Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Fenugreek Gum on The Rheological and Qualitative Properties of Pan Bread

This research was conducted to investigate the effects of adding levels of Fenugreek seeds gum 0.5-2% on the qualitative and rheological properties of wheat flour (70% extraction). The results of the chemical composition of wheat flour and flour contain fenugreek gum showed that there is an increase in moisture content, protein, fat, ash and carbohydrate as the level of fenugreek gum replacement was increased. Ratios of wet gluten, dry gluten, gluten index and colour in wheat flour contain fenugreek gum was found to be significantly higher (P ≤ 0.05) compared with the control. The rheological properties of flour prepared with an adding of fenugreek seeds gum were estimated. The adding of fenugreek gum caused changes in the rheological properties of dough, The adding of fenugreek gum to the dough led to increased water absorption and dough stability. However, the development time, degree of softening and farinoghraph quality number were decreased. The sensory evaluation of loaf for different treatments showed better acceptability for the blend flour in comparison with the control. However, it can be recommended that the panelists. preferred2% of Fenugreek seeds gum replacement.
CarfRCNN: A Two-stage Effective Model For Instance Segmentation

With the rapid development of deep learning, many instance segmentation models have achieved good results in accuracy and time. But here are still many problems. In this paper, we proposed a two-stage model CarfRCNN. We proposed CAResNet to change the structure of the backbone, making the feature extraction of the input image more refined. At the same time, we also added the CRF module to add smooth constraints to the pixels, so that the segmentation mask fits the target contour more closely. We train and test on the COCO datasets, and compare with the Mask R-CNN. The experimental results prove that the model we proposed can greatly improve the accuracy.
Modification of the Constitutive Model of Concrete Material Considering the Effect of Structural Size

As the size of the structure increases, the size effect on the mechanical properties of the structure cannot be ignored. During the service period, the reinforced concrete structure will be affected by the size effect. At present, there is no item concerning material size effect in the current regulations in my country. This paper summarizes the existing size effect theory and finds a function that expresses the size effect of concrete. The peak strength size effect coefficients of domestic and foreign researchers are analyzed, and the logarithmic function form is used to calculate the peak strength size effect coefficients obtained by various researchers. The average value was fitted. We found the size effect coefficient of the ultimate strain of concrete based on the test results, and took the size effect coefficient of the ultimate strain of the concrete with a cube side length of 150 mm as one for analysis. Finally we revised the constitutive relationship of concrete.
Assessment of coral reefs damaged due to MV Pazifik ran aground in the Sape Strait using an aerial photography approach and species distribution modeling

Indonesia as an archipelagic country has a high biodiversity of coral reefs but is very vulnerable to various threats, one of the causes of damage to coral reefs is by ship aground. The damage causes minor injuries to the degradation of the reef structure. The location of the grounding sometimes on the small islands with calm currents, but sometimes in straits with strong currents. However, the assessment must be carried out, so it is necessary to develop an approach to assessing that. The purpose of this study is to assess the damage of coral reefs, to quantify the extent of damage, and identify species of corals affected. The observation used ground truth (underwater photo transect), aerial photography, machine learning, and species distribution modeling. The results obtained from aerial photography show that the MV Pazifik has damage to coral reefs reaching 613.63 m2. Based on the coral genera distribution model, it was found 35–55 genera (control location), while at the ship aground, were found 3–5 genera. Therefore, the control location is a coral reef ecosystem that is dominated by corals, while at the ship aground it can be the habitat for several hard coral genera.
Seaweed biomass derived (Ni,Co)/CNT nanoaerogels: efficient bifunctional electrocatalysts for oxygen evolution and reduction reactions

Developing earth-abundant, active and stable electrocatalysts which operate in two-electrode rechargeable metal–air batteries, including both oxygen evolution and reduction reactions (OER and ORR), is vital for renewable energy conversion in real application. Here, we demonstrate a three-dimensional (3D) bifunctional nanoaerogel electrocatalyst that exhibits good electrocatalytic properties for both OER and ORR. This material was fabricated using a scalable and facile method involving the pyrolysis of (Ni,Co)/CNT alginate hydrogels derived from sustainable seaweed biomass after an ion exchange process. The bifunctionality for oxygen electrocatalysis as shown by the OER–ORR potential difference (ΔE, the OER and ORR potentials are taken at the current densities of 10 mA cm−2 and −3 mA cm−2 in 0.1 M KOH, respectively) could be reduced to as low as 0.87 V, comparable to the state-of-the-art non-noble bifunctional catalysts. The good performance was attributed to the ternary Ni/NiO/NiCo2O4 catalytic center for charge transfer and 3D hierarchical mesoporous hybrid framework for efficient mass transport. More importantly, the Zn–air battery fabricated with the hybrid nanoaerogel as a bifunctional electrocatalyst displays very high energy efficiency (58.5%) and long-term stability. Prospectively, our present work may pave a new way to develop earth-abundant and low cost high-performance bifunctional electrocatalysts for rechargeable metal–air batteries.
Preparation and Evaluation of Plant Extract Microcapsules Using Chitosan

Research on microencapsule formulation and evaluation of ethanol extract by spray drying method was carried out to determine the effect of chitosan addition. It is hoped that from this research, chitosan microcapsule preparations can increase the benefits and stability of the extract. Microcapsule evaluation includes: microcapsule water content test, microcapsule size distribution using a Scanning Electron Microscope (SEM). The microcapsules formed were characterized by antioxidant activity using the 2,2-diphenyl-1-pikrilhidrazil (DPPH) method and total phenol content (folin-ciocalteu method). The results of the morphological evaluation showed that the microcapsules were evenly spherical for all formulas and they had an average moisture content of 4,612 ± 0.02. The anti-oxidant activity of DPPH-SA increased with the increasing concentration of added chitosan. Micro encapsules of the extract without chitosan and with the addition of 0-1% chitosan has antioxidant activity of 85,876 ± 1,897% and 86,014 ±0.570-86,725 ±0.313. In the TPC (Total Phenol Content) test, the results were 5.00 ± 0.01% and 5.49 ± 0.01-8.98 ±0.02%. Based on the research, it could be concluded that the microencapsulation with the addition of chitosan was able to increase the stability of the extract so that the antioxidant activity and total phenolic content could be higher.
Semi-supervised Learning Techniques for Speech Emotion Recognition

The core objective of this paper is to explore semi-supervised learning methods for recognizing emotions contained in speech. Semi-supervised learning techniques are used when the availability of labeled examples are sparse. There are different methods that are used in the semi-supervised settings. These techniques include generative model, graph based methods, metric based methods etc., The speech emotion data is considered for this experiment. Speech signal contains emotion specific data also. Emotion dependent features are extracted from the speech. This paper aim to enhance some existing techniques for semi-supervised learning that are used for speech emotion recognition.
Machine Learning in Smart Home Energy Monitoring System

In order to solve the insufficiency of the existing smart home energy monitor ing system in autonomous adaptability, a smart home energy monitoring system based on machine learning and embedded technology is proposed. The system uses a gatewa y to collect sensor data, and then uses a cloud computing platform running Hadoop and machine learning algorithms to learn and identify user behaviours to achieve autonom ous decision-making capabilities. Through the analysis of examples, it can be seen that the solution greatly improves the humanization of the smart home system.
A review of EEG acquisition, processing and application

As the study about the human body goes forward, some breakthrough in human biological signals is made, especially in EEG signal. At the same time, people are getting to know more about the benefits of EEG. Therefore, EEG research is enjoying a stronger demand around the world. Nowadays, EEG research is carried in several fields and different stages, such as the acquisition of EEG, the processing of EEG, energy recovery of EEG, etc. Scholars are mainly searching for behavioral and electrophysiology, neural network, multi-lead electroencephalogram, non-invasive brain imaging technology, etc. And the current research frontier and the main trend is the comprehensive study of brain function and disease at the molecular, cellular, and global levels and the understanding of the brain structure principle from the brain development process. In this review, three aspects are chosen to study: the processing of EEG, the acquisition of EEG and EEG application. Four EEG acquisition methods, five ways of EEG processing system and three patterns of application are introduced. EEG acquisition system comprises scalp electrodes, a signal isolation amplifier, an analog-to-digital converter, and a wireless transmission module. Low power consumption, small size and portability are the main directions. The research status and difficulties of suitable electrodes, high integration acquisition chip, and analysis algorithm are analyzed and predicted. Due to the rapid development of EEG signal processing, the application of EEG has developed from medical rehabilitation to driving safety, education assistance, and entertainment consumption.
The effect of microwave power and feed to solvent ratio on antioxidant activity and volatile compounds profile of crystal seedless guava leaves essential oils extracted using microwave-assisted hydrodistillation

This work utilized by-product of crystal seedless guava leaves due to its cultivation for commercial fruit production persuaded to prune guava trees branches. Essential oil (EO) extracted from plant biomass is a source of natural antioxidants due to the presence of essential components. This study was outlined to explore the potential of antioxidants and identify the chemical composition of EO extracted from crystal seedless guava using Microwave-assisted hydrodistillation (MAHD). The essential oils (EOs) were extracted using various microwave powers and feed to solvent (F/S) ratios. The two independent variables showed significantly affect on IC50 value of EOs, thereby there were several various IC50 values in DPPH free radical scavenging assay from EOs depends on their extraction condition. The best condition resulting in IC50 of EO was as low as 36.23 ± 2.07 µg/mL with a microwave power of 600 W and F/S ratio of 1:7 (w/v). The five major constituents of EO from the best extraction condition were caryophyllene (44.98%), caryophyllene oxide (14.96%), trans-nerolidol (9.16%), humulene (5.10%), and aromandendrene (5.10%). The study concluded that MAHD method possessed significant change in antioxidant activity and volatile compounds of EO extracted from crystal guava leaves.
Mask Detection and Epidemic Prevention System

The rapid spread of the coronavirus (COVID-19) has led the world into a huge pandemic crisis. As of November, more than 50 million people have been infected. In response to the transmission characteristics of COVID-19, the World Health Organization (WHO) has identified wearing masks as the most effective anti-epidemic measure in public places and crowded places. In order to ensure that people entering these areas wear masks, this paper designs a complete epidemic prevention system. Utilizes deep learning, embedded technology and LoRa wireless communication. It is characterized by low cost and flexible communication methods, which can well meet actual epidemic prevention needs.
79GHz Substrate Integrated Waveguide antenna for short-range radar applications

It is evident that radar sensing is robust technology compared to camera or LiDAR in the case of autonomous vehicles. Since, they are capable of working even under harsh weather conditions like thick fog, glaring sun, etc. The 77-81GHz frequency band for short range radar provides a global framework for automotive radar by preventing collisions, reducing traffic accidents and thus improvingvehicular safety. The proposed antenna resonates at 79GHz. Below-10dB, return loss characteristic greater than -35dB is obtained in the band of 77.5GHz to 80.4GHz (i.e., impedance bandwidth of 2.86GHz) and gain of above 8dB is achieved. Rectangular vias and complementary concentric square loops of the metamaterial structure are introduced to suppress the surface waves and to achieve the desired frequency of resonance. The feed line and the metamaterial structure are positionedaccordinglyto provide a better impedance matching. VSWR obtained is 1.04 and the antenna is linearly polarized and has a low profile. The total dimension of the antenna is 1.96×2.26×0.127mm3. The antenna structure can be used in an array for automotive radar applications.
A Rectangular Monopole Filtenna with U-shaped Slot for 5G Applications

A filtering antenna for 5G applications is proposed in this study, where the resonant element has a U-shaped slot embedded into the microstrip feed line so that no extra size is required. The proposedfiltenna has a very compact size of only 35 * 47 mm 2. Using CST Microwave Studio Suite, a comprehensive parametric analysis was done to evaluate and optimize the characteristics of the proposed filtenna. The filtenna is aimed for the 5G 3.4-3.8 GHz, as it covers a bandwidth of 412MHz (3.395-3.807GHz) witha good reflection coefficient of -22.96dBat the center frequency. The U-shaped slot enhances the lower shoulder of the antenna frequency response and has no effect on the radiation at the desired band.
High-efficiency and high-precision identification of transmitting orbital angular momentum modes in atmospheric turbulence based on an improved convolutional neural network

In this paper, we have proposed an improved convolutional neural network model based on the ShuffleNet V2 network for recognizing the orbital angular momentum (OAM) modes for the OAM based free space optical communication systems in the environments of atmospheric turbulence (AT). The network is trained by inputting the intensity images of the Laguerre Gaussian beams, which can effectively finish the training process due to its special designs, and can recognize the OAM modes with high accuracy. Compared with previous works for the single and multiplexing OAM modes, the proposed network model has high-precision and high-efficiency characteristics. Especially for the multiplexing OAM modes, our proposed system can achieve the recognition accuracy of 99.5% under strong AT and long-distance transmission. In addition, in order to prove that our system has good generalization ability and strong robustness, we used the trained model to test several groups of data obtained under untrained AT intensities, and the results showed that our model could still maintain high accuracy under the untrained AT intensities, which is very important to the realization of high-capacity optical communication technologies based on OAM in the future
A Real-time Multi-target tracking method based on Deep Learning

In view of complex model and poor real-time performance of current multi-target tracking algorithms, a real-time online multi-target tracking method based on deep learning is proposed. Firstly, the detector is used to detect the target in video image space and obtain its detection frame. After that, the position, coordinates and motion of the next frame of the target are predicted by the Kalman filter. Then Complete Intersection over Union (CIOU) is used as the distance measure to calculate the overlap between the detection box and the prediction box, and further generate the similarity matrix. Finally, the Hungarian algorithm finds optimal matching in the bipartite graph formed by the two boxes, so as to realize the data association between multiple targets. Experimental evaluation shows that this method finishes real-time and online multi-target tracking well.
Predicting user interaction behavior on government microblogs: a machine learning approach

Nowadays, the Chinese government usually publishes policies via social media, where everyone can respond to the messages. Exploring the interaction behaviors of millions of users can help the government collect the users’ opinions and make decisions. We use the machine learning algorithm Multilayer Perceptron Network model to predict the user’s interactive behaviors under their comments/replies. We found that three kinds of features are useful in predicting user interaction behaviors: the Doc2vec word vector features, the user attributes, and the user history posting information. The experiments show the effectiveness of the neural network-based model, which provides a way to optimize the formulation and implementation of public policies.
Investigations on piezo actuated micro XY stage for vibration-assisted micro milling

Vibration assisted machining is an advanced non-conventional precision machining technique aiming at improving the machining performance by superimposing a small amplitude, high-frequency vibration either on the tool or on the workpiece. This article presents the mechanical design, electromechanical simulation, and experimentation on the developed prototype of the flexural hinged micro XY stage for the vibration-assisted micro-milling system. The micro XY stage comprises three layers of flexural hinge structure surrounding the central parallel kinematic structure. The finite element analysis method is adopted to evaluate the static structural stiffness and harmonic behaviour. Two multilayer piezoelectric stack actuators drive the micro XY stage in X and Y directions. The experimental results show that the micro XY stage has a vibrating work area of 17.06 µm × 17.11 µm with a hysteresis nonlinearity and cross-coupling displacements on both axes. Therefore, an electromechanical model is essential to compensate for the hysteresis behaviour and cross-coupling displacements. Open-loop tracking control experiments determine the accuracy of the developed electromechanical model. Implementing a combined hysteresis and cross-coupling displacement compensation approach into the electromechanical model resulted in an open-loop tracking error of 7% for the synchronised circular path and a maximum deviation of 0.6 µm from the linear path.
Identification of Five Types Kampar River Fish With Genetic Approach

Fish identification in the mainland waters of Riau Province is generally carried out based on morphological characteristics. To identify fish species accurately in a short time, the DNA Barcoding method with a molecular approach is used. Five types of fish were taken from Kampar River and coded as MJ313, PJ36, SJ35, BJ33, and GJ41. The genes of these five fish were amplified, and PCR products were sequenced and analyzed using bioinformatics software. The editing of the sequencing results and the determination of the nucleotide composition were analyzed using Mega5 software. DNA sequences were aligned using the ClustalW software. The sequences obtained are then compared with GeneBank data using Basic Local Alignment Search Tools (BLAST). The results showed that the species with the code MJ313 and GJ41 were Channa striata, PJ36 were Osteochilus hasseltii, BJ33 were Mystus sp. Based on the sample examination, the species coded SJ35 cannot be determined because the marker band is low so that it is not visible. The results of the analysis on species coded SJ35 indicate that there is no COI report for species coded SJ35 on GeneBank.
Effects of rainforest transformation to monoculture cash crops on soil living ants (Formicidae) in Jambi Province, Sumatra, Indonesia

Ants (Formicidae) are fundamental components of almost every terrestrial ecosystem, especially in the tropics. While epigaeic ants are extensively studied, hypogaeic, soil living ants are still neglected to a large extent. To remedy this, in this paper we explore the effects of rainforest transformation cash crop monocultures on abundance, richness and community composition of soil living ants (Formicidae). Ants in this study were procured as a by-product of extensive sampling of soil meso- and macrofauna along a land-use gradient from lowland rainforest via jungle rubber to monocultures of rubber and oil palm in Jami Province, Sumatra, Indonesia. Sampled in 32 plots of 50 * 50 m each, with three 16 cm * 16 cm * 5 cm soil cores each, we collected 2.079 worker ant individuals, belonging to 90 morphospecies from 37 genera and six subfamilies. Land use had a significant effect on abundance and richness, while distance-based community composition was not affected. Cumulatively, lowland rainforest had the highest number of ant species exclusively living in it, and the highest average ant abundance, although multiple comparison tests did not detect significant differences. We also found highest species richness in the lowland rainforest in one of the two investigated landscapes, while not significantly different from the agricultural systems in the other. High abundance variances among the sample sites suggest inadequacy of the sampling method, however. Despite that, our study provides a first glimpse into hypogaeic ant community responses to rainforest conversion to cash crop monocultures in Sumatra, Indonesia.
In vitro anti-cholesterol and anti-hypertensive activity of stem bark the Flacourtia rukam

Flacourtia rukam belongs to family Salicaceae and Indonesia is known as rukem. F. rukam is popular among people especially Musi Banyuas in South Sumatera, Indonesia for the treatment of hypertensive. Investigated effect anti-cholesterol and anti-hypertensive activity from extracts the stem bark of F. rukam have been done. The anti-cholesterol activity was measured by the photometric method by reaction cholesterol with Liebermann-Burchard reagent and anti-hypertensive activity using Angiotensin Converting Enzyme (ACE) inhibitory method. The crude ethanol extract showed the highest anti-cholesterol activity compared to the fractions with IC50 value of 157.88 mg/L. Crude ethanol extract also contained the highest phenolic and flavonoid content compared to fractions. The anti-cholesterol activity of extracts is equivalent to the total phenolic and flavonoid contained. In an anti-hypertensive activity study, the crude ethanol extracts exhibited the percentage of ACE inhibitory activity with IC50 119.82 mg/L. The results indicated that the stem bark of F. rukam might reduce or control the cholesterol levels and blood pressure. Anti-cholesterol and anti-hypertensive activity the stem bark of F. rukam is higher in extract form compared to fractions (synergistic).