Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmented Translation Algorithm of Complex Long Sentences Based on Semantic Features

The progress of the times is inseparable from communication. If a country wants to develop well, it must learn from each other. Accurate language translation can better let people understand what they want to express. Therefore, language translation is becoming more and more important in the current social communication. Although there is a lot of research on translation, in many cases there will be inaccurate translations. Therefore, finding an accurate translation method is what many people need. Aiming at the problem that the translation of complex long sentences is prone to errors, this paper proposes a sentence segmentation algorithm, which is a method of dividing the long sentence into multiple independent clauses and then translating it. The segmentation algorithm uses the semantic features of the Concept Hierarchical Network (HNC) theory to segment clauses. The segmentation algorithm is integrated with a rule-based baseline translation system. The BLEU value of the integrated translation system reaches 0.1898, which is higher than that before the integration. The system has increased by 30%. Experimental results prove that the proposed method can effectively improve the effect of patent translation.
Android and Cloud-Based Application Development to Predict Remaining Age of Four-Wheeled Vehicle Brake Pad with Varied Driving Behaviour

One important component in a vehicle is the braking system. The main function of the braking system is to provide deceleration to stop the vehicle speed. One important part of the braking component is the brake lining. But to know the physical condition of the brake lining, it is necessary to dismantle the wheel and brake components. So that one of the initial stages of development is to make an application that can predict the remaining life of the brake lining without having to do the demolition. Therefore, this study will design the calculation with the physical condition monitoring approach of the brake lining itself. In addition, in this study, the author will try to find a relationship between driving behaviour and its effect on the wear rate of the brake lining. After testing the road with 3 different driving behaviours, it was found that the driver with eco behaviour would consume 0.42%, normal behaviour as much as 1.65% and sport behaviour as much as 44.96% of the thickness of the original brake lining. There was a significant result in sports driving behaviour because in this behaviour the braking pressure and temperature will be very high when compared to eco and normal. Furthermore, the application error which obtained from the application was higher than the other two behaviour.
Video Abnormal Event Detection Based on Optical Flow and 3DCNN

With the advancement of technology, the detection of abnormal events in videos has extensive theoretical and practical significance. Most events in videos are normal events, but a small number of abnormal events contain a lot of information. How to identify individual abnormal events from massive videos is the focus of concern. The paper made full use of the spatio-temporal continuity of video images. Firstly, a space-time cube was constructed by integrating optical flow information; secondly, a multi-layer three-dimensional convolutional neural network model was constructed; finally, the entire process of video abnormal event detection was realized based on this model. The experiment on the UCSD dataset showed that the method in this paper can effectively detect abnormal events in videos.
Evaluation of total phenolic, flavonoid contents, antioxidant and cytotoxicity activities of various parts of Phaleria macrocarpa (Scheff.) Boerl fruit

The objective of this study was to evaluate the total phenolic and flavonoid contents and to investigate antioxidant and cytotoxic activities of ethanol extract of pericarp, mesocarp and seeds of Phaleria macrocarpa (Scheff.) Boerl. The total phenolic content was determined as Gallic acid (GAE) equivalent, and flavonoid contents were determined as Quercetin (QE) equivalent. 2,2-diphenyl-1-picrylhydrazyl (DPPH) method was used to measure the antioxidant activity. The cytotoxicity activities were tested against breast cancer cell lines T47D using WST-1 (4-[3-(4-Iodophenyl)-2-(4-nitro-phenyl)-2H-5-tetrazolio]-1,3-benzene sulfonate) assay. The ethanol extract of pericarps, rich in flavonoid (43.88 ± 2.33), exhibited the most potent antioxidant activities to DPPH (IC50: 16.01 μg/mL). However, a high amount of phenolic content (66.57 ± 2.33 mg GAE/g) was found in the ethanol extract of P. macrocarpa mesocarp. The ethanol extract of P. macrocarpa seeds exhibited high cytotoxic activities against T47D cell lines in a dose-dependent manner compared to mesocarp and pericarp extracts. These results suggested that the ethanol extract of P. macrocarpa seeds could be used as an anticancer against T47D cell lines.
Characterization of Hydrochars Produced by Hydrothermal Carbonization of Lignin, Cellulose, d-Xylose, and Wood Meal

Hydrothermal carbonization of cellulose, lignin, d-xylose (substitute for hemicellulose), and wood meal (WM) was experimentally conducted between 225 and 265 °C, and the chemical and structural properties of the hydrochars were investigated. The hydrochar yield is between 45 and 60%, and the yield trend of the feedstock is lignin > WM > cellulose > d-xylose. The hydrochars seem stable below 300 °C, and aromatic structure is formed in all of these hydrochars. The C content, C recovery, energy recovery, ratio of C/O, and ratio of C/H in all of these hydrochars are among 63–75%, 80–87%, 78–89%, 2.3–4.1, and 12–15, respectively. The higher heating value (HHV) of the hydrochars is among 24–30 MJ/kg, with an increase of 45–91% compared with the corresponding feedstock. The carbonization mechanism is proposed, and furfural is found to be an important intermediate product during d-xylose hydrochar production, while lignin hydrothermal carbonization products are made of polyaromatic hydrochar and phenolic hydrocha.
Hidden Markov Model energy conservation approach for continuous monitoring of vital signs in geriatric care applications

In the recent healthcare crisis engendered by the Covid19 pandemic, wireless body area networking devices have started to play a significant role in mitigating the health problems of the elderly. The energy conservation of the device during the temporary disconnection of the sensor node can play a vital role in the broader acceptance of this technology. Here, a probabilistic hidden Markov model (HMM) is used for energy conservation, a relatively less explored area of energy conservation approach within the field of wireless sensor networks. Since the vital signs of heart rate and blood pressure are highly correlated, the heart rate and blood pressure readings are taken for model development. The classification of normal and critical data is based on the probability of observation sequences in the particular model. The hidden states are estimated using the observation sequence and the HMM parameters during disconnection. Accuracy between 0.9 and 1.0 is obtained for different series. Dynamic threshold limits are included for more adaptability of the model for varying physiological conditions of the patients. The energy conservation possible using the model is discussed. This model presents a novel approach to energy conservation using HMM, which will help continuous home monitoring of vital parameters in geriatrics.
Prediction of Corrosion Resistance of B10 Copper-Nickel Alloy Based on Optimal Convolutional Neural Network

Aiming at the defects of insufficient number of corrosion resistance prediction models and simple feature extraction of B10 copper-nickel alloy, a corrosion resistance prediction model of B10 copper-nickel alloy based on optimized convolutional neural network is proposed. The convolutional neural network architecture for grain boundary image characteristics is proposed to conclude a step-by-step convolution operation by analysing the traditional convolution operation process, and it proves theoretically that such a step-by-step convolution operation can reduce the consumption of additional parameters; learning pool proposed single-channel operation can reduce the loss of feature information; the multi-layer feature fusion learning strategy makes the expressive force of deep network extraction features diversified. The results of multiple experiments demonstrate that the improved algorithm introduced can improve the prediction accuracy of the model in image classification comparing with the traditional convolutional neural network model, and the improved convolutional neural network model can better achieve the prediction of corrosion resistance of B10 copper-nickel alloy.
A Hybrid Deep Learning Framework for Estimation of Elbow Flexion Force via Electromyography

Real-time and accurate estimation is beneficial for intention recognition, muscle rehabilitation evaluation and artificial limb control. However, it is difficult to estimate the elbow flexion force accurately. The aim of our model is to estimate elbow flexion muscle force, which can be used for elbow joint health assessment and prosthetic control studies. This paper proposed an end-to-end deep learning framework by fusing Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) neural network with attention mechanism, which is more suitable for time series EMG signal to improve the feature extraction ability and achieve a high flexion force estimation accuracy. Experimental results indicated that the proposed method can automatically extract the proper features of elbow motion behaviors without professional knowledge in feature extraction model. Moreover, the experimental result shows that the proposed framework performs well in the accuracy and generalization ability, outperforming the state-of-the-art methods.
Dual-Loop Iterative Learning Control of Robot Manipulator for Human-Robot cooperation

This paper proposed a dual-loop iterative learning control strategy for robot manipulator in the situation of repetitive human-robot interaction. By utilizing the contact force between the human and the manipulator, a force-based loop is first conducted to produce the unknown desired trajectory of the manipulator. According to Lyapunov’s method and using the back-stepping technique, an adaptive iterative learning controller is presented to fulfill the trajectory tracking task of the robot manipulator, in spite of the model uncertainties and the unknown disturbances. The proposed control scheme is verified by numerical simulation on a robot manipulator with two degrees-of-freedom.
Convolutional Bi-directional Long Short Term Memory Network based Dynamic Fault Diagnosis for Transformer DGA

Dissolved gas analysis (DGA) method is one of the important methods to detect early internal faults in transformers. Aiming at the shortcomings of feature extraction and online modelling in the analysis model based on shallow layer, a transformer DGA online fault diagnosis method combining convolutional neural network and bidirectional long short term memory network is proposed in this paper. By using multi-time characteristic gas data as the input of the diagnostic model, combined with convolution and pooling, the fault sensitive features were extracted, and the time dimension features were extracted by using the recursive neural network with gated structure. Under various evaluation criteria, it is compared with convolutional neural network, bidirectional long short-term memory (Bi-LSTM) network and dynamic support vector machine. The experimental results show that the online transformer diagnosis model proposed in this paper, which considers both time and space characteristics, has higher prediction accuracy.
Self-regulated learning of first-year students at physics education department, FKIP – Universitas Syiah Kuala

This study examines the problem of how the criteria for self-regulated learning of first-year students at the Physics Education Department and aims to determine the self-regulated learning of freshman year students in the department. Quantitative research methods and descriptive approaches used in this study. A total of 61 students in 2017 became research subjects and obtained 83.60% of students believed in their abilities (criteria; very independent), 75.40% of students were satisfied with their effort (criteria; independent), 70.50% of students were able to make decisions and have learning initiatives (criteria; independent), and 62.30% of students are responsible for their studies (criteria; independent). That shows the Physics Education Department, FKIP Unsyiah class of 2018 has the self-regulated learning with independent to very independent criteria. The results of the study become a reference for the preparation and improvement of the curriculum.
Optimal control of boiler system based on IoT and energy consumption simulation

The energy consumption of building heating in China is enormous and reducing heating energy consumption is of great significance to promote energy saving and emission reduction in China. A reasonable boiler heating strategy can effectively reduce heating energy consumption. This paper presents an optimal control method based on the IoT system and an interface that makes Energy Plus in the cloud server work together with the IoT system. The boiler can make corresponding adjustments to the outlet temperature according to weather conditions and other factors to achieve energy-saving. The results show that the intelligent control method proposed in this paper can reduce the boiler outlet temperature by 2-8 °C and can reduce energy consumption by about 15%.
A Summary of wearable textiles power generation

With the advent of a series of wearable devices such as Google Glass, electronic bracelets, etc, people have become more and more interested in wearable devices, but because of the short power supply period, the function is greatly limited. So the use of fabric to generate electricity has slowly come into the eyes of researchers, to understand the history of fabric power generation, this paper mainly analyzes and summarizes the methods of power generation for different types of fabrics from domestic and foreign research, and proposes corresponding solutions based on practical problems. Secondly, the impact of the choice of materials on the actual power generation, including output performance, output stability is further outlined.
The Use of SPOT 6 and RapidEye Imageries for Mangrove Mapping in the Kembung River, Bengkalis Island, Indonesia

High-resolution satellite imageries believed to map the mangrove area well. Two satellite imageries with almost similar spatial resolution were expected to achieve this objective, including SPOT (Satellite Pour l’Observation de la Terre) 6 and a RapidEye. Two images covered the same area and acquired under the equivalent season at Kembung River, Bengkalis Island, Riau Province. A comparison was tested on several objectives. First, a comparison was conducted band by band through examining their statistical spectral analysis. RapidEye had a higher variance in all visible bands but low at NIR. Second, spectral separability using Jeffrey-Matusita distance was executed with three-band composites (SPOT 6/VNIR; RAPIDEYE/VNIR and RAPIDEYE/VNIR+RedEdge). SPOT 6 had better separability than RapidEye imageries compositions (VNIR+RedEdge and VNIR), with an overall accuracy of 79.9%, 75.3%, and 67.3%, respectively. Third, mangrove land cover classification. Three pixel-based classification rules were used, including a general classifier, maximum likelihood (ML), and two advanced classifiers, i.e., neural network (NN) and support vector machine (SVM). Results indicated that the use of SPOT 6 produced better overall accuracy than the other two image composition for each classifier. SVM was promising in the mangrove mapping algorithm compare to NN and ML.
The development of power take-off technology in wave energy converter systems: A Review

Utilizing ocean wave energy as a renewable energy source has become the object of rapid research. Energy conversion technology continues to evolve to seek more efficient, cheaper forms of investment, operation, and maintenance and are environmentally friendly. The converter type and PTO hold the key to the efficiency of the whole system. This literature review paper examines various general concepts and innovations of wave activated body converters and commonly used and innovative power take-off systems with a focus on controlling efforts in maximizing the generated power, challenges and efforts to develop a PTO control system as well as various research conducted by various parties.
Sensory analysis and head-space aroma volatiles for the characterization of capers from different geographic origin

Abstract Characterization of capers (Capparis spinosa L. Capparaceae) was achieved by measurement of aroma compounds and sensory descriptors. Different capper samples (the floral buds of the plants) coming from Pantelleria and Salina Islands (Italy) were analyzed. Head-space aroma volatiles were extracted and analyzed by SPME-GC-MS. In all, more than one-hundred volatile compounds were identified and the quantitative results have been expressed as percentage of total peak area. Aliphatic acids and aldehydes were the most abundant chemical classes in all the samples whereas aromatic alcohols distinguish the samples from different origin. Among sulfur compounds, methyl-isothiocyanate was the major one, followed by benzyl-isothiocyanate. The sensory profile of capers was defined by a trained panel, measuring the intensity of nineteen descriptors. All the data were statistically correlated. Multivariate analysis applied to the volatile and sensory data allowed distinguishing the samples in relation to the area of origin.
Research on BIM Reconstruction Method Using Semantic Segmentation Point Cloud Data Based on PointNet

As the construction industry is shifting from the construction of new buildings to the maintenance and use of existing buildings in recent years, the demand for automated building information models (BIM) creation is increasing. This paper uses the deep learning network PointNet to perform semantic segmentation on the public S3DIS point cloud data set, which means to assign the same type of point cloud building components in the data set to the same label, and the bounding box algorithm is been used to obtain the outer contour parameters of the segmented point cloud building components. Finally, the Dynamo, which is one of the Revit plug-in, is used to perform parametric modeling according to the obtained parameters, and generates the BIM corresponding to the point cloud data set. The experimental results show that the method proposed in this paper can complete the parametric creation of BIM with high completeness based on the efficient segmentation of point clouds.
Estimating pile length uncertainty with Kriging

Piles are used to transfer loads from the structure into the bearing soil layer. The accurate estimation of the pile lengths is a complex statistical problem that includes several spatial and measurement-based uncertainties. Commonly, bearing subsurface is modeled with linear triangular networks, and geometry related uncertainty is considered with overall safety factor. Currently, it is common practice is to solve the unknowns in this problem with simplistic borehole analysis and engineering judgment. Our paper presents a statistical pile length model based on the borehole uncertainty analysis. Modern geotechnical designing contains multiple steps including ground investigations, computer modeling, and expert decision making. All the included steps add uncertainty to the design process. The problem with these traditional methods is that the sources and the magnitudes of the uncertainty are not visible to the designer, which can lead to non-optimal decisions. With the help of the state-of-the-art probabilistic models, a design pipeline can be constructed that propagates the uncertainty from process to process and is transparent about its sources and magnitudes. In our paper, we show how the probabilistic pipeline approach enables us to make more informed decisions. This is due to uncertainty propagation from ground investigations to the 3D volumetric model. Given the uncertainty, we can estimate the mean and variance of the future costs and the impact that different design decisions have. The paper presents a practical example where probabilistic models are utilized to improve decision making.
Influence of oil pollution on the surface of the microbial community and catalase activity of sod - podzolic soil

The article provides data on the influence of different levels of oil pollution on the structure of the microbial community and the activity of catalase in sod-podzolic soil during the cultivation of barley and wheat. It also presents data on the effect of biopreparations based on oil-oxidizing bacteria on the total number of microorganisms, microscopic fungi, actinomycetes and catalase activity. Based on the analysis of the microflora of oil-contaminated soddy-podzolic soil, it was found that oil pollution affects the total number of microorganisms, the qualitative and quantitative composition of micromycetes. and actinomycetes. It was also found that small concentrations of oil stimulate the number of microbiota and catalase activity. The introduction of biological products based on oil-oxidizing bacteria has a beneficial effect on the indicators of the general biological activity of the soil. It is noted that the selection of a phytomeliorant is of great importance in the reclamation of non-contaminated soil.
Variety-Specific Soybean Yield with Respect to Nitrogen-Fixing Symbiosis on the Northern Boundary of the Crop’s Geographical Range

The effects of inoculating nitrogen-fixing symbionts on soybean yield were tested in the experimental field of the Krasnoyarsk State Agrarian University, 50 km north of Krasnoyarsk. Strain 634 was tested in 2017–2018 on the Zaryanitsa variety. Strain 634 and Spontaneous, discovered in Krasnoyarsk forest steppes, were tested in 2019 on the precocious Zaryanitsa and Eos varieties (Siberian ecotype). Treating Zaryanitsa soybean with Strain 634 induced a yield of 0.197 kg/m2 compared to the control yield of 0.167 kg/m2 (p = 0.037). The number of nodules peaked in Zaryanitsa inoculated with the biological Spontaneous: 104 nodules per root. Spontaneous reduced the height of Zaryanitsa plants (p = 0.0606) but increased the seed weight (p = 0.0282). Eos was inoculated with two strains; compared to the controls, it had lower productivity metrics such as the number of branches (p = 0.0771), beans (p = 0.0483), seeds (p = 0.0142), and seed weight (p = 0.0381). However, the preservation rates were 16% higher in case of Strain 634, 22% in case of Spontaneous. Factor analysis (varimax rotation) identified two factors: one comprised the indicators related to the development of photosynthesis organs (the number, weight, and area of leaves) and to nodulation (the number, weight, and moisture of nodules). The other one comprised the yield structure components and the number of plants per unit of area. Eos yield rose by 0.02 kg/m2 and 0.048 kg/m2 when inoculated with Strain 634 and Spontaneous, respectively.