Miracle-dz/UniFAR-FTUs huggingface dataset:
Layer-wise optimization of elliptical laminates with curvilinear fibers

Abstract The lay-ups of symmetric variable stiffness composite laminated elliptical plates with curvilinear fibers are optimized for the maximum frequencies of the lowest three modes using the layer-wise optimization method. Two design variables and three constraints are considered. The fiber paths are constructed using the method of shifted paths. The laminate is modeled as one curved hierarchical finite element. The formulation is based on the first-order shear deformation theory. The blending function method is used to model accurately the geometry. New results for the optimal lay-ups that maximize the natural frequencies of the lowest three modes are obtained. The method is validated based on convergence test and comparison with published results for symmetric constant stiffness composite laminated elliptical plates with rectilinear fibers. A parametric study is performed showing the effects of aspect ratio, thickness ratio, modulus ratio, number of layers, and boundary constraint on the optimal solutions.
Effects of different exercise loads on serum betatrophin (ANGPTL-8/lipasin) and cartonectin (CTRP-3) levels in metabolic syndrome

Abstract Objectives The number of studies examining the circulating level change of betatrophin and cartonectin in metabolic syndrome applying different loads of exercise is limited. The purpose of the present study was to investigate how different loads of exercises regulate the betatrophin and cartonectin levels in metabolic syndrome induced rats. Methods A total of 24 Wistar-Albino male rats were used in the study. Rats were divided into four groups as follows; G1: control group (fed with standard diet and tap water), G2: metabolic syndrome group (without exercise application), G3: metabolic syndrome + aerobic exercise group (aerobic exercise applied), G4: metabolic syndrome + anaerobic exercise group (anaerobic exercise applied). Betatrophin and Cartonectin levels were determined by ELISA method in serum samples. Results There was a statistically significant difference in betatrophin levels between the groups and this differentiation was caused by G2 (p <0.05). Cartonectin levels were not significantly different between groups (p> 0.05). Conclusions It can be concluded that anaerobic exercises have more positive effects on glucose balance in metabolic syndrome than aerobic exercises, and by regulating betatrophin levels, anaerobic exercises indicate this effect.
Classification of Malaria Parasitized and Uninfected Images Using Machine Learning Approach

Human health is an important concern in the current scenario. Different diseases have adverse effect on human society. Malaria parasite is one of them. So, it is very much essential for the classification of malaria parasitized and uninfected cases at the earliest so that preventive measures can be taken accordingly. Machine Learning (ML) plays an important role for the identification, classification as well as analysis of different medical images which can help in accelerating the diagnosis process. In this work, an attempt has been made for the classification of malaria parasitized and uninfected cases from the analysis of different images using ML based methods. In this paper, the ML based methods such as k-Nearest Neighbor (k-NN), Neural Network (NN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), AdaBoost (AB) and Naïve Bayes (NB) are used for such classification. The methods are evaluated using Classification Accuracy (CA) performance parameter. This work is carried out using Orange 3.26. 0.
AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots

Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot ‘Snail’ with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.
Artificial magnetic conductor using split ring resonators and its applications to antennas

An artificial magnetic conductor using the split‐ring resonators (SRRs) printed on conductor‐backed dielectric substrate is presented. The simulation and measurement verify that the magnetic conductor is successfully accomplished around the resonant frequency. As an antenna application, the characteristics of a horizontal wire antenna placed above the SRR array, as well as a conducting ground plane and a grounded dielectric slab, are investigated and compared. A maximum gain of 4.34 dBi is obtained for the wire antenna above the SRR array. The results confirm that the split resonators can successfully be used to replace the antenna ground plane in order to improve their performance. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 329–334, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21341
On the evaluation of the high-frequency load line in active devices

In this work a de-embedding technique oriented to the evaluation of the load line at the intrinsic resistive core of microwave FET devices is presented. The approach combines vector high-frequency nonlinear load-pull measurements with an accurate description of the reactive nonlinearities, thus allowing one to determine the actual load line of the drain–source current generator under realistic conditions. Thanks to the proposed approach, the dispersive behavior of the resistive core and the compatibility of the voltage and current waveforms with reliability requirements can be directly monitored. Different experiments carried out on a gallium nitride HEMT sample are reported.
A new energy-efficient hierarchical clustering algorithm for wireless sensor networks

Recent advances in wireless communications and microelectro-mechanical systems have motivated the development of extremely small, low-cost sensors that possess sensing, signal processing and wireless communication capabilities. A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. Our algorithm generates a hierarchy of clusterheads and observes that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.
Circumstances and outcomes of falls in hospital for adults with communication disability secondary to stroke: A qualitative synthesis

BACKGROUND: Falls are a significant patient safety concern in hospital. Adult patients with stroke, and those with communication disability, are at an increased risk of falls during their hospital admission compared to patients without stroke or communication disability. OBJECTIVE: The aim of this review is to determine the circumstances and outcomes of falls in hospitalised patients with communication disability following stroke. METHOD: A qualitative synthesis of 16 papers according to the Generic Reference Model of patient safety. This is a secondary analysis of studies in a systematic review of the association between communication disability after stroke and falls in hospitalised patients. RESULTS: In studies including participants with communication disability, falls commonly occurred at the patient bedside, during the day, and in transfers. However, no studies provided individual or group specifically detailing the circumstances and outcomes of falls of the included participants with communication disability. CONCLUSION: Research to date provides scant evidence on the circumstances and outcomes of falls in hospital patients with communication disability after stroke. This review performs a useful function in highlighting a glaring gap in the literature and the urgent need to enrich hospital falls prevention research that includes patients with communication disability following stroke. Findings of this review are discussed in relation to providing a framework for analysis of for future research.
Substrate-integrated divider networks in LTCC with optimized tolerance / isolation properties for Ka-Band satellite systems

Low Temperature Cofired Ceramics (LTCC) technology is increasingly used for creating high-rel solutions, especially in class S products deployed in space. Current research, executed by the german and european space agencies, focuses inter alia on new, compact beam forming networks for Ka-band multiple feed per beam antennas. These networks consist of a high number of dividers and must provide a high isolation between neighbouring cells to ensure compliance with frequency plan regulations. While the LTCC process brings a number of advantages, its thick film printing process and the tolerances of available resistive paste systems introduce difficulties in implementing typical Wilkinson divider networks. This paper presents a novel concept to implement tolerance-optimized dividers, taking special focus on the properties of the LTCC process.
Extraction and Characterization of Agricultural Discarded Sesbania Aculeata Stem Waste as Potential Alternate for Synthetic Fibers in Polymer Composites

ABSTRACT Natural fibers have a wide range of potential applications in green composite manufacturing industrial sectors due to their renewable eco-friendly nature and remarkable properties. In the present work, the physico-chemical, thermal, tensile and morphological properties of Sesbania Aculeata Fibers (SAFs) are presented in first time. Chemical analysis results show that SAFs have relatively higher α-cellulose (70.1 wt.%) with lower hemicelluloses (10.2 wt.%), lignin (12.1 wt.%) and wax (0.6 wt.%) content. Structural analysis of SAFs was performed through fourier transform infrared (FTIR), energy dispersive X-ray microanalyzer (EDX) and NMR spectroscopy. X-ray diffraction (XRD) analysis evidenced that SAFs have a crystallinity index (CrI) of 65.5% with a crystalline size of 4.6 nm. The mechanical properties of the SAFs were investigated through a single fiber tensile test and fiber pull out test. The thermal stability of the SAFs was examined by thermogravimetric analysis (TGA). Based on the results, it is predicted that the SAFs can be used as a potential reinforcement in the polymer matrix composite structure.
Impacts of additive uniaxial strain on hole mobility in bulk Si and strained-Si p-MOSFETs

Hole mobility changes under uniaxial and combinational stress in different directions are characterized and analyzed by applying additive mechanical uniaxial stress to bulk Si and SiGe-virtual-substrate-induced strained- Si (s-Si) p-MOSFETs (metal-oxide-semiconductor field-effect transistors) along ⟨110⟩ and ⟨100⟩ channel directions. In bulk Si, a mobility enhancement peak is found under uniaxial compressive strain in the low vertical field. The combination of ⟨100⟩ direction uniaxial tensile strain and substrate-induced biaxial tensile strain provides a higher mobility relative to the ⟨110⟩ direction, opposite to the situation in bulk Si. But the combinational strain experiences a gain loss at high field, which means that uniaxial compressive strain may still be a better choice. The mobility enhancement of SiGe-induced strained p-MOSFETs along the ⟨110⟩ direction under additive uniaxial tension is explained by the competition between biaxial and shear stress.
Ultrasonic transducers for cure monitoring: design, modelling and validation

The finite element method (FEM) has been applied to simulate the ultrasonic wave propagation in a multilayered transducer, expressly designed for high-frequency dynamic mechanical analysis of polymers. The FEM model includes an electro-acoustic (active element) and some acoustic (passive elements) transmission lines. The simulation of the acoustic propagation accounts for the interaction between the piezoceramic and the materials in the buffer rod and backing, and the coupling between the electric and mechanical properties of the piezoelectric material. As a result of the simulations, the geometry and size of the modelled ultrasonic transducer has been optimized and used for the realization of a prototype transducer for cure monitoring. The transducer performance has been validated by measuring the velocity changes during the polymerization of a thermosetting matrix of composite materials.
DenseSENet: more accurate and robust cross-domain iris recognition

Abstract. With the broad application of iris recognition systems, iris images acquired under cross-sensor and cross-spectrum conditions are increasingly common. Differences in the information stored in these images place high requirements for accuracy and robustness on cross-domain iris recognition algorithms, and current algorithms have not performed well in cross-domain iris recognition. Hence, we propose a dense squeeze and excitation network (DenseSENet) to extract common features in cross-domain iris images and use real-valued features for cross-domain matching. DenseSENet is a deep convolutional neural network based on an attention mechanism. It uses the batch normalization-rectified linear unit-Conv1 × 1 structure to increase the nonlinearity of the network and repeatedly uses features through dense connections to effectively prevent overfitting. The proposed dense squeeze and excitation structure extracts more accurate and stable features by learning the relations between channels and assigning corresponding attention values. The structure of global average pooling-fully connected-softmax is utilized to obtain compact and normalized real-valued feature templates to effectively reduce the size of the iris template. The proposed cross-domain iris recognition framework based on DenseSENet is end-to-end, which transforms the cross-domain matching problem to a many-to-one classification problem. Experiments are conducted on publicly available cross-sensor and cross-spectral iris databases. The results show that the proposed real-valued feature matching based on DenseSENet has better matching accuracy and robustness than the state-of-art cross-domain matching algorithm on two publicly available cross-sensor and cross-spectral iris databases.
PLANAR LASER DIAGNOSTICS AT HIGH REPETITION RATES: ACQUISITION AND ANALYSIS OF TRANSIENT COMBUSTION PROCESSES

This contribution highlights recent advances in laser diagnostics at high repetition rates. Based on recent improvements in all-solid-state, diode-pumped laser and CMOS camera technology, well known methods such as Mie scattering, particle image velocimetry and planar laser-induced fluorescence are adapted and extended to high repetition rates in the kHz-regime and ap
Large Eddy Simulation of Flow and Heat Transfer in a Staggered 45° Ribbed Duct

Results from large eddy simulation (LES) of fully developed flow in a staggered 45° ribbed duct are presented with rib pitch-to-height ratio P/e = 10 and a rib height to hydraulic diameter ratio e/Dh = 0.1. The nominal Reynolds number based on bulk velocity is 47,300. Mean flow and turbulent quantities, together with heat transfer and friction augmentation results are presented. The flow is characterized by a helical vortex behind each rib and a complementary cross-sectional secondary flow, both of which result from the angle of the rib with respect to the mean flow. Averaged velocity profiles at the duct center show excellent agreement with experiments and heat transfer predictions agree well with experiments. Turbulent kinetic energy, shear stress, and heat transfer augmentation ratios show a strong correlation to the rib vortex and the secondary flow. Overall, heat transfer is augmented by a factor of 2.3 compared with a smooth duct and matches experimental data within 2%. Copyright © 2004 by ASME
Adaptive-Learning-Based Vehicle-to-Vehicle Opportunistic Resource-Sharing Framework

With an ever-increasing number of connected devices on roads, it becomes unsustainable to provide nearby specialized execution resources (compute and storage) for servicing innovative applications. Moreover, the vehicular environment being inherently ad hoc and opportunistic, not to mention highly mobile, makes it unsuitable to use traditional cloud computing due to delayed and interrupted services. Thus, there is a possibility to introduce potential collaboration among nearby connected vehicles. However, the underlying decision model for the selection of the most suitable vehicle for task offloading is challenging in such a dynamic environment. In this study, we propose a collaborative vehicular computing framework that adopts online learning for efficient task assignment between local and neighboring computing resources. The underlying workload adaptive task offloading intends to balance out the workload across neighboring vehicles. The framework is compared against three techniques including two adaptive learning techniques in terms of service delay, efficiency, task delivery rate, task failures, and learning regret. The results demonstrate the effectiveness of the proposed resource-sharing network, improving service quality and throughput for servicing innovative intelligent transportation applications.
How data-sharing nudges influence people's privacy preferences: A machine learning-based analysis

INTRODUCTION: Many online services use data-sharing nudges to solicit personal data from their customers for personalized services. OBJECTIVES: This study aims to study people’s privacy preferences in sharing di ff erent types of personal data under di ff erent nudging conditions, how digital nudging can change their data sharing willingness, and if people’s data sharing preferences can be predicted using their responses to a questionnaire. METHODS: This paper reports a machine learning-based analysis on people’s privacy preference patterns under four di ff erent data-sharing nudging conditions (without nudging, monetary incentives, non-monetary incentives, and privacy assurance). The analysis is based on data collected from 685 UK residents who participated in a panel survey. Their self-reported willingness levels towards sharing 23 di ff erent types of personal data were analyzed by using both unsupervised (clustering) and supervised (classification) machine learning algorithms. RESULTS: The results led to a better understanding of people’s privacy preference patterns across di ff erent data-sharing nudging conditions, e.g., our participants’ preferences are distributed in a space of 48 possible profiles more sparsely than we expected, and the unexpected observation that all the three data-sharing nudging strategies led to an overall negative e ff ect: they led to a reduced level of self-reported willingness for more participants, comparing with the case of no nudging at all. Our experiments with supervised machine learning models also showed that people’s privacy (data-sharing) preference profiles can be automatically predicted with a good accuracy, even when a small questionnaire with just seven questions is used. CONCLUSION: Our work revealed a more complicated structure of people’s privacy preference profiles, which have some dependencies on the type of data nudging and the type of personal data shared. Such complicated privacy preference profiles can be e ff ectively analyzed using machine learning methods, including automatic prediction based on a small questionnaire. The negative results on the overall e ff ect of di ff erent data-sharing nudges imply that service providers should consider if and how to use such mechanisms to incentivise their consumers to share personal data. We believe that more consumer-centric and transparent methods and tools should be used to help improve trust between consumers and service providers.
Computer Vision Techniques to Collect Helmet-Wearing Data on Cyclists

Several studies have shown that cyclists can reduce the risk of severe head injuries by wearing a helmet. A system is proposed to collect cyclist helmet usage data automatically from video footage. Computer vision techniques are used to track the moving objects and then to analyze the object trajectories and speed profiles to identify cyclists. Image features are extracted from a region around the cyclist's head. Support vector machines determine whether the cyclist is wearing a helmet. The system can be approximately 90% accurate in cyclist classification when provided with accurate tracks of the cyclist's head. Even for situations in which obtaining video to track a cyclist is challenging, the proposed method provides an effective retrieval system, potentially reducing the number of video records that must be analyzed manually to find instances of cyclists not wearing helmets.
Spontaneous coronary artery dissection presented as posterior ST-Elevation myocardial infarction.

A 48-year-old female, head nurse in a neighboring hospital, during her morning occupation at the hospital, felt an acute pain in the mid-chest, with radiation in her left shoulder. She was a non-smoker, with no cardiovascular history or predisposing factors for atherosclerotic disease, under treatment with thyroid hormone replacement due to hypothyroidism. Although a mild ST-segment depression was revealed at the 12-lead electrocardiogram left precordial leads, the appearance of an ST-segment elevation at the posterior leads V7, V8, and V9 was in favor of posterior – lateral ST-Elevation Myocardial Infarction (STEMI) [Figure 1].