bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on risk evaluation of internet strategic transformation of manufacturing enterprises based on bp artificial neural network
Authors: Honglei Huang , Jiaming Bai , Tong Liu
Published: April 2021

For manufacturing enterprises to successfully enter the “Industry 4.0” era and establish advantages in the new wave of industrial revolution, they must use Internet thinking to transform manufacturing enterprises and promote the in-depth integration of informatization and industrialization under the premise of managing and controlling risks, to achieve transformation and upgrading. The innovation and management of the risks of manufacturing enterprises’ Internet strategic transformation directly affects the success or failure of enterprises’ transformation. On the basis of constructing the risk evaluation index system of manufacturing enterprise’s Internet strategy transformation, a manufacturing enterprise’s Internet strategy transformation risk evaluation model was constructed based on BP artificial neural network, and typical enterprises were selected to carry out practical application. The results show that the BP artificial neural network structure of the risk evaluation of the Internet strategic transformation of the manufacturing enterprises includes 18 layers of network input layer, 4 nodes of network hidden layer and 4 neurons of output layer, and the application effect of model is good.
Crisis analysis and prevention and control of financial leverage imbalance in shareholder equity pledge based on DANP model
Authors: Fang Fang
Published: April 2021

The process of economic globalization is accelerating, and the financial risks that listed companies need to face are more complicated. In order to clarify the impact of shareholder equity pledge on financial leverage imbalance crisis, this study explores financial leverage imbalance crisis of shareholder equity pledge based on DANP model. Fully consider the financial leverage effect and the potential risks of shareholder equity pledge, use the DANP financial leverage imbalance early warning model to analyze the impact of shareholder equity pledge on the financial leverage imbalance crisis, establish a financial leverage imbalance early warning evaluation index system, and preprocess the early warning evaluation indicators. Determine the constant weight of each evaluation index, and obtain the final financial leverage imbalance crisis early warning evaluation value based on the determined constant weight. Results show that the DANP model can accurately analyze the financial leverage imbalance crisis in the case of shareholder equity pledges, clarify the company’s financial status in the case of shareholder equity pledges, and propose to standardize equity pledge behavior and improve the company’s internal control system in response to the crisis of shareholder equity pledge financial leverage imbalances. Improve the three prevention and control measures of the independent director system to enhance the ability of enterprises to resist the crisis of financial leverage imbalance.
Stage performance layout design based on artificial intelligence technology
Authors: Li Gelang
Published: April 2021

The main purpose of stage performance arrangement is to improve the audience’s satisfaction with the stage performance. For this purpose, the stage performance arrangement design method based on artificial intelligence technology was explored. In the process of stage performance action retrieval, the offline link uses the method of frame distance definition based on quaternion in artificial intelligence technology to construct the structure of motion database digraph; the real-time link determines the corresponding vertex and edge in the digraph. According to the vertices and edges in the directed graph, the performer’s trajectory was determined, and the multi-resolution filtering algorithm in artificial intelligence technology was used to extract the motion path in the performer’s trajectory. The target path of stage performance arrangement was obtained by mapping the performer’s motion path through zoom processing. Based on the goal path, by arranging the control position of the performer’s root joint and correcting the direction of the performer, the movement track of the performer in the stage performance process was reconstructed, and the stage performance layout design was realized by combining with the position arrangement of the stage performance motion segment. The experimental results show that the method can accurately plan the performers’ path and improve the user’s satisfaction with the stage performance.
Research on literary intelligent translation based on improved optimization model
Authors: Liu Hongjian
Published: April 2021

Machine translation is widely used in people’s daily life and production, occupying an important position. In order to improve the accuracy of literary intelligent translation, this study explores literary intelligent translation based on improved optimization model. According to semantic features, machine translation was used to create a semantic ontology optimization model that includes an encoder and a decoder, and a conversion layer including a forward neural network layer, a residual connection and a normalization layer were added to the semantic ontology optimization model between the encoder and the decoder, the conversion layer was used to achieve grammatical conversion, which improves the accuracy of intelligent translation of the semantic ontology optimization model. Results show that the BLEU value of using this method to translate literary sentences can reach 17.23 when the number of training steps is 8000, and the training time is low, the translation result has a low correlation misalignment rate, which can meet the user’s literary translation needs.
Teaching ability evaluation of ideological and political teachers based on big data fuzzy clustering
Authors: Fu Jinhui
Published: April 2021

To improve the accuracy and utilization of the evaluation of ideological and political teachers’ teaching ability, this study proposes an evaluation method of ideological and political teachers’ teaching ability based on big data fuzzy clustering. According to the detected information resources and the characteristics that the smaller the average square residual value in the clustering is, the higher the similarity of information required by users is, the user needs to solve the minimum mean square residual value problem and information of given double cluster. The problem of information resource detection was transformed, and the missing features of the data were supplemented. The clustering minimum means square residual value problem with missing data was solved by using the idea of quadratic function, to realize the detection of information resources needed by ideological and political teachers in big data. Through the optimization process of the evaluation model of ideological and political teachers’ teaching ability, the method of big data fuzzy clustering and information fusion was used to cluster and fuse the information resources needed by teachers, to realize the evaluation of ideological and political teachers’ teaching ability. Results show that through the evaluation test of ideological and political teachers’ teaching ability, the evaluation accuracy and utilization rate of this method is higher than 95% in the evaluation cycle. The filling principle of multi-dimensional data missing features is better in the detection of information resources needed by users and promotes the improvement of Ideological and political teachers’ teaching ability.
The application and regulation of administrative discretion in the era of artificial intelligence
Authors: Li Li , Tian Tian , Zhao Yanmin
Published: April 2021

The advent of the era of artificial intelligence makes it possible for administrative subjects to use intelligent machines and systems to engage in administrative activities. Among them, the administrative discretion, which is the core of administrative law, is particularly concerned about the use of artificial intelligence. In the era of weak artificial intelligence, intelligent administrative discretion has been widely used in all aspects of administrative law enforcement, but there is a phenomenon that administrative subjects are negligent in exercising discretion. Looking forward to the era of strong artificial intelligence, artificial intelligence machines or systems may have the ability and power to independently exercise administrative discretion, but they cannot become the real administrative discretion subject. Intelligent administrative discretion is conducive to administrative efficiency and guarantees the fairness of administrative behavior, but it also faces legal risks such as unfair results of discretion, opaque algorithm settings, and weakening of government functions. Only by strengthening the legal basis, protecting the rights of the counterparty, improving the accuracy of the algorithm, and improving the status of the administrative subject can the administrative discretionary behavior under the background of artificial intelligence be effectively regulated.
Research on the architecture of food safety risk intelligence early warning system
Authors: Zhang Yu , Zhang Yarui , Li Xiaocui
Published: April 2021

Food safety supervision involves all aspects of production, processing and sales. True, reliable and complete intelligence can realize the traceability of the entire process of food safety production, thereby ensuring that food safety incidents are controllable from the source. However, most studies only analyze the food safety risk identification and early warning from the perspective of information flow from the theoretical level, and lack specific applications at the practical level. Therefore, this study analyzes the system requirements and the overall business process of the system, expounds the goals and principles of system design, designs the overall framework of the system, and finally elaborates on the realization of its functions of the different functional modules of the system, so as to provide the early warning system development provides decision support and reference. Finally elaborates the realization of its functions according to the different functional modules of the system, so as to provide decision support and reference for the development of early warning system.
An integrated approach for the selection of operation strategy using fuzzy TOPSIS, sensitivity analysis, and MOLP
Authors: Tham Tran Thi , Doan Linh Thi Truc , Amer Yousef , Lee Sang Heon
Published: April 2021

Operation strategy plays an important role in business improvement and calls for many research attention in recent years. This study aims to propose an integrated approach to determine the most appropriate operational strategies in their companies under multi-conflicting objectives with a limited budget. The novel approach is developed by using the combination of Fuzzy Technique for Order Preference by Similarity to Ideal Situation (Fuzzy TOPSIS), Sensitivity Analysis (SA) and Multi-Objective Linear Programming (MOLP) model. The operation strategies are evaluated through five objectives such as Productivity, Quality, Cost, Time and Importance score. The importance scores of all strategies are firstly obtained from the Fuzzy TOPSIS method. The sets of the weight of criteria are then established by using SA while MOLP approach is used to select appropriate strategies under multi-conflicting objectives with limited resources. A case study with 110 possible scenarios of operational strategies from An Giang Fisheries Import Export Joint Stock Company in Vietnam is considered to illustrate the practicability of the proposed approach. The results found that the proposed approach is suitable to make a decision on operation strategy.
Anomalies detection in the application layer with new combined methods in IoT networks
Authors: Gholi Beik Adeleh Jafar , Shiri Ahmad Abadib Mohammad Ebrahim , Rezakhani Afshin
Published: April 2021

Today, due to increasing dependence on the internet, the tendency to make smart and the Internet of things (IoT), has risen. Also, detecting attacks, and malicious activity as well as anomalies on the internet networks, and preventing them from different layers is a necessity. In this method, a new hybrid model of IWC clustering and Random Forest methods are introduced to identify normal and abnormal conditions. It also shows unauthorized access and attacks to different layers of the Internet of Things, especially the application layer. The IWC is a clustering and improved model of the k-means method. After being tested, evaluated, and compared with previous methods, the proposed model indicates that identifying anomalies in, its data has been efficient and useful. Unlabeled data from the Intel data set IBRL is used to cluster its input data. The NSL-KDD data set is also used in the proposed method to select the best classification and identify attacks on the network.
Multiple-attribute decision making method based on power generalized maclaurin symmetric mean operators under normal wiggly hesitant fuzzy environment
Authors: Liu Peide , Zhang Pei
Published: April 2021

A normal wiggly hesitant fuzzy set is a very useful tool to mine the potential uncertain information given by decision makers, which is considered as an extension of hesitant fuzzy set and can improve the effectiveness of decision making. Power average operator can relieve the impact on decision result of unreasonable data, and the generalized Maclaurin symmetric mean operator (GMSM) is an extension of Maclaurin symmetric mean operator with wider range of applications, which can consider the relationship among decision attributes. By integrating the advantages of them, in this paper, we develop the normal wiggly hesitant fuzzy power GMSM (NWHFPGMSM) operator and its weighted form based on the distance measure of two normal wiggly hesitant fuzzy elements, then we further discuss their properties and some special cases. Thus, a new multi-attribute decision making method based on the NWHFPGMSM operator under normal wiggly hesitant fuzzy environment is proposed. Finally, we select some examples to illustrate the effectiveness and superiority of the proposed method in this paper through comparison and analysis with other methods.
A multi-layer approach for advanced persistent threat detection using machine learning based on network traffic
Authors: Xuan Cho Do , Duong Duc , Dau Hoang Xuan
Published: April 2021

Advanced Persistent Threat (APT) is a dangerous network attack method that is widely used by attackers nowadays. During the APT attack process, attackers often use advanced techniques and tools, thus, causing many difficulties for information security systems. In fact, to detect the APT attacks, intrusion detection systems cannot rely on one technique or method but often combine multiple techniques and methods. In addition, the approach for APT attack detection using behavior analysis and evaluation techniques is facing many difficulties due to the lack of characteristic data of attack campaigns. For the above reasons, in this paper, we propose a method for APT attack detection based on a multi-layer analysis. The multi-layer analysis technique in our proposal computes and analyzes various events in Network Traffic to detect and synthesize abnormal signs and behaviors in order to make conclusions about the existence of APT in the system. Specifically, in our proposal, we will use serial 3 main layers for the APT attack detection process including i) Detecting APT attacks based on analyzing abnormal connection; ii) Detecting APT attacks based on analyzing and evaluating Suricata log; iii) Detecting APT attacks based on analyzing behavior profiles that are compiled from layers (i) and (ii). To achieve these goals, the multi-layer analysis technique for APT attack detection will perform 2 main tasks: i) Analyzing and evaluating components of Network Traffic based on abnormal signs and behaviors. ii) building and classifying behavior profile based on each component of network traffic. In the experimental section, we will compare and evaluate the effectiveness of the APT attack detection process of each layer in the multi-layer analysis model using machine learning. Experimental results have shown that the APT attack detection method based on analyzing behavior profile has yielded better results than individual detection methods on all metrics. The research results shown in the paper not only demonstrate the effectiveness of the multilayer analysis model for APT attack detection but also provide a novel approach for detecting several other cyber-attack techniques.
Distance functions, upper approximation operators and Alexandrov fuzzy topologies
Authors: Oh Ju-Mok , Kim Yong Chan
Published: April 2021

In this paper, we introduce the notions of join preserving maps using distance spaces instead of fuzzy partially ordered sets on complete co-residuated lattices. We investigate the properties of Alexandrov fuzzy topologies, distance functions, join preserving maps and upper approximation operators. Furthermore, we study their relations and examples. We prove that there exist isomorphic categories and Galois correspondences between their categories.
Research on energy consumption prediction of office buildings based on comprehensive similar day and ensemble learning
Authors: Yu Junqi , Zhang Tianlun , Zhao Anjun , Xie Yunfei
Published: April 2021

Energy consumption prediction can provide reliable data support for energy scheduling and optimization of office buildings. It is difficult for traditional prediction model to achieve stable accuracy and robustness when energy consumption mode is complex and data sources are diverse. Based on such situation, this paper raised an approach containing the method of comprehensive similar day and ensemble learning. Firstly, the historical data was analyzed and calculated to obtain the similarity degree of meteorological features, time factor and precursor. Next, the entropy weight method was used to calculate comprehensive similar day and applied to the model training. Then the improved sine cosine optimization algorithm (SCA) was applied to the optimization and parameter selection of a single model. Finally, an approach of model selection and integration based on dominance was proposed, which was compared with Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), with a large office building in Xi ‘an taken as an example to analysis showing that compared with the prediction accuracy, root mean square percentage error (RMSPE) in the ensemble learning model after using comprehensive similar day was reduced by about 0.15 compared with the BP model, and was reduced by about 0.05, 0.06 compared with the SVR and LSTM model. Respectively, the mean absolute percentage error (MAPE) was reduced by 12.02%, 6.51% and 5.28%. Compared with several other integration methods, integration model based on dominance reduced absolute error at all times. Accordingly, the proposed approach can effectively solve problems of low accuracy and poor robustness in traditional model and predict the building energy consumption efficaciously.
Prototype Network for Text Entity Relationship Recognition in Metallurgical Field Based on Integrated Multi-class Loss Functions
Authors: Chen Wei , Chen Junqiu , Xian Yantuan
Published: April 2021

It is of great significance to recognize the metallurgical entity relations in order to construct the Knowledge graph of Metallurgical Literature and to further understand the metallurgical literature. However, there are few researches on the textual entity relations in metallurgical fields either few marked Corpora. The syntactic structure of the same entity relationship category is relatively simple and has strong domain characteristics. The traditional entity relationship model can not identify the domain entity relationship well. Meanwhile the syntactic structure of the same entity relations class is relatively simple, and the syntactic structure is relatively simple in the recognition of entity relations in metallurgy field. Furthermore, the entities with similar syntactic structure often have the same entity relations and the different words in the sentence have different contribution to the entity relations. In order to solve the mentioned problems, this paper will combine the algorithm that can highlight the syntactic structure in sentences and improve the accuracy of the model with the Algorithm that can highlight the contribution of words in sentences and the loss function level integration is carried out in the framework of small sample prototype network, so as to maximize the advantages of each algorithm and improve the accuracy –firstly, in the coding layer of the prototype network, we use the CNN algorithm which can highlight the important words in the sentences and the TreeLSTM algorithm which can parse the sentences in the text so that the syntactic relations between the words in the sentences can be acted on in the relation recognition, the sentences are coded together by two algorithms, then, the EUCLIDEAN distance loss is calculated by using this high quality coding and the prototype coding, finally, the traditional entity relation recognition model with Attention Mechanism is integrated into the loss function, further highlighting the decisive role of important words in text sentences in relation recognition and improving the generalization of the model. The results showed that compared with the traditional methods such as CNN, RNN, PCNN and Bi-LSTM, the proposed method in this paper has better performance in the case of small sample data set.
A multi-grained aspect vector learning model for unsupervised aspect identification
Authors: Shi Jinglei , Guo Junjun , Yu Zhengtao , Xiang Yan
Published: April 2021

Unsupervised aspect identification is a challenging task in aspect-based sentiment analysis. Traditional topic models are usually used for this task, but they are not appropriate for short texts such as product reviews. In this work, we propose an aspect identification model based on aspect vector reconstruction. A key of our model is that we make connections between sentence vectors and multi-grained aspect vectors using fuzzy k-means membership function. Furthermore, to make full use of different aspect representations in vector space, we reconstruct sentence vectors based on coarse-grained aspect vectors and fine-grained aspect vectors simultaneously. The resulting model can therefore learn better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect identification and topic coherence of the extracted aspect terms.
The Adolescent/Young Adult Self-Management and Independence Scale (AMIS-II): Expanding evidence for validity and reliability1
Authors: Ridosh Monique M. , Stiles-Shields Colleen , Stern Alexa , Winning Adrien M. , Anderson Lara , Sawin Kathleen J. , Holmbeck Grayson N.
Published: April 2021

PURPOSE: The purpose of this longitudinal study was to evaluate the internal consistency reliability and construct validity of the Adolescent/Young Adult Self-Management and Independence Scale-II (AMIS-II), an interview-based measure of self-management for youth with chronic health conditions. METHODS: A diverse sample of adolescents and young adults (AYA) with spina bifida (SB) (n = 64 AYA; mean 20.88; age range 18–25 years) completed an AMIS-II interview. Six years earlier, parents from 55 families completed questionnaires that assessed children’s responsibility for SB-related care (Sharing of Spina Bifida Management Responsibilities) and their ability to perform skills across condition-related tasks (The Spina Bifida Independence Survey). Parents also reported on child’s communication skills, adaptive behaviors, and independent management of finances (Adaptive Behavior Assessment System-Second Edition; Scales of Independent Behavior-Revised). Descriptive and correlational analyses were conducted to assess the construct validity and the internal consistency reliability of the AMIS-II. RESULTS: The AMIS-II demonstrated excellent internal consistency reliability (AMIS-II total scale α= 0.95; subscales α= 0.90 –0.91). Evidence in support of construct validity was found in associations between the AMIS-II and measures of child responsibility for SB-related care, ability to perform condition-related skills, and adaptive behaviors (r’s = 0.378 –0.777; p’s < 0.05). CONCLUSION: This study provides further evidence of strong reliability and validity for the AMIS-II. Additional research with this measure will facilitate a better understanding of factors related to self-management behaviors in adolescents and young adults with spina bifida.
Rancang Bangun Mesin Pemasang Snap Ring untuk Mengurangi Cycle Time pada Assembling Transmission FF di PT. AWI
Authors: Kristiawan Timotius Anggit , Abidin Zaenal , Laksono Panji Setya , Nugroho Wahyu Isti
Published: April 2021

Sejalan dengan tujuan pemerintah untuk mewujudkan Indonesia sebagai negara industri yang tangguh pada tahun 2025, perlunya dukungan terhadap industri komponen otomotif dalam proses produksi untuk memenuhi kubutuhan dalam negeri. Latar belakang penelitian ini adalah target cycle time pada assembling transmission FF tidak tercapai (melebihi target waktu yang ada di TSK PT. Akashi Wahana Indonesia), dikarenakan terdapat proses pemasangan snap ring yang masih manual. Penyebabnya adalah proses pemasangan snap ring dilakukan secara manual menggunakan tenaga manusia dimana proses pemasangannya menggunakan tuas yang tinggi yaitu sejajar dengan bahu operator serta dalam satu shift kerja harus memasang snap ring pada 495 produk. Pemasangan dengan cara ini dimungkinkan mengakibatkan kelelahan pada operator, karena posisi tuas yang kurang ergonomis. Data aktual cycle time assembling transmission FF sebesar 98,015 detik, adapun TSK assembling transmission FF sebesar 96 detik. Berdasarkan permasalahan tersebut maka perlu adanya solusi yaitu dengan membuat rancang bangun mesin press pneumatik semi otomatis berbasis PLC untuk pemasangan snap ring pada input shaft gear dan mengurangi cycle time pada assembling transmission FF line. Pengujian yang dilakukan yaitu pengujian cycle time pada assembling transmission FF. Hasil yang diperoleh dari penelitian ini adalah mesin press pneumatik untuk pemasangan snap ring dapat mengurangi cycle time sebesar 3,48%.
Modern approaches to learning biotechnologies in the conditions of digitalization of technological education
Authors: Novikova N. N. , Tyapkin S. E.
Published: April 2021

The article discusses approaches to teaching the "Biotechnology" module in modern technological education in the context of its digital transformation. On the example of using digital simulations and models, the methodological aspects of using educational tools that allow organizing the interactive cognitive activity of students when caring for plants are described. The experience of using a remote controlled hydroponics system in technology lessons is described. A description of the means and tools, software for the remote hydroponics system and a methodology for constructing technology lessons for growing indoor and vegetable plants are given. It is noted that the creation and application of a hydroponics system is a unique example of interdisciplinary integration of knowledge in ecology, biology, informatics and technology. The proposed approaches to the use of modern digital means make it possible to activate the cognitive activity of students in the study of modern biotechnologies and in practice to apply basic knowledge of programming.
Characterization of Polycyclic Aromatic Hydrocarbons and Bioaugmentation Potential of Locally Isolated Beneficial Microorganisms Consortium for Treatment of Tar-Balls
Authors:   Faculty of Science and Natural Resources, Universiti Malaysia Sabah, UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia, Zahari Nur Zaida , Baharudzaman Erma Hani ,   Faculty of Science and Natural Resources, Universiti Malaysia Sabah, UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia, Tuah Piakong Mohd ,   Faculty of Science and Natural Resources, Universiti Malaysia Sabah, UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia, Cleophas Fera Nony ,   Faculty of Science and Natural Resources, Universiti Malaysia Sabah, UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia, Vun Leong Wan ,   Faculty of Science and Natural Resources, Universiti Malaysia Sabah, UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia
Published: April 2021

Oil spills are one of the environmental pollutions that commonly occur along coastal areas. Tar-balls are one of the products that come from the oil spill pollution. In this study, tar-ball pollution was monitored at 10 points along the coastline of Marintaman Beach in Sipitang, Sabah, Malaysia. This research determined the physical characteristics, composition, and concentration of Polycyclic Aromatic Hydrocarbons (PAHs) in tar-balls. The total number of tar-balls collected was 227 (n=227). The tar-balls were observed in various shapes and the sizes were recorded in the range of 0.1 cm to 6.9 cm. The composition and concentration of Polycyclic Aromatic Hydrocarbons (PAHs) in the outer and inner layer of tar-balls were determined. The results showed that the main Polycyclic Aromatic Hydrocarbons (PAHs) compounds were found in inner layers of the tar-balls with benzo (g,h,i) perylene (72.26 mg/kg), flourene (59.87 mg/kg), dibenzo (a,h) anthracene (44.48 mg/kg), indeno (1,2,3-c,d) pyrene (78.18 mg/kg), and benzo (e) fluoranthene (45.70 mg/kg), respectively. Further research was done with the bioaugmentation study of locally isolated beneficial microorganisms (LIBeM) consortium for treatment of tar-balls in an Aerated Static Pile (ASP) bioreactor system. The results showed that, after 84 days of treatment, this consortium, consisting of C. tropicalis-RETL-Cr1, C. violaceum-MAB-Cr1, and P. aeruginosa-BAS-Cr1, was able to degrade total petroleum hydrocarbon (TPH) by 84% as compared to natural attenuation (19%). The microbial population of this consortium during the biodegradation study is also discussed in this paper.
Tontemboan Affixes Notes in Teaching English Tenses
Authors: Kumayas Tirza
Published: April 2021

Teaching English language is a big challenge in Minahasa. Students are still unable to master or even comprehend English even after eleven years learning the language at primary and secondary levels. This research entitled “Tontemboan Affixes Relates to English Tenses” aims to identify, classify and describe the affixes and analyze the similarities and differences of affixes used in both languages, English and Tontemboan. This research used descriptive method, which includes preparation, collecting the data, and analyzing the data. The result showed that some affixes in Totemboan language have different functions of affixes in English. Simple present tense functions the habitual action by adding ma’ before the morpheme, following the verb. The present continuous tense which is formed by adding ma’ as prefix has a global meaning. Past continuous tense indicated by adding the prefix ma’ and simple past tensi indicated by adding a base prefix and infix with the initials are consonant.
Analysis of Income on the Partnership Program-based Broiler Business in Regency of North Minahasa, North Sulawesi, Indonesia
Authors: Kalangi L S , Lombogia S O B , Regar M N
Published: April 2021

The partnership program-based broiler farming business (main-plasma), located in Regency of North, Province of North Sulawesi, Indonesia is performed due to the limit of capital, skill, market access and lack of ability in projecting fluctuated market demand. In addition, Covid-19 pandemic has provenly affected broiler farming business, showed by declining broiler’s demand and production, particularly on the decreasing income of farmers. Hence, the objective of this research is to examine if it is there is a difference among total of DOC (Day-Old Chick) input, feed conversion ratio (FCR), mortality rate, weight of harvest, raising period, and harvest time, pre- and during Covid-19 pandemic, and analyze some impacts of the existing Covid-19 pandemic and some different variables on the income of the partnership program-based broiler farming business. Technically, the purposive sampling was employed as the sampling method in selecting districts, villages, and farmers. Then, data was analyzed by the paired t-test sample using SPSS 22 and multiple-regression analysis by E-views 11 program. As the result, the paired t-test sample shows that variable of income, total of DOC (Day-Old Chick), FCR, mortality rate, rate of broiler’s weight, raising period, and harvest time in pre- and during Covid-19 pandemic was significantly different, where the value of Sig. (2-tailed) was < 0.05, while, the variable of total of DOC and raising period had significantly positive impact on income, variable of FCR and harvest time had significantly negative impact on farmer’s income. Variable of dummy pandemic could decrease farmer’s income in the partnership program-based broiler farming business; however, it was unimportant.
Modulatory Effects of Memantine on Neuronal Response Properties in Rat Barrel Cortex
Authors:   Physiology-Pharmacology Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran., Nasiri Mahin , Kaeidi Ayat ,   Physiology-Pharmacology Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.,   Department of Physiology and Pharmacology, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran., Fatemi Iman ,   Research Center of Tropical and Infectious Diseases, Kerman University of Medical Sciences, Kerman, Iran., Azin Mahdieh ,   Physiology-Pharmacology Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.,   Department of Physiology and Pharmacology, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran., Bannazadeh Mahboobeh ,   Physiology-Pharmacology Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran., Allahtavakoli Mohammad ,   Physiology-Pharmacology Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.,   Department of Physiology and Pharmacology, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran., Roohbakhsh Ali ,   Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran., Shamsizadeh Ali ,   Physiology-Pharmacology Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.,   Department of Physiology and Pharmacology, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
Published: April 2021

Introduction: Memantine as N-Methyl-D-Aspartic Acid (NMDA) receptor antagonist is used in some neurological disorders. Moreover, memantine presents modulatory effects on the somatosensory information processing in healthy subjects. This study investigated the effects of memantine on electrophysiological properties of barrel cortex neurons in male rats. Methods: Single unit recording was used to evaluate the electrophysiological properties of barrel cortex neurons. The neural responses to the Principal Whisker (PW), Adjacent Whisker (AW), and combined displacement of two whiskers [20 ms Inter-Stimulus Intervals (ISIs)] were recorded before and 2 hours after memantine gavage (10 mg/kg). A Condition Test Ratio (CTR) was calculated for assessing inhibitory interactions. Results: Two hours after memantine gavage, neuronal ON and OFF responses to PW deflection were decreased. Furthermore, CTR for both ON and OFF responses was decreased following memantine administration. Conclusion: The current study demonstrated that memantine modified neural response properties in the rat barrel cortex.
Pharmacological and Molecular Evidence of Neuroprotective Curcumin Effects Against Biochemical and Behavioral Sequels Caused by Methamphetamine: Possible Function of CREB-BDNF Signaling Pathway
Authors:   Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran., Gholami Mina , Hozuri Farzad ,   Department of pharmaceutical chemistry, Faculty of Pharmaceutical Chemistry, Pharmaceutical Sciences Branch, Islamic Azad university (IUAPS), Tehran, Iran., Abdolkarimi Setayesh ,   Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Pharmaceutical Sciences Branch, Islamic Azad university (IUAPS), Tehran, Iran., Mahmoudi Mahsa ,   Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Pharmaceutical Sciences Branch, Islamic Azad university (IUAPS), Tehran, Iran., Motaghinejad Majid ,   Research Center for Addiction and Risky Behaviors (ReCARB), Iran Psychiatric Center, Iran University of Medical Sciences, Tehran, Iran., Safari Sepideh ,   Razi Drug Research Center, Iran University of Medical Sciences, Tehran, Iran., Sadr Samira ,   Research and Development Department, Parsian-Exir-Aria Pharmaceutical Company, Tehran, Iran.
Published: April 2021

The neuroprotective impact of curcumin and the role of CREB-BDNF signaling in this way was evaluated in methamphetamine (METH)-induced neurodegeneration in rats. Sixty adult male rats were randomly split into 6 groups. While normal saline and 10 mg / kg METH were administered intraperitoneally in Groups 1 and 2, Groups 3, 4, 5 and 6 received METH (10 mg/kg) and Curcumin (10, 20, 40 and 80 mg/kg respectively) simultaneously. Morris Water Maze (MWM), oxidative hippocampal, antioxidant, inflammatory, apoptotic, and CREB and BDNF were assessed. We've found that METH disturbs learning and memory. Concurrent curcumin therapy (40 and 80 mg / kg) decreased cognitive disturbance caused by METH. Multiple parameters, such as lipid peroxidation, oxidized form of glutathione (GSSG), interleukin 1 beta (IL-1β), tumor necrosis factor alpha (TNF-α) and Bax, increased by METH therapy, although the reduced type of glutathione (GSH), Bcl-2, P-CREB and BDNF concentrations in the hippocampus decreased. Different doses of curcumin adversely attenuated METH-induced apoptosis, oxidative stress and inflammation, but enhanced concentrations of P-CREB and BDNF. Curcumin-caused neuroprotection against METH-induced neurodegeneration is conducted by P-CREB / BDNF signaling pathway activation.
EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach
Authors:   Department of Electronics and Communication Engineering, Panjab University Chandigarh, Chandigarh, India., Kaur Chamandeep , Singh Preeti ,   Department of Electronics and Communication Engineering, Panjab University Chandigarh, Chandigarh, India., Sahni Sukhtej ,   Department of Psychiatry, Cheema Medical Complex, Mohali, India.
Published: April 2021

Introduction: Several computer-aided diagnosis systems for depression are suggested for use by clinicians to authorize the diagnosis. EEG may be used as an objective analysis tool for identifying depression in the initial stage to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on Empirical Mode Decomposition (EMD) ( with Detrended Fluctuation Analysis (DFA) and wavelet packet transform. Initially, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further Wavelet Packets Decomposition (WPD)-based evaluation is applied to extract the cleaner signal. Results: Simulations were conducted on real EEG databases for depression to demonstrate the effects of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE were identified. The obtained results indicated improved signal-to-noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Additionally, Random Forest and SVM (Support Vector Machine)-based classification revealed the improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique equaled 98.0% and 97.21% for the EMD- DFA technique for RF and SVM, respectively, compared to the proposed method. Furthermore, the classification performance for both classifiers was compared with and without denoising to highlight the effects of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in a better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of the EMD approach.
VCfMRI: A Matlab Toolbox for Visualization and Conversion of fMRI Data Modalities
Authors:   Ankara Yıldırım Beyazıt University, Ankara, Turkey., Jaber Hussain , Aljobouri Hadeel ,   Al-Nahrain University, Iraq., Kocak Orhan ,   Kırıkkale University, Turkey., Algin Oktay ,   Bilkent University, Ankara, Turkey., Cankaya Ilyas ,   Ankara Yıldırım Beyazıt University, Ankara, Turkey.
Published: April 2021

In this work, a novel conversion and visualization fMRI (VCfMRI) toolbox is proposed. The VCfMRI tool is enabled to read, write 3-D volume data (.dcm, .nii, .img, hdr and .mat format) as well as multi conversion operations between them are performed in the same package. In the current work, real fMRI data are used and all data are acquired by MRI scanner type Siemens/3T in National Magnetic Resonance Research Center (UMRAM)-Bilkent University. About 62 analyses functions have been implemented and incorporated in analysis about 7 GUI tools for multiple conversions of fMRI modalities, reading/writing and viewing in all fMRI data formats, visualizing 3-dimensional (sagittal, coronal and horizontal slices) statistical and non-statistical neuroimaging, thresholding and overlaying viewing. The presented package is a simple tool to address several issues that related to complexity in visualizing and conversion between multi-formats of fMRI data. This work enables the user to visualize and deals with fMRI data in an easy way especially for physicians, healthcare specialists and researchers whose faced challenges about how handling with these type of data.
Evaluation of the GABAA Receptor Expression and the Effects of Muscimol on the Activity of Wide Dynamic Range Neurons Following Chronic Constriction Injury of the Sciatic Nerve in Rats
Authors:   Department of Physiology, Faculty of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran, Sadeghi Mehdi , Manaheji Homa ,   Department of Physiology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran, Zaringhalam Jalal ,   Department of Physiology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Haghparast Abbas ,   Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Nazemi Samad ,   Department of Physiology, Faculty of Medicine, Sabzevar University of Medical Sciences, Sabzevar, Iran., Bahari Zahra ,   Department of Physiology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Noorbakhsh Seyed Mohammad ,   Science Beam Institute, Tehran, Iran
Published: April 2021

Introduction: The modality of γ-aminobutyric acid receptors (GABAA) in control of dorsal horn neuronal excitability and inhibition of sensory information is ambiguous. The aim of the present study was to investigate the expression of GABAA receptor and the effects of its agonist muscimol on wide dynamic range (WDR) neuronal activity in the chronic constriction injury (CCI) model of neuropathic pain. Methods: Adult male Wistar rats weighing 200 to 250 g were used for the induction of CCI neuropathy. 14 days after surgery, muscimol (0.5, 1, and 2 mg/kg i.p.) was injected. Then, the behavioral tests were performed. Thereafter, the animals were sacrificed, and the lumbar segments of the spinal cords were collected for Western blot analysis of the GABAA receptor α1 subunit expression. The electrophysiological properties of WDR neurons were studied by single unit recordings in separate groups on the 14th day after CCI. Results: The outcomes indicated the development of thermal hyperalgesia and mechanical allodynia after neuropathy; nonetheless, the expression of GABAA receptor α1 subunit did not change significantly. Moreover, the evoked responses of the WDR neurons to electrical, mechanical, and thermal stimuli were signiﬁcantly increased. 14 days after CCI, muscimol administration decreased thermal hyperalgesia, mechanical allodynia, and hyper-responsiveness of the WDR neurons in CCI rats. Conclusion: It confirms that the modulation of the spinal GABAA receptors after nerve injury can offer further insights to design new therapeutic agents in order to reduce the neuropathic pain symptoms.
Minocycline Enhance the Restorative Ability of Olfactory Ensheathing Cells by the Upregulation of BDNF and GDNF Expression After Spinal Cord Injury
Authors:   Department of Animal Physiology, Faculty of Biology, Kharazmi University, Tehran, Iran., Pourkhodadad Soheila , Oryan Shahrbanoo ,   Department of Animal Physiology, Faculty of Biology, Kharazmi University, Tehran, Iran., Hadipour Mohammad Mehdi ,   Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran., Kaka Gholamreza ,   Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran., Sadraie Seyed Homayoon ,   Department of Anatomy, School of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Published: April 2021

Introduction: Spinal Cord Injury (SCI) is a global public health issue that results in extensive neuronal degeneration, axonal and myelin loss, and severe functional deficits. Neurotrophic factors are a potential treatment for reducing secondary damage, promoting axon growth; they are responsible for inducing myelination after injury. Olfactory Ensheathing Cells (OECs) and minocycline have promoted locomotor function after SCI. The present study investigated the neuroprotective effects of combined treatment with minocycline and OECs on spinal cord injury related to Brain-Derived Neurotrophic Factor (BDNF) and Glial Derived Neurotrophic Factor (GDNF) expressions after SCI. Methods: Adult female rats were used to experimental SCI by weight compression method. Rats received an intraperitoneal minocycline injection (90 mg/kg) immediately after SCI and 24 h after injury. OECs were transplanted one week after the injury. The hindlimb function was assessed using Basso Beattie Bresnahan (BBB) locomotor rating scale and Electromyography (EMG). After 5 weeks, the spinal cord segment centered at the injury site was removed for histopathological analysis. Immunohistological and western blot assays were performed to observe the expression of NeuN, BDNF, GDNF, and Myelin Basic Protein (MBP). Results: SCI induced the loss of locomotor function with decreased BDNF and GDNF expressions in the injury site. Minocycline+OECs increased the score of the BBB locomotor scale and increased spared tissue in the injury site. Immunohistochemical results suggested that NeuN expression significantly increased in the minocycline+OECs group than other groups. Moreover, electromyography amplitude in treated rats was increased compared to the control group. BDNF, GDNF, and MBP expressions and the number of ventral motor neurons increased further by minocycline+OECs in SCI rats. Conclusion: The present study provides evidence that minocycline may facilitate recovery of locomotor function by OECs by increasing BDNF and GDNF expressions following SCI.