Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-based microburst identification using a hybridized extended Kalman filter with genetic algorithm

Microburst (MB) wind shear is one of the most important meteorological dangers threatening the aircraft (AC) safety and the life of passengers. Though there are some ground-based 3D Lidar systems to detect low-level MB wind shears to alert the pilots, there have been fewer scientific attempts to identify model-based MB parameters via AC onboard air and position data. The latter refers to the development and identification of an acceptable MB model upon which an automatic flight control (AFC) system can be designed to control the AC through wind shear microburst. In essence, accurate knowledge of MB model is an essential prerequisite for design and analysis of AFC systems that can safely fly the AC against microbursts, especially in crucial phases of flight such as takeoff and landing. The present study focuses on online estimation of MB parameters whose results pave the way for effective MB autopilot designs for safe flights through MB. The proposed task is accomplished via a model-based approach using the AC six degrees of freedom (6 DoF) equations of motion (EOM) integrated with the latest verified model of the MB utilizing the extended Kalman filter (EKF). In addition to a sensitivity analysis to determine the key MB model parameters, the performance of the estimation process is enhanced via a hybridization of the genetic algorithm (GA) with the EKF. The results are promising and indicate that the proposed scheme can identify the MB model parameters with sufficient accuracy needed for online applications with AFC design.
Energy-Efficient Power Allocation in Downlink Multi-Cell Multi-Carrier NOMA: Special Deep Neural Network Framework

Energy-efficient resource allocation for multi-cell multi-carrier non-orthogonal multiple access (MCMC-NOMA) is a challenging task due to the interference and other related factors, which makes obtaining an applicable solution in the real-time is even more challenging. In this paper, we aim to design a model capable of efficient resource allocation in real-time. We formulate our problem as energy efficiency (EE) maximization. First, we propose an iterative solution to handle user scheduling and power allocation, which is not suitable for real-time application. Next, we design a dual-pipeline augmented deep convolutional neural network (ADCNN) to handle the power allocation in real-time. The first pipeline is to extract high quality features and spatially connect and refine them using attention-based network. The second pipeline is to extract low-quality spatial features. Because more discriminative features are obtained through fusion of the high and low quality features, a better prediction of power allocation can be obtained. Simulation results show the adequacy of the proposed model for the real-time application and larger problems compared with other models such as deep neural network (DNN).
PAGAMENTO POR SERVIÇOS AMBIENTAIS COMO ESTRATÉGIA PARA FORTALECIMENTO DA AGRICULTURA FAMILIAR EM UM MUNICÍPIO PAULISTA

Este estudo analisa dois programas municipais, inter-relacionados e complementares, um voltado ao fortalecimento da agricultura familiar fruticultora e boas práticas de uso do solo, e outro ao pagamento por serviços ambientais, visando restauração florestal atrelada à provisão de água para abastecimento público em Louveira (SP). O objetivo foi identificar junto a gestores locais os fatores que influenciam o ambiente operacional dos programas, limitando ou fomentando sua efetividade, categorizando-os entre pontos fortes, fracos, potencialidades e ameaças. Foram realizadas entrevistas semiestruturadas com representantes de organizações estratégicas para sua execução, e os fatores são apresentados e analisados com a utilização de uma matriz SWOT. Dois fatores foram apontados com frequência como ameaças à continuidade dos programas: a dependência de decisões polí­ticas e fonte de recursos para custeá-los. Praticar a sustentabilidade por meio de boas práticas, a preocupação ambiental e o pagamento de subvenção ao agricultor foram as principais forças identificadas. Todas as fraquezas e algumas ameaças referem-se à participação e entendimento dos agricultores em relação aos programas, o que indica que são necessárias estratégias fomentem a aproximação dos agricultores aos programas, tornando-os mais que apenas receptores das ações desenvolvidas.
Privacy‐preserving CNN feature extraction and retrieval over medical images

Online medicine diagnosis based on pathological images has been regarded as a pervasive method due to the advances in electronic healthcare and Internet of Things (IoT), however, it also causes storage and computing stress on the local IoT devices. To solve this problem, a nature way is to outsource images to cloud servers. Unfortunately, a range of security and privacy issues arise while delegating both storage and computing to the untrusted external servers. In this paper, we present a privacy‐preserving feature extraction and retrieval scheme over medical images, which allows images storage and processing on two separate cloud servers. We share the images using secret sharing technology and design a set of secure two‐party computation protocols between the two cloud servers. Then a privacy‐preserving convolutional neural networks (CNN) framework is constructed to achieve feature extraction, classification and retrieval of images in the encrypted domain. We analyse and evaluate our scheme in terms of both security and efficiency. The results indicate that the proposed secure protocols in our scheme can significantly reduce the computation overhead while protecting the privacy of images as well as the data generated during execution on cloud servers and final results. The performance of our scheme in image feature extraction, classification and retrieval is at a similar level comparable to the scheme based on original CNN in plaintext.
Development of industrial scale PVC nanocomposites with comprehensive enhancement in dielectric properties

This study aims to develop large-scale polyvinyl chloride (PVC) nanocomposites for industrial application with power cables. To achieve this goal, PVC/silicon dioxide and PVC/titanium dioxide nanocomposites were fabricated with two different loadings of nanoparticles: 0.3 and 0.6 wt. %, in the presence of a suitable coupling agent that was used to reduce the agglomeration of nanoparticles and enhance the compatibility with polymer matrix. The coupling agent used in this study was the amino silane, and the process followed in the nanocomposites preparation was the melt blending method. The dielectric properties of these synthesised nanocomposites were studied by measuring the AC dielectric breakdown strength under the uniform field, then compared with the simulation results. The relative permittivity ( ɛ r), loss tangent (tan δ ), and DC electrical conductivity ( σ ) were also measured under frequencies ranging from 20 Hz to 1 MHz. In addition, the internal discharge measurements are performed using the traditional needle-plane configuration with the help of phase-resolved partial discharge (PD) analyser. This technique is used to analyse the PDs activity with respect to the phase angle of the applied voltage. It was found that the dielectric breakdown strength and PD resistance of the prepared samples are increased higher than that of the neat PVC; however, the ɛ r, tan δ , and σ at 50 Hz are decreased.
A Hybrid Load Matching Method for WPT Systems to Maintain High Efficiency Over Wide Load Range

The efficiency of a wireless power transfer (WPT) system is highly dependent on the load resistance. However, the load resistance, such as the battery, is not constant during the charging process. Therefore, a crucial challenge for a WPT system is to keep high efficiency over wide load range. In this work, a hybrid load matching method for WPT systems is proposed to achieve high efficiency. The basic principle of this system is to use a hybrid load matching method, i.e., adding an <inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> resonant matching network and choosing the rectifier operation modes (full-/half-bridge), to convert the load to an optimal range. Then, the system efficiency can be kept at the top regions of the efficiency curves. Finally, a prototype is built to validate the performance of the WPT system with the proposed approach. The experimental results show that the optimal load of the WPT system can be transformed from 30 to 60, 120, and 240 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>, and the efficiency of the proposed system can be maintained above 89% from 10 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> (527.8 W) to 600 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> (8.64 W).
Gunshots Localization and Classification Model Based on Wind Noise Sensitivity Analysis Using Extreme Learning Machine

The gunshot event localization and classification have numerous real-time applications. The study is also useful for steering the video camera and guns in the directed direction. This paper proposes a framework that can be used for a surveillance system to accurately localize and classify the type of gunshots impregnated with wind noise. The main contribution of this paper is the localization of the gunshot for the very first time using Hadamard product with wavelet de-noising in windy conditions. We have evaluated our framework on airborne gunshots acoustic dataset, and a derived (simulated) sound dataset, as an offline scenario, using four microphones’ geometry. For localization, the proposed system outperformed with an accuracy of 99.95%. The other contribution is a sensitivity-based comprehensive examination of gunshot sound signals, with normal to strong wind noise of varying SNRs, for machine learning and deep learning classifiers to categorize the type of gunshots. For classification, it has been found, not known before for the gunshots dataset, that ELM is robust for original, normal, and strong windy environments with an accuracy of 93.01%, 91.61%, and 88.11% respectively with the threshold SNR. A comprehensive comparison of recent techniques with the proposed approach has also been added.
Making the Rounds: Exploring the Role of Circulating Tumor DNA (ctDNA) in Non-Small Cell Lung Cancer

Advancements in the clinical practice of non-small cell lung cancer (NSCLC) are shifting treatment paradigms towards increasingly personalized approaches. Liquid biopsies using various circulating analytes provide minimally invasive methods of sampling the molecular content within tumor cells. Plasma-derived circulating tumor DNA (ctDNA), the tumor-derived component of cell-free DNA (cfDNA), is the most extensively studied analyte and has a growing list of applications in the clinical management of NSCLC. As an alternative to tumor genotyping, the assessment of oncogenic driver alterations by ctDNA has become an accepted companion diagnostic via both single-gene polymerase chain reactions (PCR) and next-generation sequencing (NGS) for advanced NSCLC. ctDNA technologies have also shown the ability to detect the emerging mechanisms of acquired resistance that evolve after targeted therapy. Furthermore, the detection of minimal residual disease (MRD) by ctDNA for patients with NSCLC after curative-intent treatment may serve as a prognostic and potentially predictive biomarker for recurrence and response to therapy, respectively. Finally, ctDNA analysis via mutational, methylation, and/or fragmentation multi-omic profiling offers the potential for improving early lung cancer detection. In this review, we discuss the role of ctDNA in each of these capacities, namely, for molecular profiling, treatment response monitoring, MRD detection, and early cancer detection of NSCLC.
Increasing safe design practice within the engineering curriculum

Context: The Australian Work Health and Safety Strategy 2012-2022 contains two national Action Areas of direct relevance to Engineering Educators: Healthy and safe by design and Health and safety capabilities. The need for designs to be safe, and for student engineers to develop competencies in this area, is not new. However, poor design of machinery plant and powered tools continues to kill and injure Australian workers. Safe Work Australia (2014) reports that between 2006 and 2011 63, workrelated deaths were determined to be caused by the unsafe design of machinery, plant and power tools, or design-related factors contributed to the fatality. A further 125 fatalities were considered as possibly design-related. It is sad fact that many of these deaths were preventable with existing design solutions. Good design can eliminate (or minimise the impact of) the major physical, biomechanical and psychosocial hazards associated with work. From an engineering education perspective it is necessary to increase awareness amongst educators and students of these processes such that consideration of safe design is inherent to the engineering design process and not simply an added regulatory requirement. Purpose: Safe design is not a separate activity or series of activities, but is integral to the engineering process regardless of sector or discipline. This paper reviews the role of engineering educators in understanding, promoting and embedding safe design principles within the engineering curricula. Approach: The paper explores how safe design has been incorporated into engineering education since the early 1990s, and assesses the effectiveness of available resources and teaching practice. Changes to the legislative environment throughout this time are also described, to provide context and articulate implications for engineering educators. Results: The importance of safe design is recognised and resources do exist to support engineering educators to embed safe design principles within curriculum. The paper provides a series of recommendations to mainstream the available resources, highlights characteristics of effective practice and identifies areas for further professional development of engineering educators who are not familiar with safe design principles. Conclusions: In order to develop graduates who are safe design practitioners, the model of engineering design introduced within the engineering curriculum must demonstrate that safe design is an inherent user requirement for all projects. This requires engineering educators to be familiar with human centred engineering design and how this impacts traditional technical design outcomes.
Physiological and Pathological Functions of Neuronal Hemoglobin: A Key Underappreciated Protein in Parkinson’s Disease

The expression of Hemoglobin (Hb) is not restricted to erythrocytes but is also present in neurons. Hb is selectively enriched in vulnerable mesencephalic dopaminergic neurons of Parkinson’s disease (PD) instead of resistant neurons. Controversial results of neuronal Hb levels have been reported in postmortem brains of PD patients: although neuronal Hb levels may decline in PD patients, elderly men with higher Hb levels have an increased risk of developing PD. α-synuclein, a key protein involved in PD pathology, interacts directly with Hb protein and forms complexes in erythrocytes and brains of monkeys and humans. These complexes increase in erythrocytes and striatal cytoplasm, while they decrease in striatal mitochondria with aging. Besides, the colocalization of serine 129-phosphorylated (Pser129) α-synuclein and Hb β chains have been found in the brains of PD patients. Several underlying molecular mechanisms involving mitochondrial homeostasis, α-synuclein accumulation, iron metabolism, and hormone-regulated signaling pathways have been investigated to assess the relationship between neuronal Hb and PD development. The formation of fibrils with neuronal Hb in various neurodegenerative diseases may indicate a common fibrillization pathway and a widespread target that could be applied in neurodegeneration therapy.
Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM

Background and Objectives: Clinical diagnosis has become very significant in today’s health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosis of brain tumors can be improved by an early and precise diagnosis based on magnetic resonance imaging. For computer-aided diagnosis methods to assist radiologists in the proper detection of brain tumors, medical imagery must be detected, segmented, and classified. Manual brain tumor detection is a monotonous and error-prone procedure for radiologists; hence, it is very important to implement an automated method. As a result, the precise brain tumor detection and classification method is presented. Materials and Methods: The proposed method has five steps. In the first step, a linear contrast stretching is used to determine the edges in the source image. In the second step, a custom 17-layered deep neural network architecture is developed for the segmentation of brain tumors. In the third step, a modified MobileNetV2 architecture is used for feature extraction and is trained using transfer learning. In the fourth step, an entropy-based controlled method was used along with a multiclass support vector machine (M-SVM) for the best features selection. In the final step, M-SVM is used for brain tumor classification, which identifies the meningioma, glioma and pituitary images. Results: The proposed method was demonstrated on BraTS 2018 and Figshare datasets. Experimental study shows that the proposed brain tumor detection and classification method outperforms other methods both visually and quantitatively, obtaining an accuracy of 97.47% and 98.92%, respectively. Finally, we adopt the eXplainable Artificial Intelligence (XAI) method to explain the result. Conclusions: Our proposed approach for brain tumor detection and classification has outperformed prior methods. These findings demonstrate that the proposed approach obtained higher performance in terms of both visually and enhanced quantitative evaluation with improved accuracy.
Peripheral Nerve Stimulation for Treatment of Cluneal Neuropathy Case Study

Chronic low back pain is a prevalent and sometimes debilitating condition. This case report describes a 69-year-old female presenting with axial spine pain. The pain was inadequately controlled by opioids as she was treated unsuccessfully with hydrocodone and remained to have the pain between 7/10 and 10/10. Peripheral neural stimulation (PNS) was trialed and then used to control her pain. PNS is a device-based treatment option that appears effective in a subset of patients. It has been effectively used to treat many different chronic pain syndromes. The patient responded well to the treatment, with her pain intensity going down to between 2/10 and 5/10 on the same scale. She was able to discontinue her use of opioids. PNS can be a safe and effective treatment in patients who have not responded well to pharmacologic analgesia.
A Well-Balanced Unified Gas-Kinetic Scheme for Multicomponent Flows under External Force Field

The study of the evolution of the atmosphere requires careful consideration of multicomponent gaseous flows under gravity. The gas dynamics under an external force field is usually associated with an intrinsic multiscale nature due to large particle density variation along the direction of force. A wonderfully diverse set of behaviors of fluids can be observed in different flow regimes. This poses a great challenge for numerical algorithms to accurately and efficiently capture the scale-dependent flow physics. In this paper, a well-balanced unified gas-kinetic scheme (UGKS) for a gas mixture is developed, which can be used for the study of cross-scale multicomponent flows under an external force field. The well-balanced scheme here indicates the capability of a numerical method to evolve a gravitational system under any initial condition to the hydrostatic equilibrium and to keep such a solution. Such a property is crucial for an accurate description of multicomponent gas evolution under an external force field, especially for long-term evolving systems such as galaxy formation. Based on the Boltzmann model equation for gas mixtures, the UGKS leverages the space–time integral solution to construct numerical flux functions and, thus, provides a self-conditioned mechanism to recover typical flow dynamics in various flow regimes. We prove the well-balanced property of the current scheme formally through theoretical analysis and numerical validations. New physical phenomena, including the decoupled transport of different gas components in the transition regime, are presented and studied.
Driver Behavioral Cloning for Route Following in Autonomous Vehicles Using Task Knowledge Distillation

Planning appropriate driving trajectory for route following is an important function for autonomous driving. Behavioral cloning, which allows automatic trajectory learning and improvement, has been effectively used in driving trajectory planning. However, existing behavioral cloning methods always rely on large scales of time-consuming, laborious, and reliable labels. To address this problem, this paper proposes a new off-policy imitation learning method for autonomous driving using task knowledge distillation. This novel method clones human driving behavior and effectively transfers the driving strategies to domain shift scenarios. The experiment results indicate that our method can lead to satisfactory route-following performance in realistic urban driving scenes and can transfer the driving strategies to new unknown scenes under various illumination and weather scenarios for autonomous driving.