bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying the higgs-gauge boson couplings signs at leptonic colliders
Authors: Hamdellou H , Ahriche A 
Published: February 2021

Abstract
We investigate in this work the Higgs-gauge bosons couplings hVV (V = W, Z) through different final states at e
+
e
− colliders. These couplings could be modified with respect to the SM in many SM extensions, as

g
h
V
V

=

κ
V

g

h
V
V

S
M

, either via radiative corrections or if the Higgs is composite. Therefore, we consider the final states

b

b
¯

+

E

T

@

250

G
e
V

,

h

W
+

W
−

→

b

b
¯

+

ℓ
+

ℓ
−

+

E

T

@

500

G
e
V

; and ZW
+
W
− → jj + ℓ+ℓ− + ɆT
@ 1 TeV within the corresponding luminosity at the ILC. Then, by considering that there will be no significant deviation with respect to the SM at HL-LHC, i.e., |δκV
/κV
| ≤ 0.04, we show that the sign of the hVV couplings can be probed to be κV
= ±1.
Rare nonleptonic Bs
-meson decays as a probe for new physics beyond the standard model
Authors: Faisel G. , Tandean J. 
Published: February 2021

Abstract
The recent measurements on a number of b → sμ
+
μ
− processes have manifested anomalous results which could be early evidence for the presence of new physics beyond the standard model in b → s transitions. Supposing this to be the case, we entertain the possibility that a heavy Z′ boson is responsible for these anomalies and that it also affects the rare nonleptonic decays of the

B
¯

s

meson which receive substantial contributions from the so-called penguin diagrams. The majority of these

B
¯

s

decay modes are not yet discovered, and within the standard model their rates have been estimated to be relatively suppressed. Taking into account various constraints, we find that the Z′ effects can enlarge the rates of a few of the modes, especially

B
¯

s

→

η

π
0

,

ϕ

π
0

,

η
ω
,

ϕ
ω

, considerably above their standard-model expectations. Consequently this Z′ scenario may be experimentally testable in the near future.
Design and Internet of Things Development of Network Teaching Resource Base System for Educational Technology
Authors: Zhou Xuefang , Li Xiaoyan , Su Na 
Published: February 2021

Abstract
With the deepening of the application of Internet of Things in education, its role in promoting education and teaching has become increasingly obvious, but the corresponding is that the application of Internet of Things in education has remained in the experimental stage, and can not be applied on a large scale. Therefore, according to the characteristics of the educational application of the Internet of Things, this paper designs and completes a new educational resource database management system of the Internet of Things. The system consists of three parts: the core layer, the access layer and the performance layer. It has good expansibility and shields the complexity of the Internet of Things technology, and meets the needs of ordinary teachers and students to rapidly develop the educational application of the Internet of Things. The research results show that through the analysis of the status quo of network teaching in colleges and universities, a web-based intra-campus network teaching system software platform is established to solve the problems in higher education. At the same time, the experimental system also helps the experimenter to deeply study the basic technology of the Internet of Things.
Multi-scale Convolutional Recurrent Neural Network and Data Augmentation for Polyphonic Sound Event Detection
Authors: Zhang Keming , Cai Yuanwen , Ren Yuan , Ye Ruida , Zhang Xianwei , He Liang 
Published: February 2021

Abstract
We propose Multi-scale convolutional recurrent neural networks (MCRNN) and data augmentation methods to detect polyphonic sound event with few training data. MCRNN consists of Multi-scale convolutional neural networks (MCNN) and recurrent neural networks (RNN). MCNN concatenates the higher level features extracted using multiple convolution kernels with different scales from the time domain and frequency domain at the same time. RNN is able to capture the longer term temporal context characteristics. A novel background spectrum random replacement (BSRR) data augmentation method is applied to expand training data, which uses standard normal distribution data with randomly selected position and length instead of the original time-domain, frequency-domain or time-frequency domain background spectrum features. Our method is tested on the datasets of DCASE 2019 Task3 (T3). The experimental results showed that the MCRNN and BSRR data augmentation method are efficient. We achieved better results than the first place and the single advanced on the T3 by applying BSRR and SpecAugment data augmentation method simultaneously. On the evaluation dataset (T3-eval), our best result shows 0.05 and 0.975 of error rate (ER) and F1 respectively. Our method got the best performance and relatively improved 17% and 1% than the corresponding values of the single advanced.
Research and Application of Agricultural Internet of Things Technology in Intelligent Agriculture
Authors: Yue Shuyu , Du Yirong , Zhang Xiao 
Published: February 2021

Abstract
China’s modernization of agriculture has been accelerating, the Internet of things and other high-tech level of rapid improvement, and intelligent agriculture is the main direction of modern agriculture. Firstly, this paper summarizes the technology of Internet of things and intelligent agriculture, and summarizes the intelligent technology and internet technology applied in the control process of agricultural production in the process of intelligent agriculture Through listing: Intelligent Agricultural Monitoring System based on Onenet Open Platform, intelligent agricultural irrigation system based on Internet of things technology, apple tree pest control expert system based on Internet of things, practical cases of agricultural product marketing platform based on Internet of things, the paper studies the application of Internet of things technology in Environmental Monitoring, irrigation, pest control, and marketing of agricultural products. In order to provide theoretical basis for farmers.
Logistic Regression Models in Predicting Heart Disease
Authors: Zhang Yingjie , Diao Lijuan , Ma Linlin 
Published: February 2021

Abstract
This paper predicts the risk of suffering from heart disease among the elderly by exploring the feasibility of using logistic regression models. Through the technology of data mining, the main pathogenic factors of heart disease were found, and the incidence of heart disease was predicted by using the regression model. The accuracy of logistic regression model was compared with other explored algorithms, and I found that the logistic regression model was worthy of research in the field of heart disease prediction.
Modified Deep Belief Network Model and Its Application
Authors: Yin Peng 
Published: February 2021

Abstract
Aiming at the problem that the traditional deep belief network (DBN) model has long training time and it is difficult to find the global optimum because of the fixed step. A modified DBN model was proposed. In the DBN pre-training phase, the adaptive step (AS) was introduced into the Contrastive Divergence (CD) algorithm to solve the problem of finding the global optimum difficulty because of the fixed step, improving the accuracy and oscillation resistance. In the fine-tuning phase, the BFGS quasi-newton method (QNM) was used to speed up the convergence and reduced the training time. The modified DBN model was applied to the fuel consumption prediction of the descending section of the aircraft. The experimental outcomes prove that compared with the traditional DBN and its variant model, the modified DBN model improves the convergence speed and the prediction accuracy.
Research and Implementation of Open Domain Question Answering System Based on DuReader Dataset and BIDAF Model
Authors: Guo Zechen , Wan Fucheng , Ma Ning 
Published: February 2021

Abstract
At present, major search engines emerge in endlessly, and the pop-up result list of search keywords makes people dizzy. In order to improve this problem and make the question query more concise and convenient, this article is based on the DuReader data set and reproduces BiDAF (Bi-Directional Attention Flow) The model builds an intelligent question answering system in the open field. The final model can extract answers to questions more accurately after training. This article embeds deep learning technology into the system and uses intelligent chat to show them.
Research on Public Opinion and Early Warning Analysis Model of Network Emergencies Based on Decision Tree
Authors: Wang Peipei 
Published: February 2021

Abstract
Taking the existing network public opinion cases as the research object, the paper analyzes key factors affecting the network public opinions by the machine learning method and the big data technology statistical technology, and builds the early warning system of public opinions based on this. The decision tree model is also built to realize analysis and early warning, and finally the “Qingdao Sky-price Prawns” incident is cited to illustrate the feasibility of the early warning by classification proposed in this paper.
Biomedical Engineering for Medical Assistance “Stress and Acceleration Analysis for Damage Half-skull as A Consequence of Concrete Impact on a Protective Helmet” (Mechanical and Computer Engineering)
Authors: Sakly Houneida , Said Mourad , Tagina Moncef 
Published: February 2021

Abstract
The dynamic analysis of fluid mechanics CFD applied to the medical field is one of the most important structural problems. They impose additional mechanical constraints on the construction of the 3D model to be processed and reduce its performance. As the acceleration forces of the concrete mainly excite these contacts between the skull and the Helmet, they could taken into consideration in this proposed system (helmet, concrete, and skull). Our aim is to evaluate the stress measurements in an accidental half-skull 3D with the computer fluids dynamics (CFD) tools basing on the mechanical and physical properties of the wall the skull. The second contribution is to analyze the acceleration of a concrete and the impact in the stress evaluation on half skull with and without helmet.
Improved Social Force Model for Emergency Evacuation Scene with Limited Field of Vision
Authors: Xin Zhou , Xin Feng , Tai Ma , Xin Zhang 
Published: February 2021

Abstract
Pedestrian movement modeling and simulation is an important way to formulate an emergency evacuation plan. The performance of the pedestrian movement model directly determines the accuracy of the simulation experiment results. Pedestrian movement in the real world is a very complicated process, and this process is susceptible to a variety of external factors (e.g. road conditions, visibility, etc.) and internal factors (e.g. psychological states, etc.) which making it difficult to model pedestrian movement. The social force model draws on the principles of mechanics and proposes a modeling scheme from the perspective of social force which greatly simplifies the process of pedestrian movement modeling. However, the traditional social force model considers the general situation of pedestrian movement and does not target the emergency evacuation scene, which has certain limitations. In this paper we start with the social force model and introduces the concepts of external guides and internal panic emotions to improves the model. In the scene, the influence of the attraction of the guide and the psychological state of the pedestrian on the movement process of the pedestrian are considered. So that the model is more suitable for the emergency evacuation scene with limited field of vision. It also provides a reference for emergency evacuation plans in public places. We take the subway station as the research object and uses Anylogic software to build simulation experiments. The experimental results show that the improved model we proposed in this paper is less than the traditional model in terms of evacuation time by 8.4%, and is better than the traditional model in terms of exit pedestrian density by 21.5%.
Study of Credit Evaluation Algorithm Based on Iterative Principle of Unified Platform
Authors: Li Xiaotang 
Published: February 2021

Abstract
Because retail consumers are difficult to authenticate, they are difficult to manage, it leads the credit management of C2C e-commerce is very difficult. At present, most C2C e-commerce transactions are considered too simple in the credit evaluation algorithm, it can’t really reflect the integrity of the user. In this paper proposes the credit evaluation algorithm based on iterative principle bases on transaction history, on the basis of considering the transaction amount, the transaction time and the credibility of the users, with the method of iterative calculation, give full consideration to the effects of time of credit for the success of the deal to provide reliable basis, it can effectively distinguish malicious users and integrity users, thus reducing the credit fraud behavior, such as improving the security of C2C e-commerce.
A Hybrid Total Logistics Forecasting Method Combined with ARIMA and BPNN
Authors: Wang Jianjun , Xing Yaqian , Zhang Shuo , Zhou Yan , Li Li , Nian Tiantian 
Published: February 2021

Abstract
In recent years, with the continuous improvement of China’s economic level, Chinese people’s living standards are also improving, so that the vigorous development of the e-commerce industry and the number of the total amount of social logistics have increased year by year, which has brought great impetus to the development of the logistics industry. Based on the background, it requires a scientific and reasonable forecasting result of the total amount of logistics for the logistics industry investment. This paper employs an ARIMA and BPNN combined method to forecast the total logistics situation during the “14th Five-Year Plan” period, the results show that the combination forecasting method can take into account the advantages of each single forecast model, and its forecast effect is more ideal.
Research on the Construction of Intelligent Learning System Based on Big Data
Authors: Yang Zhengan 
Published: February 2021

Abstract
Internet technology and online learning are in a period of high integration. The learner-centered online learning environment is no more restricted by distance. Facing the large amount of learning resources on the Internet, learners often have no choice. The problem cannot be solved in time, so it is imperative to build an intelligent learning system that according to the characteristics of learners. This article analyses the design of an intelligent learning system based on big data, and analyses the key points of the fit between big data and personalized learning features. According to the design principles of intelligent learning system based on big data, it is divided into four categories: learning strategies, learning tools, learning resources, and learning activities. To build an intelligent learning system in three dimensions. Then the recommended mode of resources in the intelligent learning system is introduced in detail. According to the above design ideas of the intelligent learning system based on big data, a personalized learning platform is constructed, and the structure, functions, and development technology of the platform are elaborated.
An Elite Adaptive Niche Evolutionary Algorithm for Duty Clustering Problem in SoWSN
Authors: Bai Jinbin , Tian Min , Li Jiangquan 
Published: February 2021

Abstract
The recent success of emerging low power wireless sensor networks technology has encouraged researchers to create novel duty cycle design algorithm in this area. Since “sensors are constrained in sensing capabilities”, duty cycle design plays a crucial role in maximizing the point coverage rate, while most researches for duty cycle design are related to a duty cycle design algorithm. But unfortunately, the ideal duty cycle design requires an exhaustive search over all combinations of the allowed combinations. In this letter, we present a new elite adaptive niche evolutionary algorithm (EANEA) for duty cycle design problem in Service-oriented wireless sensor networks (SoWSN). In order to extend the network life cycle, we designed an objective function for SoWSN. We also give an EANEA which, depending on a powerful niche operator, blends the merits of both elite selection and adaptive adjusting for the channel assignment problem. Simulation results show that the shown algorithm can achieve a higher point coverage rate over genetic quantum algorithm (QGA) and Shuffled Frog-Leaping Algorithm (SFLA). Moreover, the optimization employs an elite selection to initialize the parameters and avoid local optima proficiently.
Policy mixes for more sustainable smart home technologies
Authors: Sovacool B K , Furszyfer Del Rio D D , Griffiths S 
Published: February 2021

Abstract
Smart home technologies refer to devices that provide some degree of digitally connected, automated, or enhanced services to household occupants. Smart homes have become prominent in recent technology and policy discussions about energy efficiency, climate change, and the sustainability of buildings. Nevertheless, do they truly promote sustainability goals? Based on an extensive original dataset involving expert interviews, supplemented with a review of the literature, this study elaborates on an array of social, technical, political, and environmental risks facing smart home innovation, with clear implications for research, policy, and technology development. Only with a more thoughtful and coordinated mix of policies in place will smart home adoption begin to fulfill some of the sustainability objectives their advocates continually promise.
Enhancing the performance of solar stills for desalination of the sea water
Authors: Saiful S A M , Shams S , Prasad D M R , Ratnayake U 
Published: February 2021

Abstract
The purpose of this research is to determine whether the production output of distilled water from desalination by distillation using a hybrid solar still coupled with Photovoltaic panels can enhance the overall efficiency of the system for desalinating the seawater. Desalination by distillation has not yet reached the global market as the output rate is significantly less compared to other methods such as reverse osmosis (RO). This research developed a working hybrid prototype solar still that utilized both passive and active heating sources to assess its performance and competitiveness with other solar stills in the market. The outcome of this research indicates that a solar still on its own is efficient enough in removing Total Dissolved Salts (TDS) for safe drinking water. However, it cannot compete with the performance of alternatives such as RO desalination units and is highly dependent on solar energy. Although, the results from this study show that the use of solar reflectors can increase the performance by 21%. This study concludes that a new technology/concept is needed to fill in the gap between solar stills and RO units whereby it can match the performance of a typical RO desalination unit and maintain the same capital and running costs of a typical solar still.
Preliminary study of water quality surrounding the petrochemical plants at Teluk Kalong industrial area
Authors: Ibrahim Mohd H , Mohamed Rabiatul M , Aziz Azharin S. Abdul , Chandran Sairam 
Published: February 2021

Abstract
Wastewater from industry could lead to water pollution if not properly treated. This research aims to analyze quality of effluent discharge surrounding the petrochemical plants at Teluk Kalong Industrial Area. The purpose is to identify of uncertain level of Biochemical Oxygen Demand (BOD), Total suspended Solids (TSS), heavy metal zinc and copper. These properties could affect water quality if the effluent is not properly treated prior to discharge on water surface. In order to obtain the aim of this research, the laboratory analysis has been carried out on the waste water discharged from the petrochemical industry area. An experiment was conducted and discovered that the minimum value for BOD, TSS, Zn and Cu was 35.55mg/l, 21.667mg/l, 0.006mg/l, and 0.001mg/l accordingly. The maximum value for BOD, TSS, Zn and Cu was 60.150mg/l, 742.667mg/l, 0.839mg/l and 0.044mg/l accordingly. As not all parameters obtained satisfactory to the local standard, it is recommended further research be performed to ensure appropriate result complying to the regulatory standards.
Water Quality Index (WQI) classification of rivers in agriculture and aquaculture catchments
Authors: Baharudin Fauzi , Kassim Jalina , Imran Siti Nurulhuda Mohd , Wahab Mahyun Ab 
Published: February 2021

Abstract
Water quality is always affected by a wide range of human and natural factors which is commonly a result of mismanagement of land, dumping of rubbish and unintentional introduction of chemical material into the river. The rivers in Kuala Selangor and Sabak Bernam districts which lie in active agriculture and aquaculture activities are also not spared from pollution. This study focused on water quality investigation and source of pollution identification in seven rivers in Kuala Selangor and Sabak Bernam. The water quality parameters investigated were Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), pH and Ammoniacal Nitrogen (AN). Water Quality Index (WQI) was then calculated based on Department of Environmental guidelines. Based on the WQI results, Sungai Selangor and Sungai Tengi were classified as Class III showing slightly polluted, while Sungai Buloh, Sungai Nibong, Sungai Haji Dorani, Sungai Besar (Bagan Cina), Sungai Besar (Parit Timur) and Sungai Bernam were classified as Class IV or polluted. The industrial, residential, agricultural activities in the surrounding area of the rivers that were suspected of being sources of river pollution were also identified.
Stabilized leachate treatment by using combination of struvite precipitation and coagulation-flocculation methods: RSM optimization
Authors: Hurairah Sarah Nadira , Halim Azhar Abdul , Aziz Nurhazirah Abdul 
Published: February 2021

Abstract
Leachate is a form of pollution from landfills with pollutants in organic and inorganic forms. Stabilized leachate is characterized by the presence of organic materials with high ammoniacal nitrogen concentrations (>400mg/L), high chemical oxygen demand (COD) concentrations (<4000mg/L) and the ratio of BOD/COD is low (<0.1). In this study, struvite precipitation used to remove high ammonia content and coagulation-flocculation using poly-aluminium chloride (PAC) as coagulant agent and cationic polymer as flocculant agent used for COD and colour removal. Response surface methodology (RSM) is applied to determine the optimum parameters and interaction effects of the four main factors that influence the efficiency of treatment used; coagulant dosage, flocculant dosage, pH and Mg-N-P ratio on COD, NH3-N and colour removal. Optimum parameters obtained from the study were the coagulant dosage of 2250 mg/L, flocculant dosage of 14 mg/L, pH 7, and Mg: N: P ratio 1.1. The combination of struvite precipitation and coagulation-flocculation method results a percentage of removal of COD 48.6%, NH3-N 92.8% and color 98.8%
Adsorption of ibuprofen using activated carbon derived from Dillenia Indica peels
Authors: Fadzail Fadirah , Hasan Masitah , Mokhtar Zulfakar 
Published: February 2021

Abstract
Adsorption of ibuprofen (IBP) onto a low-cost activated carbon, prepared at a laboratory scale from Dillenia Indica peels, has been investigated. The effect of initial ibuprofen concentration (20-100 mg/L) was studied. The equilibrium data obtained at 30 °C were analyzed by isotherms and kinetics study. The Langmuir and Freundlich isotherms were used to explain the experimental data. While pseudo-first-order and pseudo-second-order models were applied for adsorption kinetic at different initial concentrations of ibuprofen. It was found that the adsorption process obeyed Freundlich isotherm and pseudo-second-order kinetic model. The activated carbon presented adsorption capacities of 7.5075 mg/g.