Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep‐tow and sea beam studies of dueling propagating ridges on the East Pacific Rise near 20°40′S

Nontransform ridge axis discontinuities, such as propagating rifts, overlapping spreading centers (OSCs) and saddle points, occur frequently along the mid-ocean ridge system. We used Sea Beam and deep-tow to study an example of a relatively large nontransform offset near 20°40′S on the East Pacific Rise (EPR). The two spreading center tips are 15 km apart and separated by a diffuse shear zone. A broad V-shaped wake of disturbed topography and magnetics occurs on the flanks of the EPR near the offset. On a fine scale, the offset is tectonically much more complex than either the typical OSC or propagating rift structures for two reasons. First, it is a “dueling propagator” in which structures produced by successive short-term episodes of northward and southward propagation have overprinted each other, with migration toward the south prevailing over the long term. Second, its offset distance is such that its tectonic behavior may be transitional from that of a propagating rift to that of an OSC system. The offset's rate of migration to the south has been ∼20 mm/yr over the last 2 m.y. , far less than total opening rate of 162 mm/yr. Distinct propagation events may have been much more rapid, however, surging north and south at rates of 160–230 mm/yr. In the process, several fossil spreading ridges 30–50 km long have been abandoned and rafted to the side. We propose a fine scale tectonic evolution in which a propagating spreading center either curves inside or runs outside of a soon to be abandoned, propagating rift tip. We call this process “self-decapitation. ” Dueling propagation at 20°40′S may be caused by competing magmatic sources at shallow points along the EPR: one to the north near 20°S and one to the south near 21°30′S. The 20°40′S dueling propagator occurs at a long-wavelength low in the axial depth profile between these competing sources. In general, the division of spreading centers into segments separated by ridge axis discontinuities occurs at low points along the axis and may be caused by the pattern of upper mantle convection and the factors governing flow of magma along the strike of the ridge.
Calculation Analysis and Improvement Measures of Lightning Strike Trip Rate for Collection Lines of Wind Farm in High Altitude Mountain Area

At present, lightning tripping has become a serious threat to the safe operation of high-altitude mountain wind farms. This paper completes the risk assessment and analysis of the winding trip of the whole collection line by establishing a three-dimensional electrical geometric model that can consider the influencing factors such as complex terrain, tower structure and line sag in mountainous areas; Then, the electromagnetic transient simulation software is used to model and calculate the counterattack trip risk of collector line; Finally, the lightning trip risk of the whole collector line is evaluated and the improvement measures are put forward. The calculation results show that after correcting the insulator flashover voltage in high altitude areas, the calculated direct lightning trip rate of the collector line of the wind farm is 5.4061 times /100km.year, which is basically consistent with the 5.0158 times /100km.year converted from the statistical data of lightning trip fault information of the wind farm; The tripping rate of wind power collection line in mountainous area is obviously affected by the ground inclination angle. The valley terrain within the span will make the towers on both sides prone to lightning flashover; The lightning protection effect of more than 94.74% of the poles and towers of the collection line is not ideal. Among them, the lightning flashover risk of the poles and towers caused by lightning counterattacks is the highest. Counterattack tripping has become the biggest threat to the safe operation of the collection line of a wind farm in mountainous areas; It is difficult to effectively reduce the counterattack trip rate by reducing the tower grounding resistance and increasing the insulation level. A lightning arrester can be selected as the main lightning protection measure.
A Hand-Eye Calibration Method for Skin in Situ Printing Based on Feature Point Extraction by Point Cloud Segmentation

Skin burn treatment is an urgent problem in the clinic. Skin in situ printing technology was expected to treat skin burns with accurate in situ construction of skin tissue. The first step in the skin in situ printing is to calibrate the hand-eye of the printhead and scanner. However, the accuracy of the existing industrial hand-eye calibration methods is difficult to be applied to the treatment of burned skin. According to the high precision requirement of skin in situ printing, a hand-eye calibration method based on point cloud segmentation feature point extraction is proposed in this paper. Firstly, the statistical filtering method, cluster segmentation method, and RANSAC method are used to remove noise points, plane fitting, and feature point extraction for the scanned point clouds. Then, the feature points processed by the point cloud were matched with the feature points in the coordinate system of the three-dimensional measurement platform and the rotation translation matrix between the coordinate system of the three-dimensional printing platform. The coordinate system of the binocular laser three-dimensional scanner was obtained by the singular value decomposition method. Finally, the wrong cloud data is removed in turn. The hand-eye calibration experimental results show that compared with the method without feature point extraction by point cloud segmentation, the proposed method reduces the average distance and maximum error of the XYZ axis by 78.477% and 82.301%, respectively. Compared with other hand-eye calibration methods, the average distance error is increased from 0.060mm to 0.040mm. It is proved that the hand-eye calibration based on feature point extraction by point cloud segmentation can meet the needs of skin in situ printing, which makes preparation for subsequent skin in situ printing experiments and makes it possible to treat skin burns in situ clinically.
An efficient drug synergy model based on deep learning for malignant diseases

Malignant diseases are the leading cause of mortality worldwide. Drug resistance is one of the significant reasons for mortality in malignant diseases. Chemical treatment involves the administration of many drugs, either all at once or in a specific order, intending to minimize the size of a tumor or eliminate it. With the combination of drugs or drug synergy, the therapeutic response of drugs is greater than the expected additive monotherapy. The identification of synergistic drugs with clinical trials is a time-consuming and expensive task. Due to a lack of available resources, it is impracticable to screen all of the essential synergistic drugs. The introduction of artificial intelligence (AI) has not only altered our perspective but also presented new approaches to old challenges. One area where significant progress has been made because to AI advancements is the prediction of drug synergy based on deep learning in the treatment of malignant diseases. In this paper, we provide a unique deep learning-based drug synergy model for malignant diseases. The proposed model is used to determine the most successful synergistic drug combinations, and it may also increase the practicality of exploring new drug combinations.
Commercial Indian Bamboo Species Classification on matK DNA Barcode Sequences using Machine Learning Techniques with K-mer

Bamboo, a grass, belongs to the Poaceae family, with 1642 species from 116 genera worldwide. It has exceptional physical, chemical, and mechanical qualities, which allow it to be employed in over a thousand different ways and contribute to a trade value of USD 2.76 billion. Bamboo is grown using rhizomes, tissue culture, or short branch cuttings without any other checks resulting in incorrect species identification and categorisation. Therefore, the classification or identification of these bamboo use its DNA barcode sequences with a K-mer based method, and machine learning (ML) is the most excellent strategy for resolving issues with the conventional or traditional categorisation of the species. A DNA barcode is a brief genetic signature that helps identify the species to which an organism belongs. It is possible to extract a useful feature from genome sequences using K-mer based approaches, which may then be used to increase comparison accuracy. In this research, we evaluate the classification performance of four supervised ML models on the DNA-barcode sequence of six Indian commercial bamboo species with a different K-mer combination. For this classification, we choose matK barcode region and supervised ML models such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). The results analysis of these models on the matK DNA sequence with different K-mers demonstrates that the classification capabilities of the GBM approaches are quite promising, and it has an accuracy of 95.3% on average.
The Value of Ambiguity Quantification in Distributionally Robust Economic Dispatch Models for the Wind-Penetrated Power System

When developing operation schedules for power systems with high penetration of renewable energy sources (RESs), it is critical to accurately quantify the inherent uncertainty of RESs to hedge against suboptimal strategies and potential security issues. In this paper, a data-driven two-stage distributionally robust economic dispatch (TS-DRED) model based on an L1-norm-based ambiguity set is first developed, where the misspecification of uncertain wind generation is incorporated. Then, by exploring the explicit relationship between the robustness level of the TS-DRED model and the historical wind data, two effective metrics are constructed to quantitatively show the value of quantifying the ambiguity in the DRO-based framework, compared to its stochastic and robust counterparts. The case study validates the feasibility of the proposed metrics, and demonstrates that overlooking the ambiguity in wind uncertainty may lead to suboptimal operation strategies.
Resonance Damping in Active Power Decoupled Single-Phase Grid-tied Differential Buck Inverter

The single-phase differential buck inverter allows active decoupling of the double-line frequency ripple power without employing the additional switching devices. The ripple energy storage is realized by two filter capacitors connected between the ac side terminals and the negative dc bus. The grid current control is attained by controlling the differential mode voltage of the ac side capacitors. The active power decoupling is achieved by controlling the common mode voltage such that the ripple power on the dc side is eliminated, allowing the use of a small and reliable film capacitor on the dc side sufficient to avoid the switching frequency current from the dc source. The present work comprehensively details the plant modeling and control design for the differential buck grid-tied inverter. It is analytically shown that the system’s differential mode and common mode plants are highly under damped due to the presence of LCL and LC resonances, respectively. Subsequently, the design of active damping loops is presented and shown to improve system stability. Further, the controller designs for grid current and active power decoupling are discussed. Finally, simulation results are presented to validate the analytical inferences derived in the paper.
Using Open Technology to Bring Computational Thinking Activities to the Outdoors

Many educational resources are available that teach children computational thinking and visual programming. As part of this initiative, we develop computational puzzles from well-known computational thinking activities. Inspired by the idea of geocaching, we made our puzzles accessible and fun for the whole family by embedding them into a scavenger hunt. We describe our approach that frames computational puzzles as an outdoor family activity. Our project was launched in 2021 and has received informal positive feedback.
Anti-Deflection and Interoperability Study of WPT Based on Distributed Transmitting Coils

The offset of the relative position between the receiver and transmitter in the radio energy transmission system will affect the system transmission efficiency to a certain extent. In this paper, the full-range offset resistance and interoperability of the coupling mechanism are investigated in two aspects. A proposed many-to-one coupling mode is proposed, the transmit coil type is a planar distributed transmit unit coil combination, and the open mode of the distributed transmit coil is studied for different receive coil types, axial offset conditions and rotational offset conditions of the position mode to achieve interoperability and wide-range all-directional offset resistance of the receive coil. To improve the degree of freedom of the distributed transmit coil, a distributed series control logic circuit is proposed, and the resonance parameters of the transmit coil are designed for the series connection condition to reduce the effect of cross-coupling. The search strategy for receiving coil position detection is proposed by combining the magnetic field characteristics of the distributed transmit coil and the pickup voltage detection method based on the LCC-S resonant topology. An experimental setup is built to verify the feasibility of achieving anti-deflection and interoperability.
New Modified 4:2 Approximate Compressors for Low-Power Applications

Energy efficiency is an important design concern in most digital and embedded systems. In particular, hardware projects often require high-speed and low-power multipliers. Compressors usually implement the partial products reduction of a multiplier to reduce the critical path and the power consumption. An emerging approach that can be applied in error-tolerant applications to improve the overall performance is the approximate computing. In this paper, two power-efficient 4:2 approximate compressors are proposed and evaluated at the transistor level in a 7 nm FinFET technology. The proposed circuits show improvements in power and delay compared to state-of-the-art approximate compressors, reducing the PDP on average by 66.7% and 79.1%, respectively. Furthermore, operating at near-threshold voltage, the energy efficiency can be improved by 43% and 52% compared to nominal operation.
QLEN: A Load Distribution Algorithm to Improve QoS Factors Among Fog Devices

This article entails the proposition of a heuristic multi-objective algorithm for load distribution among fog devices. The decision for routing is made based on a weighted heuristic. The proposed algorithm, QLEN aims to improve three quality of service factors, namely L (load balancing efficiency), E (energy consumption) and N (network usage); hence the name QLEN. The importance of these parameters can be varied according to the application. This is done by adjusting the weights to suit the needs of the incoming tasks. For the default use case, the weights are tuned for the optimization of all three of the parameters. These optimal weights are calculated using stochastic gradient descent. QLEN is compared against fundamental load balancing algorithms such as Round Robin, Throttled Round Robin and Priority Based Load Balancing algorithm. The environment simulates a health monitoring application, using the iFogSim simulator. The results show that QLEN consistently outperforms the compared algorithms with respect to the targeted parameters. The efficiency of load distribution achieved by QLEN on average performs 1.69% better than all the three algorithms; at the same time showing 54.86% lesser network usage, 3413.33 MJ lesser energy consumption and 10.39% reduced redirection to the cloud. The algorithm also scales efficiently, showing better performance as the number of fog devices increases.
Multidimensional Evaluation of Online Asynchronous Discussion for Blended Learners

As an essential part of online and offline blended teaching, the conversation session can not only activate the classroom atmosphere, but also help learners understand the course. significance. In recent years, more and more educational researchers have focused their attention on offline conversations and created relevant development paths, but paid less attention to online asynchronous discussions. With the development of blended learning and online learning, it is important to develop an evaluation system specifically for asynchronous online conversation sessions. The research is based on the SSD-SLD multi-dimensional classroom evaluation system, taking the online asynchronous discussion session of the physics teaching theory course as an example, using mixed research methods to evaluate the online asynchronous discussion session, and drawing conclusions and enlightenments.
Surface Defect Detection of Fabric Based on Improved Faster R-CNN

As an indispensable part of industrial production, industrial defect detection plays a vital role in ensuring product quality. In recent years, deep learning technology provides a new solution for industrial defect detection. More and more researchers apply deep learning technology to industrial defect detection tasks, and the existing methods have achieved good research results. Therefore, based on the classical deep learning detection algorithm Faster R-CNN, this paper conducted research on surface defects of fabric as the detection object. The main research contents of this paper are as follows:Firstly, fabric data sets containing 15 kinds of defects are selected as the research object of this paper, and the defect data format is processed and converted into the coco 2017 format. Then, according to the characteristics of fabric surface defect data set, an algorithm for detection of fabric surface defects based on improved Faster R-CNN was proposed.Based on the analysis of various convolutional neural networks, ResNet50 and ResNet101 are selected as the feature extraction backbone network of the algorithm in this paper, and the deformable convolution is embedded into ResNet, so that the model can adapt to the irregular shape of defects. In view of different surface defect scales, K-means clustering algorithm was used to customize the aspect ratio of anchor frame and optimize the original anchor frame parameters.ROI-Align is used to replace ROI Pooling, and blinear interpolation algorithm is used to accurately locate the anchor frame, which solves the position mismatch problem caused by two rounds of ROI Pooling. Finally, experiments were designed on the fabric dataset and aluminum profile data published on the Tianchi platform to demonstrate that the improved Faster R-CNN based fabric surface defect detection algorithm can achieve higher defect detection accuracy.
IoT based automated mosquito based disease Detection

Mosquitoes in India has a wide impact in spreading diseases like, dengue, Malaria, Zika virus, Chikangunia. They cause a wide casualty in terms of life due to mosquito. The paper finds the actual environmental parameters causing the wide spread and breeding of mosquitoes in the state of Uttarakhand. The paper provide a real time design of IoT based product for finding the areas of uttarakhand which got affected from mosquitoes on real time basis based on environmental parameters responsible for providing the favourable conditions for these mosquitoes to breed and spread in the area which causes such type of diseases due to mosquitoes.
Accretion of a New England (U.S.A.) Salt Marsh in Response to Inlet Migration, Storms, and Sea-level Rise

Sediment accumulation rates were determined at several sites throughout Nauset Marsh (Massachusetts, U.S.A.), a back-barrier lagoonal system, using feldspar marker horizons to evaluate short-term rates (1 to 2 year scales) and radiometric techniques to estimate rates over longer time scales (137Cs,210Pb,14C). The barrier spit fronting theSpartina-dominated study site has a complex geomorphic history of inlet migration and overwash events. This study evaluates sediment accumulation rates in relation to inlet migration, storm events and sea-level rise. The marker horizon technique displayed strong temporal and spatial variability in response to storm events and proximity to the inlet. Sediment accumulation rates of up to 24 mm year−1were recorded in the immediate vicinity of the inlet during a period that included several major coastal storms, while feldspar sites remote from the inlet had substantially lower rates (trace accumulation to 2·2 mm year−1). During storm-free periods, accumulation rates did not exceed 6·7 mm year−1, but remained quite variable among sites. Based on137Cs (3·8 to 4·5 mm year−1) and210Pb (2·6 to 4·2 mm year−1) radiometric techniques, integrating sediment accumulation over decadal time scales, the marsh appeared to be keeping pace with the relative rate of sea-level rise from 1921 to 1993 of 2·4 mm year−1. At one site, the210Pb-based sedimentation rate and rate of relative sea-level rise were nearly similar and peat rhizome analysis revealed thatDistichlis spicatarecently replaced this onceS. patenssite, suggesting that this portion of Nauset Marsh may be getting wetter, thus representing an initial response to wetland submergence. Horizon markers are useful in evaluating the role of short-term events, such as storms or inlet migration, influencing marsh sedimentation processes. However, sampling methods that integrate marsh sedimentation over decadal time scales are preferable when evaluating a systems response to sea-level rise.
Ordovician back arc foreland and Ocloyic thrust belt development on the western Gondwana margin as a response to Precordillera terrane accretion

The Famatina belt, southern central Andes, records a circa 470 m.y. shortening episode (Ocloyic orogeny) affecting the peri‐Gondwanan back arc basin in response to the accretion of the Precordillera terrane. Collision created distinct features across and along the margin, some of which persisted into the present Andean structure. From east to west, the Ocloyic deformation is recorded across the telescoped region of Famatina, represented by (1) an east vergent fold‐thrust belt (EVFTB) involving a major angular unconformity between Early and Middle Ordovician rocks, (2) a highly deformed volcano‐sedimentary belt, and (3) an east vergent ductile shear zone affecting Famatinian granites. Axis attitudes of Ordovician refolded anticlines in the EVFTB show west‐east tectonic transport, in agreement with kinematics of Ocloyic high‐strain belts, indicating shortening orthogonal to the present Pacific margin. Eastward displacement of the Ordovician volcanic arc at ∼27°S latitude together with a documented sinistral slip zone across the proto‐Andean margin suggests tectonic indentation.