Miracle-dz/UniFAR-FTUs huggingface dataset:
Generation and regulation of electromagnetic pulses induced by hybrid laser pulses interacting with solid targets

In inertial confinement fusion, electromagnetic pulses (EMPs) can be produced during high-power laser interacting with solid targets, which are intimately related to laser intensity and laser energy. In this study, EMPs generated by hybrid laser pulses coupling with targets are recorded and analyzed. The results indicate that a single picosecond laser gives birth to the most intense EMPs, but they are remarkably suppressed when a nanosecond laser-shooting target is triggered before the picosecond and femtosecond laser. One possible hypothesis is proposed based on x-rays inducing pre-ablation that generates pre-plasma at the surfaces of the picosecond target and femtosecond target, leading to a sharp drop both in the energy and number of the emitting hot electrons and protons. The findings will deepen our understanding of the mechanism of EMPs’ generation and will also open a new avenue to regulate EMPs by hybrid laser pulses.
Research on Partial Discharge Location in Transformer Based on Adaptive Particle Swarm Optimization Algorithm

As the requirements for safe operation of the power system become increasingly high, the study of transformer partial discharge location will become more important, the intelligent algorithm is introduced for partial discharge location in transformer is a very effective means. This paper proposes an adaptive particle swarm optimization algorithm of transformer partial discharge location, through the analysis of experimental verification, this algorithm has high precision and fast convergence speed to avoid premature convergence etc.
3D Laser Scanning of Railway Line

this railway route between towns Trencin and Nove Mesto n/Vahom . For this fact, the experimental measurements of the representative parts of such a railway 3D laser scanning is very useful method to reach digital terrain model as the common output of map making. The paper deals with an application of 3D laser scanning in railway track measurements, which belong to the field of engineering surveying. Experimental measurements were carried out on the ballast-less railway construction with the purpose to determine its track geometry by the combination of terrestrial and laser scanning methods. Measurements were carried out in conformity with conditions involved in corresponding technical standards. Surveyors used a variety of firmware, CAD and mathematical software for laser scanning processing to reach the expected outputs in adequate volume, accuracy and reliability.
Identifikasi Jamur Rhizosfer di Lahan Tanaman Bawang Merah (Allium ascalonicum L.) Bergulma di Desa Bendang Raya Kecamatan Tenggarong

Penelitian bertujuan untuk mengetahui jenis jenis jamur rizosfer pada tanaman bawang merah di Desa Bendang Raya Kecamatan Tenggarong. Penelitian ini dilaksanakan pada bulan November 2019 sampai Januari 2020 di Laboratorium Hama Penyakit Tumbuhan Fakultas Pertanian Universitas Mulawarman. Pengambilan sampel tanah rizosfer dilakukan pada lahan yang bergulma terus (tanpa pengendalian gulma), bergulma empat minggu, bergulma tiga minggu, bergulma dua minggu dan bergulma satu minggu. Pengambilan sampel pada kedalaman 0-20 cm. Diambil 12 titik sampel, dikomposit, lalu diambil 1 kg untuk dianalisis di laboratorium. Hasil penelitian ini menunjukkan bahwa kepadatan populasi jamur pada lahan bergulma terus (tanpa pengendalian gulma) adalah 34x103 terdiri dari 4 jenis Jamur yaitu; Penicillium sp.1, Rhizoctonia sp. , Gonytrichum sp. , Aspergillus sp. pada lahan bergulma empat minggu terdapat 30x103 terdiri dari 3 jenis jamur yaitu Aspergillus sp. , Botrytis sp. dan Aureobasidium sp. , pada lahan bergulma tiga minggu terdapat 24x103 terdiri dari 3 jenis jamur yaitu Pythium sp. , Fusarium sp. , dan Aspergillus sp. , pada lahan bergulma dua minggu terdapat 23x103 terdiri dari 2 jenis jamur yaitu: Pennicillium sp. , Trichoderma sp. dan pada lahan bergulma satu minggu terdapat 10x103 yang terdiri dari 2 jenis Jamur yaitu Trichoderma sp. dan Aspergillus sp.
Prediction of cutting tool life based on evolutionary neural network

Artificial neural network was introduced to predict cutting tool life,and neural network-based prediction model of cutting tool life as well as the optimizing selection of machining parameters were proposed.In the prediction process,there were some disadvantages in Back Propagation(BP) algorithm,such as low convergence speed,easily falling into local minimum point and weak global search capability.To settle these problems,a genetic algorithm was used to train BP neural network to replace classical learning algorithms.An evolutionary neural network learning algorithm was developed.Results of simulations and experiments showed that the evolutionary neural network based on genetic algorithm could effectively overcome the shortcoming of falling into local minimum point.This method could obtain higher prediction accuracy.As a result,it provided theoretical basis for the establishment of cutting tool requirements planning,the account of its cost and the selection of machining parameters,as well as reduced the cost in Manufacturing Execution System(MES).
Palm area detection for reliable hand gesture recognition

Hand gesture recognition applications require as a first step a reliable identification of the hand region and its subdivision into fingers and palm areas. In this work the hand is firstly extracted from the depth data acquired by a depth sensor (e.g., Microsoft Kinect). Then the center of the palm and the hand orientation are identified. Finally circular and elliptical shapes are fitted on the extracted samples in order to reliably identify the palm and fingers area. The proposed approach has been tested inside a simple gesture recognition scheme and preliminary results show its reliability.
A Novel Implementation of Siamese Type Neural Networks in Predicting Rare Fluctuations in Financial Time Series

Stock trading has tremendous importance not just as a profession but also as an income source for individuals. Many investment account holders use the appreciation of their portfolio (as a combination of stocks or indexes) as income for their retirement years, mostly betting on stocks or indexes with low risk/low volatility. However, every stock-based investment portfolio has an inherent risk to lose money through negative progression and crash. This study presents a novel technique to predict such rare negative events in financial time series (e.g., a drop in the S&P 500 by a certain percent in a designated period of time). We use a time series of approximately seven years (2517 values) of the S&P 500 index stocks with publicly available features: the high, low and close price (HLC). We utilize a Siamese type neural network for pattern recognition in images followed by a bootstrapped image similarity distribution to predict rare events as they pertain to financial market analysis. Extending on literature about rare event classification and stochastic modeling in financial analytics, the proposed method uses a sliding window to store the input features as tabular data (HLC price), creates an image of the time series window, and then uses the feature vector of a pre-trained convolutional neural network (CNN) to leverage pre-event images and predict rare events. This research does not just indicate that our proposed method is capable of distinguishing event images from non-event images, but more importantly, the method is effective even when only limited and strongly imbalanced data is available.
Optimisation method of extrusion dies with a thin and complex shape

Abstract Extrusion dies are usually manufactured with a rectangular pocket in front of the actual die opening. Finite element simulations demonstrate that curved or slant shaped pockets can assist in a more homogenous material flow in terms of velocity and shear stresses. An efficient optimisation algorithm has been developed for different pocket widths of slant die pockets. The algorithm incorporates finite element simulations to predict improvements in material flow. An example of a die with a great aspect ratio is presented and compared with experimental results.
An Integrated Pharmacology-Based Strategy to Investigate the Potential Mechanism of Xiebai San in Treating Pediatric Pneumonia

Xiebai San (XBS) is a traditional Chinese medicine (TCM) prescription that has been widely used to treat pediatric pneumonia since the Song dynasty. To reveal its underlying working mechanism, a network pharmacology approach was used to predict the active ingredients and potential targets of XBS in treating pediatric pneumonia. As a result, 120 active ingredients of XBS and 128 potential targets were screened out. Among them, quercetin, kaempferol, naringenin, licochalcone A and isorhamnetin showed to be the most potential ingredients, while AKT1, MAPK3, VEGFA, TP53, JUN, PTGS2, CASP3, MAPK8 and NF-κB p65 showed to be the most potential targets. IL-17 signaling pathway, TNF signaling pathway and PI3K-Akt signaling pathway, which are involved in anti-inflammation processes, immune responses and apoptosis, showed to be the most probable pathways regulated by XBS. UPLC-Q/Orbitrap HRMS analysis was then performed to explore the main components of XBS, and liquiritin, quercetin, kaempferol, licochalcone A and glycyrrhetinic acid were identified. Molecular docking analysis of the compounds to inflammation-associated targets revealed good binding abilities of quercetin, kaempferol, licochalcone A and liquiritin to NF-κB p65 and of quercetin and kaempferol to Akt1 or Caspase-3. Moreover, molecular dynamics (MD) simulation for binding of quercetin or kaempferol to NF-κB p65 revealed dynamic properties of high stability, high flexibility and lowbinding free energy. In the experiment with macrophages, XBS markedly suppressed the (Lipopolysaccharides) LPS-induced expression of NF-κB p65 and the production of pro-inflammatory cytokines IL-6 and IL-1β, supporting XBS to achieve an anti-inflammatory effect through regulating NF-κB p65. XBS also down-regulated the expression of p-Akt (Ser473)/Akt, Bax and Caspase-3 and up-regulated the expression of Bcl-2, indicating that regulating Akt1 and Caspase-3 to achieve anti-apoptotic effect is also the mechanism of XBS for treating pediatric pneumonia. Our study helped to reveal the pharmacodynamics material basis as well as the mechanism of XBS in treating pediatric pneumonia.
Efficient Second‐Harmonic Generation from Silicon Slotted Nanocubes with Bound States in the Continuum

Optical materials with centrosymmetry, such as silicon and germanium, are unfortunately absent of second‐order nonlinear optical responses, hindering their developments in efficient nonlinear optical devices. Here, a design with an array of slotted nanocubes is proposed to realize remarkable second harmonic generation (SHG) from the centrosymmetric silicon, which takes advantage of enlarged surface second‐order nonlinearity, strengthened electric field over the surface of the air‐slot, as well as the resonance enhancement by the bound states in the continuum. Compared with that from the array of silicon nanocubes without air‐slots, SHG from the slotted nanocube array is improved by more than two orders of magnitude. The experimentally measured SHG efficiency of the silicon slotted nanocube array is high as 1.8 × 10−4 W−1, which is expected to be further engineered by modifying the air‐slot geometries. The result can provide a new strategy to expand nonlinear optical effects and devices of centrosymmetric materials.
Full broadband processing including total Q compensation in the presence of gas

Summary Recent efforts in marine broadband processing have largely been focused on source and receiver deghosting. To fully recover the frequency bandwidth of seismic data, the anelastic nature of the earth needs to be taken into account. In addition, in the presence of gas anomalies, attenuation of seismic waves will cause further degradation in the resolution of migrated images. By quantifying the attenuation of seismic energy using quality factor Q, we can model the intrinsic absorptive nature of the earth as background Q and the localized absorptive bodies, e.g. gas pockets, as anomalous Q. Using the frequency information and the amplitude information of the data to estimate the background Q (FS-QTomo) and anomalous Q (A-QTomo), respectively, the attenuation effects of the earth can be compensated by the application of Q-PSDM in the presence of gas with the resultant combined Q model. The accuracy of these processes is further enhanced by broadband processing with deghosting in terms of better estimation of the centroid frequency for FS-QTomo and deeper penetration of low frequency signal through the gas bodies for A-QTomo. Thus, the interplay of deghosting and Q tomography provides a full broadband processing workflow for restoring the distortion of amplitude, frequency and phase caused by the combined effects of the earth’s anelasticity and gas pockets. We applied our workflow on a field data example and demonstrated through this first case study that high resolution broadband seismic data with improved signal to noise ratio (S/N) can be obtained.
Pushing the Efficiency of StereoNet: Exploiting Spatial Sparsity

: Current CNN-based stereo matching methods have demonstrated superior performance compared to traditional stereo matching methods. However, mapping these algorithms into embedded devices, which exhibit limited compute resources, and achieving high performance is a challenging task due to the high computational complexity of the CNN-based methods. The recently proposed StereoNet network, achieves disparity estimation with reduced complexity, whereas performance does not greatly deteriorate. Towards pushing this performance to complexity trade-off further, we propose an optimization applied to StereoNet that adapts the computations to the input data, steering the computations to the regions of the input that would beneﬁt from the application of the CNN-based stereo matching algorithm, where the rest of the input is processed by a traditional, less computationally demanding method. Key to the proposed methodology is the introduction of a lightweight CNN that predicts the importance of reﬁning a region of the input to the quality of the ﬁnal disparity map, allowing the system to trade-off computational complexity for disparity error on-demand, enabling the application of these methods to embedded systems with real-time requirements.
How Primary School Curriculums in 11 Countries around the World Deliver Food Education and Address Food Literacy: A Policy Analysis

(1) Background: As one of the biggest drivers of health and climate change, the food system has unrealised potential to influence consumption toward affordable, healthy, sustainable diets. A range of policy levers, including mandating food education, are needed. Schools are considered the best place for food education and childhood is a crucial period when eating habits that persist into adulthood are formed. Food education as part of the curriculum is crucial in generating population shifts in food systems improvements. The purpose of this policy analysis was to analyse mandatory curriculums in different countries to explore the ways in which primary school food education addresses food literacy. (2) Methods: This study analyses how food education within primary school education policy, in 11 countries, addresses Food Literacy (FL). It is the first study of this kind. A case study methodology was employed, and curriculum policy content analysis was conducted using a Food Literacy framework. (3) Results: Each country has a curriculum dedicated to food education, supported by food education in non-food curriculums. There is no standardized approach to primary school food education policy, no consensus in primary food education nomenclature or what curriculums constitute. Curriculums focus on cooking and health topics, but significantly less on social-cultural, equity, and sustainability issues. (4) Conclusion: How primary curriculums around the world deliver food education policy to address FL varies enormously. All 11 countries have dedicated food curriculums, supported by non-food curriculums, but there is no consensus as to what food education is called or constitutes. Countries rarely deal with FL comprehensively. The most comprehensive are single, detailed food curriculums, complemented by non-food curriculums where food knowledge and skills progress clearly and are the intended learning outcome.
Preoperative Factors Predicting the Preservation of the Posterior Cruciate Ligament in Total Knee Arthroplasty

Predicting the successful preservation of posterior cruciate ligament (PCL) in total knee arthroplasty (TKA) is an important step for preoperative planning to secure the satisfactory outcomes. We aimed to examine the preoperative factors predicting the successful preservation of the PCL in cruciate‐retaining TKA and the outcome of sacrificing the PCL.
Semi-automatic CNN Architectural Pruning using the Bayesian Case Model and Dimensionality Reduction Visualization

Visualization techniques have been applied to reasoning about complex machine learning models. These visual approaches aim to enhance the understanding of black-box models’ decisions or guide in hyperparameters configuration, such as the number of layers and neurons/filters in deep neural networks. While several works address the architectural tuning of convolutional neural networks (CNNs), only a few works face the problem from a semi-automatic perspective. This work presents a novel application of the Bayesian Case Model that uses visualization strategies to convey the most important filters of convolutional layers for image classification. A heatmap coordinated with a scatterplot visualization emphasizes the filters with the most contribution to the CNN prediction. Our methodology is evaluated on a case study using the MNIST dataset.
A Survey of NOMA for VLC Systems: Research Challenges and Future Trends

Visible light communication (VLC) has become a promising technology for high data rate communications and an attractive complementary to conventional radio frequency (RF) communication. VLC is a secure, energy efficient and cost-effective technology that exploits the existing infrastructure, particularly in indoor environments, for wireless data transmission. Nevertheless, the main limitation of developing high data rate VLC links is the narrow modulation bandwidth of light-emitting diodes (LEDs), which is in the megahertz range. The power domain nonorthogonal multiple access (PD-NOMA) scheme is envisioned to address several challenges in VLC systems. In this paper, we present a detailed overview of PD-NOMA based VLC systems. Moreover, we introduce insights on some PD-NOMA VLC system constraints and challenges such as power allocation, clipping effect, MIMO and security. Finally, we provide open research problems as well as possible directions for future research to pave the way for the implementation of PD-NOMA VLC systems.
Tilt-shift eddy current probe impact on information value of response signal

: This article deals with the possibility for increasing of the informational value of a response signal using tilt-shift eddy current probe. Numerical simulations based on the FEM method using the OPERA 3D software as well as gained experimental results are presented. The simulated cracks are evaluated at the selected eddy current probe tilts and shifts with respect to conductive plate to obtain additional data needed for its evaluation and localization. Obtained simulation results are compared and discussed with the experimental results.
Suburban Road Networks to Explore COVID-19 Vulnerability and Severity

The Delta variant of COVID-19 has been found to be extremely difficult to contain worldwide. The complex dynamics of human mobility and the variable intensity of local outbreaks make measuring the factors of COVID-19 transmission a challenge. The inter-suburb road connection details provide a reliable proxy of the moving options for people between suburbs for a given region. By using such data from Greater Sydney, Australia, this study explored the impact of suburban road networks on two COVID-19-related outcomes measures. The first measure is COVID-19 vulnerability, which gives a low score to a more vulnerable suburb. A suburb is more vulnerable if it has the first COVID-19 case earlier and vice versa. The second measure is COVID-19 severity, which is proportionate to the number of COVID-19-positive cases for a suburb. To analyze the suburban road network, we considered four centrality measures (degree, closeness, betweenness and eigenvector) and core–periphery structure. We found that the degree centrality measure of the suburban road network was a strong and statistically significant predictor for both COVID-19 vulnerability and severity. Closeness centrality and eigenvector centrality were also statistically significant predictors for COVID-19 vulnerability and severity, respectively. The findings of this study could provide practical insights to stakeholders and policymakers to develop timely strategies and policies to prevent and contain any highly infectious pandemics, including the Delta variant of COVID-19.
The COVID-19 pandemic impacts psychiatric outcomes and alcohol use among college students

ABSTRACT Background The COVID-19 pandemic has imposed fundamental challenges on nearly every area of life. Objective The purpose of the current study was to expand on the literature on the impact of the pandemic on college students by a) examining domains of impact of the pandemic on psychiatric and alcohol outcomes and b) controlling for pre-pandemic outcomes. Method Participants included 897 college students (78.6% female) from a larger longitudinal study on college student mental health. Structural equation models were fit to examine how COVID-19 impact (exposure, worry, food/housing insecurity, change in social media use, change in substance use) were associated with PTSD, anxiety, depression, suicidal ideation, and alcohol phenotypes. Models were fit to adjust for pre-pandemic symptoms. Results No effects of COVID-19 exposure remained after adjusting for earlier outcomes. COVID-19 worry predicted PTSD, depression, and anxiety, even after adjusting for earlier levels of outcomes (β’s: .091–.180, p’s < .05). Housing/food concerns predicted PTSD, anxiety, and depression symptoms as well as suicidal ideation (β’s: .085–.551, p’s < .05) after adjusting for earlier levels of symptoms. Change in media use predicted alcohol consumption (β’s: ± .116−.197, p’s < .05). Change in substance use affected all outcomes except suicidality (β’s: .112–.591, p’s < .05). Conclusions Domains of COVID-19 impact had differential effects on mental health and substance outcomes in college students during the first wave of the coronavirus pandemic. Future studies should examine the trajectory of these factors on college student mental health across waves of the pandemic. HIGHLIGHTS SEMs were used to find that five correlated domains of COVID-19 impact were associated with mental health and substance outcomes in college students during the first wave of the coronavirus pandemic.