Miracle-dz/UniFAR-FTUs huggingface dataset:
Geophagy and nutrient supplementation in the Ngorongoro Conservation Area, Tanzania, with particular reference to selenium, cobalt and molybdenum

Wildlife and livestock in the Ngorongoro Conservation Area (NCA), Tanzania, commonly practise geophagy. We investigated the nutrient content of nine earth licks and adjacent topsoils in the NCA, and for comparative purposes, two salt pans in north-eastern Tanzania, licks in central Tanzania and a lick near Puerto Maldonado, Peru. The licks had no consistent pattern of nutrient enrichment relative to unutilized topsoils, although the Na content of licks was greater than that of the adjacent topsoils at all but one site. The three largest licks in the NCA (Gibbs Farm, Seneto and Ascent Road) were enriched relative to topsoils and global averages in Se (maximum of 4.7 mg kg−1), Co (maximum of 107 mg kg−1) and/or Mo (maximum of 7.4 mg kg−1). We suggest that licks do provide supplemental Na, but that Se, Co and/or Mo at the largest licks provide easily overlooked nutritional benefits and are perhaps the primary target for geophagy at these sites. Subsoil zones of clay deposition are likely to be enriched in various elements through illuviation or mineral precipitation from solution. Animals may use the taste of NaCl as a clue for locating such zones where they are likely to find a greater quantity of micronutrients relative to other soils. These findings have consequences for conservation and pastoralism in that these large licks may be key resources, providing micronutrients that are essential for maintaining the health and fecundity of animal as well as human populations in the region.
Signal-processing Challenges in Leveraging 100 Gb/s Wireless THz

This paper presents a wireless link based on a Tx and a Rx RF front-end module operating at a LO frequency of 230 GHz. The LO frequency generation paths of the Tx and the Rx are driven using two independent and unlocked frequency synthesizers. Due to this operation mode, the Tx LO leakage is down-converted in the Rx, generating a DC modulated tone that, if not properly handled, will prevent the system to operate appropriately. This paper analyses this effect and presents a solution to mitigate this impairment. Applying this, and using a 16-QAM modulation scheme, a maximum data-rate of 80 Gbps with an EVM of 12.5 % was achieved at a 1-meter distance.
Optimising Low-Voltage Transformer Tap Settings in Distribution Networks

In this paper, a practical method of determining the optimal tap setting of no-load distribution tap-changing transformers is proposed. The uptake of distributed energy resources impacts the risk of distribution systems violating voltage constraints. Setting no-load transformer tap settings appropriately can mitigate some of this risk, but changing these taps requires an outage to the customer and must be infrequent. Hence, the optimisation of these tap settings must consider loading for at least a whole year to account for seasonal variation. An evolution strategy is used to determine these settings based on an average loading case. The performance of this method is measured with a normalised objective function. Monte Carlo simulations are used to determine the probability that the network voltages on the secondary side of the transformer terminals violate the required voltage constraints once this optimal set of taps is established. This algorithm was tested on a real distribution feeder, and generates a sufficiently-optimal set of taps without significant computation time. Furthermore, it can provide information about areas of a given distribution system that may require augmentation from a network planning perspective as more distributed resources are gradually introduced.
A Depression Recognition Method for College Students Using Deep Integrated Support Vector Algorithm

The infinite increase in population, the pressure of survival, and the pressure of learning make the competition between people more and more fierce. Some college students have also been in a state of anxiety and panic for a long time, and mental health diseases have shown an explosive growth trend. The development of social networks such as Weibo, QQ, and WeChat not only provides more convenient communication methods for college students, but also provides a new emotional vent window for college students. They can record their living conditions in real time through social networks and interact with friends to express emotions and relieve stress. At the same time, the development of social networks has also provided a new way for the detection of depressed users. The current computer technology analyzes the user’s social network data to detect the user’s depression. This study uses text-level mining of Sina Weibo data from college students to detect depression among college students. First, collect text information of college student users in Sina Weibo, and construct the text information into input data that can be used for machine learning. Deep neural networks are used for feature extraction. An deep integrated support vector machine(DISVM) algorithm is introduced to classify the input data, and finally realize the recognition of depression. DISVM makes the recognition model more stable and improves the accuracy of depression diagnosis to a certain extent. Simulation experiments verify that the proposed depression recognition scheme can detect potential depression patients in the college student population through Sina Weibo data.
Direct Measurement Combined with S Transform Based Fault Detection Method on Radial-bus Multi-terminal DC System

High voltage dc (HVDC) power system is a promising technology due to its easy integration in asynchronous networks and its high efficiency on long-distance transmission. However, its wide application faces the challenge of fault detection and protection. The rapid-growing dc fault current requires fast detection speed and sufficient discrimination between the fault condition and other transients. In this paper, a method combined with direct measurement on the dc current and S transform analysis is proposed. Basically, the direct measurement method monitors the moment when the current exceeds the threshold amplitude and S transform analysis is responsible for identifying whether the overcurrent is caused by a dc fault on the monitoring line. The effectiveness of this proposed method is tested on a 4-bus radial multi-terminal dc (MTDC) system in MATLAB/Simulink. The detection time and sensitivity with different fault resistances and locations are summarized. The sensitivity variation of different lines over the fault period is shown to support the validity of this method.
Software Cost Estimation Based on Dolphin Algorithm

Accurate Software Effort Estimation is of high importance with regard to Software Project Management. It can be specified as the process for predicting Effort regarding costs, needed for developing software products. A lot of techniques related to software effort estimation were carried out for developing models that are generating optimal estimation accuracy. Swarm intelligence is one such technique. The process-related in selecting the optimum estimation algorithm is expert dependent and complex. The presented study optimizes the estimation using the COCOMO II models by two models: the first model applied the dolphin algorithm, the second model applied suggested hybrid dolphin and bat algorithm (DolBat). By applying the two models on two data set and evaluate with the use of Magnitude of Relative Error(MRE) and Mean Magnitude of Relative Error(MMRE). The results indicate that the dolphin algorithm has better than previous algorithms but the (DolBat) is the best to get the coefficient value of the COCOMO II model.
Designing a neural network for vehicle type classification using extended Haar features

Vehicle type classification method using artificial neural network (ANN) is proposed in this paper. The presented method relies on ANN to recognize between car, motorcycle and non-vehicle images. Extended Haar feature (EHF) was used to extract unique features from motorcycle and car images. These features were used to train four designed ANNs. The focus of this research is to determine the most optimum ANN for future development, which is good in the term of accuracy and speed. The experimental results show that a more complex ANN has a good accuracy but with a slower processing speed. The most optimum ANN gives 94% of accuracy and 51ms of processing speed to classify a single image.
Research on trajectory similarity matching model based on spatiotemporal trajectory big data

In order to effectively match the target vehicle with the trajectory of the passenger's mobile phone to be matched, this research proposed a trajectory similarity matching model of spatiotemporal trajectory big data. The model mainly adopts two advanced measurement methods: structural measurement of spatiotemporal trajectory and motion state measurement of spatiotemporal trajectory. The GPS trajectory of a vehicle traveling on a certain road within a time interval was used as the target trajectory, and the similarity between the trajectories was calculated. Finally, the number of mobile phone trajectories matching the target vehicle was determined, and then the number of mobile phones matching the target vehicle was obtained. Experimental results verify that the model could effectively match the target trajectory with the mobile phone trajectory, which was in line with the expected effect.
CMOS Ring Amplifier for Bio-Signal LNA

Low power wireless medical devices have attracted great research interests, which need the low noise amplifier (LNA) to amplify bio-signals. And power efficient amplification in scaled CMOS technologies remains a persistent challenge. Ring amplification technique, with a similar structure to CMOS ring oscillators, has the potential to achieve high gain with minimal power dissipation as well as efficient amplification in scaled environments. CMOS ring amplifiers possess the benefits of scalable power-delay production and efficient slew-based charging. So, the application of the ring amplification technique to low power LNA design is investigated in the paper. The approach of ring amplifier with single-ended structure is not suitable for LNA design. Then the pseudo differential amplifier with float sampling scheme is proposed, which is suitable for the purpose of noise reduction with chopper technique. This structure can also eliminate the need for additional common mode feedback in differential processing of signals. Experiments simulation has verified that the bio-signal can be properly processed with the provided design.
Does Word2Vec encode human perception of similarityƒ A study in Bangla

The quest to understand how language and concepts are organized in human mind is a neverending pursuit undertaken by researchers in computational psycholinguistics; simultaneously, on the other hand, researchers have tried to quantitatively model the semantic space from written corpora and discourses through different computational approaches - while both of these interacts with each other in-terms of understanding human processing through computational linguistics and enhancing NLP methods from the insights, it has seldom been systematically studied if the two corroborates each other. In this paper, we have explored how and if the standard word embedding based semantic representation models represent the human mental lexicon. Towards that, We have conducted a semantic priming experiment to capture the psycholinguistics aspects and compared the results with a distributional word-embedding model: Bangla word2Vec. Analysis of reaction time indicates that corpus-based semantic similarity measures do not reflect the true nature of mental representation and processing of words. To the best of our knowledge this is first of a kind study in any language especially Bangla.
Continuous blood pressure prediction based on hierarchical adaptive algorithm

Continuous hierarchical adaptive blood pressure prediction method based on pulse condition is proposed in this paper, the linear blood prediction models and the network training parameters of different population and different pulse conditions can be obtained through a large number of experimental data. For one measurement, the first level blood prediction based on different pulse conditions is carried on to get the corresponding linear blood prediction model in order to get the primary predictive blood pressure. Then the trained BSO-BP neural network is used to adjust the blood pressure of the second stage by the pulse characteristic values, and the final blood pressure can be obtained. The experimental results showed that the blood pressure of patients with hypertension and normal people are improved by the proposed method. The results of continuous measurement also show great follow-up ability, strong ability to capture abnormal blood pressure, which means the method can predict hypertension effectively.
Research on Multi-class Weather Classification Algorithm Based on Multi-model Fusion

In daily life, the animal husbandry, aquaculture, agriculture, and transportation industries that people engage in are all affected by the weather. There is no doubt that accurate, convenient and fast identification of various types of weather is of great significance in production and life. In this article, we collected and labeled a dataset containing 9 types of ground weather images. In addition, we use ResNet convolutional neural network and DenseNet convolutional neural network to build the network structure, and perform probability discrimination on the output results of each model to increase the recognition rate. We trained and tested on the collected weather dataset. Experimental results show that the method has better performance in weather recognition.
A Survey on Flexible Exoskeleton Robot

Current exoskeleton robots limit the movement comfort and flexibility of the wearer, and have safety problem, due to strong rigidity actuator and small material impact resistance. In contrast, the flexible exoskeleton robots are characterized by high flexibility, high environmental adaptability, high compatibility and multi-functionality, and they have now become a trend of exoskeleton development. The development and research status of flexible exoskeleton robots are summarized. Furthermore, by their actuating mechanisms, flexible exoskeleton robots are summarized as motor, rope and pneumatic. The difficulties of the current flexible exoskeleton and the main research challenges are summarized, and the prospect of future research is put forward.
Research on Reactive Power Control of the HVDC Transmission System

DC converter station needs to consume a large amount of reactive power when transmitting active power. Insufficient or excessive reactive power will cause fluctuations in the AC voltage, which will affect the safety and stability of the entire system in severe cases. To maximize the reactive power balance of the converter station, this study proposes a new principle of active power distribution: the power distribution between the two DC circuits in the project takes the minimum reactive power change of the converter station as the objective function and uses GSO optimization The algorithm optimizes the power distribution coefficient of the two times to maintain the reactive power balance of the converter station as much as possible; the power distribution between the two internal poles of the DC is proportional to the absolute value of the DC voltage of the two poles to ensure that each time Internal ground electrode current balance. Simulation results verify the correctness, robustness, and superiority of the proposed active power allocation principle.
Research on polygraph technology based on ballistocardiogram signal

Polygraph technology has always been concerned by the society. At present, there are many technologies used in the field of lie detection research. By summarizing the characteristics of the current technology, this paper proposes a multi-channel concealed polygraph technology based on ballistocardiogram (BCG), which can make up for the shortcomings of the existing technology. In this paper, the BCG signal, image signal, speech signal and galvanic skin response (GSR) signal are used to preprocess and extract the features. Using the extracted features to train the classification model, we can get an overall accuracy of 78%, and the area under the curve (AUC) score is 0.84. This experiment proves the effectiveness and advantages of BCG signal for lie detection, and also provides an effective method for the study of concealed polygraph technology.
Edge Computing in Smart Hydropower Station: Sinusoidal Noise Rejection for PD Monitoring

For the problem that pulses are overwhelmed by sinusoidal noise, and thus are difficult to extract accurately, a new method to suppress sinusoidal noise in partial discharge signal based on spectrum differential and spectrum correction was proposed. The proposed method employs Hanning windows FFT to reveal the spectrum of original signal, which will be used to difference, and obtain differential spectrum. Then the classical threshold criterion is adopted to determine the frequencies of sinusoidal signals from the differential spectrum. Combination with spectrum feature of Hanning window, the frequencies are corrected to estimate the waveform of the sinusoidal noise, which will be subtracted from original signal to extract discharge signals. The results show that the proposed method is superior to wavelet transform and partial energy ratio based denoising techniques.
Research on adaptive cruise following control strategy based on model prediction

In this paper, an adaptive cruise following strategy is designed based on the model prediction, and an appropriate inter-distance strategy is designed. By taking three indicators of traceability, comfort and safety as the quantitative indicators of ACC performance, a comprehensive performance indicator function is established based on inter-distance strategy so as to obtain the desired acceleration of the vehicle following strategy. Simulink and Carsim are employed to establish a system simulation model, and the simulation results are compared and analyzed. The simulation results show that the vehicle-following strategy based on model prediction not only satisfies vehicle-following driving, but also enhance the driving comfort
Early maternal behavior of wild cheetahs: implications for captive husbandry

Against a background of poor breeding success and cub survival in captive cheetahs, a knowledge of the early maternal behavior in the wild may yield useful information for comparison with the captive situation. This paper documents the types of lair sites used by mothers to conceal their newborn cubs in the wild situation, and details patterns of maternal behavior observed during this period. Four kinds of lairs were used by cheetah mothers, and the amount of protection from the elements and predators was assessed for each. Lair type appeared to have little effect on cub survival. Cubs were concealed in lairs for 8.2 weeks on average, although larger litters tended to leave lairs earlier. During this period, cubs were moved to new lairs approximately every 5.6 days. Cubs were left for an average of 9.6 hours while their mothers went hunting, but mothers virtually always returned to them around nightfall, even if they failed to catch any prey. Maternal neglect and cub abandonment, which account for many cub deaths in captivity, are abnormal in the wild, except when prey is very scarce. Improved husbandry techniques, such as the provision of multiple, secluded nest boxes, and remote monitoring conditions should promote breeding success in captivity. In addition, noise and human disturbance should be minimized.
Challenges of Migrating Legacies Web to Mobile: A Systematic Literature Review

The multi-device era comes thanks to mobile computing which gives remote access to resources from anywhere changing the people’s life and opening new business opportunities. However, the legacy systems do not render the content properly in mobile devices because they were thought to be only compliant with Web browsers. Economic availability is often the reason why these have not been modernized. This work proposes a systematic literature review about the approaches used for the portabilization or modernization of web 1.0 business applications to mobile devices in the period 2006-2017, from SCOPUS, IEEE, and ACM. The search obtained 824 articles, 44 were selected and classified with respect to focus, scope, type of research and type of contribution. The results obtained allowed us to reach conclusions about the state of the subject and to determine the research gaps, such as the need for better use of the mobile characteristics because the adaptations are mostly basic. In addition, an approach is proposed and compared with the aforementioned them.
Real-time Vehicle License Plate Detection by Using Convolutional Neural Network Algorithm with Tensorflow

Detection of vehicle license plates (VLP) become a challenging issue because of the variability in conditions and the types of the license plate. There are several solutions use stationary cameras with a limited angle and specific resolution and also for a specific license plate type. Unfortunately, license plate detection is a challenging issue when vehicles in the open environments and images are taken from a particular range by low cost cameras. Vehicle license plates can be detected with computer vision technology in real-time video conditions. In this paper, we propose vehicle license plates detection system by using convolutional neural network (CNN) with tensorflow. The research was conduct in three step process such as data pre-processing, training/testing process, and interpretation result. Using CNNs algorithm in tensorflow with 25,000 steps and 8 batches on the training process can produce a training model of vehicle license plates detection with high accuracy around 70-99%.
Study on Induced Factors and Power Variations of the Steady-State Visual Evoked Potential Subharmonics

Harmonic and subharmonic components are contained in the spectrum of the steady-state visual evoked potential (SSVEP). These components are widely used in the brain-computer interface (BCI) technology and neuroscience research. Subharmonics cannot be easily induced, and the mechanism of the power variation of these components are ambiguous. In this paper, the induced factors and power variation of the SSVEP subharmonics are studied by a visual cortex network model simulation. The SSVEP subharmonics with orders higher than 1/2 are easily suppressed by noise. Therefore, the power variation of the SSVEP subharmonics with 1/2 order, and their induction factors are further analyzed. Simulation results show that the stimulus frequency, stimulus intensity, and noise intensity are three important parameters affecting the generation and power variations of the SSVEP subharmonics. If the stimulus intensity is weak, the 1/2 subharmonics cannot be induced. When the stimulus intensity increases to a certain value, 1/2 subharmonics appear in the SSVEP, and their power increases sharply with the increasing stimulus intensity. However, when the stimulus intensity exceeds a certain threshold, the 1/2 subharmonics remain constant. The bandwidth of the stimulus frequency, which can induce 1/2 subharmonics increases with increasing stimulus intensity under the same noise condition. Generation of 1/2 subharmonics and its power are suppressed by noise.