Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Frequency Three-Level Gate Driver for GaN HEMT Bridge Crosstalk Suppression

In order to suppress the crosstalk in gallium nitride (GaN)-based bridge configuration, this article proposes a novel high-frequency three-level gate driver (HFTGD), and the crosstalk suppression with switching frequency up to 5 MHz can be achieved. The capacitor–diode circuit generates a negative voltage to prevent the false turn-on of the device under the impact of positive crosstalk. Pulsewidth modulation generator works together with comparator circuit to rapidly clamp the negative voltage to zero level, thereby suppressing the negative crosstalk. In particular, HFTGD utilizes only two passive components to produce the negative voltage and also has the advantages of good high-frequency stability and adjustable negative voltage depth. The control signal of the zero-level clamp circuit is digitally implemented, resulting in a shorter clamp time and minimal impact on the switching speed of the GaN high electron mobility transistor (HEMT). In addition, only a single power supply and a single control signal are required for HFTGD, which reduces the cost and control complexity. The effectiveness of HFTGD for positive and negative crosstalk suppression is demonstrated in a double-pulse test based on GS66504B.
Relationships between Radarsat SAR data and surface moisture content of agricultural organic soils

A time series of 10 C-band fine beam images were acquired over the West Monteregie region (Quebec) in 1999 to examine the potential of Radarsat to detect surface moisture content of cultivated organic soils. Soil moisture data were simultaneously collected from 10 fields cropped to carrots, potatoes, lettuces, and onions. The analysis was conducted at two levels: (i) a temporal scale (daily average value); and (ii) a spatial scale (10 fields). At the first level, we found that the average radar backscatter ) was quite well correlated to the seasonal change of the average moisture content (R 2=0.75), although the dates with a strong vegetation growth decreased the relationship. Comparison with data acquired for mineral soils indicates that organic soils exhibit a lower backscatter, especially at lower moisture content levels, suggesting that soil types should be considered prior to map soil moisture changes. At the field scale, the relationship was much lower (R 2=0.44). The quality of the relationship was proportional to vegetation cover and appeared to be crop-dependent. Best results were obtained with onions and carrots. Considering only bare organic soils, the result was satisfactory (R 2=0.79) and similar to those published on mineral soils.
Low power 3 degree-of-freedom Lorentz force MEMS mirror for optical applications

A low power three-degree-of-freedom scanning micro-mirror is presented. The 2 × 2 mm mirror is a gimbaless structure, directly supported by single crystal micro-springs. It is actuated by Lorentz force and is able to tilt about two axes and has linear motion in a third axis. The transient and frequency responses of the micro-mirror are analyzed. The Lagrange’s equations of motions, describing the dynamic behavior of the system are presented, and show a good agreement with the experimental results. The fabricated MEMS mirror demonstrated a tilt angle of 22. 8º at 247.5 Hz about y -axis, and 13. 3º at 292.7 Hz about x -axis, in a 0.1 T magnetic field and 20 mA current on the mirror. Power consumption was 2.6 mW of power in tilting motions in resonant operation. With a total dc drive current of 110 mA, 232 µ m linear motion was achieved.
Understanding Customer Intention to Use E-Payment for Online Shopping

The emergence of e-commerce has sparked a huge change in consumer behavior, transforming how individuals purchase and conduct transactions. E-payment has become a pillar of this digital transition. However, some customers are still hesitant to adopt e-payment despite its advantages. This paper aims to unravel the factors influencing customer intentions to use e-payment for online shopping. Based on a review of existing literature on the Technology Acceptance Model (TAM Model) and additional external factors, a proposed model was developed to test consumer's intention to use e-payment for online shopping. The result of this study would be useful to understand consumers’ behaviors in employing electronic transactions when making payments for online purchases. This paper also provides valuable insights for e-payment service providers and online retailers on how to promote sustainable online shopping in the future.
Effect of Internet-based versus face-to-face Cognitive Behavioral Therapy for Dependent Personality Disorder: A randomized clinical trial

Background: Dependent Personality Disorder (DPD) is the most prevalent personality disorder, which is associated with several negative consequences. Objectives: The current study aimed to investigate the effect of ICBT on DPD and compare it with face-to-face CBT in a sample of DPD patients. Method: The participants were adults with DPD recruited from various counseling and psychological clinics in Tehran, Iran. They were randomly assigned to receive face-to-face CBT ( n = 20) or ICBT ( n = 20). Depression-Anxiety-Stress Scale-21 (DASS-21) and Interpersonal Problems Questionnaire (IIP-32) were completed at pre-and post-treatment. Result: Analysis of Covariance (ANCOVA) showed that the face-to-face CBT group had significantly lower interpersonal problem scores than the ICBT group (F=23.81, p<0.05). However, no significant difference was observed between the face-to-face CBT and ICBT groups in anxiety (F=3.68, p<0.05) and depression (F=1.70, p<0.05). Conclusion: The findings support the effects of both ICBT and face-to-face CBT in treating patients with DPD. While both ICBT and face-to-face CBT demonstrated favorable outcomes, our study suggests an advantage of face-to-face CBT in addressing interpersonal problems among these patients. Further research is needed to investigate the long-term effects of ICBT to enhance traditional approaches for DPD.
MICROPLASTIC DISTRIBUTION IN RIVER SEDIMENT: A CASE STUDY AT U-TAPHAO, SOUTHERN THAILAND

Microplastic is everywhere in the aquatic ecosystem including river sediment. In this study, seven sediment sampling stations were selected along the basin, from upstream to downstream (7 stations) of the U-Taphao river for 4 months (February, April, June, and August) in 2022. The total number of microplastic particles found was 1,470 pieces, consisting of 814 fibers (55.37%) and 656 fragment particles (44.63%) from the four sampling months. Station 5 showed the highest concentration of microplastics, followed by station 1. Seven polymers were found throughout the study: PE, PP, PET, rayon, copolymer, PA, and PVA. Polymer analysis from microplastic samples by FTIR polymer analysis showed significant differences in the frequency of polymer types found in each month (p < 0.001).
BUSINESS MODEL OF NON-GOVERNMENTAL ORGANIZATIONS FOR THE DEVELOPMENT OF THE EDUCATION SECTOR IN MOZAMBIQUE: A CONSTRUCTIVIST APPROACH

This research aims to determine the business model of non-governmental development organizations (NGDOs) of the education sector in Mozambique. The work adopts a constructivist methodological approach in two stages: (i) inductive, centered on the construction of a proposal of social business model Canvas from the observation of 15 NGDOs operating in the education sector in the country, based on a theoretical framework solidly supported in the literature; (ii) deductive, testing the proposed model using a panel technique that allowed the refinement and reconstruction of the proposal initially formulated. The paper contributes to the literature on social business models by making a pioneering validation of knowledge built in this area and in a field of application not yet explored. The contribution to international relations lies at the level of the data collected and information produced on international cooperation in Mozambique.
AmpelogrAphic chArActerizA tion of Some grApe cultiv ArS (Vitis Vinifera l.) grown in South-weStern region of turkey

Ten grapevine cultivars (Vitis vinifera l.) were characterized according to the international grape descriptor lists. The morphology of various vine organs of some autochthonous and hybrid cultivars was described at several phenological stages, and pomologycal characteristics were determined during growth cycle. With respect to the ampelographic characters, great differences were observed among the studied cultivars. The relationships between the cultivars were further assessed via UPGMA dendrogram analysis. Certain characteristics were anticipated to play particular role in the constitution of the ampelographic dendrogram. Particular data observed in this study on some autochthonous grapevine cultivars would help to prevent disappearing local cultivars and to preserve germplasm collection.
Test particle motion around brany black hole immersed in an Test particle motion around brany black hole immersed in an external asymptotically uniform magnetic field external asymptotically uniform magnetic field

We investigate circular motion of charged and neutral particles around non-rotating black hole immersed in an external asymptotically uniform magnetic ﬁeld. The eﬀects of braneworlds on innermost circular stable orbits have been considered. Shown that innermost circular orbits (ISCO) decreases in de-creasence of both brane charge and particle charge. Moreover, we have investigated energy extraction from black holes in braneworld through collision of two particles. Obtained that the presence of the brane charge parameter causes to decrease of the value of center of mass energy of colliding two charged particles, it means the brane charge acts as an additional gravity.
Atomic and Close-to-Atomic Scale Manufacturing: Status and Challenges

Next-generation lithography techniques such as Extreme Ultraviolet Lithography have started to reach their physical limits and will not be able to meet the requirements of future Post-Moore Era Integrated Circuit chips that will be based on quantum, photonic, and DNA computing. These future chips and the next generation of quantum products will require sub-10nm and even atomic-scale functional features. Promising candidates for atomic and close-to-atomic scale manufacturing include well-established tip-based techniques such as Scanning Tunnelling Microscopy (STM) and Atomic Force Microscopy (AFM), however, they suffer from severely low throughput, although parallel tips have been suggested to increase the throughput. The integration of these techniques with others such as AFM in Scanning Electron Microscopy has created new hybrid techniques that have greatly enhanced the capabilities of the standalone process. On the other hand, higher throughput techniques like atomic layer etching (ALE) suffer from poor process control and defects despite being promising candidates due to the self-limiting nature of the processes. Studies into laser processing techniques are being investigated to test the feasibility of laser beam-based atomic scale precision manufacturing. Furthermore, the recent progress in quantum simulations has promoted the development of the optical tweezer towards atomic scale manufacturing.
Evolutionary Algorithm-Based Optimal Wiener-Adaptive Filter Design: An Application on EEG Noise Mitigation

Electroencephalogram (EEG) signals are well-known nonstationary brain signals of lower strength. Due to their small amplitude, they attract other biomedical artifacts from the surroundings. This research mainly focuses on removing artifacts from the EEG. The presented work uses the recent metaheuristics to efficiently design a Wiener-based adaptive noise mitigation structure (WANMS). Many powerful evolutionary optimization algorithms (EOAs), such as particle swarm optimization algorithm (PSOA), moth flame optimization algorithm (MFOA), symbiotic organism search optimization algorithm (SOSOA), and the student psychology-based optimization algorithm (SPBOA), have been applied for the optimal design of WANMS. The proposed structure is analyzed with various noisy signals, such as electrooculogram (EOG) and electrocardiogram (ECG) with white Gaussian noise (WGN). Among all the metaheuristic algorithms applied to WANMS, the SPBOA-based WANMS has performed better with improved signal-to-noise-ratio (SNR) and minimal mean-squared-error (MSE) values. The results obtained through the proposed technique ensure its supremacy compared to other state-of-the-art techniques. Hence, the proposed method can be utilized for EEG signal enhancement.
EFFECT OF SUMMER IRRIGATION ON POPULATION DYNAMICS AND REPRODUCTIVE STATUS OF ADULT SITONA LEPIDUS IN THE WAIKATO

Populations of adult Sitona lepidus were sampled from five irrigated and five non-irrigated dairy pastures in the Waikato from November 1998 to April 1999. Under irrigated conditions, the presence of a second peak of adults with significantly more tenerals present provides evidence of a second generation over the summer months. The importance of the role of moisture in the development of S. lepidus was further demonstrated by the differences in the development of eggs in the calyces of sexually mature females. Egg development was significantly greater in females in irrigated pastures. Onset of rain after a dry period resulted in rapid egg development in females in non-irrigated pastures. These findings explain why the summer dry La Nina weather conditions in the Waikato led to farmer observations of a reduced impact of S. lepidus on clovers in Waikato pastures in 1998 and 1999.
Multiobjective Modular Strategic Planning Framework for Low-Altitude Missions Within the Urban Air Mobility Ecosystem

This article introduces a multimetric multiconstraint strategic path planning framework applicable to unstructured urban airspace. The planner is based on a modular and scalable approach to handle several information sources and aspects characterizing urban flight scenarios, such as risk and weather maps, landing site locations, navigation requirements, and mobile and fixed obstacle characteristics. This information is coupled with dynamic constraints and unmanned aerial vehicle specifications to derive a flyable and safe path connecting a start position and a destination. Strategies for data gathering and synthesis, used to keep a reduced computational burden, are described along with the path planner algorithm. The latter consists in three steps specifically developed to handle both static and time-varying information. A multi-objective cost function with variable weighting coefficients has been implemented so that the most relevant factors for the considered applications can be selected in an adaptive fashion. The performance of the developed algorithms is tested by investigating the planner behavior when changing its inputs as well as the cost function weighting coefficients, demonstrating the ability of the planner in returning an efficient and safe trajectory.
Real‐time capture of seismic waves using high‐rate multi‐GNSS observations: Application to the 2015 Mw 7.8 Nepal earthquake

The variometric approach is investigated to measure real‐time seismic waves induced by the 2015 Mw 7.8 Nepal earthquake with high‐rate multi‐GNSS observations, especially with the contribution of newly available BDS. The velocity estimation using GPS + BDS shows an additional improvement of around 20% with respect to GPS‐only solutions. We also reconstruct displacements by integrating GNSS‐derived velocities after a linear trend removal (IGV). The displacement waveforms with accuracy of better than 5 cm are derived when postprocessed GPS precise point positioning results are used as ground truth, even if those stations have strong ground motions and static offsets of up to 1–2 m. GNSS‐derived velocity and displacement waveforms with the variometric approach are in good agreement with results from strong motion data. We therefore conclude that it is feasible to capture real‐time seismic waves with multi‐GNSS observations using the IGV‐enhanced variometric approach, which has critical implications for earthquake early warning, tsunami forecasting, and rapid hazard assessment.
Withdrawal of Fluid from a Poroelastic Layer

This paper analyzes the problem of a sink segment extracting fluid from a poroelastic layer bounded by two semi-infinite impermeable elastic strata. This problem can be reduced to one of the classic elasticity theory, with the gradient of the pore pressure field in the reservoir acting as a pseudo-body force, and the jumps of pore pressure at the interfaces between the reservoir and the adjacent layers inducing pseudo-tractions. The solution of this elastic problem is obtained using integral transforms. The asymptotic behaviors are then emphasized and conceptual models are introduced that capture the salient features of the small and large time asymptotes.
Wave dissipation over macro-tidal saltmarshes: Effects of marsh edge typology and vegetation change

ABSTRACT To achieve sustainable coastal management and planning, the interaction between fine-grained - in particular, vegetated - intertidal environments and incoming waves needs to be better understood. Previous studies have established that wave attenuation over saltmarshes can be significantly greater than over unvegetated intertidal surfaces. However, detailed, quantitative information on the effect of marsh elevation in the tidal frame, marsh width, seaward marsh edge configuration (e.g. cliffed versus ramped marshes), seasonal changes in marsh surface roughness (e.g. creek density, vegetation composition) and incident wave conditions, however, has been lacking. Based on a 10-month-long wave/tide dataset from two sites on the Dengie marshes, eastern England, this study addresses the effect of (i) marsh edge topography; (ii) marsh width; (iii) inundation depths; and (iv) seasonal changes in marsh surface vegetation cover on wave height and wave energy dissipation. Directional waves and water levels were recorded at 21 locations across both shallow-sloping and cliffed (cliff height of ca. 1.5m) intertidal profiles. In addition, changes in marsh surface vegetation cover and composition were recorded on a seasonal basis. Wave height attenuation over 310m of the shallow-sloping profile averaged 92 % over the monitoring period. Further analysis shows that the most rapid reduction in wave heights occurs over the most seaward 10 meters of permanent saltmarsh vegetation, where wave height attenuation averaged 2.1% and 1.1% per meter at the shallow-sloping and cliffed site respectively. Across the mudflat and the saltmarsh as a whole, wave height dissipation rates were significantly lower with an average of 0.1% and 0.5% per meter respectively. The presence of a saltmarsh cliff increased average wave heights by up to 0.5% per meter. Observed wave height attenuation showed a seasonal pattern at both sites (average wave energy attenuation near the marsh edge was highest in September – November and lowest in March – July) and appears to be linked to the cycle of seasonal vegetation growth. The study provides criteria for the assessment of the wave dissipation potential of marshes characterised by different widths, edge configurations and slopes, variability of water depths, and seasonal variations in vegetation cover/density.
Enhancing Apple Leaf Diagnosis Through Deep Learning Techniques

Apple is a type of fruit, which is very nutrient-rich and offers a variety of health advantages. They are also rich in antioxidants and fiber. Consuming them is associated with a reduced chance of developing many chronic conditions, including diabetes, a condition coronary heart disease, as well as cancer. Aside from aiding in weight loss, apples may also enhance digestive and cognitive well-being. Apple Leaf, on the other hand, has a lot of fiber and can be helpful when you're sick to increase your appetite. Apple leaf is thought to have anti-inflammatory qualities and is high in antioxidants. The methods of detecting apple leaf disease are presented in this research. It is dependent on a study that develops a CNN (Convolutional Neural Network) to categorize a completely distinct variety of apple leaf diseases. If a diseased leaf is discovered, this program will detect the type of the disease. The system in question uses cutting-edge deep learning techniques as well as transfer learning and convolutional neural networks. The outcome demonstrates the CNN model that has been retrained performs better when detecting the disease of the leaf. The conventional methods to classify leaf disease have had an accuracy ranging around 95%. Hence, to classify the leaf disease more accurately and preferably, Convolutional neural networks-based deep learning for image recognition is proposed. This is a lightweight PlantifyDr Dataset on Kaggle that serves to identify leaf photos using the DNN. With the help of VGG16, the accuracy achieved in this work is about 97.50% on the Apple Leaf Dataset. It presents good accuracy in classifying the type of disease when it is compared with many other approaches found in the literature
Enhancing Singing Performances: Novel Method for Automatic Vocal Pitch Correction

Our research presents a novel method for automatically correcting the pitch of human vocals using a data-driven approach. Unlike existing commercial systems, which often shift vocal notes to predefined pitches or the closest pitch in a twelve-tone scale, here pitch is treated as a continuous value. This allows for greater flexibility in singing performances, including improvisation and harmonization. The system is developed and a neural network is trained by using a dataset of 4,702 karaoke from Sumel Inc. which had a great intonation. This model is trained in such a way that it learns to make corrections using the incorrect intonations also for the different pitch variations. The flaws in the previous work was monotonous pitch consideration within a controlled environment. To overcome this our system was proposed, the network architecture also integrates the convolutional layers which is then processed by gated recurrent units, which have shown promising performance in real-world score-free singing pitch correction, also known as auto tuning. The anticipated convolutional neural network accurateness is 93 % and error rate 7%.
An Intelligent Method for Predicting Skin Disease Using OM-Extreme Learning Machine

In terms of prevalence, skin disorders rank towards the top. Various organisms, including fungi, bacteria, allergens, viruses, and so on, can trigger skin illnesses. Improved speed and accuracy in the diagnosis of skin diseases has resulted from the development of lasers and Photonics-based medical technology. However, there is now a severe lack of affordable options for such diagnostics. Therefore, at the outset, image processing techniques aid in constructing an automated screening system for dermatology. To aid in the categorization of skin disorders, feature extraction is essential. Several methods exist where computer vision can aid in the diagnosis of skin disorders. Skin infections are frequent in Saudi Arabia because of the country's scorching, desert climate. This proposed method adds to the literature on the early diagnosis of skin diseases. To help diagnose skin conditions, to recommended using image processing. This technique uses digital images of the affected skin to determine the disease kind through the application of image analysis. The method to offer straightforward, quick, and can be done with just a camera and a computer.Preprocessing, feature extraction for segmentation, and model performance evaluation make up the suggested method. Preprocessing makes use of a histogram and an RGB to Grayscale conversion. Segmentation is performed with the help of adaptive thresholding. The GLCM, IQA, and complete reference methods are all utilized for feature extraction. Finally, OM-ELM is utilized to assess the model's performance. Comparing the suggested method to ELM and AE-ELM, it fares quite well.