Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women’s Survey

Abstract There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and time points amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother–child dyads from the longitudinal Southampton Women’s Survey were analysed. Continuous diet quality indices (DQI) were derived using principal component analysis from interviewer-administered FFQ collected in mothers pre-pregnancy, at 11- and 34-week gestation, and in offspring at 6 and 12 months and 3, 6–7 and 8–9 years. A forward modelling approach from 1 to 6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian information criteria, probability of class assignment, ratio of the odds of correct classification, group membership and entropy. Both methods suggested that five classes were optimal, with a strong correlation (Spearman’s = 0·98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM = 4 % and GBTM = 5 %), poor-medium (23 %, 23 %), medium (39 %, 39 %), medium-better (27 %, 28 %) and best (7 %, 6 %). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.
Half-Mode Slab Air-Filled Substrate Integrated Waveguide (SAFSIW)

This paper reports the proposal and development of a half-mode slab air-filled substrate integrated waveguide, a solution of compromise between half-mode air-filled substrate integrated waveguide (SIW) and half-mode SIW. Theoretical analysis and simulated results are presented to understand the operation mechanism. For experimental validation, a transition to SIW is reported and used in a back-to-back configuration. With low-cost FR-4 to cover the structure, the measured results show an insertion loss of 1.1 ± 0.17 dB together with a reflection coefficient better than −21.5 dB over the entire Ka-band. Using two transmission lines of different lengths, an attenuation of 0.298 dB/cm is obtained at 33 GHz. The half-mode slab air-filled SIW provides high degrees of freedom to control the attenuation and phase constants for the design of microwave and millimeterwave components including couplers and leaky-wave antennas.
Proposal for an Active PV Array to Improve System Efficiency During Partial Shading

Owing to time-varying external environmental influences, undesirable string mismatches frequently occur in photovoltaic (PV) power generation systems, which will cause conventional PV systems to experience extremely severe power losses. Under partial shading conditions (PSCs) caused by passing clouds, neighboring buildings, and animal excrement, the characteristic curve of the PV string becomes complicated with multiple peaks, which increases the difficulty of determining the maximum power point (MPP) of the array. However, it is essential to ascertain the global maximum power point (GMPP) to extract the full power of each PV module. Therefore, we propose a new type of PV generation module constituting an active PV array (APV) system as well as its control strategy. It can ensure that the system continuously operates in the optimal state when the solar irradiation intensity drops drastically due to unanticipated environmental variables. Depending on the experiment of two PV arrays under identical conditions, the efficiency and reliability of the APV system are substantiated. Meanwhile, in the case where the external environmental conditions are deliberately changed, the experimental results fully illustrate the adaptability of the APV system to diverse harsh conditions.
A new topology of resonant inverter including a piezoelectric component

This document introduces a new resonant inverter topology based on the Φ2 inverter. This new topology uses multiple resonance to pass fundamental and third harmonic at the switch terminals by filtering second harmonic with a piezoelectric component. This helps to reduce the voltage stress on the switch. Adding a piezoelectric resonator allows a better frequency stability and a better performance than a classic LC circuit. The structure and the design of the topology are described. Experimental results demonstrating the feasibility of the topology and a comparison with simulation results are done. The prototype works at 1.4MHz for an input voltage of 20V, an AC output voltage of 20V and an output power of 15W.
Study on the mechanical properties of ultra-low dielectric film by tensile test

The ultra-low dielectric constant (ULK) materials is implemented in the copper/low-k interconnection structures in industry in order to minimize the RC delay and crosstalk noise. The mechanical property of ULK dielectrics is weak compared to traditional dielectrics. So it poses a significant risk to the device reliability, not only in the fabrication process, but also in packaging processes and reliability tests. When using numerical simulations to assess the structural integrity associated with low-k integration, accurate mechanical property of ULK materials is also an important parameter that needed to be measured. In this paper, we present a method of measuring the mechanical properties of ULK materials. This method is suitable for a variety of materials which can be deposited or grown on a silicon substrate. The fabricating processes mainly include lithography, temporary bonding, grinding, Si etching and debonding. In addition, we designed two kinds of test structures. One is with pre-crack, and the other is without pre-crack. Each structure has two kinds of geometry sizes. The tensile test was done by using the in-situ tensiometer apparatus with uniaxial stretching. The stress-strain diagrams of the specimens show an elastic modulus $\mathbf{E=1.2}$ GPa and tensile strength is about 13.5 Mpa for the structure without pre-crack, which is lower than that of bulk low-k material. The samples with two kinds of geometry sizes have similar modulus values which can be converted by the measured results from the diagram. While other specimens show a critical energy release rate ${\mathrm{G}_{\mathrm{c}}=0.25\mathrm{J}/\mathrm{m}^{2}}$ for the structure with pre-crack, which is also lower than that of bulk material. The results illustrate that the ability to resist fracture of low-k thin films is weak. It provides a possible method to measure the mechanical properties of ULK thin films.
DOHMO: Embedded Computer Vision in Co-Housing Scenarios

This paper presents DOHMO, an embedded computer vision system where multiple sensors, including intelligent cameras, are connected to actuators that regulate illumination and doors. The system aims at assisting elderly and impaired people in co-housing scenarios, in accordance with privacy design principles. The paper provides details of two core elements of the system: The first one is the BOX-IO controller, a fully scalable and customizable hardware and software IoT ecosystem that can collect, control, and monitor data, operational flows and business scenarios, whether indoor or outdoor. The second one is the embedded 3DEverywhere intelligent camera, a device composed of an embedded system that receives input data provided by a 3D/2D camera, analyzes it, and returns the metadata of this analysis. We illustrate how they can be connected and how simple decision mechanisms can be implemented in such a framework. In particular, illumination can be triggered on and off by the detected presence of people, overcoming the limitations of typical sensors, while doors can be opened or closed based on person trajectories in an intelligent manner. To substantiate the proposed system, numerous experiments are performed in a lab and a co-housina scenario.
Computer 3D-modeling of viscoelastic deformation of the aorta

In this study the viscoelastic deformation of human aortic bifurcation was investigated. An algorithm of automated geometric model construction was implemented in the parametric form. We considered the vessel as a hollow cylinder with the walls as an isotropic material. The analysis was carried out using the finite element method. The load was set as close as possible to the real one using a sphygmogram. Different types of pulse waveforms were presented: typical pulse waveform with normal arterial pressure, typical waveform of old myocardial infarction and arrhythmia and considered their influence on the hysteresis loops.
Ocean Oil Spill Classification with Polarimetric SAR Based on VGG16 Multi-Feature Extraction

Fully polarimetric synthetic aperture radar (PolSAR) technology performs well in oil spill detection because of its rich target scattering information. Aiming at the problem that oil spill detection scheme based on polarization feature and deep learning has large sample requirements and high information redundancy, we propose a classification scheme combining with RBFSVM classifier and VGG16 based on transfer network. The ability of our method to distinguish different oil films is analyzed through a radarsat-2 POLSAR image of the North Sea oil spill experiment.
Angular Power Distribution in 60 GHz Wireless Uplink for Vehicle-to-Infrastructure Scenarios

In emerging fifth-generation (5G) networks, millimeter-wave bands will be used. Due to high atmospheric attenuation, the 60 GHz band is planned to be used for indoor and outdoor line-of-sight communication. Numerous uses of this band for vehicle-to-infrastructure (V2I) connectivity are indicated in the literature. In this paper, we propose a novel method for determining angle-of-arrival power distribution in wireless uplink. In this method, the three-dimensional geometry-based stochastic channel model is used for generating propagation paths. A multi-ellipsoid geometry of this model is based on the synthesized power delay profile obtained in the measurement campaign for the analyzed V2I scenario. Finally, we describe the future directions of mapping environmental influence on the angular power distribution.
Interference Mitigation Against FMCW Signals Applicable Into IEEE 802.11ad for Intra-Vehicle Communications

It is expected that IEEE 802.11ad is used as intra-vehicle communications for high-speed transmission. Currently, since motion detection by frequency modulated continuous wave (FMCW) signals in mmW has been available, issues on interference between IEEE 802.11ad and FMCW signals have been raised. To solve the issues, an interference mitigation method applicable into IEEE 802.11ad against FMCW signals has been proposed. In this method, FMCW signals are detected by summation of the energy in frequency domain of interfered received signals. Interference suppression is achieved by nulling the frequencies where it is determined that FMCW signals are detected. The method does not require any changes of IEEE 802.11ad specifications. Simulation results show that significant improvement on BER has been achieved by introducing the method over SIR range from 0 dB to 8 dB. Experiment on the method has been conducted to evaluate performance of the method in a vehicle. The results obtained by the experiment have shown that the method successfully suppresses FMCW signals.
A 0.6-to-1.2 V Scaling Friendly Discrete-Time OTA-Free ΔΣ-ADC for IoT Applications

This work presents a scaling-friendly Discrete-Time (DT) 2nd order ΔΣ ADC using a passive switched capacitor (SC) integrator and a novel VCO-Frequency Delta-Sigma Modulator (FDSM). The proposed VCO-FDSM employs a linear VCO with negative feedback. A novel all-digital frequency quantizer quantizes the VCO frequency into a multibit output with inherent dynamic element matching (DEM) property, which provides the 1st order mismatch noise shaping in the feedback DAC. The passive SC integrator at the input provides an additional order of noise shaping to the VCO-FDSM. This highly digital architecture facilitates a trade-off between the supply voltage and the operational bandwidth (BW) and makes the design suitable for energy-efficient IoT applications. The test chip fabricated in 65nm CMOS technology operates at the supply voltages ranging from 0.6 V to 1.2 V and achieves a 75.7dB peak SNR in 3.5kHz BW, consuming 7.78 µW with a FoMs of 162.2dB at a 1V supply voltage.
High-Isolation Dual-Polarized Leaky-Wave Antenna With Fixed Beam for Full-Duplex Millimeter-Wave Applications

In this article, a high-isolation dual-polarized leaky-wave antenna (LWA) with a fixed beam is proposed for full-duplex millimeter-wave (mm-wave) applications. The proposed antenna consists of a dual-polarized LWA array and an orthogonally differential feeding network. The proposed dual-polarized LWA array is developed based on the proposed LWA unit cell, which is suitable for differential feed, and the open stopband is eliminated. The proposed feeding network provides two pairs of orthogonally differential excitation for the LWA array to realize the fixed beam with orthogonal polarization in boresight direction; the high interport isolation is implemented as well. The prototype for demonstration purposes is fabricated and measured. The measured results show that the proposed antenna exhibits an operating band from 27.6 to 29.5 GHz with a maximum gain of up to 24 dBi. Furthermore, the measured interport isolation is higher than 51 dB within the operating band, where the maximum isolation is up to 60 dB. The measured cross-polarization levels are also very low, less than −27 dB. The proposed antenna features high gain, high interport isolation, and good co-/cross-polarization isolation, achieving a good Tx/Rx separation for the in-band full-duplex applications at mm-wave frequencies.
Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning

Accurate diagnosis of corn crop diseases is a complex challenge faced by farmers during the growth and production stages of corn. In order to address this problem, this paper proposes a method based on K-means clustering and an improved deep learning model for accurately diagnosing three common diseases of corn leaves: gray spot, leaf spot, and rust. First, to diagnose three diseases, use the K-means algorithm to cluster sample images and then feed them into the improved deep learning model. This paper investigates the impact of various k values (2, 4, 8, 16, 32, and 64) and models (VGG-16, ResNet18, Inception v3, VGG-19, and the improved deep learning model) on corn disease diagnosis. The experiment results indicate that the method has the most significant identification effect on 32-means samples, and the diagnostic recall of leaf spot, rust, and gray spot disease is 89.24 %, 100 %, and 90.95 %, respectively. Similarly, VGG-16 and ResNet18 also achieve the best diagnostic results on 32-means samples, and their average diagnostic accuracy is 84.42% and 83.75%. In addition, Inception v3 (83.05%) and VGG-19 (82.63%) perform best on the 64-means samples. For the three corn diseases, the approach cited in this paper has an average diagnostic accuracy of 93%. It has a more significant diagnostic effect than the other four approaches and can be applied to the agricultural field to protect crops.
A Compact Reconfigurable 1-D Periodic Structure in GaAs MMIC with Stopband Switching, Dual-Band Operation and Tuning Capabilities

This paper presents a systematic study of a compact and reconfigurable periodic structure in GaAs MMIC technology. Compactness is achieved by the introduction of spiral inductors in a conventional unit cell without disturbing the reactive loading mechanism. The proposed architecture exhibits a 28.3% wider stopband with 62.6% smaller footprint compared to a conventional structure. The compactness and bandwidth improvement in the proposed structure is explained with the help of dispersion and circuit analysis. The reconfigurability built into the design using PIN diodes allows stopband switching, dual-band operation and tuning capabilities with the mere use of a single reactive load in its unit cell. To the best of the authors knowledge, it is the first time a reconfigurable MMIC implementation is realized using the proposed structure or even the conventional design. As a guide to design, sensitivity analysis to filter performance is presented for important structure parameters. Switching element parasitics are discussed in two ways: firstly, with the design and measurement of structures with idealized switching conditions and in second, with the circuit and full-wave EM modelling of the finite periodic structure with the actual PIN diodes. The on-chip measurements of the fully reconfigurable filter show excellent agreement with simulations.
Designing a generalised reward for Building Energy Management Reinforcement Learning agents

The reduction of the carbon footprint of buildings is a challenging task, partly due to the conflicting goals of maximising occupant comfort and minimising energy consumption. An intelligent management of Heating, Ventilation and Air Conditioning (HVAC) systems is creating a promising research line in which the creation of suitable algorithms could reduce energy consumption maintaining occupants' comfort. In this regard, Reinforcement Learning (RL) approaches are giving a good balance between data requirements and intelligent operations to control building systems. However, there is a gap concerning how to create a generalised reward signal that can train RL agents without delimiting the problem to a specific or controlled scenario. To tackle it, an analysis and discussion is presented about the necessary requirements for the creation of generalist rewards, with the objective of laying the foundations that allow the creation of generalist intelligent agents for building energy management.
Comparison between two numerical methods for the computation of thermal conductivities of particulate composites: FEM and GeoDict

The thermal interface material in direct contact with chip, termed as TIM1, is a kind of composite material with thermally conductive particulate fillers. To better understand the thermal conduction mechanism and promote the design of TIM1, numerical studies are very essential. In this work, we compare two numerical methods, finite element method (FEM) and GeoDict, to compute the effective thermal conductivity of the particulate composites, in which the interface thermal resistance is considered. Firstly, we employed the Monte-Carlo algorithm as well as energy minimization method to generate the representative volume elements (RVEs), and the particulate fillers are dispersed randomly in the RVEs. The interface thermal resistance is introduced in the numerical modeling by an imperfect interface between various components. The effective thermal conductivity is evaluated over RVEs by FEM and GeoDict, respectively, with the efficiency and accuracy of the two algorithms compared. The drawback of FEM is that its pre-treatment is quite difficult for particulate composites of high filler content because of the requirements of fine mesh, which also calls for a large demand of memory. In contrast, GeoDict solved the problem using fast Fourier transform algorithm based on pixels of the RVEs, showing high efficiency and can be extended to any kind of additives. Finally, we show that GeoDict could be an efficient tool for high-throughput screening of formulas of TIM1 for advanced electronic packaging.
Semi-supervised Anomaly Detection Algorithm using Probabilistic Labeling (SAD-PL)

To detect abnormal data via semi-supervised learning, unlabeled data are generally assumed to be normal data. This assumption, however, causes inevitable performance degradation when a small fraction of abnormal data is included in the unlabeled dataset. To overcome the degradation and to maintain stable detection performance, we propose a semi-supervised anomaly detection algorithm using probabilistic labeling (SAD-PL) for unlabeled data. The proposed SAD-PL is composed of two steps: (1) estimating local outlier factor (LOF) scores of latent vectors from both labeled and unlabeled data and (2) estimating labeling probability on the unlabeled data by using the prior missing probability of the labeled data via the Neyman-Pearson (NP) criterion. The SAD-PL runs iteratively by using the proposed complementary learning functions until the rate of label changes is lower than the predefined threshold. Experimental results reveal that the SAD-PL shows superior detection probability over the existing algorithms and stable performance regardless of the normal to abnormal data ratio in unlabeled data and the ratio of change variation of unlabeled data statistics to labeled data statistics.
Mechanical Response of BNNS-reinforced Aluminum Composites under Uniaxial Compression

Boron nitride nanosheet (BNNS) has been widely used as the reinforcing material in metal matrix composites because of excellent thermal, electrical and mechanical properties. Chirality directions of boron nitride nanosheet include armchair and zigzag. In this paper, the influence of temperature on the mechanical reponses of aluminum (Al) and BNNS/Al composite under uniaxial compression loads haa been investigated by molecular dynamics (MD) simulations. MD simulation results show that there is a negative relationship between temperature and ultimate stress of the above material, and ultimate stress decreases as the temperatue increases. The relationship between critical strain and temperature is the same response. In addition, BNNS plays a significant role in improving ultimate stress and Young's modulus of the nanocomposite, despite its small volume fraction. However, compression loads along different chiral directions can also result in some diversities of mechanical response. For different temperature conditions, the ultimate stress of zigzag BNNS/Al composite is bigger than others in general, and armchair BNNS/Al composite shows the better temperature stability.
TCP-Aware OFDMA Transmission Based on Traffic Intensity in Downlink and Uplink Directions in IEEE 802.11ax Wireless LANs

In recent years, wireless local area networks (WLANs) have become widespread, and the number of mobile stations (MSs) connected to WLANs and the number of WLANs installed have been increasing. In addition, owing to the diversification of applications using WLANs, transmission opportunities have been proportionally greater in the uplink direction, from MSs to base stations (BSs), than ever. Furthermore, most of this data is transmitted using Transmission Control Protocol (TCP). Therefore, WLANs need to provide opportunities for frame transmission in each direction according to the ratio of TCP traffic intensity. In this study, we propose a TCP-aware frame transmission method that makes effective use of Orthogonal Frequency Division Multiple Access (OFDMA) transmission as introduced in the IEEE 802.11ax standard to improve the TCP throughput of the entire WLAN while realizing the allocation of such frame transmission opportunities. In an environment where TCP flows are transmitted in both uplink and downlink directions, the proposed method transmits each frame by using OFDMA transmission and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) adaptively and selectively according to the buffering status of TCP data segments and ACKs in each direction.