bluuebunny/crossref_metadata_2025 huggingface dataset:
An Exploratory Study of the Factors that Promote and Delay Sustainable EcoTourism Development in Mansehra
Authors: Khan Usman , Khan Faisal , Malik Muhammad Faizan
Published: October 2020

Geographically Mansehra district is situated on Karakoram Highway that links to ancient Taxila, Kashmir, Gilgit-Baltistan, China, and Central Asia. It is the central point for visitors, transportation, exchange, and business in the northern zone. This vicinity is constantly cherished by visitors for its rich potential of tourism attractions with delightful nature, magnificent valleys, and high altitude mountains for adventures and nature lovers. However, this research seeks to make contributions to the present literature of heritage management and tourism development through exploring the rich tourism potential in Mansehra. Exploratory in nature, the study adopts a qualitative technique combining primary and secondary data analysis and scholars observation. The findings reveal that Mansehra has no longer yet been scientifically explored and developed as a tourism destination. The general problems that preclude the development of tourism activities in Mansehra are lack of awareness, basic tourism infrastructure, scientifically exploration and documentation, developmental plan, waste management, conservation, and poor management of cultural and natural sites.
Evaluating the Sustainability of Public Debt in the Presence of Current and Fiscal Accounts Deficit in Developing Countries
Authors: Khan Ayaz , Ul Haq Zahoor , Iqbal Javed
Published: October 2020

We estimate the public debt sustainability for 53 developing countries divided into different regions using data from 1996 to 2017. Sustainability of public debt calculation is based on theoretically derived models with necessary and sufficient conditions. Current study empirically validates the importance of least evident saving-investment gap along with other variables in public debt sustainability issue. Findings show that current account, fiscal account and saving-investment gap imbalances cause unsustainable public debt for all different regions which is a matter of great concern for each region. Policy makers of the developing countries must bring policies to promote investment activities backed by saving not by debt.
Economic Growth, Foreign Direct Investment and the Environment: An Empirical Investigation for SAARC Countries
Authors: Yousaf Abida , Erum Naila , Bibi Fozia
Published: October 2020

The study tests the validity of the environmental Kuznets curve (EKC) for SAARC countries by using closed and open economy Models. The Peroni Panel Cointegration technique along with FMOLS estimation techniques have been used for empirical analysis by using the data from 1972-2015. The long run and short run estimates of the closed economy model reveals positive and significant relationship between Per capita GDP, per capita GDP2 and the carbon emissions that deny the existence of EKC. The findings of open economy model signify that FDI not only helps to transfer cleaner technologies, but it enables the producers to use less pollutant technologies for the production purposes. Moreover, an increase in the forest area is helpful for reducing the carbon emissions. Finally, population density and energy consumption are proved significant contributors of carbon emissions. The study suggests that effective policies should be followed for reducing emissions, regulating FDI-environment and per capita GDP environment relationship.
Modeling the flexural behavior of corroded reinforced concrete beams with considering stirrups corrosion
Authors: Tan Nguyen Ngoc , Kien Nguyen Trung
Published: October 2020

The reinforcement corrosion is one of the most dominant deterioration mechanisms of existing reinforced concrete structures. In this paper, the effects of the stirrup corrosion on the structural performance of five corroded beams have been simulated using the finite element model with DIANA software. These tested beams are divided into two groups for considering different inputs: (i) without corroded stirrups in flexural span, (ii) with locally corroded stirrups at different locations (e.g. full span, shear span, middle span). FE model has been calibrated with experimental results that were obtained from the four-point bending test carried out on the tested beams. This study shows that the stirrups corrosion should be received more attention in the serviceability limit state since its considerable effect on flexural behavior. Based on a parametric study, it shows that the effect of the cross-section loss of tension reinforcements on the load-carrying capacity of the corroded beam is more significant than the bond strength reduction.

Keywords:

reinforced concrete; beam; stirrup corrosion; finite element model; flexural nonlinear behavior.
Two-scale design of porosity-like materials using adaptive geometric components
Authors: Hoang Van-Nam
Published: October 2020

This paper is an extension of our recent work that presents a two-scale design method of porosity-like materials using adaptive geometric components. The adaptive geometric components consist of two classes of geometric components: one describes the overall structure at the macrostructure and the other describes the structure of the material at the microstructures. A smooth Heaviside-like elemental-density function is obtained by projecting these two classes on a finite element mesh, namely fixed to reduce meshing computation. The method allows simultaneous optimization of both the overall shape of the macrostructure and the material structure at the micro-level without additional techniques (i.e., material homogenization), connection constraints, and local volume constraints, as often seen in most existing methods. Some benchmark structural design problems are investigated and a selected design is post-processed for 3D printing to validate the effectiveness of the proposed method.

Keywords:

topology optimization; concurrent optimization; porosity structures; two-scale topology optimization; adaptive geometric components.
Phân tích ứng xử cố kết nền đất yếu gia cố PVD có xét đến sự giảm khả năng thoát nước phi tuyến theo chiều sâu
Authors: Phú Nguyễn Bá
Published: October 2020

Nhiều nghiên cứu cho thấy rằng tốc độ cố kết nền đất yếu có gia cố bấc thấm (PVD) chịu ảnh hưởng trực tiếp tới khả năng thoát nước của PVD. Tuy nhiên, khả năng thoát nước của PVD (qw) thường giảm đáng kể theo độ sâu cắm bấc, điều này dẫn đến làm chậm quá trình cố kết trong nền. Bài báo này tiến hành phân tích cố kết thấm đối xứng trục của khối đất trụ tròn có gia cố PVD, trong đó qw được xem như giảm phi tuyến theo chiều sâu cắm PVD. Kết quả phân tích cho thấy rằng sự giảm phi tuyến theo chiều sâu của qw  gây cản trở đáng kể cho quá trình cố kết của đất nền, và kết quả này thể hiện rõ hơn ở độ sâu lớn hơn trong nền đất yếu. Tuy nhiên, qw càng lớn thì sự ảnh hưởng này càng nhỏ. Lời giải giải tích được sử dụng phân tích các công trình thực tế với trường hợp chiều dày lớp đất yếu lớn, kết quả cho thấy rằng việc dự đoán ứng xử cố kết xét đến sự giảm phi tuyến của qw  cho kết quả phù hợp với dữ liệu quan trắc tại hiện trường.

Từ khóa:

khả năng thoát nước; ứng xử cố kết; độ thấm của đất; nền gia cố bấc thấm; lời giải giải tích.
Phân tích và thực nghiệm xác định hình dạng tháp Tensegrity ba tầng
Authors: Hiếu Bùi Quang , Quân Võ Doãn , Khanh Hoàng Quốc , Luận Dương Minh , Lâm Trần Phước , Đạt Nguyễn Hữu
Published: October 2020

Kết cấu Tensegrity ngày càng được ứng dụng rộng rãi trong các công trình dân dụng vì độ tin cậy cao, sử dụng vật liệu hiệu quả, vượt nhịp lớn và có khả năng đóng mở. Đây là loại kết cấu bao gồm các thanh chịu nén không liên tục nằm trong các dây cáp chịu kéo liên tục. Phân tích hình dạng cho kết cấu này là cần thiết trong giai đoạn thiết kế ban đầu để tìm ra hình dạng mà tại đó ứng suất trước trong các cấu kiện là tự cân bằng với nhau. Bài báo này áp dụng phương pháp mật độ lực tương thích trong việc xác định hình dạng của tháp Tensegity ba tầng. Quy trình thực nghiệm bằng thanh gỗ và dây cáp thép để minh chứng cho kết quả phân tích cũng được giới thiệu trong bài báo này.

Từ khóa:

kết cấu Tensegrity; phân tích hình dạng; phương pháp mật độ lực tương thích; tháp Tensegrity ba tầng.
Tài nguyên văn hóa di sản – Cơ sở dữ liệu cho ngành công nghiệp văn hóa tại Việt Nam
Authors: Thảo Phan Thị Phương
Published: October 2020

Bài báo phân tích các nội dung liên quan tới mô hình khai thác tài nguyên văn hóa di sản kỹ thuật số tại Việt Nam. Di sản là kết tinh văn hóa, lịch sử, truyền thống còn tồn tại và được lưu giữ tới ngày nay. Vì vậy Di sản là một dạng tài nguyên có hàm lượng văn hóa cao, thích hợp cho các hoạt động kinh tế xã hội. Phát triển ngành Công nghiệp văn hóa gắn với sử dụng Tài nguyên văn hóa di sản, đặc biệt là sử dụng thương hiệu Di sản thế giới sẽ tạo ra sản phẩm văn hóa chất lượng, có thương hiệu, bên cạnh đó chính sản phẩm văn hóa còn giúp lưu giữ quảng bá các giá trị di sản. Tuy nhiên nhiệm vụ đưa Di sản thành tài nguyên văn hóa để khai thác, nhằm đem lại hiệu quả về kinh tế vừa không ảnh hưởng tới công tác bảo tồn bảo trì di sản là một nhiệm vụ phức tạp với nhiều vấn đề liên quan. Bài báo sẽ phân tích tổng quan về hệ thống tài nguyên di sản tại Việt Nam, tiềm năng và phương pháp khai thác cho ngành Công nghiệp văn hóa và gợi mở giải pháp thiết lập hệ thống cơ sở dữ liệu Tài nguyên văn hóa di sản phục vụ cho ngành Công nghiệp văn hóa tại Việt Nam.

Từ khóa:

tài nguyên văn hóa; di sản; công nghiệp văn hóa; mô hình không gian.
Nghiên cứu sử dụng phế thải xây dựng và thải phẩm công nghiệp chế tạo gạch bê tông rỗng
Authors: Tuân Ngô Kim , Nhất Lâm Duy , Thới Nguyễn Trọng , Khải Phạm Quang , Khanh Ngô Văn
Published: October 2020

Phế thải từ phá dỡ các công trình ở Việt Nam hiện nay chưa được quản lý chặt chẽ và gây ra nhiều vấn đề ô nhiễm môi trường, lãng phí tài nguyên có thể tái tạo. Bên cạnh đó, một lượng không nhỏ thải phẩm của các nhà máy sản xuất vật liệu xây dựng như bê tông khí chưng áp (AAC) không được tái sử dụng mà đang chôn lấp hoặc đổ thải. Việc nghiên cứu tái sử dụng phế thải xây dựng và thải phẩm công nghiệp là rất cần thiết và có ý nghĩa lâu dài. Bài báo đưa ra một số kết quả nghiên cứu sử dụng cốt liệu tái chế từ phế thải xây dựng và thải phẩm công nghiệp là phế phẩm bê tông khí chưng áp (AAC) để chế tạo gạch bê tông rỗng có khả năng thoát nước mưa, ngăn ngừa ngập lụt và góp phần làm giảm hiệu ứng nhiệt đô thị. Kết quả nghiên cứu có lợi ích kép về mặt bảo vệ môi trường. Gạch bê tông rỗng có tổng độ rỗng từ 22,2% đến 41,2% (bao gồm độ rỗng giữa các hạt và độ rỗng bên trong hạt AAC và cốt liệu tái chế), tốc độ thoát nước từ 2,3 mm/s đến 9,1 mm/s, độ hút nước đạt từ 5,1% đến 16,5%, cường độ nén dao động từ 6,5 MPa đến 19,3 MPa tùy theo độ rỗng thiết kế và hàm lượng AAC sử dụng. Gạch bê tông này phù hợp để ứng dụng tại các bãi đỗ xe, vỉa hè, lối đi bộ, tránh được hiện tượng ngập lụt, đọng nước cũng như có tác dụng giảm hiệu ứng nhiệt đô thị.

Từ khóa:

phế thải xây dựng; bê tông rỗng; hút nước và giữ nước.
Flexible Epistemologies: Gypsy/roma Thinking and Anthropology Theory
Authors: Pontrandolfo Stefania , Solimene Marco
Published: October 2020

This article reflects on the conceptual debt that anthropology has developed towards the peoples it studies, by exploring the case-study of Gypsy/Roma anthropology. We argue that ethnographically-grounded research has enabled anthropologists to access and incorporate Gypsy/Roma visions and practices of the world. The flexible Gypsy epistemologies, which Gypsies/Roma use in the social and cultural construction of particular forms of identity and mobility, have thus translated into a specific practice of theory, which has provided more adequate tools for grasping the complexity of reality and contributed to a decolonialisation of anthropological thought.
Commons Research and Pastoralism in the Context of Variability
Authors: Blau Jill Philine
Published: October 2020

Research has shown that pastoralism and the management of the commons are connected (Bollig and Lesogorol 2016). In this article I discuss how the concept of variability, which emerged from discussions of dryland ecologies in the 1980s (Homewood 2008), can inform and enhance research on the commons and vice versa. Research on the commons can further elucidate the understanding of pastoralist practices. I conclude with reflections drawn from some empirical examples in the literature, the use of the socio-ecological systems (SES) framework, and discuss the benefits and potential problems when applied to heterogeneous and flexible pastoralist practices and to the pastoral management of the commons.
Nomadic Digital Ethnography and Engagement
Authors: Hahn Allison
Published: October 2020

The availability of information and communication technologies (ICTs) such as cell phones, WIFI connections, and social media has broadly changed communication norms amongst mobile pastoralists. Scholars and development organisations have reported on the end results of digital tools, for example by examining the ability of governments and development organisations to send early-warning weather reports through enhanced cellular access; the use of SMS to engage in deliberative polling; and the use of WIFI connections to provide banking services. However, researchers have not yet fully addressed how these tools are changing the communicative norms and ethnographic research methods used between researchers and mobile pastoralists. These changing communicative norms embed relations that inform academic understanding of the opportunities that arise from the interplay of complex forms of social and economic variability as experienced by herders.

This paper draws from the fields of Communication and Anthropology to understand how these same ICTs have changed the complex communication between herders and researchers through the establishment of new communicative networks. I ask how new communicative networks impact on both existing and emerging ethnographic research practices and how the emergent ‘digital field’ of research might open space for new communicative networks and research projects. Then, I propose that digital ethnography may be one way in which both herders and researchers can respond to variability while establishing research projects wherein herders are recognised both as participants in a research project and as co-producers of knowledge.
Afterword: The Infrastructures of Difference
Authors: Joks Solveig , Østmo Liv , Law John
Published: October 2020

How might we think about the fluidities of those who live in high variability environments when they butt up against state and disciplinary stabilities? This Afterword explores this question by distinguishing between infrastructures of stability and infrastructures of fluidity. The differences between these – which the paper calls the infrastructures of difference – are not simply conceptual, methodological and epistemological, but also deeply embedded in normative, metaphysical, institutional and material relations. This explains why they are so resilient, and why the infrastructures of stability so powerfully enact the bias against variability of pastoralists, Roma and indigenous groups. However, the Afterword also argues that in practice stabilities and fluidities are entangled, relational, and are never mutually exclusive. Instead they go together fractally, so that stabilities lie within fluidities, and fluidities within stabilities. Finally, the Afterword rehearses the political and intellectual implications of this by touching on the tactics used by those who champion fluidities in the face of powerful stabilities. The lesson here appears paradoxical, but it is not: to be fluid is (also) to include stability.
Apps für Kindergartenkinder: Lernen oder Aufmerksamkeitsraub? – Anforderungen an Lernapps aus kognitionspsychologischer Perspektive
Authors: Cordes Anne-Kristin , Egert Franziska , Hartig Fabienne
Published: October 2020

Der vorliegende Beitrag befasst sich mit der Frage, wie Lernapps für Kinder in der frühen Kindheit gestaltet werden müssen, um Lernprozesse zu ermöglichen. Dazu wird multimediales und digitales Lernen aus kognitionspsychologischer Sicht beleuchtet. Theoretische Modelle legen nahe, dass die Verarbeitung neuer Informationen im Arbeitsgedächtnis über zwei Kanäle erfolgt, einen akustischen und einen visuellen Kanal. Doch sind die Verarbeitungsressourcen des Arbeitsgedächtnisses begrenzt: Neben der Verarbeitung relevanter neuer Informationen und der Integration neuen und bereits gespeicherten Wissens werden Ressourcen – je nach Aufmerksamkeitslenkung – durch die Verarbeitung irrelevanter Reize oder durch die Inhibition dieser Reize gebunden. Diese und weitergehende theoretische Annahmen bestätigen sich in empirischen Studien zu multimedialem Lernen. Daraus lassen sich Hinweise ableiten, welche Anforderungen Lernapps erfüllen sollten, um den Lernprozess zu ermöglichen und zu unterstützen. So reduziert sich die Beanspruchung des Arbeitsgedächtnisses durch das Weglassen irrelevanter Informationen und gleichkanaliger Redundanzen sowie die Beschränkung auf nur wenige Wahlmöglichkeiten. Erleichtert wird das Lernen (komplexer Inhalte) zudem durch die Hervorhebung essentieller Informationen, Vorentlastung und die parallele Darbietung gleicher Informationen über beide Verarbeitungskanäle. Die Berücksichtigung und Verbreitung dieses kognitionspsychologischen Wissens im Bereich der (Medien-)Pädagogik wie auch der App-Entwicklung/ Informatik birgt die Chance, kindliche Lernprozesse durch die Entwicklung und den Einsatz adäquater digitaler Medien zu ermöglichen und optimal zu unterstützen.
Differences in Clinical Presentation of Behavioral and Psychological Symptoms of Dementia in Alzheimer’s Disease According to Sex and Education Level
Authors: Chang Kuo-Hsuan , Huang Chin-Chang , Chen Chiung-Mei , Wu Hsiu-Chuan , Kuo Hung-Chou
Published: October 2020

Background: The behavioral and psychological symptoms of dementia (BPSD) seriously affect the quality of life of patients with Alzheimer’s disease (AD) and their caregivers. Objective: We aimed to identify associations between demographic/genetic factors and clinical presentations of BPSD. Methods: In a cohort of 463 AD patients with BPSD, we retrospectively analyzed sex, education level, AD severity (assessed using the Clinical Dementia Rating and Mini-Mental Status Examination), and BPSD severity (assessed using the Neuropsychiatry Inventory, NPI). Severe BPSD was defined as NPI ≥10 for 3 consecutive years. Results: Among patients with severe BPSD (NPI ≥10), we observed more female patients (62.96%) and a lower level of education (6.03±4.77 years) as compared to those with mild BPSD (NPI <10) (female: 51.09%, p = 0.007; education years: 7.91±4.93, p < 0.001). Females had a lower level of education (5.72±4.50 years) and higher scores for depression/dysphoria (1.22±2.05) compared with males (education: 8.96±4.89 years, p < 0.001; depression/dysphoria: 0.78±1.42, p = 0.047). Patients with a high level of education (defined as ≥12 years) had higher scores for appetite/eating (0.90±2.02) than did those without (0.69±1.79; p = 0.001). Genetic analysis showed similar total and subscale NPI scores between patients with and without APOE4 and with and without the GRN rs5848 genotype. Conclusion: Our findings indicate potential contributions of sex and education to the presentation of BPSD. Further study is warranted to provide models for tailoring therapeutic programs to individual AD patients according to these factors.
The Israel Registry for Alzheimer’s Prevention (IRAP) Study: Design and Baseline Characteristics
Authors: Ravona-Springer Ramit , Sharvit-Ginon Inbal , Ganmore Ithamar , Greenbaum Lior , Bendlin Barbara B. , Sternberg Shelley A. , Livny Abigail , Domachevsky Liran , Sandler Israel , Ben Haim Simona , Golan Sapir , Ben-Ami Liat , Lesman-Segev Orit , Manzali Sigalit , Heymann Anthony , Beeri Michal Schnaider
Published: October 2020

Background: Family history of Alzheimer’s disease (AD) is associated with increased dementia-risk. Objective: The Israel Registry for Alzheimer’s Prevention (IRAP) is a prospective longitudinal study of asymptomatic middle-aged offspring of AD patients (family history positive; FH+) and controls (whose parents have aged without dementia; FH–) aimed to unravel the contribution of midlife factors to future cognitive decline and dementia. Here we present the study design, methods, and baseline characteristics. Methods: Participants are members of the Maccabi Health Services, 40–65 years of age, with exquisitely detailed laboratory, medical diagnoses and medication data available in the Maccabi electronic medical records since 1998. Data collected through IRAP include genetic, sociodemographic, cognitive, brain imaging, lifestyle, and health-related characteristics at baseline and every three years thereafter. Results: Currently IRAP has 483 participants [mean age 54.95 (SD = 6.68) and 64.8% (n = 313) women], 379 (78.5%) FH+, and 104 (21.5%) FH–. Compared to FH–, FH+ participants were younger (p = 0.011), more often males (p = 0.003) and with a higher prevalence of the APOE E4 allele carriers (32.9% FH+, 22% FH–; p = 0.040). Adjusting for age, sex, and education, FH+ performed worse than FH–in global cognition (p = 0.027) and episodic memory (p = 0.022). Conclusion: Lower cognitive scores and higher rates of the APOE E4 allele carriers among the FH+ group suggest that FH ascertainment is good. The combination of long-term historical health-related data available through Maccabi with the multifactorial information collected through IRAP will potentially enable development of dementia-prevention strategies already in midlife, a critical period in terms of risk factor exposure and initiation of AD-neuropathology.
Decoupling control of bearingless PMSM with LSSVM inverse system and IMC
Authors: Li Ke , Ling Feng , Sun Xiaodong , Yang Zebin
Published: October 2020

In this paper, a novel decoupling control scheme combining least squares support vector machines (LSSVM) inverse models and 2-degree-of-freedom (DOF) internal model controllers (IMC) is employed in the decoupling control system of the bearingless permanent magnet synchronous motor (BPMSM). This scheme can be used to enhance the control properties of high-precision, fast-response, and strong-robustness for the BPMSM system, and effectively eliminate the nonlinear and coupling influence. It introduces LSSVM inverse models into the original BPMSM system to constitute a decoupled pseudo-linear system. In addition, the particle swarm optimization algorithm (PSO) is used to optimize parameters of the LSSVM, which improves its fitting ability and prediction accuracy. What is more, the internal model control scheme is used to design additional closed-loop controllers, thereby improving the robustness of the entire control system. Therefore, this scheme successfully combines the advantages of the LSSVM inverse models and the internal model controller. It can enhance the stability and the static as well as dynamic properties of the whole BPMSM system while independently adjusting the tracking and interference rejection performances. The effectiveness of the proposed scheme has been verified by simulation results at various operations.
MPTC of a six-phase PMSHM with auxiliary voltage vectors
Authors: Xu Hao , Chen Long , Sun Xiaodong
Published: October 2020

Permanent magnet synchronous hub motors (PMSHMs) have been gradually introduced into the applications of electric vehicles. In order to output more torque, many researchers turned their research direction to six-phase motors. Because it is composed of two sets of three-phase windings, there will be interference between the windings, affecting the performance of the motor. In order to improve the steady and dynamic-state performance of permanent magnet six phase synchronous motor, a predictive torque control method based on multi vector model is proposed in this paper. Finally, experimental results show the effectiveness of this method.
A precision-based diagnostic model ADOBE-accurate detection of breast cancer using logistic regression approach
Authors: Venkatesh Veeramuthu , Anishin Raj M. M. , Mohamed Sajith K. , Anushiadevi R. , Suriya Praba T.
Published: October 2020

Cancer is a prevalent disease which comes in several forms. The need of the hour in cancer research is to be able to diagnose cancer in its early stages. The furthermost common forms of cancer among women us breast cancer. In recent times, there has been a drastic increase in the number of breast cancer cases among women. As a wide range of medical data is available in electronic form and with easy access to Machine Learning(ML) techniques disease progression risk evaluation has been made easier. These ML tools can aid in giving us complex insights from the massive amounts of available data. Some of the techniques used for developing predictive models for perfect decision making in cancer research are Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs). Although it is acceptable that ML is used to predict cancer progression, we need some level of validation. In this paper, we have come up with a review of several ML methods in modelling cancer progression. We discuss several predictive models based on supervised ML techniques and the inputs given by users, along with the data available. The results that were obtained from Logistic Regression show us that this method gave a significantly higher accuracy than most other classifiers. The best accuracy is 98.2%, however, the best precision and recall is 100 and 98.60% correspondingly.
Artificial Neural Network based SVPWM for Five Level Cascaded H-bridge Inverter fed Grid connected PV System
Authors: Palanisamy R. , Mohana Sundram K. , Selvakumar K. , Boopathi C.S. , Selvabharathi D. , Vijayakumar V.
Published: October 2020

An Artificial Neural Network (ANN) based Space Vector Pulse Width Modulation (SVPWM) for five level cascaded H-bridge inverter (CHBI) fed grid connected photovoltaic (PV) system. The multilevel inverter topologies are offers better performance compare conventional two level inverter like reduced total harmonic distortion, less electromagnetic interferences and voltage stresses across switches are low. The ANN based SVPWM generates the switching pulses for cascaded H-bridge inverter; it improves the accuracy in reference vectors tuning and identification, which leads to improve the inverter output voltage, better utilization of dc-link voltage and controlled output current. The ANN control makes the implementation of SVPWM becomes simple and minimizes the intricacy in tracking reference vector and calculation of switching time; it is suitable for any type of non-linear systems. This proposed system is energized using PV system and it is boosted using dc-dc boost converter, and the output of CHBI is synchronized with grid connected system using coupled inductor. The simulation and experimental results of ANN based SVPWM for CHBI is verified using simulink-matlab and DSP processor.
Applying data mining on customer relationship management system to discover forgotten effects
Authors: Urrutia Angélica , Rojo Fabiola , Nicolas Dra. Carolina , Ahumada Roberto
Published: October 2020

Companies need to know customer preferences for decision-making. For this reason, the companies take into account the Customer Relationship Management (CRM). These information systems have the objective to give support and allow the management of customer data. Nevertheless, it is possible to forget causal relationships that are not always explicit, obvious, or observables. The aim of this study on new methodologies for finding causal relationships. This research used a data analysis methodology of a CRM. The traditional analysis method is the Theory of Forgotten Effects (TFE), which is considered in this work. The new approach proposed in this article is to use Data Mining Algorithms (DMA) like Association Rules (AR) to discover causal relationships. This study analyzed 5,000 users’ comments and opinions about a Chilean foods industry company. The results show that the DMA used in this work obtains the same values as the TFE. Consequently, DMA can be used to identify non-obvious comments about products and services.
A systematic decision making for functional training in competitive sports training
Authors: Tong Wenxia , Li Bo
Published: October 2020

Decision-making is a vital aspect of any sport and competitive success, in particular open, strong, competitive team sports like soccer, volleyball, basketball, and rugby. However, in the notational analysis, it has largely ignored. To successfully win in any game and compete in every sport for individuals and teams at the national and international level, all success factors must be reconsidered to ensure a better winning decision. Hence, in this paper, Exploratory Hybridized Structural Equation Modeling Framework (HSEMF) has been proposed for decision making on functional training competitive sports training. The training environment will allow competitors to weigh up their options, determine, and mistake. However, the main feature of the environment is that athletes must be informed if they make mistakes to ensure that they do not take part in the future. The feedback should be provided to improve the performance of the athlete. The decision accuracy and performance indicators have been evaluated. Overall, the results did not show a causal relationship between changes in decision making after implementing the learning modules. However, it has provided moderate evidence of improved reaction time due to the learning modules for decision-making.
An approach to study medicine in China based on artificial intelligence analysis model for international students’
Authors: Li Yuanyuan , Wan Xuehong
Published: October 2020

This study utilizes empirical research method to compile the questionnaire for the motivation of Chinese medical students coming to China. The survey was conducted on 653 medical students from 55 origin countries and 8 universities in China. In addition to motivation research, we also analyzes the relationship between the factors such as university, educational background, gender and origin of students and the motivation. The results show that safe and stable social environment, profound history and culture, and a large number of universities are the most important motives for foreign students to study medicine in China. There are significant differences in the motivations of international medical students from different genders, universities, and educational background to study abroad, and but there were no significant differences among students from different countries of origin.
Ice and snow talent training based on construction and analysis of artificial intelligence education informatization teaching model
Authors: Liu Shuo , Wang Jin
Published: October 2020

Based on the analysis of the artificial intelligence education informatization teaching model for the training of ice and snow talents, this article first builds an artificial intelligence education informatization teaching model, reads and organizes a large number of documents such as big data and personalized teaching of ice and snow talent training. It needs to analyze the existing problems and condense the relevant concepts of educational big data, personalized teaching. We personalized teaching systems based on big data and elaborate on the related theoretical basis. It constructed a visual analysis framework for artificial intelligence teaching data, discussed the realization process and mechanism of data visualization of mixed ice and snow talent cultivation from the two dimensions of timeliness and media form, and introduced the mixed data of numerical and text for visual processing methods. We discussed the interactive presentation process of the visualization results. The research explores the impact of artificial intelligence on the elements of ice and snow talent training instruction design one by one and uses the paradigm migration analysis framework to prove that the ice and snow talent training instruction design paradigm in the context of artificial intelligence has produced a migration.
Improving the effectiveness of middle school physical education classroom teaching
Authors: Chen Changxiang , Zhou Qian
Published: October 2020

The quality of Physical Education (PE) education in high schools is closely related to interactive educational efficiency in classrooms. Teachers and students can improve their interest in learning through classroom interaction. Teachers can adjust educational programs according to the existing shortcomings of physical education, stimulate students’ interests in sports, and reduce student tensions and learning pressures. Students can increase their enthusiasm and creativity in sports, thereby enhancing students’ sports skills. Therefore, in a practical teaching process, it’s important to emphasize enhancing the effectiveness of interactive instruction in the classroom. This makes it possible to develop sports instruction. This paper analyzes how to effectively improve the effects of classroom interactions in a lower secondary school, and proposes a concrete teaching method for physical education. First, this paper explains the importance of improving the effectiveness of classroom education for junior high school students, and analyzes the present state of PE classroom education, and proposes an improvement strategy including physical education, and rationalizes students’ physical and mental development to stimulate students’ interest in sports. The classroom is innovative education and means that students improve their classroom enthusiasm.
Piano playing teaching system based on artificial intelligence – design and research
Authors: Liu Meichen , Huang Jieru
Published: October 2020

In recent years, with the rise of piano teaching, many people began to learn to play the piano. However, the expensive piano teaching cost and its unique teaching model that teachers and students are one to one have caused the shortage of piano education resources, and people learn piano playing has become a luxury activity. The use of computer multimedia software for piano teaching has become a feasible way to alleviate this contradiction. This paper proposes the design of an intelligent piano playing teaching system based on neural network, studies the realization method of the piano teaching system, presents a method of evaluating piano playing by using neural network model for the difficulties in computer piano teaching, that is, computer teaching is one-way knowledge transfer without interaction. In addition, this paper simulates the teacher to guide the students to carry on the playing practice, which is of great significance to the teaching of the piano.
Artificial intelligence model for real-time monitoring of ideological and political teaching system
Authors: Luo Ying
Published: October 2020

In ideological and political teaching, students have more serious problem behaviors in the classroom, including distracted, dazed, inattentive, and sleeping. In order to improve the efficiency of ideological and political teaching, based on artificial intelligence technology, this paper constructs a real-time monitoring system for ideological and political classrooms based on artificial intelligence algorithms, and builds model function modules according to the actual needs of ideological and political teaching monitoring. Moreover, this study makes reasonable calculations on the information monitoring and information transmission parts and installs a different number of monitoring equipment in different fixed locations according to the needs of signal monitoring. In addition, this paper designs a control experiment to study the system performance and verify the parameters from multiple aspects. The research results show that the system model constructed in this paper is stable in ideological and political teaching and has certain effects.
An artificial intelligence fuzzy system for improvement of physical education teaching method
Authors: Gaobin  , Huan Nan Cao , Zhen Zhong Liu
Published: October 2020

There are certain disadvantages in the traditional physical education teaching model. In order to improve the advanced nature of physical education teaching methods, this paper builds a physical education evaluation system based on artificial intelligence fuzzy algorithm. The system uses fuzzy control instructions as the basis to combine human language and mechanical language, so that the machine can recognize human working language habits and execute commands according to the instructions. Moreover, in this study, the trapezoid function is selected as the membership function, and the improved particle optimization algorithm is used to capture the student’s motion process and the motion vector decomposition, and the system structure model is constructed based on the functional requirements analysis. In addition, this study conducts system performance analysis through experimental teaching methods. The research results show that this system can effectively promote the reform of teaching methods in physical education and has a certain practical effect.
An artificial intelligence based speech model for linguistics teaching
Authors: Conghai Hu , Qianqian Zhao , Jie Guo
Published: October 2020

The linguistic artificial intelligence teaching model can be assisted by the intelligent speech recognition model. The traditional speech recognition algorithm has certain problems, so it cannot effectively eliminate speech noise. Based on the advantages of the linguistics teaching model, this article combines the linguistics teaching model and the artificial intelligence model to build an artificial intelligence assisted teaching model that can be used for classroom teaching. Moreover, this study improves the traditional algorithm and constructs an artificial intelligence linguistics teaching model based on the improved algorithm. The filtering part of noise includes preliminary filtering of speech signals based on the short-term energy detection method, and further detection and recognition of preliminary filtering speech signals based on the artificial intelligence model detection method. After these two steps of filtering and recognition, the voice file is sent to the client for processing and control. In addition, this study set up a control experiment to analyze the performance of the model. The research results show that the algorithm in this paper has a certain effect.
Particle swarm optimization neural network for research on artificial intelligence college English classroom teaching framework
Authors: Juan Dong , Hong Wei Yu
Published: October 2020

This paper based on the algorithm of particle swarm optimization neural network, the university English classroom training framework with artificial intelligence is researched and designed, and a personalized learning path based on an improved binary particle swarm algorithm based on the non-linear increase of inertial weights and the exploration of unknown space is proposed. The recommendation method improves the algorithm’s convergence speed and convergence accuracy. It is easy to jump out of the local optimum through the improvement of the algorithm, thereby solving the problem of low recommendation accuracy of the personalized learning path and improving the recommendation efficiency. To verify the recommended effect of the model and algorithm, this paper designs a simulation experiment and a learning platform that take the college English course as an example to verify the running performance and practical application effect of the proposed method. The above experiments show that the proposed method can improve the matching degree of the personalized learning path and the needs of learners, and improve the accuracy of application in personalized learning path recommendation.
An artificial intelligence data mining technology based evaluation model of education on political and ideological strategy of students
Authors: Rong Zheng , Gang Zheng
Published: October 2020

The student’s political and ideological practices is a vital portion of education, and it is related to optimization of task based on fundamental scenario in establishing morality. In order to establish a scientific, reasonable and operable evaluation model for students’ ideological education, and evaluate the status of college students’ ideological education. In this paper, firstly, in view of the shortcomings of evaluation objectives, single evaluation methods, lack of pertinence of evaluation indicators and subjectivity of evaluation standards in the current evaluation system of university students’ ideological and political education, the basic principles for constructing evaluation models of university students’ ideological and political education are put forward. Secondly, in case to meet changing needs of the times, an artificial neural network algorithm based on artificial intelligence data mining and a traditional multi-layer fuzzy evaluation model are designed to evaluate the ideological and political education of college students. This newly proposed model integrates learning, association, recognition, self-adaptive and fuzzy information processing, and at the same time, it overcomes their respective shortcomings. Finally, an example analysis is carried out with a nearby university as an example. The evaluation results display that the evaluation model of students’ ideological education established in this paper is in good agreement with the previous evaluation results. It fully shows that the comprehensive evaluation model of fuzzy neural network for college students’ ideological and political education established in this paper is scientific and effective.
AI based research on exploration and innovation of development direction of piano performance teaching in university
Authors: Chen Yanjie , Zheng Na
Published: October 2020

This paper investigates the cognition status of information piano education for teachers and students in a university, which mainly includes a summary of the piano teaching status in a university and make an analysis and summary of the investigation results. In addition, this paper puts forward the direction of the network information reform and construction for piano majors in Colleges and universities, mainly including three aspects, that is, taking piano “micro class” teaching to arm traditional classroom teaching, using the new media to build a networked piano learning environment, and building the piano teaching “MOOC” platform.
Role of AI for application of marketing teaching –A research perspective
Authors: Hu Jiheng , Liu Boya , Peng Hao
Published: October 2020

With the arrival of the era of artificial intelligence, based on the problems existing in the teaching process of marketing specialty, combined with the future business development trend and the core needs of enterprise operation, this paper analyzes the system reform of the practical courses of artificial intelligence and marketing specialty. With the rapid development of computer technology, intelligence has gradually become an important means to solve problems in various industries. In this paper, the modern media as a means, marketing teaching in Colleges and universities as the research background, through the establishment of the depth of marketing in Colleges and Universities Based on artificial intelligence network research learning platform, build a post-modern media communication perspective system. Based on probabilistic neural network and from the perspective of modern media marketing application system construction, the paper proves that the artificial intelligence prediction based on probabilistic neural network has good convergence, fault tolerance and data processing ability through MATLAB. Finally, this paper takes the pricing strategy in marketing as an example, and focuses on the application of artificial intelligence technology in marketing teaching from four aspects: preparation before class, implementation in class, consolidation after class and marketing teaching examination. According to the function and application of the theory of artificial intelligences in marketing teaching, we can find out that teachers must deeply understand the situation of each student’s artificial intelligences, so as to use the theory of artificial intelligences to change the traditional view of students and talents, and teach students according to their aptitude, so as to achieve better teaching effect.
Japanese translation teaching corpus based on bilingual non parallel data model
Authors: Guo Zheng , Jifeng Zhu
Published: October 2020

In recent years, with the development of Internet and intelligent technology, Japanese translation teaching has gradually explored a new teaching mode. Under the guidance of natural language processing and intelligent machine translation, machine translation based on statistical model has gradually become one of the primary auxiliary tools in Japanese translation teaching. In order to solve the problems of small scale, slow speed and incomplete field in the traditional parallel corpus machine translation, this paper constructs a Japanese translation teaching corpus based on the bilingual non parallel data model, and uses this corpus to train Japanese translation teaching machine translation model Moses to get better auxiliary effect. In the process of construction, for non parallel corpus, we use the translation retrieval framework based on word graph representation to extract parallel sentence pairs from the corpus, and then build a translation retrieval model based on Bilingual non parallel data. The experimental results of training Moses translation model with Japanese translation corpus show that the bilingual nonparallel data model constructed in this paper has good translation retrieval performance. Compared with the existing algorithm, the Bleu value extracted in the parallel sentence pair is increased by 2.58. In addition, the retrieval method based on the structure of translation option words graph proposed in this paper is time efficient and has better performance and efficiency in assisting Japanese translation teaching.
Effectiveness of ideological and political education reform in universities based on data mining artificial intelligence technology
Authors: Xiaoyang Huang , Junzhi Zhao , Jingyuan Fu , Xiuxia Zhang
Published: October 2020

Relying on the reform of the learning field curriculum system of ideological and political education courses in colleges and universities, the association rules between data mining and artificial intelligence technology are used to mine the association relationship between the software professional courses of the computer department, and the ideas and methods of optimizing the course setting are proposed. Discuss the reference value of the whole environment, multi-dimensional space-time, inter-subjectivity theory to the model construction from the theoretical level and the guiding significance of the Internet governance thought to the reform of ideological and political education in universities and colleges The survey results analyze the problems existing in the reform and construction of ideological and political education in colleges and universities, and propose improvements and optimization measures and the four systems of “systematic design outlined in the outline, teamwork coordination of team building, network expansion of environmental construction, and dual promotion of quality assurance". The countermeasures to improve the effectiveness of the ideological and political education in universities, experience summarization, theoretical analysis and empirical research, the idea of constructing a “three-stage full environment” network ideological and political education model for colleges and universities is proposed. Through in-depth understanding and analysis of the relevant knowledge of data mining artificial intelligence technology, the relevant methods of data mining artificial intelligence technology are used to solve the timeliness problems of ideological and political education reform in colleges and universities, so that relevant managers can timely learn relevant information in complex issues to provide solutions for further decision-making.