Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation

In this study, the Convolution Neural Network (CNN) algorithm is applied for non-destructive evaluation of aluminum panels. A method of classifying the locations of defects is proposed by exciting an aluminum panel to generate ultrasonic Lamb waves, measuring data with a sensor array, and then deep learning the characteristics of 2D imaged, reflected waves from defects. For the purpose of a better performance, the optimal excitation location and sensor locations are investigated. To ensure the robustness of the training model and extract the feature effectively, experimental data are collected by slightly changing the excitation frequency and shifting the location of the defect. The high classification accuracy for each defect location can be achieved. It is found that the proposed algorithm is also successfully applied even when a bar is attached to the panel.
Clustering in doelvolgalgoritmen van radarsystemen

Clustering van doelen en metingen is een belangrijk onderdeel in doelvolgalgoritmen van radarsystemen, zoals het Multiple Hypotheses algoritme. Clustering is de bottleneck binnen een Multiple Target Tracking systeem als veel doelen real-time gevolgd moeten worden. Het is daarom van belang dat een efficient clusteringalgoritme in het systeem is gei'mplementeerd. Verschillende clusteringalgoritmen worden beschreven evenals hoe deze zijn aangepast, gei'mplementeerd en getest voor toepassing binnen een Multiple Target Tracking systeem.
Spurges (Euphorbia L., Euphorbiaceae) of the Boreal Eurasia. I. Section Paralias Dumort

The section Paralias L. of the genus Euphorbia L. in the Circumboreal floristic region is revised using traditional taxonomic approach. The section is represented in the region by 16 species of 3 subsections. A special attention is paid to the E. nicaeensis All. aggregate. E. nicaeensis s. str. is regarded as almost strictly Mediterranean species differing from other members of the aggregate by nectarias with appendicules («horns»), even if very short. E. glareosa Pall. ex Bieb. is characterized by small (10–20 cm) and thin (1.5–2 mm) stems and usually 5 pseudoumbell rays. E. pannonica Host and E. stepposa Zoz ex Prokh. have stems (20)25–70 cm high, 2.5–4 mm diam. and psudoumbell of (5)7–16 rays. E. pannonica differs from E. stepposa by pilose fruits and/or presense of small «glareose» indumentum.
G-Quadruplexes in Repeat Expansion Disorders

The repeat expansions are the main genetic cause of various neurodegeneration diseases. More than ten kinds of repeat sequences with different lengths, locations, and structures have been confirmed in the past two decades. G-rich repeat sequences, such as CGG and GGGGCC, are reported to form functional G-quadruplexes, participating in many important bioprocesses. In this review, we conducted an overview concerning the contribution of G-quadruplex in repeat expansion disorders and summarized related mechanisms in current pathological studies, including the increasing genetic instabilities in replication and transcription, the toxic RNA foci formed in neurons, and the loss/gain function of proteins and peptides. Furthermore, novel strategies targeting G-quadruplex repeats were developed based on the understanding of disease mechanism. Small molecules and proteins binding to G-quadruplex in repeat expansions were investigated to protect neurons from dysfunction and delay the progression of neurodegeneration. In addition, the effects of environment on the stability of G-quadruplex were discussed, which might be critical factors in the pathological study of repeat expansion disorders.
Semantic Segmentation Approaches in Crop Row Detection

Crop row detection is a vital task in precision agriculture. Earlier works for solving this task follow traditional computer vision based methodologies. However, in recent years deep learning based approaches are being adopted for solving this task. Among various deep learning methodologies, semantic segmentation has found to be most successful for obtaining meaningful representation of images in a plethora of domains, such as, medical image analysis and autonomous driving. Scene parsing is a subcategory of semantic segmentation where all objects of interest in a scene are color coded as a way to simultaneously classify and localize their presence. In this way, scene parsing technique is a very good fit for solving crop row detection task; However, no existing research has yet ventured this direction. In this work we investigate the performance of five latest semantic segmentation methodologies on real-life crop row datasets for solving the crop row detection task. Our experimental results validate that most of the semantic segmentation methods provide substantially good results for solving the crop row detection task; Importantly, LinkNet architecture provides the best results among the competitors. We also discuss various reallife challenges for solving crop row detection in real-life scenarios.
The Impact of COVID-19 on Human Mobility: A Case Study on New York

COVID-19 has dramatically changed people’s mobility patterns. This report aims to analyze the impact of COVID-19 on people’s mobility through statistics and comparing the visits of POIs (Point-Of-Interests) in New York State in 2019 and 2020. The report uses data from SafeGraph, which is a data company. The raw data contains POI visits across the United States in 2019 and 2020. Considering the analysis size and difficulty of the data, POI visits from New York State are extracted for analysis, and POI locations are classified according to the tags provided by the source data. The scale of analysis is from macro to micro, and they are the total POI visits data of New York State based on different ways in 2019 and 2020, the POI visits of CBG (Census Block Group) division in New York City, and three representative POI samples to do individual analysis. The analysis methods are: (1) use line plot and bar plot statistics to compare the trends of POI visits data from 2019 to 2020, and (2) make the spatial visualization comparison, which includes grid map, scatter map, heatmap, and OD map, between the first peak of epidemic impact in the first full week of April 2019 and April 2020, and the scope is narrowed to New York City. Wherein the OD maps are drawn based on the CBG division. Compared to related work, the analysis object includes CBG, categories, and individual POI. In addition, the analysis method combines statistical graphs and spatial visualizations and explores the policy impact of the New York City government. This report adopts more multidimensional analysis methods and objects to improve the comprehensiveness and reliability of the analysis content.
A deep convolutional neural network-based method for identifying weed seedlings in maize fields

Compared to the previous use of machine learning algorithms for recognition, its disadvantages have small accuracy recognition speed is slow. In this paper, we extend the idea of weed recognition to deep convolutional neural networks, and after effective training can get a network model with relatively good robustness, which can provide great help for weeding in corn fields. We choose the deep convolutional model for comparison with the common weeds in corn fields as the dataset, and we can get that the VGG16 model has better recognition effect on weeds compared with other convolutional models through experiments. Through multiple rounds of training, the F1-values of VGG16 model for Black-grass, Charlock, Cleavers, Common chickweed, and Loose silky-bent were 0.971, 0.945, 0.949, 0.959, and 0.958, respectively, and we selected different optimizers for the VGG16 model to retrain these weed datasets to obtain the SGD optimizer with the The combined performance is the best. In summary, by combining deep learning in machine vision to weed identification, we can effectively reduce the labor cost in agricultural farming as well as can better increase the yield of corn.
A Novel Approach for Short-Term Energy Forecasting in Smart Buildings

Efficient energy management is required for optimal energy consumption. The building sector consumes 40% of the total global energy production and is expected to reach 50% by 2050. With the soaring price of electricity, buildings need economical and efficient energy management. Recent advances in artificial intelligence and the Internet of Things (IoT) have inspired researchers working in smart building management to harness the potential of these technologies for forecasting energy consumption in smart buildings. This article proposes a novel hybrid deep learning model consisting of convolutional neural network (CNN) and recurrent neural network (RNN) to predict hourly energy consumption for smart buildings. Experimental results demonstrate that the CNN-gated recurrent unit (GRU) model, with an accuracy of 97%, outperforms the state-of-the-art techniques.
Characterizing persistent disturbing behavior using longitudinal and multivariate techniques

Persistent disturbing behavior (PDB) refers to a chronic condition in therapy-resistant psychiatric patients. Since these patients are highly unstable and difficult to maintain in their natural living environment and even in hospital wards, it is important to properly characterize this group. Previous studies in the Belgian province of Limburg indicated that the size of this group was larger than anticipated. Here, using a score calculated from longitudinal psychiatric registration data in 611 patients, we characterize the difference between PDB patients and a set of control patients. These differences are studied both at a given point in time, using discriminant analysis, as well as in terms of the evolution of the score over time, using longitudinal data analysis methods. Further, using clustering techniques, the group of PDB patients is split into two subgroups, characterized in terms of a number of ordinal scores. Such findings are useful from a scientific as well as from an organizational point of view.
What Next for the Post Covid Global Economy: Could Negative Supply Shocks Disrupt Other Fragile Systems?

There is a reasonable likelihood that that the next global economic crisis could threaten the future of democracy. The economic system is a complex, adaptive system (CAS) subject to “tipping points” when underlying stresses lead to crisis. Moreover, the economic system is nested within a number of other CAS; political, environmental and public health among others. Looking forward, recurrent negative supply shocks imply a dangerous future of higher real interest rates and debt distress leading to either deflation (private debt distress) or higher inflation (sovereign debt distress). Such problems could threaten democratic political systems that are already showing signs of significant stress themselves. The paper finishes with some reflections on policy alternatives.
ON COHERENCE OF ENDOMORPHISM RINGS

Abstract Let R be a ring and U a left R-module with S=End(RU). The aim of this paper is to characterize when S is coherent. We first show that a left R-module F is TU-flat if and only if HomR(U,F) is a flat left S-module. This removes the unnecessary hypothesis that U is Σ-quasiprojective from Proposition 2.7 of Gomez Pardo and Hernandez [‘Coherence of endomorphism rings’, Arch. Math. (Basel)48(1) (1987), 40–52]. Then it is shown that S is a right coherent ring if and only if all direct products of TU-flat left R-modules are TU-flat if and only if all direct products of copies of RU are TU-flat. Finally, we prove that every left R-module is TU-flat if and only if S is right coherent with wD(S)≤2 and US is FP-injective.
Using Surface Pressure Variations to Categorize Diurnal Valley Circulations: Experiments in Owens Valley

Abstract Harmonic analysis has been applied to data from nearly 1000 Automatic Surface Observation System (ASOS) stations over the United States to extract diurnal pressure signals. The largest diurnal pressure amplitudes (∼200 Pa) and the earliest phases (∼0600 LST for surface pressure maximum) were found for stations located within deep mountain valleys in the western United States. The origin of these unique characteristics of valley pressure signals is examined with a detailed study of Owens Valley, California. Analysis of observational data from the Terrain-Induced Rotor Experiment (T-REX) project shows that the ratio of the valley surface pressure to temperature amplitude can be used to estimate the daily maximum mixed-layer depth H. On days with strong westerly winds above the valley, the mixed layer is found to be shallower than on quiescent days because of a flushing effect in the upper parts of the valley. Idealized two-dimensional Weather Research and Forecasting Model simulations were used to .
Resource Efficient Profiling of Spatial Variability in Performance of Regression Models

Scientists design models to understand phenomena, make predictions, and/or inform decision-making. This study targets models that encapsulate spatially evolving phenomena. Given a model, our objective is to identify the accuracy of the model across all geospatial extents. A scientist may expect these validations to occur at varying spatial resolutions (e.g., states, counties, towns, and census tracts). Assessing a model with all available ground-truth data is infeasible due to the data volumes involved. We propose a framework to assess the performance of models at scale over diverse spatial data collections. Our methodology ensures orchestration of validation workloads while reducing memory strain, alleviating contention, enabling concurrency, and ensuring high throughput. We introduce the notion of a validation budget that represents an upper-bound on the total number of observations that are used to assess the performance of models across spatial extents. The validation budget attempts to capture the distribution characteristics of observations and is informed by multiple sampling strategies. Our design allows us to decouple the validation from the underlying model-fitting libraries to interoperate with models constructed using different libraries and analytical engines; our advanced research prototype currently supports Scikit-learn, PyTorch, and TensorFlow.
Exploring the mutation tendency of a specific region in the virus taxonomic groups using data mining techniques

The GDNQ motif is mainly found in nonsegmented (-)ssRNA viruses and plays a role in the catalytic and polymerase activity pathways. However, in some recently reported dsRNA mycoviruses, the GDNQ motif of the (-)ssRNA virus group is found instead of the GDD motif commonly found in the dsRNA virus group. Therefore, in this study, data mining techniques were used to explore the mutation tendency of a specific region in the virus taxonomic groups. Viral protein sequences were obtained from the NCBI Virus data repository, and the performance of several known string-searching algorithms (BF, KMP, and BM) was evaluated to establish an optimal strategy for extracting useful information from sequence big data. Then, the pattern-matching performance of the algorithm was tested to detect whether the conserved region sequence was a common motif type or a mutated motif type. As a result of the analysis, it was confirmed that the BF method produced the fastest results. However, the BM method recorded higher accuracy. Through the virus mutation tendency derived from the analysis results, it was confirmed that 7.1% of dsRNA viruses had a mutated motif type based on the total data analyzed and that it was relatively recent that ssRNA virus motif variants began to be discovered in dsRNA viruses. It is expected that additional in-depth analysis of the host range and infection route of viruses with the mutation will help understand the evolution of dsRNA and ssRNA mycoviruses.
Experimental evidence for the global acidification of surface ocean at the Cretaceous–Palaeogene boundary: the biogenic calcite-poor spherule layers

Abstract The massive amount of impact-generated atmospheric CO2 at the Cretaceous-Palaeogene boundary (KPB) would have accumulated globally in the surface ocean, leading to acidification and CaCO3 undersaturation. These chemical changes would have caused a crisis of biocalcification of calcareous plankton and enhanced dissolution of their shells; these factors together may have played a crucial role in forming the biogenic calcite-poor KPB spherule layers observed at numerous oceanic sites and marine (now on land) sites in Europe and Africa. Experimental data and observations indicate that the deposition spherule layer probably lasted only a few decades at most.
Microscale mapping of oscillatory flows

We present an optofluidic method that allows the two-dimensional vectorial near-field mapping of oscillatory flows with micron-scale resolution. An oscillatory flow created by a microsource (an optically trapped silica particle set to oscillate in a dipole-type mode) is detected by another twin silica particle independently trapped and located in the vicinity of the source. Fourier analysis of the motion of the detecting particle at different points in space and time renders the vectorial velocity map around the oscillating microsphere. The method introduced here paves the way for in-situ characterization of fast mixing microscale devices and for new detection methods able to provide location and recognition (due to the field pattern) of moving sources that may be applied to both artificial and living microobjects, including macromolecules, cells, and microorganisms.
A review of a method for dynamic load distribution, dynamical modeling, and explicit internal force control when two manipulators mutually lift and transport a rigid body object

The report reviews a method for modeling and controlling two serial link manipulators which mutually lift and transport a rigid body object in a three dimensional workspace. A new vector variable is introduced which parameterizes the internal contact force controlled degrees of freedom. A technique for dynamically distributing the payload between the manipulators is suggested which yields a family of solutions for the contact forces and torques the manipulators impart to the object. A set of rigid body kinematic constraints which restricts the values of the joint velocities of both manipulators is derived. A rigid body dynamical model for the closed chain system is first developed in the joint space. The model is obtained by generalizing the previous methods for deriving the model. The joint velocity and acceleration variables in the model are expressed in terms of independent pseudovariables. The pseudospace model is transformed to obtain reduced order equations of motion and a separate set of equations governing the internal components of the contact forces and torques. A theoretic control architecture is suggested which explicitly decouples the two sets of equations comprising the model. The controller enables the designer to develop independent, non-interacting control laws for the position control andmore » internal force control of the system. « less
Image Geolocation by Non-Expert Crowd Workers with an Expert Strategy

Identifying the location where a photo was taken is an important operation in many applications such as the disaster response if it is not associated with the location information. This process usually is done by experts who are familiar with the geolocation. However it is not guaranteed that we can find such expert workers. In this paper, we explore an approach to improve the quality of image geolocation with an workflow implement experts’ strategy for the image geolocations. The result of preliminary experiment suggested that our approach is effective in improving the accuracy of non-expert geolocation.
Calculation and Analysis of Communication Network Survivability in SCADA Communication System

The power optical communication network is a dedicated communication network serving power grids, and its survivability is critical to the safe and stable operation of power grids. In this paper, the reliability of communication networks is analyzed, and a set of probabilistic shared risk links that can accurately measure the probability of service failures is calculated; Finally, based on the probabilistic risk management link set, three shared development path algorithms with different reliability are proposed. This article carries on simulation calculation and comparative analysis of these three typical algorithms to the CERNET network topology for specific business. This is of great significance for ensuring the safe and reliable operation of the grid system and reducing the occurrence of large-scale grid accidents.
Modeling and Performance Evaluation of a Grid-Connected Photovoltaic/Wind Hybrid Power System

Using renewable energy technologies in Egypt returned to the abundance of solar irradiation and wind speed. The purpose of this paper is to investigate the dynamic modeling and performance analysis of grid-connected hybrid renewable energy system (HRES). This system consists of constant speed wind turbine (CSWT) and photovoltaic (PV) station integrated with the utility grid. Maximum power point tracking (MPPT) technique and unity power factor operation are obtained in PV station by using a controlled power converter. The performance of HRES is examined using MATLAB Simulink software. Moreover, the system is validated through realistic data based on actual wind speed and solar radiation measurements for Zafarana, Suez Gulf region, Egypt. The simulated results prove the effectiveness of the controlled converter for capturing maximum energy along with working with unity power factor. Depending on the simulation results, the electricity production of the HRES per GWh is estimated.
Opuntia ficus-indica Fruit: A Systematic Review of Its Phytochemicals and Pharmacological Activities

The use of Opuntia ficus-indica fruits in the agro-food sector is increasing for a multiplicity of players. This renewed interest is, in part, due to its organoleptic characteristics, nutritional value and health benefits. Furthermore, industries from different sectors intend to make use of its vast array of metabolites to be used in different fields. This trend represents an economic growth opportunity for several partners who could find new opportunities exploring non-conventional fruits, and such is the case for Opuntia ficus-indica. O. ficus-indica originates from Mexico, belongs to the Cactaceae family and is commonly known as opuntia, prickly pear or cactus pear. The species produces flowers, cladodes and fruits that are consumed either in raw or in processed products. Recent publications described that consumption of the fruit improves human health, exhibiting antioxidant activity and other relevant pharmacological activities through enzymatic and non-enzymatic mechanisms. Thus, we provide a systematic, scientific and rational review for researchers, consumers and other relevant stakeholders regarding the chemical composition and biological activities of O. ficus-indica fruits.