Miracle-dz/UniFAR-FTUs huggingface dataset:
Rice biophysical parameter retrieval with optical satellite imagery: a comparative assessment of parametric and non-parametric models

Abstract This article presents a comparison of parametric and non-parametric models in rice biomass and leaf area index (LAI) retrieval using optical satellite imagery. Four parametric models including the linear, quadratic, logarithmic and exponential models, and four non-parametric models that include RF, SVM, kNN, and GBDT were applied, respectively, on the optical satellite dataset. GBDT produced the most accurate biomass estimates (RMSE of 191.8 g/m2) before heading and the quadratic model produced the most accurate biomass estimates (RMSE of 364.7 g/m2) after heading. RF registered the most accurate LAI estimates (RMSE of 0.79 m2/m2) before heading, whereas the quadratic model recorded the most accurate LAI estimates (RMSE of 1.04 m2/m2) after heading. Non-parametric models outperformed their parametric counterparts at before heading, whereas the reverse is the case after heading. These findings provide a guide to the optimal choice of empirical models for rice biomass and LAI retrieval with optical imagery.
Evaluate Short-Term Outcomes of abciximab in ST-Segment Elevation Myocardial Infarction Patients Undergoing Percutaneous Coronary Intervention: A Meta-Analysis of Randomized Clinical Trials

Objectives This meta-analysis was to verify the short-time efficacy and safety of abciximab in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). Background Abciximab has long-term efficacy in patients with STEMI undergoing PCI, but the short-term efficacy is still controversial. Methods We conducted a systematic review and meta-analysis compared with or without abciximab in patients with STEMI undergoing PCI. The relevant randomized controlled trials were included by searching PubMed, EMBASE, Cochrane Library, and Web of Science databases and other sources. The relative risk (RR) and 95% confidence intervals (CI) of outcomes were calculated by the fixed-effects model. Results Ten randomized controlled trials with 5008 patients met inclusion criteria. There were no significant differences in risk of all-cause death at 30-day (RR 0.79, CI 0.55–1.12, P=0.18), major bleeding (1.37, 0.93–2.03, P=0.11), and transfusion (1.23, 0.94–1.61, P=0.13) between the two groups. However, there were significant differences in risk of all-cause death at 6 months (0.57, 0.36–0.90, P=0.02), recurrent myocardial infarction (0.55, 0.33–0.92, P=0.02), repeat revascularization (0.58, 0.43–0.78, P=0.0004), final TIMI flow <3 (0.77, 0.62–0.96, P=0.02), minor bleeding (1.29, 1.02–1.63, P=0.04), and thrombocytopenia (2.04, 1.40–2.97, P=0.0002). Conclusions The application of abciximab can lead to a lower risk of reinfarction, revascularization, and all-cause death at 6 months, but a higher risk of minor bleeding, and thrombocytopenia.
ROBUST INDOOR POINT CLOUD CLASSIFICATION BY FUSING LSTM NEURAL NETWORKS WITH SUPERVOXEL CLUSTERING

Abstract. To address the problems of lack of training data and inaccurate classification of existing 3D point cloud data segmentation and classification methods, this paper proposes a high-precision classification algorithm for indoor point clouds by fusing LSTM neural network and super voxels. The algorithm first performs super voxel segmentation on the original point cloud and uses it as the basic unit for machine learning classification, and then introduces LSTM (Long Short-Term Memory) neural network to model the super voxel domain relationship and optimize the classification results. Finally, the accuracy of the proposed method is evaluated based on open dataset, and the experimental results show that 83.2% classification accuracy can be achieved in the open dataset.
The Effectiveness of Nutritional Education Interventions on Dietary Intake in Young Black Males: A Near-Empty Systematic Review

The incidence of several diet and lifestyle-related diseases, previously seen only in adults, is increasing in prevalence in young people. The Black population, and particularly Black males, are at high risk of developing lifestyle-related diseases. Adolescence and young adulthood are considered a transitional period with increasing independence and responsibility, along with the development of lifelong lifestyle habits. This systematic review aimed to establish which methods and approaches to nutritional education interventions are the most effective in improving the nutritional/dietary intake in healthy young Black males. Eligibility criteria were designed using PICOS and included controlled trials of nutrition education interventions designed to improve dietary intake in healthy young Black or mixed-race males aged 14–21 years old. Medline, Cinahl and Scopus were searched in April 2021, resulting in 20,375 records being screened, and subsequently 72 full-text articles were reviewed. Risk of bias was assessed using the ROBINS-I tool. One study met the eligibility criteria. Results are presented in a narrative format as meta-analysis was not possible. This systematic review revealed a lack of evidence on the effectiveness of nutritional education interventions in this high-risk population. Limitations are noted and recommendations have been made.
Threshold mixed data sampling models with a covariate-dependent threshold

ABSTRACT This paper introduces a threshold mixed data sampling model with a covariate-dependent threshold (TMIDAS-CDT), which allows for a threshold effect in the relationship between dependent and independent variables sampled at different frequencies, and allows for threshold regimes depending on a time-varying threshold being modelled as a linear function of informative covariates. We develop the estimation procedure for the model, and suggest test statistics for threshold effect, threshold constancy and the equal weighting scheme in aggregating high-frequency data. We conduct Monte Carlo simulations to investigate the performance of the estimation and testing procedures. The simulation results support that the estimation procedure works well in finite samples, and the test statistics have good size and power properties.
Gate/Drain Coupled Barrier Lowering Effect and Negative Threshold Voltage Shift in Schottky-Type p-GaN Gate HEMT

To assess GaN power transistors’ capability to maintain a decent E-mode operation, <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> at high <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}$ </tex-math></inline-formula> is measured for Schottky-type p-GaN gate HEMT, and an excessive negative <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> shift is observed. With <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}} =1$ </tex-math></inline-formula> V, <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> is around 1.3 V at <inline-formula> <tex-math notation="LaTeX">${I}_{\text {D}} =1$ </tex-math></inline-formula> mA, but <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> drops by ~0.6 V when measured at a high <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}$ </tex-math></inline-formula> of 100 V. In comparison, ohmic-type p-GaN gate HEMT only shows a negligible <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> shift up to <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}} =100$ </tex-math></inline-formula> V. A gate/drain coupled barrier lowering (GDCBL) effect is proposed to explain the appreciable <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> shift in Schottky-type p-GaN gate HEMT. Upon high <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}$ </tex-math></inline-formula>, the potential of the floating p-GaN layer is raised by the drain through the capacitive coupling between p-GaN and drain (<inline-formula> <tex-math notation="LaTeX">${C}_{\text {DP}}$ </tex-math></inline-formula>). The positive potential of the p-GaN layer then lowers the energy barrier along the gated channel, resulting in a reduced <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula>. This effect is confirmed by the dependence of measured negative <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> shift upon the property of gate/p-GaN contact and also by the numerical simulations that reveal the change in band diagrams upon high <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}$ </tex-math></inline-formula>.
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level of complexity and the huge amount of data, mobility profiling faces huge challenges. Digital Twin (DT) technology paves the way for cost-effective and performance-optimised management by digitally creating a virtual representation of the network to simulate its behaviour. In order to capture the complex spatio-temporal features in traffic scenario, we construct alignment diagrams to assist in completing the spatio-temporal correlation representation and design dilated alignment convolution network (DACN) to learn the fine-grained correlations, i.e., spatio-temporal interactions. We propose a digital twin mobility profiling (DTMP) framework to learn node profiles on a mobility network DT model. Extensive experiments have been conducted upon three real-world datasets. Experimental results demonstrate the effectiveness of DTMP.
Evolution of Preset Void and Damage Characteristics in Aluminum during Shock Compression and Release

It is well known that initial defects play an essential role in the dynamic failure of materials. In practice, dynamic tension is often realized by release of compression waves. In this work, we consider void-included single-crystal aluminum and investigate the damage characteristics under different shock compression and release based on direct atomistic simulations. Elastic deformation, limited growth and closure of voids, and the typical spall and new nucleation of voids were all observed. In the case of elastic deformation, we observed the oscillatory change of void volume under multiple compression and tension. With the increase of impact velocity, the void volume reduced oscillations to the point of disappearance with apparent strain localization and local plastic deformation. The incomplete or complete collapsed void became the priority of damage growth under tension. An increase in sample length promoted the continuous growth of preset void and the occurrence of fracture. Of course, on the release of strong shock, homogeneous nucleation of voids covered the initial void, leading to a wider range of damaged zones. Finally, the effect of the preset void on the spall strength was presented for different shock pressures and strain rates.
Enhance COVID-19 Mortality Prediction with Human Mobility Trend and Medical Information

In this work, we study national and state-level COVID-19 pandemic data in the United States with the help of human mobility trend data and auxiliary medical information. We analyze and compare various state-of-the-art time-series prediction techniques. We assess a spatio-temporal graph neural network model which forecasts the pandemic course by utilizing a hybrid deep learning architecture and human mobility data. Nodes in the graph represent the state-level deaths due to COVID-19 at any particular time point, edges represent the human mobility trend and temporal edges correspond to node attributes across time. We also study statistical modeling and machine learning techniques for mortality prediction in the United States. We evaluate these techniques on both state and national level COVID-19 data in the United States and claim that the SARIMAX and GCN-LSTM model generated forecast values using exogenous hospital information variables can enrich the underlying model to improve the prediction accuracy at both levels. Our best machine learning models perform 50% and 60% better than the baseline on an average on the national level and state-level data, respectively, while the statistical models perform 63% and 42% better.
Public Employee Use of Social Media at Work: Competency, Collaboration, and Communication of Workplace Policy

Although the growth of social media has changed the way employees communicate at work, our understanding of the related workplace dynamics, particularly in public organizations, is still embryonic. This study fills these research gaps by testing hypotheses, drawn from social cognitive theory and social capital theory, using two sets of data on social media usage patterns and workplace practices among public employees. Our survey data (n = 1,360) analysis revealed that most respondents (more than 72%) spent at least an hour per day on social media while at work, for both work- and non-work-related purposes. Furthermore, public employees with higher levels of social media competence (technical understanding and impact assessment) were more likely to report effective collaboration and seek assistance when needed. The results of scenario-based randomized survey experiment (n = 600) show that the perceived fairness of social media-related termination decisions (or “get dooced”) was influenced by the presence of an explicit workplace social media policy.
Magnetite Nanoparticles in Magnetic Hyperthermia and Cancer Therapies: Challenges and Perspectives

Until now, strategies used to treat cancer are imperfect, and this generates the need to search for better and safer solutions. The biggest issue is the lack of selective interaction with neoplastic cells, which is associated with occurrence of side effects and significantly reduces the effectiveness of therapies. The use of nanoparticles in cancer can counteract these problems. One of the most promising nanoparticles is magnetite. Implementation of this nanoparticle can improve various treatment methods such as hyperthermia, targeted drug delivery, cancer genotherapy, and protein therapy. In the first case, its feature makes magnetite useful in magnetic hyperthermia. Interaction of magnetite with the altered magnetic field generates heat. This process results in raised temperature only in a desired part of a patient body. In other therapies, magnetite-based nanoparticles could serve as a carrier for various types of therapeutic load. The magnetic field would direct the drug-related magnetite nanoparticles to the pathological site. Therefore, this material can be used in protein and gene therapy or drug delivery. Since the magnetite nanoparticle can be used in various types of cancer treatment, they are extensively studied. Herein, we summarize the latest finding on the applicability of the magnetite nanoparticles, also addressing the most critical problems faced by smart nanomedicine in oncological therapies.
Mathematical modeling of economic losses caused by forest fire in Ukraine

In this publication, the authors analysed the current state of economic and environmental damage caused by forest fires in Ukraine. Uncontrolled spread of fire not only causes significant economic damage, but also has dire consequences for the ecology. The burning of large areas of forest leads to a radical change in the ecosystems of the affected region, which can eventually lead to unpredictable results. Thus, the negative indicators of forestry are: quantity of forest fires, units; area of forest lands covered by fires; losses caused by forest fires; burned and damaged forest. It should be noted that the indicators of the consequences of forest fires do not mathematically correlate with the loss of wood cover in Ukraine, which are determined by the data remote sensing of the earth (Forest Global Watch). At the same time, a data set Global Forest Watch on the loss of wood cover concerted with a number of institutions, including the University of Maryland, Google, USGS і NASA, and this resource uses satellite imagery Landsat to display the loss of yearly tree cover with a resolution of 30 × 30 meters. Existing approaches to calculating economic losses are described. The authors’ approach to the determination of losses from forest fires, which is based on statistical and mathematical methods (correlation analysis, mathematical modelling) of scientific research, is proposed. The proposed mathematical model makes it possible to estimate the amount of economic damage caused by forest fires in Ukraine.
GLOBALLY OPTIMAL POINT CLOUD REGISTRATION FOR ROBUST MOBILE MAPPING

Abstract. Point cloud registration algorithms have been studied for several decades. In the SLAM domain, dense local convergence based methods are typically used to register consecutive scans. Since these procedures are not globally optimal, it happens that they converge to a wrong local minimum. This can lead to gross errors during mapping and can make entire datasets unusable. We introduce a new branch and bound based point cloud registration method that is globally optimal. The method is able to reliably determine the global optimum within a given parameter search space. We show how this method can be used in a mapping system as a fallback function to correct gross errors. Using various public datasets, we demonstrate the capabilities of the method.
Associative and Physical Mapping of Markers Related to Fusarium in Maize Resistance, Obtained by Next-Generation Sequencing (NGS)

On the basis of studies carried out in the last few years, it is estimated that maize diseases cause yield losses of up to 30% each year. The most dangerous diseases are currently considered to be caused by fungi of the genus Fusarium, which are the main culprits of root rot, ear rots, and stalk rot. Early plant infection causes grain diminution, as well as a significant deterioration in nutritional value and fodder quality due to the presence of harmful mycotoxins. Therefore, the aim of the research was to identify new markers of the SilicoDArT and SNP type, which could be used for the mass selection of varieties resistant to fusarium. The plant material consisted of 186 inbred maize lines. The lines came from experimental plots belonging to two Polish breeding companies: Plant Breeding Smolice Ltd., (Co., Kobylin, Poland). Plant Breeding and Acclimatization Institute—National Research Institute Group (51°41′23.16″ N, 17°4′18.241″ E), and Małopolska Plant Breeding Kobierzyce, Poland Ltd., (Co., Kobierzyce, Poland) (50°58′19.411″ N, 16°55′47.323″ E). As a result of next-generation sequencing, a total of 81,602 molecular markers were obtained, of which, as a result of the associative mapping, 2962 (321 SilicoDArT and 2641 SNP) significantly related to plant resistance to fusarium were selected. Out of 2962 markers significantly related to plant resistance in the fusarium, seven markers (SilicoDArT, SNP) were selected, which were significant at the level of 0.001. They were used for physical mapping. As a result of the analysis, it was found that two out of seven selected markers (15,097—SilicoDArT and 58,771—SNP) are located inside genes, on chromosomes 2 and 3, respectively. Marker 15,097 is anchored to the gene encoding putrescine N-hydroxycinnamoyltransferase while marker 58,771 is anchored to the gene encoding the peroxidase precursor 72. Based on the literature data, both of these genes may be associated with plant resistance to fusarium. Therefore, the markers 15,097 (SilicoDArT) and 58,771 (SNP) can be used in breeding programs to select lines resistant to fusarium.
Tuning Of Velocity PID-Fuzzy Power System Stabilizer By Particle Swarm Optimization

Power system stabilizer (PSS) is used to damping power system oscillation. It will act as supplementary feedback through the generator excitation system, which produce a component of electrical torque in phase with speed variation. Fuzzy controllers are nonlinear. It is more difficult to set the fuzzy controller gains compared to conventional PID controller. This research proposed velocity (PID-FPSS) its gains are tuned by particle swarm optimization technique (PSO). The objective is to damp local-area oscillation that occur following power system small or large disturbances. The effectiveness of the proposed technique is illustrated by applying the velocity PID-FPSS to a single-machine infinite bus power system that is typically used in the literature to test the performance of power system stabilizers. The simulation has been conducted in MATLAB, SIMULINK (R2013a) package. A comparison between the proposed PID-FPSS and a well-designed robust power system stabilizer (RPSS) confirms the superiority of the proposed technique.
Model predictive control method for CHB multi-level inverter with reduced calculation complexity and fast dynamics

Finite control set model predictive control (MPC) method needs calculations corresponding to the total number of voltage vectors made by power converters. This operating principle of the MPC leads to an increase in the computational load and number of calculations, which dramatically increase proportional to the increase in the level of cascaded H-bridge (CHB) inverters. However, it is difficult to consider all voltage vectors to find the optimal vector for high-level CHB inverters because of the short sampling time. This study proposes a new MPC algorithm to reduce the number of calculations for multi-level CHB inverters, in which only three voltage vectors are considered as optimal vectors regardless of the level of CHB inverters. Consequently, compared with the conventional MPC method for CHB inverters, the proposed MPC method requires fewer calculations and reduces the computational load without affecting the system performance and dynamic response.
Agile and conventional methodologies: an empirical investigation of their impact on software quality parameters

II KEY TERMS . III DECLARATION . IV ETHICS CLEARANCE . V ACKNOWLEDGEMENTS . VI DEDICATION . VII CHAPTER 1: BACKGROUND AND INTRODUCTION . 1 1.1 BACKGROUND . 1 1.2 PROBLEM STATEMENT . 4 1.3 RESEARCH QUESTION . 6 1.4 RESEARCH OBJECTIVES . 7 1.5 RESEARCH MOTIVATION AND RELEVANCE . 8 1.6 RESEARCH DESIGN AND METHODOLOGY . 8 1.7 DEFINITION OF KEY TERMS AND CONCEPTS . 9 1.7. 1 DEFINITION OF AGILITY . 9 1.7. 2 SYSTEM AND INFORMATION QUALITY DEFINITION . 10 1.7. 3 SCOPE AND SOFTWARE QUALITY CRITERIA . 11 1.7. 3.1 SYSTEM QUALITY CRITERIA . 11 1.7. 3.2 INFORMATION QUALITY CRITERIA . 12 1.8 OUTLINE OF DISSERTATION CHAPTERS . 12 CHAPTER 2: LITERATURE REVIEW . 14 2.
On Feature Selection in Short-Term Prediction of Backbone Optical Network Traffic

The knowledge about future traffic volumes is beneficial for the network operators in many areas. Short-term forecasting of multiple traffic types helps with efficient resource utilization by enabling near real-time adjustment. An important issue is the choice of a suitable prediction model to obtain the most accurate traffic forecasts. A machine learning (ML) algorithm picked for this task can be further tuned by an appropriate feature selection. In this paper, we propose three models containing sets of additional input features to improve the prediction quality of different ML algorithms. We evaluate our models on multiple datasets containing diverse types of network traffic. In extensive numerical experiments, we prove the high prediction quality of ML regression algorithms aided by our proposed additional features. Obtained mean absolute percentage errors (MAPE) are, depending on the predicted traffic type, as little as 1–10%.