabricate carbon fiber versions of the aircraft reciprocating engine valve cover. The carbon fiber valve covers were evaluated for dimensional accuracy and quality while the 3D printed composite mold was evaluated for durability and dimensional stability. The data collected from this study provided valuable information in the understanding of 3D printed composite molds, potential improvements for the molds, and considerations for future tooling design.

Keywords: additive manufacturing, carbon fiber, composite tooling, molds

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Hung-Jen Lin, Chien-Hao Wang, Chien-Peng Huang, Yu-Sheng Tseng, En-Cheng Yang, Joe-Air Jiang

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Bees’ incoming and outgoing behavior is a decisive index which can indicate the health condition of a colony. Traditional methods for monitoring the behavior of honey bees (Apis mellifera) take too much time and are highly labor-intensive, and the lack of automation and synchronization disables researchers and beekeepers from obtaining real-time information of beehives. To solve these problems, this study proposes to use an Internet of Things (IoT)-based system for counting honey bees’ incoming and outgoing activities using an infrared interruption technique, while environmental factors are recorded simultaneously. The accuracy of the established system is verified by comparing the counting results with the outcomes of manual counting. Moreover, this highly -accurate device is appropriate for providing quantitative information regarding honey bees’ incoming and outgoing behavior. Different statistical analysis methods, including one-way ANOVA and two-way ANOVA, are used to investigate the influence of environmental factors, such as temperature, humidity, illumination and ambient pressure, on bees’ incoming and outgoing behavior. With the real-time data, a standard model is established using the outcomes from analyzing the relationship between environmental factors and bees’ incoming and outgoing behavior. In the future, smart control systems, such as a temperature control system, can also be combined with the proposed system to create an appropriate colony environment. It is expected that the proposed system will make a considerable contribution to the apiculture and researchers.

Keywords: ANOVA, environmental factors, honey bee, incoming and outgoing behavior

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Authors: Feng Li, Qian Peng

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The mass start speed skating (MSSS) is a new event for the 2018 PyeongChang Winter Olympics and will be an official race for the 2022 Beijing Winter Olympics. Considering that the event rankings were based on points gained on laps, it is worthwhile to investigate the pacing behavior on each lap that directly influences the ranking of the race. The aim of this study was to detect the pacing behavior and performance on MSSS regarding skaters’ level (SL), competition stage (semi-final/final) (CS) and gender (G). All the men's and women's races in the World Cup and World Championships were analyzed in the 2018-2019 and 2019-2020 seasons. As a result, a total of 601 skaters from 36 games were observed. ANOVA for repeated measures was applied to compare the pacing behavior on each lap, and the three-way ANOVA for repeated measures was used to identify the influence of SL, CS, and G on pacing behavior and total time spent. In general, the results showed that the pacing behavior from fast to slow were cluster 1—laps 4, 8, 12, 15, 16, cluster 2—laps 5, 9, 13, 14, cluster 3—laps 3, 6, 7, 10, 11, and cluster 4—laps 1 and 2 (p=0.000). For CS, the total time spent in the final was less than the semi-final (p=0.000). For SL, top-level skaters spent less total time than the middle-level and low-level (p≤0.002), while there was no significant difference between the middle-level and low-level (p=0.214). For G, the men’s skaters spent less total time than women on all laps (p≤0.048). This study could help to coach staff better understand the pacing behavior regarding SL, CS, and G, further providing references concerning promoting the pacing strategy and decision making before and during the race.

Keywords: performance analysis, pacing strategy, winning strategy, winter Olympics

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Pixiang Wang, Shaoyang Liu, Yucheng Peng

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Polypropylene (PP) is an essential material of numerous applications in different industrial sectors, including packaging, construction, and automotive. Because the application of homopolypropylene (HPP) is limited by its relatively low impact strength and high embrittlement temperature, various types of impact copolymer PP (ICPP) that incorporate elastomers/rubbers into HPP to increase impact strength have been successfully commercialized. Crystallization kinetics of an isotactic HPP, an ICPP, and their composites were studied in this work understand the composites’ behaviors better. The Avrami-Jeziorny model was used to describe the crystallization process. For most samples, the Avrami exponent, n, was greater than 3, indicating the crystal grew in three dimensions with spherical geometry. However, the n value could drop below 3 when the ICPP content was 80 wt.% or higher and the cooling rate was 7.5°C/min or lower, implying that the crystals could grow in two dimensions and some lamella structures could be formed under those conditions. The nucleation activity increased with the increase of the ICPP content, demonstrating that the rubber phase in the ICPP acted as a nucleation agent and facilitated the nucleation process. The decrease in crystallization rate after the ICPP content exceeded 60 wt.% might be caused by the excessive amount of crystal nuclei induced by the high ICPP content, which caused strong crystal-crystal interactions and limited the crystal growth space. The nucleation activity and the n value showed high correlations to the mechanical and thermal properties of the materials. The quantitative study of the kinetics of crystallization in this work could be a helpful reference for manufacturing ICPP and HPP/ICPP mixtures.

Keywords: polypropylene, crystallization kinetics, Avrami-Jeziorny model, crystallization activation energy, Nucleation activity

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Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Yixuan Peng

Abstract:

Music tourism is the phenomenon of people visiting a place because of their association with music, as well as the process of creating an emotional attachment to a place through the connection between people and music. Music offers people the opportunity to immerse themselves in the local culture. Music tourism is increasingly recognized as an industry with economic and social impacts. People often come together for a common purpose of music at a certain time and place, such as concert, opera, or music workshop. This is very similar to the act of pilgrimage: the process of participation evokes strong emotions; it takes time and money to get to the destination; the gathering, and the emotional co-frequency. This study conducted further qualitative research using MAXQDA by applying the musical topophilia model with East Asians as interview subjects. There are three steps to traveling: before, during and after the trip. To date, 53 individuals living in East Asia have been interviewed one-on-one (online/offline) about their travel experiences. This part of the interview is limited to the two stages that are before and after travel. Based on the results of the interviews above, and as Europe has the most representative music industry and the richest variety of music genres. The " during the trip" phase of the observations and interviews were conducted in Europe and involved on-site music in Salzburg and London, including musical theater, street music, and musical pilgrimages. Interviews with 24 people were conducted in English, Chinese and Japanese. This study will use data to demonstrate the followings: the irreplaceability of music in faraway places; the identity and sense of belonging that music brings; the ethnic barriers that music crosses; and the cultural communication that music enables.

Keywords: belongingness, gathering, modern pilgrimage, anthropology of music, sociology of music

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