Miracle-dz/UniFAR-FTUs huggingface dataset:
Caracterización Morfológica y Agronómica de la Colección Nacional de Germoplasma de Jatropha curcas L.

Titulo en ingles: Morphological and Agronomic Characterisation of the Jatropha curcas L. Colombian Germplasm collection. RESUMEN: Con el objetivo de estimar la diversidad genetica de la coleccion colombiana de germoplasma de Jatropha curcas L. , con un total de 246 accesiones colectadas en estado silvestre en distintas regiones del pais, se llevo a cabo una caracterizacion morfologica y agronomica mediante la medicion de 27 caracteristicas geneticas de tipo cualitativo y cuantitativo. El estudio se baso en metodos de estadistica multivariada para hallar la variabilidad genetica entre las accesiones. Las distancias obtenidas en el dendograma posibilitaron formar 18 grupos al estimar el coeficiente de disimilaridad DICE entre los caracteres asociados a componentes de produccion, caracteristicas agronomicas y descriptores de interes especifico, asociados a 10 componentes principales que logran explicar el 78,25 % de la variacion. La caracterizacion morfologica permitio determinar la variabilidad genetica dentro de las accesiones en especial los descriptores de disposicion de arquitectura de planta, porte de las plantas, longitud de la parte de la rama no lignificada y color del latex. La importancia del estudio radica en el manejo y uso de estas accesiones en programas de mejoramiento genetico tradicional o asistido por marcadores moleculares conforme al aprovechamiento de la especie en la agroindustria con fines energeticos en la produccion de agrocombustibles. Palabras clave: Descriptores, Variabilidad Genetica, Jatropha curcas L. , Agrocombustibles. ABSTRACT: In order to determine the genetic diversity of the colombian germplasm collection of Jatropha curcas L. , established with a total of 246 wild accessions collected in different regions of the country, it was carried out a morphological and agronomical characterization using 27 genetic traits either qualitative or quantitative type. The study was based on multivariable statistics statistical to determine the genetic variability among accessions. The distances in the dendrogram made possible to stablish 18 groups based on the DICE dissimilarity coefficient between the characters associated with yield components, agronomic characteristics, and specific descriptors of interest related to 10 principal components obtained from 20 quantitative evaluated descriptors. Morphological characterization allowed us to determine the presence of genetic variability within accessions especially descriptors available to plant architecture, plant size, length of the not lignified branch, latex color and this study of the Jatropha germplasm collection allows the handling and use of accessions in traditional plant and molecular markers assisted programs according to the use of the species for energy purposes in the production of agrofuels. Key words: Descriptors, Genetic Variability, Jatropha curcas L. , Agrofuels.
Tri-Band Antenna for Compact Wireless Sensor Nodes Operating at 24/28/39 GHz

5G communication technology is an advanced arena of exploration that enables the communication with lower latency at ultra high speed. Planar antenna structuraes are fetching interest in 5G technology due to its multiband operating frequency and compact dimensions. In this work, tri-band, wide band antenna structure is designed. Antenna has covered 24/28/39 GHz bands with wide impedance bandwidth of 1.5/1/2.1 GHz respectively. T-shaped slot is printed in the ground plane to tune the resonance frequency. 24-GHz frequency spectrum covers the Industrial Scientific and medical band and used for short range communication; 28-GHz and 39-GHz bands are suitable for cellular and device to device communication. Antenna has good radiation efficiency of 92%, 79% and 95%, gain of 5.43 dB, 2.86 dB and 4.73 dB. Energy pattern shows the dipole like radiation characteristics at 24/28/39 GHz respectively.
Ultrafast Feature Extraction for Lithium-Ion Battery Health Assessment

Machine learning (ML) becomes an important technology in battery health assessment. The mapping from feature usually extracted from charging voltage or temperature to unmeasurable state of health (SOH) can be found by training a ML-based SOH estimator. However, the feature may become invalid when operation conditions change or be inaccessible from incomplete charging. For tackling these challenges, various entropies are investigated thoughtfully. Afterwards, spectral entropy and its variants, i.e., composite multi-scale entropy and hierarchical entropy are screened out. Ultrafast SOH feature extraction is therefore achieved where only 2 seconds of voltage data is needed. Finally, the effectiveness of the proposed method is verified by using the accelerated aging dataset from NMC batteries.
Modes of rifting in magma‐rich settings: Tectono‐magmatic evolution of Central Afar

Recent research in Afar (northern Ethiopia) has largely focused on the formation of the present‐day ocean‐continent transition at active segments (e.g., Manda Hararo). However, the Oligo‐Miocene history of extension, from the onset of rifting at ~25 Ma to the eruption of the massive Stratoïd flood basalts at ~4 Ma, remains poorly constrained. Here we present new structural data and radiometric dating from Central Afar, obtained along a zone stretching from the undeformed Oligocene Ethiopian plateau to the Manda Hararo and Tat'Ale active volcanic segments. Basaltic and rhyolitic formations were mapped in two key areas corresponding to the proximal and distal parts of a half‐rift. We present a balanced composite cross section of Central Afar, reconstructed using our new data and previously published geophysical data on the crustal structure. Our main findings are as follows: (1) Extension during the Mio‐Pliocene corresponds to a “wide rift” style of rifting. (2) The lower crust has been underplated/intruded and rethickened during rifting by magmatic injection. (3) Our restoration points to the existence of midcrustal shear zones that have helped to distribute extension in the upper crust and to localize extension at depth in a necking zone. Moreover, we suggest that there is a close relationship between the location of a shear zone and the underplated/intruded material. In magma‐rich environments such as Central Afar, breakup should be achieved once the initial continental crust has been completely replaced by the newly, magmatically accreted crust. Consequently, and particularly in Afar, crustal thickness is not necessarily indicative of breakup but instead reflects differences in tectono‐magmatic regimes.
Physiological Reactions of Plants at CO 2 Emitting Mofettes and Thermal Effect of Emerging Gas-Probable Use for Remote Sensing

Plants growing within mofette fields (CO2 emitting gas vents) show specific adaptations according to their extreme habitat. The closer they grow to the gas emitting vents and the higher the concentration of the escaping CO2 gas, the smaller the actual height and the total biomass of the plants. For some species a concomitant decrease in total chlorophyll was found. Additionally, photosynthesis as measured using CO2 gas-exchange and fluorescence clearly followed the same rule, being higher at the margins of the area and lower close to the vents. As fluorescence can be detected via remote sensors, chlorophyll fluorescence may be used to detect physiological changes from remote. In addition an abiotic thermal parameter may help in recognising mofettes in the field. As the flux of escaping CO2 gas from the soil may influence the soil temperature at and around the vent openings the application of infrared thermovision systems may be used additionally help to confirm their existence or detect unknown ones.The emerging gas dampens the temperature fluctuations of the soil imposed by the weather: it will cool the soil, when the gas is cooler than the undisturbed surface and it will warm the soil when the gas is warmer. Subtraction of multi-temporal IR images and setting temperature difference thresholds in difference images allows to disclose this effect and thus to detect the vents as well as their spatial extension.
Design of terahertz detector based on superconducting kinetic inductance

High time-frequency sampling in terahertz band has the potential to find significant discoveries, and superconducting kinetic inductance detector (KID) has the characteristics of fast response and low readout noise, which is a favorable reserve instrument for terahertz high-speed imaging detector. In this paper, we present our design of terahertz detector based on superconducting kinetic inductance effect, including six groups of comparative experiments.
Robust Optimized Pulse Patterns for Salient Permanent Magnet Synchronous Machines

Past studies formulated optimized pulse pattern (OPP) problem for permanent-magnet synchronous machines with magnetic saliency. Calculating these OPPs requires the exact inductances, whereas in practice these values are estimated via identification methods, and may vary depending on other system quantities. To this end, we formulate robust OPPs considering the minimization of the worst-case current harmonic distortion. We show that the original problem is amenable to a size reduction via the Pareto frontier of a certain nonlinear transformation of the given uncertainty set. Case studies show the improvement via reduction and robust formulation.
Road Maintenance System using YOLOV7

Road transport is one of the most important types of transportation; it plays an important part in a country's economic growth by connecting communities and businesses, as well as giving access to education, job, social, and health services. Many factors, including weather, significant traffic volumes, large numbers of heavy vehicles, shoddy building methods and materials, and poor road maintenance, can cause damage to roads. Potholes are the most bothersome road damage/defect, causing discomfort to travelers, car damage, and accidents. Even if there aren't many big trucks in the traffic stream, their influence is noticeable. Due to their physical qualities of length, size, and acceleration/deceleration, heavy trucks have an adverse physical and psychological impact on the flow of nearby traffic. Potholes are frequently associated with accidents, and such data analysis from a basic set of variables to a broader set of variables will be beneficial to the Mumbai public in a variety of ways. This paper aims to limit the occurrence of potholes by tracking the number of vehicles plying over a certain road, classifying the vehicles into light transport vehicles and heavy transport vehicles and estimating the amount of load being borne by the road due to the heavy transport vehicles. Potholes are detected using YoloV7and then generalized to the entire city using an Android app and Google Maps.
VPWM-Based DC Voltage Balancing and Fault Diagnosis Method for a Cascaded H-Bridge Multilevel Converter

In the DC voltage balancing control of a cascaded H-bridge multilevel converter, the frequently used PI controllers have complicated parameter tuning and cause the balancing ability to degrade under the switch fault condition. Regarding this, a space vector pulse width modulation-based DC voltage balancing method is presented to flexibly adjust the cell voltage levels and maintain the DC voltage balance between the power cells to the fullest degree. Furthermore, based on the voltage level changes under different switch faults, an active vector transition scheme is used to successively diagnose single or multiple open switch faults in one power cell or different power cells. The simulation and experimental results have shown the effectiveness of balancing algorithm under the normal and fault conditions, and the diagnosis validity for multiple faults.
Hybrid Modulation for Balancing Thermal Dissipation in Dual Active Bridge Converters

High thermal stress on the power semiconductor device is one of the main causes of the power electronics converter failures. Due to an imperfect design of the hardware circuit in practice and parameter variations in volume production, the problem of unbalanced thermal stress often appears on the power semiconductor devices. For addressing this issue, active thermal control is usually utilized to reduce the thermal loading of the most stressed power semiconductor device and thus improve the converter reliability. In dual active bridge (DAB) converters, the most often utilized modulation method is phase-shift PWM control. On this basis, it is possible to mitigate the thermal loading of the most stressed device by rerouting the current flowing path. In this letter, a hybrid modulation method is presented by combining the conventional extended-phase-shift (EPS) modulation and the proposed modified EPS modulation. A 1.5 kW converter prototype is established and comparative experimental results are shown to validate the effectiveness of the hybrid modulation to do thermal management.
Multisection 3-dB Ultra-Broadband Directional Coupler Composed of Coupled-Line Sections Designed in PCB and MMIC Technologies

In this paper, we present a new 3-dB ultra-broadband directional coupler with bandwidth exceeding one frequency decade. The proposed topology is composed of three coupled-line sections and two transmission lines having unequal lengths. In comparison to the known solutions, the proposed approach requires weaker coupling of coupled-line sections which makes it easier to implement in technologies where the achievement of a strong coupling is not possible due to physical constrains. The ultra-broadband directional coupler has been designed in PCB and monolithic technologies as components operating at 1 GHz and 20 GHz center frequency, respectively. The obtained measurements and electromagnetic simulations feature good electrical properties of the developed designs and confirm applicability of the proposed concept.
Mass Screening for Low Bone Density Using Basic Check-Up Items

Given the severe impact of low bone density (LBD) on public health, and to avoid the potential damage of X-rays-based bone density measurements, this study aimed to develop a scoring system for the mass screening for LBD in women aged 50 years or older, using the basic body check-up items as variables. Five variables, including age, body mass index (BMI), systolic blood pressure (SBP), blood glucose level, and total cholesterol level (TCL), were obtained from medical check-up records of 1525 women aged 50 years or older who had done body examination between 2011 and 2018, and were used to construct a scoring system for the screening for LBD. Multivariate logistic regression was applied to investigate the putative association of the five variables with LBD. A scoring system was derived from the regression model to discriminate persons at risk of LBD from low-risk persons. An artificial neural network (ANN) model was also applied to the same task. Precision, recall, $F1$ -score, and c-statistic were adopted as evaluation metrics. Age, BMI, SBP, glucose, and TCL were significantly associated with the risk of LBD. Precision, recall, c-statistic, and $F1$ -score of the proposed scoring system were 0.66, 0.83, 0.73, and 0.74, respectively. ANNs achieved better performances in terms of all measurements. This study demonstrates the feasibility of using routine body check-up items to estimate LBD risk. Different from X-rays-based instruments, the scoring system derived from this study may serve as a postcheck-up mass screening tool to enable health practitioners to identify individuals at a risk of LBD efficiently and nonintrusively.
IoMT with Cloud-Based BC Disease Diagnosis Healthcare Framework Using Deep Convolutional Neural Network with Long Short-Term Memory

Sensing devices on the human chest are linked through the Internet of Medical Things (IoMT) to remotely monitor health data on multiple dimensions, including physiological and behavioural impulses. IoMT devices in clinical use cloud computing to generate enormous volumes of data supplemented by portable equipment's insufficient memory and computational power. An IoMT-based healthcare diagnosis model will be developed using a deep learning methodology. It is the driving force behind the development of a new IoMT-based medical system for the early detection of breast cancer (BC) using a Deep Convolutional Neural Network with Long Short-Term Memory (DeepCNN-LSTM). The suggested DeepCNN-LSTM model yielded significant gains in accuracy, recall, F-measure, and recall, as demonstrated in the extensive experimental investigation.
Characterizing Bias in Word Embeddings Towards Analyzing Gender Associations in Philippine Texts

The steady increase in computational gender bias research has been mostly done on languages for which reliable NLP packages are readily available—such as English, Chinese, and Spanish. This study expands on this area of research by using word embedding bias analysis methods in the Philippine context. To this end, Philippine media textual corpora consisting of 380 million English words and 921 million Filipino words were compiled and used to train FastText embeddings. These embeddings were then subjected to validation and to the Word Embedding Association Test (WEAT) to characterize bias in the embeddings and in the texts they were trained in. Results show that Filipino texts are associated with the heterosexual male by default, but strongest biases relate to the female and the non-heterosexual. Meanwhile, media texts written in English generally have more balanced gender associations compared to texts written in Filipino. Furthermore, the Filipino corpus links action more to the male and objects and social roles to the female. On the other hand, implicitly gendered words in English texts are mostly nouns. These results contribute to demonstrations of how WEAT can be applied in low-resource languages, such as Filipino.
Fast Switching of GaN Transistors using a Boosted Gate Voltage

This paper presents a gate-driving strategy for achieving fast switching edges from GaN HEMT transistors. Switching losses can be reduced by using zero-ohms external gate resistance, whilst further reductions are achieved by overcoming the limitations of internal gate resistance with gate voltage boosting during key regions of the switching transition.
Fruit harvesting drone based on YOLOv4-tiny algorithm

This study conducted experiments and tests to validate the performance of a mango picking drone. The results demonstrated that the utilization of the YOLOv4-tiny object detection model accurately detects and locates ripe fruits within orchards. The accelerated inference computation provided by TensorRT enhances the real-time capability, enabling unmanned aerial vehicles (UAV) to make prompt harvesting decisions. The three-point localization method calculates distances to ensure precise measurements between the UAV and the target fruits, facilitating more precise and efficient harvesting actions. Additionally, the implementation of Kalman filters in distance detection further improves the stability and accuracy of the system, effectively mitigating the impact of sensor noise and uncertainty.
Algorithm Used in Video Event Recognition & Classification with Hierarchical Modeling

Video media data is easier to transmit, produce, and distribute over the world. There has been a lot of information lost since advancements in video analysis and processing have lagged behind the data's exponential increase. The necessity for effective recovery of video data content is, thus, pressing. Because there are so many potential classifications, identifying and labeling occurrences may be difficult. In order to help human operators keep an eye on everything, researchers have built algorithms to better understand the semantic content of videos so they can better spot potential threats. The Yolo method for object detection and the hierarchical context model for event recognition are both explored in this work using a common surveillance video dataset. We hope that by employing these methods, we can enhance the efficiency with which video data material is retrieved by better identifying and classifying occurrences.
Robotic Grasp Detection Method Based on Lightweight Feature Fusion Convolutional Neural Network

To solve the problems such as the difficulty in grasping detection and inaccurate positioning of manipulators in an unstructured environment, this paper proposes a manipulator grasping detection method based on a lightweight feature fusion convolution neural network, which is used to predict the grasping position and orientation of target objects. This method takes the three-channel RGB image and single-channel depth map as the network input, constructs the one-to-one mapping relationship between the object and the pixel, effectively avoids time-consuming operations such as discrete sampling and candidate sorting, and adopts the multi-constraint prediction result evaluation method to improve the accuracy of capture detection. The experimental results show that the method in this paper can accurately predict the grasping position and posture in an unstructured environment. The success rate of grasping is 86.7%, and the processing speed of a single image is about 18ms. The balance between precision and speed is achieved, which has reference value for solving the problem of difficult robot autonomous grasping in the unstructured environment.
Mamíferos de los bosques de roble

The high rate of deforestation over the Andean forests has generated a large proportion of fragmented landscapes in the country. The distribution of Oak groves in the country was determined based on ecosystem maps. Charala and Encino Oak groves patches are the largest ones found at the east Andes and like others, due to the unfair use of this resources, has suffered a fragmentation process. Fifty five species of mammals included in 10 orders and 14 families were found in these forests. Chiroptera and Rodentia were the most representative groups. Anthropic processes had produced a 68.1% loss of the habitat, and constitutes the main threat for these forests. The size of the patches were evaluated for three mammal species categories. The patches’ area are not favorable for large-size species, intermediately to favorable to medium-size species and are favorable for small-size species. It is suggested that patches’ area effect over mammal species could relate to the decrease of species richness and of each fragment area. There are good connections between patches (only five isolated), allowing the presence of a greater species diversity. There is also a bleak plateau zone between connected patches increasing their connectivity and offering different habitats and resources for some mammal species.
Review of Charging and Discharging Strategies for EVs Based on DR

Reasonable control for charging and discharging electric vehicles (EVs) can improve the safety and economy of the electric power system. In addition, the charging cost for EV users can be significantly reduced. Demand response (DR) strategy is one of the key technologies for orderly charging and discharging EVs, which can address many problems caused by large-scale EVs interfacing with the utility and improve renewable energy utilization. A literature review on charging and discharging strategies for EVs based on DR is presented. Definition of DR is illustrated. The charging and discharging strategies of EVs based on DR is elaborated, which can be divided into price-based and incentive-based demand response strategies. Finally, comparison among different DR strategies for charging and discharging EVs is given.