bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative hypolipidemic efficacy of homeopathic mother tincture Allium Sativa Q, Curcuma Longa Q and statin in normal and cholesterol fed rabbits
Authors: Anjum Khalid Mahmood , Rasool Fayyaz Rasool , Bhatti Ehsan Mehmood , Liaquat Samina , Yousaf Muhammad Zubair , Zahid Shafaq , Khan Noor , Yameen Muhammad
Published: May 2019

Although, lipid lowering drugs regulate the blood serum lipids, they have several reported side effects. Therefore, a substance that is less toxic and yet effective would be beneficial. Here, we compared the antihypercholesterolemic effects of combinations of homeopathic mother tincture (HMT) Allium sativa Q (garlic) and Curcuma longa Q (turmeric), with standard lipid lowering drug statin. For experimentation, a total of 20 rabbits were recruited and divided into four groups with five rabbits (n = 5). Normal saline (NS) group served as control; second group served as high fat diet (HFD) group, third group received Allium sativa Q (garlic) and Curcuma longa Q (turmeric) and HFD (T and G group) while the fourth group received statin and HFD (statin group). At the end of the 8th week, blood was collected to analyze the comparative efficacy of Allium sativa Q, Curcuma longa Q and statin on total cholesterol (TC), low density lipoproteins (LDL), high density lipoproteins (HDL) and triglyceride (TG). Various parameters measured (mg/dl) in HFD rabbits were, TC (291.76±2.2), LDL (244.20±8.3) and triglycerides (243.79±4.3) increased significantly and HDL (32.19±3.3) was reduced to an alarming level as compared to the NS group after eight weeks. TC, LDL and triglycerides level reduced significantly in Tand G group while the HDL level increased significantly as compared to HFD. In statin group, TC (148.37±1.2), LDL (76.32 ±3.4) and TG (104.33 ±8.9) decreased while HDL increased (p LESS than 0.05) as compared to the HFD after eight weeks. It was therefore concluded that that homeopathic medicine Allium sativa Q and Curcuma longa Q is equally good like statin and can safely be used to regulate the circulating serum lipid profile
Method Development for Describing Content of Multitasked Interventions Using the Omaha System
Authors: Altiner Merve , Secginli Selda , Mathiason Michelle A. , Monsen Karen A.
Published: May 2019

Background

Understanding multitasking within nursing and midwifery is needed for efficient, effective, and cost-effective health services. Methods are needed to measure and analyze multitasking in alignment with the nursing process. The Omaha System operationalizes the nursing process and may be a valuable resource for multitasking analysis.

Purpose

The purpose of this study was to develop and test a method for describing intervention content of nurse/midwife multitasked interventions using the Omaha System and Time Capture Tool (TimeCaT) in a family health center in Turkey.

Methods

Eight nurses/midwives were observed with TimeCaT in a family health center in 2016. Preidentified 84 nurse/midwife interventions employed in the center were mapped on Omaha System terms and entered in TimeCaT software for data collection. Co-occurring interventions were analyzed using descriptive and inferential statistics and visualization techniques.

Results

Of 1066.8 total minutes observed, 34.0% included more than one intervention that occurred at the same time. Caretaking/parenting and postpartum problems, teaching, guidance, and counseling category, and caretaking/parenting skills target were more often multitasked than others.

Implications for Practice

It was feasible to use the Omaha System and TimeCaT to categorize, describe, and measure multitasking nursing/midwifery interventions. This method may be applied to other time–motion data when more than one co-occurring intervention is recorded.
Use of Music Therapy to Improve Cognition in Older Adults With Dementia: An Integrative Review
Authors: Domínguez-Chávez Claudia Jennifer , Salazar-González Bertha Cecilia , Murrock Carolyn J.
Published: May 2019

Background and purpose

Dementia is considered a public health priority due to physical, psychological, economic, and social repercussions in older adults, their families, and caregivers. To address this issue, healthcare providers can use music therapy as a complementary therapy. This manuscript was elaborated to analyze and synthesize the current evidence of the use of music therapy to improve cognition in older adults with dementia.

Methods

Using the Whittemore and Knafl (2005) method, databases searched were PubMed, CINAHL, Ovid, EBSCO, ProQuest, and PsycINFO, using the terms such as “Music Therapy,” “Acoustic Stimulation,” “Music,” “Cognition,” “Memory,” “Attention,” “Aged,” and “Dementia.” Studies that met the eligibility criteria were screened by title, abstract and full text. Quality of the studies were evaluated using critical appraisal tools and Cochrane “Risk of Bias” tool.

Results

A total of 211 articles were screened by title and abstract, but only 28 articles met the inclusion criteria and were full text screened. Of the 28 articles, only five studies were analyzed and synthesized as they focused specifically on music therapy and cognition. Three of the studies showed that active and active–passive approaches of music therapy produce significant effect on cognition in older adults with dementia. The common characteristic was the participation of music therapists or music teachers who delivered the music therapy sessions that resulted in improved cognition.

Implications for practice

As a safe, inexpensive intervention, it should be considered as a complementary therapy to positively impact cognition in older adults with different types of dementia and levels of severity.
Research on Deep Integration of Construction Management and Computer BIM Technology
Authors: Yang Zhongxuan ,   School of Economics and Management, Systems and Industrial Engineering Technology Research Center, Zhongyuan University of Technology No.1 Huaihe Road, Longhu Economic Development Zone, Longhu, Xinzheng, Zhengzhou, Henan 450007, China
Published: May 2019

As information development has progressed in the field of architecture in China, owing to the lack of unified information exchange standards and information integration mechanisms, it is difficult to exchange and share information between different stages and application systems during the construction lifecycle. The formation of information islands and faults hinders the application of information technology in the field of construction, thus affecting the production efficiency of the industry. In this study, construction engineering management and computer building information modeling (BIM) are deeply integrated, and an international standard for the construction industry is introduced. Moreover, a BIM information integrated building management platform is developed that combines BIM technology with the construction engineering management to realize the exchange of engineering information and shared and integrated management, in addition to providing theories, methods, technologies, and platforms.
Deep Learning SDN Intrusion Detection Scheme Based on TW-Pooling
Authors: Meng Qingyue , Zheng Shihui , Cai Yongmei ,   School of Cyberspace Security, Beijing University of Posts and Telecommunications West TuCheng Road 10, Haidian, Beijing 100876, China,   School of Computer Science and Engineering, Xinjiang University of Finance and Economics  No.449 Beijing Middle Road, Urumqi, Xinjiang Uygur Autonomous Region 830026, China
Published: May 2019

The numerical control separation in the Software-Defined Network (SDN) allows the control plane to have the absolute management rights of the network. As a new management plane of the SDN, once it is attacked, it will cause the entire network to face flaws. For this reason, this paper proposes a SDN control plane attack detection scheme based on deep learning, which can detect and respond to attacks on the SDN control plane in time. In this scenario, we propose a new pooling scheme that uses the TF-IDF idea to weight the characteristics of network traffic. Ultimately, our method achieved an accuracy of 99.8% in the SDN network’s traffic data set including 24 attack types.
Efficient Mining Recurring Patterns of Inter-Transaction in Time Series
Authors: Lorpunmanee Siriluck , Kamonsantiroj Suwatchai ,   Department of Data Science and Analytics, Suan Dusit University 228-228/1-3 Sirinthon Road, Bang Bamru, Bang Phlat, Bangkok 10700, Thailand,   Department of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok 1518 Pracharat 1 Road, Wong Sawang, Bag Sue, Bangkok 10800, Thailand
Published: May 2019

One type of the partial periodic pattern is known as recurring patterns, which exhibit cyclic repetitions only for particular time period within a series. A key property of the patterns is the event can start, stop, and restart at anytime within a series. Therefore, the extracted meaningful knowledge from the patterns is challenging because the information can vary across patterns. The mining technique in recurring patterns plays an important role for discovering knowledge pertaining to seasonal or temporal associations between events. Most existing researches focus on discovering the recurring patterns in transaction. However, these researches for mining recurring patterns cannot discover recurring events across multiple transactions (inter-transaction) which often appears in many real-world applications such as the stock exchange market, social network, etc. In this study, the proposed algorithm, namely, CP-growth can efficiently perform in discovering the recurring patterns within inter-transaction. Besides, an efficient pruning technique to reduce the computational cost of discovering recurring patterns is developed in CP-growth algorithm. Experimental results show that recurring patterns can be useful in multiple transactions and the proposed algorithm, namely, CP-growth is efficient.
SAR Target Recognition via Joint Classification of Monogenic Components with Discrimination Analysis
Authors: Zhang Zhenyu ,   School of Automation and Electrical Engineering, Zhejiang University of Science and Technology No.318 Liuhe Road, Hangzhou, Zhejiang 310023, China
Published: May 2019

This paper proposes a method using joint classification of monogenic components with discrimination analysis for target recognition in synthetic aperture radar (SAR) images. Three monogenic components, namely, phase, amplitude, and orientation, are extracted from the original image and classified by joint sparse representation for target recognition. Considering that the three components may have different discrimination capabilities for different operating conditions, the discrimination analysis is incorporated into the classification scheme. The components with low discriminability are not used in the joint classification. Afterwards, those discriminative components for a certain condition are classified to determine the target type. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) to evaluate the performance of the proposed method.
Correlation Coefficients of Refined-Single Valued Neutrosophic Sets and Their Applications in Multiple Attribute Decision-Making
Authors: Fan Changxing ,   Department of Computer Science, Shaoxing University 508 Huancheng West Road, Shaoxing, Zhejiang 312000, China
Published: May 2019

The paper presents the correlation coefficient of refined-single valued neutrosophic sets (Refined-SVNSs) based on the extension of the correlation of single valued neutrosophic sets (SVNSs), and then a decision making method is proposed by the use of the weighted correlation coefficient of Refined-SVNSs. Through the weighted correlation coefficient between the ideal alternative and each alternative, we can rank all alternatives and the best one of all alternatives can be easily identified as well. Finally, to prove this decision making method proposed in this paper is useful to deal with the actual application, we use an example to illustrate it.
Improving GNSS Navigation and Control with Electronic Compass in Unmanned System
Authors: Han Xi , Zhang Xiaolin , Liu Yuansheng ,   School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics No.37 Xueyuan Road, Haidian District, Beijing 100083, China,   College of Robotics, Beijing Union University No.97 Beisihuan East Road, Chaoyang District, Beijing 100101, China,   Beijing Engineering Research Center of Smart Mechanical Innovation Design Service, Beijing Union University No.4 Gongti North Road, Chaoyang District, Beijing 100027, China
Published: May 2019

This paper proposes a compensation technique for the global navigation satellite system (GNSS)/real-time kinematic (RTK) course angle data using an electronic compass for an unmanned system. Additionally, the proportion, integral, and derivative control based on a back-propagation neural network (BP-PID) is introduced to improve the steering safety and riding comfort. The course angle jitter was determined. Because the GNSS/RTK receiver cannot offer stable heading data under specific conditions, including but not limited to susceptibility to obstacles, complex electromagnetic environment, and fewer satellites. The compensation algorithm is based on the determination of the GNSS course angle variance ratio and the asynchronous characteristic between the GNSS and an electronic compass. The combined data provide accurate and robust navigation information for an outdoor unmanned system. To address the limitation of the in-system parameter adjustment, a back-propagation (BP) neural network is adhibited to a conventional proportion, integral, and derivative (PID) lateral control system. The BP-PID control module updates the incremental PID parameters through self-learning, and results in the smoother operation of the vehicle. The flowchart of the learning algorithm and method of calculating the parameters are presented. A typical measurement was conducted and the obtained results were compared with typical RTK navigation results. Thus, the effectiveness of the proposed compensation method was confirmed.
A Kansei-Based Sound Modulation System for Musical Instruments by Using Neural Networks
Authors: Tanaka Daisuke , Suzuki Ryuto , Kato Shigeru ,   National Institute of Technology, Niihama College 7-1 Yagumo, Niihama, Ehime 792-8580, Japan,   The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
Published: May 2019

This study describes a sound modulation system based on the use of a neural network model. The inputs to the model are a) a basic, original sound wave, and b) the degree of Kansei, while the output of the model is modulated sound depending on the degree of Kansei. The degree of Kansei is the numerical value that expresses the modulation level based on a Kansei linguistic expression, such as hardness or brilliance. In the experiment, the models are constructed for the sounds of piano and Marimba. Three types of training data are used for each sound, and the degree of Kansei is assigned manually for each dataset. By changing the degree of Kansei at the input of the model, we have validated that each model could appropriately modulate the basic sound. In addition, the modulation results are illustrated for one octave of piano sounds. The potential of our proposed model and future work are also discussed.
Combining 2D Gabor and Local Binary Pattern for Facial Expression Recognition Using Extreme Learning Machine
Authors: Liu Zhen-Tao , Li Si-Han , Cao Wei-Hua , Li Dan-Yun , Hao Man , Zhang Ri ,   School of Automation, China University of Geosciences No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China,   Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China
Published: May 2019

The efficiency of facial expression recognition (FER) is important for human-robot interaction. Detection of the facial region, extraction of discriminative facial expression features, and identification of categories of facial expressions are all related to the recognition accuracy and time-efficiency. An FER framework is proposed, in which 2D Gabor and local binary pattern (LBP) are combined to extract discriminative features of salient facial expression patches, and extreme learning machine (ELM) is adopted to identify facial expression categories. The combination of 2D Gabor and LBP can not only describe multiscale and multidirectional textural features, but also capture small local details. The FER of ELM and support vector machine (SVM) is performed using the Japanese female facial expression database and extended Cohn-Kanade database, respectively, in which both ELM and SVM achieve an accuracy of more than 85%, and the computational efficiency of ELM is higher than that of SVM. The proposed framework has been used in the multimodal emotional communication based humans-robots interaction system, in which FER within 2 seconds enables real-time human-robot interaction.
Analysis of Water Quality Conditions of Lake Hachiroko Using Fuzzy C-Means
Authors: Matsui Kai ,   Akita University 1-1 Tegata Gakuen-machi, Akita-shi, Akita 010-8502, Japan, Kageyama Yoichi , Yokoyama Hiroshi
Published: May 2019

Lake Hachiroko, Japan, has many water quality issues, evident from phenomena such as green algae blooms. Understanding the details of the surface water quality of the lake, and the effect of seasons on the quality, is important. In our previous studies, we conducted fuzzy regression analysis of remote sensing data and direct measurements of water quality. The results showed that estimation maps of water quality were well created, using only five data points of the water quality parameters. To obtain maps that are in good agreement with the experimental data, remote sensing data and water quality values should be acquired simultaneously. However, performing such simultaneous observations can affect the preparation of the water quality estimation maps. We overcame this obstacle by using fuzzy c-means clustering (FCM), and considered the effect of specific disturbances and uncertainties on the remote sensing data. Furthermore, FCM using only remote sensing data creates estimation maps in which relative water surface conditions are classified. Therefore, determining the relationship between FCM results and water quality facilitates the creation of low-cost, high-frequency water quality estimation maps. Our results indicated that FCM was particularly effective in determining the presence of suspended solids (SS) during water quality analysis. However, the relationship between FCM results and water quality has not been determined in detail. In this study, we analyzed the water quality conditions of Lake Hachiroko with FCM using the data collected by the Advanced Space-borne Thermal Emission and Reflection Radiometer on Terra and, the Operational Land Imager on Landsat-8. In addition, FCM results were compared with the maps created by fuzzy regression analysis and the actual conditions of water pollution. The results indicated that (i) the maps created using FCM are effective in determining the water surface conditions, (ii) the FCM maps using data obtained during August and September have a strong relationship with biochemical oxygen demand (BOD) and SS, and (iii) the FCM maps using data obtained during May and June have a strong relationship with chemical oxygen demand (COD), SS, and total nitrogen (T-N).
Improvement of Face Recognition with Gabor, PCA, and SVM Under Complex Illumination Conditions
Authors: Zhuang Liyun , Guan Yepeng ,   School of Communications and Information Engineering, Shanghai University 99 Shangda Road, Baoshan District, Shanghai 200444, China,   Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology No.1 Meicheng East Road, Huaian, Jiangsu 223003, China,   Key Laboratory of Advanced Displays and System Application, Ministry of Education Shanghai, China
Published: May 2019

Complex illumination condition is one of the most critical challenging problems for practical face recognition. However, numerous studies have had no effective solutions reported for full illumination variation of face images in the facial recognition research field. In order to effectively solve full illumination variation problem, we propose a novel approach for illumination normalization for facial images based on the enhanced contrast method of histogram equalization (HE) and fusion of illumination estimations (FOIE). Then, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods to process illumination normalization. Next, a support vector machine classifier (SVM) is used for face classification. Experimental results show that superior performance can be obtained in the developed approach by comparisons with some state-of-the-arts.
Crystalizing Effect of Simulated Annealing on Boltzmann Machine
Authors: Shibata Hiroki ,   Graduate School of System Design, Tokyo Metropolitan University 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Ishikawa Hiroshi , Takama Yasufumi
Published: May 2019

This paper proposes a method to estimate the posterior distribution of a Boltzmann machine. Due to high feature extraction ability, a Boltzmann machine is often used for both of supervised and unsupervised learning. It is expected to be suitable for multimodal data because of its bi-directional connection property. However, it needs a sampling method to estimate the posterior distribution, which becomes a problem during an inference period because of the computation time and instability. Therefore, it is usually converted to feedforward neural networks, which means to lose its bi-directional property. To deal with these problems, this paper proposes a method to estimate the posterior distribution of a Boltzmann machine fast and stably without converting it to feedforward neural networks. The key idea of the proposed method is to estimate the posterior distribution using a simulated annealing on non-uniform temperature distribution. The advantage of the proposed method against Gibbs sampling and conventional simulated annealing is shown through experiments with artificial dataset and MNIST. Furthermore, this paper also gives the mathematical analysis of Boltzmann machine’s behaviour with regard to temperature distribution.
Regularized Fuzzy c-Means Clustering and its Behavior at Point of Infinity
Authors: Kanzawa Yuchi , Miyamoto Sadaaki ,   Shibaura Institute of Technology 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan,   University of Tsukuba 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: May 2019

This study shows that a general regularized fuzzy c-means (rFCM) clustering algorithm, including some conventional clustering algorithms, can be constructed if a given regularizer function value, its derivative function value, and its inverse derivative function value can be calculated. Furthermore, the results of the study show that the behavior of the fuzzy classification function for rFCM at an infinity point is similar to that for some conventional clustering algorithms.
Intelligent Measurement of Spinal Curvature Using Cascade Gentle AdaBoost Classifier and Region-Based DRLSE
Authors: Zhang Liyuan ,   School of Computer Science and Technology, Changchun University of Science and Technology No.7089 Weixing Road, Changchun 130022, China, Zhao Jiashi , Jiang Zhengang , Yang Huamin
Published: May 2019

For spinal curvature measurements, because of the anatomical complexity of the spine CT image, developing an automated method to avoid manual landmark is a challenging task. In this study, we propose an intelligent framework that integrates the cascade AdaBoost classifier and region-based distance regularized level set evolution (DRLSE) with the vertebral centroid measurement. First, the histogram-of-oriented-gradients based cascade gentle AdaBoost classifier is used to detect automatically and localize vertebral bodies from computer tomography (CT) spinal images. Considering these vertebral pathological images enables us to produce a diverse training dataset. Then, the DRLSE method introduces the local region information to converge the vertebral boundary quickly. The located bounding box is regarded as an accurate initial contour. This avoids the negative impact of manual initialization. Finally, we perform vertebral centroid extraction and spinal curve fitting. The spinal curvature angle is determined by calculating the angle between two tangents to the curve. We verified the effectiveness of the proposed method on 10 spine CT volumes. Quantitative comparison against the ground-truth centroids yielded a detection accuracy rate of 98.3% and a mean centroid location error of 1.15 mm. The comparative results with existing methods demonstrate that the proposed method can accurately detect and segment vertebral bodies. Furthermore, the spinal curvature can be automatically measured without manual landmark.
Emotion Prediction and Cause Analysis Considering Spatio-Temporal Distribution
Authors: Kitaoka Saki ,   Waseda University 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan, Hasuike Takashi
Published: May 2019

This paper proposes an analytical model that clarifies the relationship between specific place and human emotions as well as the cause of the emotions using tweet data with location information. In addition, Twitter data with location information are analyzed to show the effectiveness of our proposed model. First, geotags are provided to collect Twitter data and increase the number of data for analysis. Second, training data with emotion labels based on the emotion expression dictionary are created and used, and supervised learning is done using fastText to obtain the emotion estimates. Finally, by using the result, topic extraction is performed to estimate the causes of the emotions. As a result, the transition of emotion in time and space as well as its cause is obtained.
Human Posture Recognition for Estimation of Human Body Condition
Authors: Quan Wei , Woo Jinseok , Toda Yuichiro , Kubota Naoyuki ,   Graduate School of Systems Design, Tokyo Metropolitan University 6-6 Asahigaoka, Hino, Tokyo 191-0055, Japan,   Graduate School of Natural Science and Technology, Okayama University 3-1-1 Tsushima-Naka, Kita, Okayama, Okayama 700-8530, Japan
Published: May 2019

Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.
A Fuzzy Inference-Based Spiking Neural Network for Behavior Estimation in Elderly Health Care System
Authors: Shao Shuai ,   Graduate School of Systems Design, Tokyo Metropolitan University 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Kubota Naoyuki
Published: May 2019

In recent years, population aging has become an important social issue. We hope to achieve an elderly health care system through technical means. In this study, we developed an elderly health care system. We chose to use environmental sensors to estimate the behavior of older adults. We found that traditional methods have difficulty solving the problem of excessive indoor environmental differences in different households. Therefore, we provide a fuzzy spike neural network. By modifying the sensitivity of input using a fuzzy inference system, we can solve the problem without additional training. In the experiment, we used temperature and humidity data to make an estimation of behavior in the bathroom. The results show that the system can estimate behavior with 97% accuracy and 78% sensitivity.
Causal Relationship Between FDI Flow and Technological Innovation in China and Japan
Authors: Liu Chengkun , Zhang Xiuwu , Tamamine Takashi ,   School of Economics and Finance, Huaqiao University No.269 Chenghua North Road, Fengze District, Quanzhou 362021, China,   School of Statistics, Huaqiao University No.668 Jimei Avenue, Jimei District, Xiamen 361021, China,   Faculty of Economics, Kagawa University 2-1 Sakuracho, Takamatsu, Kagawa 760-8523, Japan
Published: May 2019

The improvement of a country’s technological innovation level is influenced by the technology spillover of inward foreign direct investment (IFDI) and outward foreign direct investment (OFDI). Based on the Coe and Helpmen’s theory of international capital flow model and one-way causality measure model, this study examines the similarities and dissimilarities between the dynamic effects of IDFI and OFDI on technological innovation in China and Japan to enumerate the differences in the utilization effect of FDI between developed and developing countries. The empirical results show that the one-way causality intensity of IFDI to technological innovation in China is weaker than that in Japan, but the FDI volatility in China is stronger than that in Japan. The one-way causality intensity of OFDI to technological innovation are low both in China and Japan, and the patterns of long-term and short-term effects are not identical. According to the results of our empirical research, we draw the conclusions and proposed suggestions for using IFDI and OFDI in China and Japan.
The Credit-Increasing Mechanism of Small and Medium-Sized Enterprises: Evidence from Taizhou’s Credit Guarantee Fund
Authors: Yang Jingwen , Zhang Li , Xu Bing , Wang Chengbin , Lin Hsin-Chih ,   Research Institute of Econometrics and Statistics, Zhejiang Gongshang University 18 Xuezheng Street, Xiasha Education Park, Hangzhou, Zhejiang 310018, China,   Micro-Finance Institute Zhejiang (Taizhou), Taizhou University No.1139, Municipal Avenue, Taizhou, Zhejiang 318000, China,   Research Center for Educational System and Policy, National Academy for Educational Research (NAER) No.2, Sanshu Rd., Sanxia Township, Taipei County 237, Taiwan
Published: May 2019

Increasing the credit of small and medium-sized enterprises (SMEs) is the key to solving SMEs’ financing difficulties. Because of their small size and fixed assets, it is not easy for SMEs to get loans from mortgage or private guarantee institutions. Therefore, to alleviate the credit rationing faced by SMEs and reduce financing cost, the key is to increase corporate credit score. This study uses small and micro-businesses’ data from Taizhou city to identify the key factors affecting corporate default. The results show that enterprise scale, enterprise operation status, financial environment, and credit-increasing means are the key factors affecting enterprise default, and credit protection funds do not play a significant role. Therefore, it can be argued that at present, the credit growth of SMEs still relies mainly on fixed asset mortgage, while the role of credit protection funds needs further refinement to effectively assist SMEs to solve difficult and expensive financing.
Fuzzy Clustering Method for Spherical Data Based on q-Divergence
Authors: Higashi Masayuki ,   Shibaura Institute of Technology 3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan, Kondo Tadafumi , Kanzawa Yuchi
Published: May 2019

This study presents a fuzzy clustering algorithm for classifying spherical data based on q-divergence. First, it is shown that a conventional method for vectorial data is equivalent to the regularization of another conventional method using q-divergence. Next, based on the knowledge that q-divergence is a generalization of Kullback-Leibler (KL)-divergence and that there is a conventional fuzzy clustering method for classifying spherical data based on KL-divergence, a fuzzy clustering algorithm for spherical data is derived based on q-divergence. This algorithm uses an optimization problem built by extending KL-divergence in the conventional method to q-divergence. Finally, some numerical experiments are conducted to verify the proposed methods.
Kernel Fuzzy c-Regression Based on Least Absolute Deviation with Modified Huber Function
Authors: Oi Yusuke , Endo Yasunori ,   Department of Risk Engineering, Graduate School of Systems and Information Engineering, University of Tsukuba 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan,   Faculty of Engineering, Information and Systems, University of Tsukuba 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: May 2019

The fuzzy c-regression models are useful for datasets with various correlations. To deal with nonlinear datasets, a kernel fuzzy c-regression (KFCR) method was previously proposed. However, this method is weak for outliers because its objective function is based on the least square principle. We introduce the least absolute deviation (LAD) method with a modified Huber function into the KFCR (LAD-KFCR) to overcome the abovementioned problem. We verify the usefulness of the proposed LAD-KFCR method through numerical examples.
Nonverbal Communication Based on Instructed Learning for Socially Embedded Robot Partners
Authors: Tanaka Ryosuke ,   Intelligent Mechanical Systems, Graduate School of System Design, Tokyo Metropolitan University 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Woo Jinseok , Kubota Naoyuki
Published: May 2019

The research and development of robot partners have been actively conducted to support human daily life. Human-robot interaction is one of the important research field, in which verbal and nonverbal communication are essential elements for improving the interactions between humans and robots. Thus, the purpose of this research was to establish a method to adapt a human-robot interaction mechanism for robot partners to various situations. In the proposed system, the robot needs to analyze the gestures of humans to interact with them. Humans have the ability to interact according to dynamically changing environmental conditions. Therefore, when robots interact with a human, it is necessary for robots to interact appropriately by correctly judging the situation according to human gestures to carry out natural human-robot interaction. In this paper, we propose a constructive methodology on a system that enables nonverbal communication elements for human-robot interaction. The proposed method was validated through a series of experiments.
Interval Type-2 Fuzzy Possibilistic C-Means Clustering Based on Granular Gravitational Forces and Particle Swarm Optimization
Authors: Truong Hung Quoc ,   Le Quy Don Technical University (LQDTU) 236 Hoang Quoc Viet Road, Bac Tu Liem District, Hanoi 100000, Vietnam, Ngo Long Thanh , Pham Long The
Published: May 2019

The interval type-2 fuzzy possibilistic C-means clustering (IT2FPCM) algorithm improves the performance of the fuzzy possibilistic C-means clustering (FPCM) algorithm by addressing high degrees of noise and uncertainty. However, the IT2FPCM algorithm continues to face drawbacks including sensitivity to cluster centroid initialization, slow processing speed, and the possibility of being easily trapped in local optima. To overcome these drawbacks and better address noise and uncertainty, we propose an IT2FPCM method based on granular gravitational forces and particle swarm optimization (PSO). This method is based on the idea of gravitational forces grouping the data points into granules and then processing clusters on a granular space using a hybrid algorithm of the IT2FPCM and PSO algorithms. The proposed method also determines the initial centroids by merging granules until the number of granules is equal to the number of clusters. By reducing the elements in the granular space, the proposed algorithms also significantly improve performance when clustering large datasets. Experimental results are reported on different datasets compared with other approaches to demonstrate the advantages of the proposed method.
Design of Modified Repetitive Controller for T–S Fuzzy Systems
Authors: Zhang Manli , Wu Min , Chen Luefeng , Yu Pan ,   School of Automation, China University of Geosciences No.388 Lumo Road, Hongshan District, Wuhan 430074, China,   Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems No.388 Lumo Road, Hongshan District, Wuhan 430074, China,   Department of Electrical and Electronic Engineering, Chiba University 1-33 Yayoi, Inage, Chiba 263-8522, Japan
Published: May 2019

A repetitive controller contains a pure-delay positive-feedback loop that makes it difficult to stabilize a strictly proper system. A low-pass filter is inserted in a repetitive controller to relax the stability condition of the modified repetitive-control system at the cost of degrading the tracking performance. In this study, a modified repetitive-control approach is developed, which reaches a balance between the stability and tracking performance for a class of affine nonlinear systems based on the Takagi–Sugeno fuzzy model. First, a 2D model is established to adjust continuous control and discrete learning actions preferentially induced by exploiting the 2D property in a repetitive-control process. Then, the Lyapunov stability theory and 2D system theory are used to derive a sufficient stability condition in the form of linear matrix inequalities to design parallel-distributed-compensation-based state-feedback controllers. Finally, an application-oriented example is used, and a comparison is performed to show that an extra variable is introduced such that the developed method has a better tracking performance.
Inovasi Teknologi Tanaman Krisan yang Dibutuhkan Pelaku Usaha (Technology Innovation of Chrysanthemum Needed by Stakeholders)
Authors: Hayati Nur Qomariyah , Nurmalinda NFN , Marwoto Budi
Published: May 2019

Inovasi merupakan komponen utama dalam peningkatan daya saing. Informasi inovasi teknologi yang sesuai kebutuhan pengguna sangat diperlukan dalam penentuan prioritas program penelitian. Tujuan penelitian adalah memperoleh informasi jenis inovasi teknologi yang dibutuhkan pelaku usaha untuk mengembangkan budidaya tanaman krisan. Penelitian dilakukan di wilayah Jawa Barat, Jawa Tengah, dan Bali pada bulan Januari-Desember 2014. Pemilihan lokasi dan responden dilakukan secara purposive, yaitu dengan menentukan sebanyak 45 petani yang berasal dari 10 kelompok tani yang menanam krisan di masing-masing wilayah. Untuk mengetahui tingkat kepentingan kebutuhan inovasi teknologi dalam pengembangan agribisnis krisan berkelanjutan digunakan skala Likert lima tingkat, yaitu sangat penting, penting, cukup penting, tidak penting, dan sangat tidak penting. Penilaian kepentingan dilakukan berdasarkan perkiraan besarnya tingkat kepentingan suatu inovasi teknologi dalam pengembangan sistem agribisnis krisan. Penilaian pelaku usaha terhadap nilai kepentingan relatif jenis inovasi dari masing-masing subsistem agribisnis merupakan dasar pertimbangan untuk menentukan tingkat prioritas dalam program penelitian ke depan dengan kategori utama, prioritas, maupun potensial.

Keywords

Identifikasi; Inovasi teknologi; Krisan; Pelaku usaha

Abstract

Innovation is a key component in increasing competitiveness. Informations of technology innovation is necessary in determining the priority of research programs that meet the needs of users. The purpose of this study was to obtain information on the type of technological innovation required by business actors to develop chrysanthemum cultivation. The study was conducted in West Java, Central Java, and Bali in January-December 2014. The selection of location and respondents was done purposively by determining as many as 45 farmers from 10 farmer groups planting chrysanthemums in each region. To know the importance level of technological innovation in the development of sustainable chrysanthemum agribusiness was used five-level Likert scale, that is very important, important, important enough, unimportant, and very unimportant. Assessment of interest is based on the approximate level of importance of a technological innovation in the development of chrysanthemum agribusiness system. The appraisal of business actors on the relative importance of different types of innovation from each agribusiness subsystem is the basis of consideration to determine the priority level in future research programs with major category, priority category, and potential category categories.
Keragaan Morfo-Fisiologi Phaseolus spp. yang Ditanam Pada Ketinggian Tempat yang Berbeda (Morpho-Physiological Performance Phaseolus spp. on Different Altitudes)
Authors: Trisnawati Delfi , Triadiati NFN , Mubarik Nisa Rachmania
Published: May 2019

Permintaan masyarakat di Indonesia terhadap tanaman legumes meningkat setiap tahunnya, namun produksi legumes di Indonesia belum dapat mencukupi kebutuhan konsumen. Pertumbuhan tanaman legume dipengaruhi oleh faktor lingkungan seperti ketinggian tempat, suhu, intensitas cahaya matahari, dan kelembapan. Tujuan penelitian ini untuk menganalisis dan mengevaluasi keragaan morfo-fisiologi Phaseolus spp. yang ditanam pada dua ketinggian tempat yang berbeda. Empat jenis legumes yang digunakan adalah Jackson Wonder Bean (JWB - Phaseolus lunatus), Christmas Bean (CHB - P. lunatus), Cranberry Bean (CRB - P. vulgaris), dan White Kidney Bean (WKB - P. vulgaris). Empat macam kacang tersebut ditanam pada ketinggian tempat 1.100 m dpl. (lokasi I) dan pada ketinggian 250 m dpl. (lokasi II). Peubah fisiologi dipengaruhi oleh lokasi penelitian, sedangkan peubah morfologi dipengaruhi oleh interaksi antara ketinggian tempat dan jenis kacang. Perbedaan kondisi lingkungan memengaruhi habitus JWB (P. vulgaris). CHB (P. lunatus) menghasilkan jumlah polong tertinggi pada ketinggian tempat 1.100 m dpl., yaitu 25,7 polong diikuti dengan BB 100 biji, yaitu 158,3 g. Implikasi penelitian ini adalah pembudidayaan tanaman legumes atau kacang minor pada dua lokasi yang berbeda berdasarkan ketinggian tempat untuk mendapatkan kondisi lingkungan yang sesuai agar pertumbuhan dan produksinya optimal.

Keywords

Phaseolus lunatus; Phaseolus vulgaris; Bintil akar; Konduktansi stomata; Laju fotosintesis

Abstract

The demand of legumes in Indonesia is increasing every year. The growth performance of legumes is influenced by environmental factors such as altitude land, temperature, light intensity, and air humidity. The aims of this study were to analyze and evaluate the morpho-physiological performance of legumes (Phaseolus spp.) grown in two different altitudes. Four genotypes of bean used in this study were Jackson Wonder Bean (JWB - P. lunatus), Christmas Bean (CHB - P. lunatus), Cranberry Bean (CRB - P. vulgaris), and White Kidney Bean (WKB - P. vulgaris). The four species of beans were grown at 1,100 m asl. (location I) and 250 m asl. (location II). The physiological parameters were influenced by location, meanwhile the morphological parameter was affected by the interaction between the altitude and type of beans. The differences on environmental condition in both field affected the habits of WKB (P. vulgaris). The CHB (P. lunatus) bean produced the highest number of pods at altitude of 1,100 m asl followed by wet weight of 100 seeds is 25.7 and 158.3. The implication of this research is the cultivation of beans minor at two different locations based on the altitude to get the appropriate environmental conditions to be optimal growth and production.
Willingness to Become Deceased Organ Donors among Post-graduate Students in Selected Colleges in Kathmandu Valley, Nepal
Authors: Paneru Pragya , Uprety Samyog , Budhathoki Shyam S. , Yadav Birendra K. , Bhandari Suman L.
Published: May 2019

Introduction: Globally, there is a discrepancy between demand and availability of organs for transplantation. Transplantation is done from a living donor as well as a brain-dead/deceased donor. However, the World Health Organization (WHO) encourages deceased donor transplantation, since there is no risk to the donor. Although, the Transplant Act of Nepal 2016 opened the doors for deceased donor organ transplantation, the rate of transplantation from deceased donors is very low. Thus, this study assesses factors associated with willingness for deceased organ donation among post-graduate students of law, medicine, and mass communication streams.
Methods: A total of 9 colleges, 3 from each specialty were selected via lottery method. The total sample size calculated was 440. Self- administered questionnaire was used to collect the data. 170, 140 and 130 forms were distributed in law, medicine and mass communication respectively via convenient sampling. Multivariate analysis among the variables that had p- value <0.05 in bivariate analysis was carried out to find out the strongest predictors of willingness to be deceased organ donors.
Results: In all, 53.2% were willing to become deceased organ donors. Family permission in one's wish to donate organs, having someone in family with chronic disease, having attended any conference or general talk on organ donation, knowing a live organ donor and knowing that body will not be left disfigured after organ extraction were found to be the strongest predictors for willingness to be deceased organ donors; while lack of awareness was reported as the main barrier for the same reason.
Conclusion: There is a need for extensive awareness programs and new strategies to motivate individuals and family members for organ donation.
Key words: • Deceased Organ Donation • Willingness • Kathmandu • Nepal • Organ Transplantation • Living Donor • Deceased Donor
 
© 2019 Paneru et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.