Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of a Faster Thermal Cycling Profile on PV Cell Interconnect Cycles to Failure

The thermal cycling tests used in the photovoltaic industry are slow, requiring over a month for 200 cycles and five months for 800 cycles. A faster alternative would reduce the cost of delay for module qualification. This study investigates the correlation between a baseline thermal cycling profile and profiles that are three and six times faster. Interconnect failure data for each profile was gathered across eight splits of varying material combinations. Models were applied to the failure data and factors which impacted interconnect lifetime were identified. The results show that more cycles were required in the faster profiles for equivalency to the baseline profile. Furthermore, the equivalent number of cycles varied by a factor of two across the splits. Despite these limitations, the faster profiles offer substantial qualification time savings if the underlying failure mechanisms are understood.
Electrical Aging Tests on Conventional and Nanofilled Impregnation Resins

The main insulating system components of stator windings of rotating electrical machines are organic enamel and impregnating resin. When power electronic converters supply motors, the stator insulation system is subjected to voltage waveforms such as PWM composed by trains of pulses. Due to reflection phenomena, overvoltage can be associated to pulsed waveforms and partial discharge (PD) activity can incepts leading to the insulating system failure in short time. In this work two types of conventional and nanofilled impregnating resins, based on unsaturated polyester and solvented alkydic resin matrices, have been compared. Suitable twisted pair samples prepared by means of wires insulated with conventional enamel and by two impregnating resins have been tested. The twisted pair samples have been subjected to sinusoidal voltage waveforms, having amplitudes slightly higher than the Partial Discharge Inception Voltage. Aging tests have been performed until the insulating system breakdown, which is assumed as end-point criterion: during the tests, the PD activity has been monitored by means of a PRPDA system. For each sample, starting from the PD signals post processing, the apparent current of positive discharges has been considered: this derived quantity showed a linear behavior with the degradation time. In order to define a life model, the slopes of the straight lines have been put in relation with the related lifetimes by means of the inverse power law.
Identification of Hypertension by Mining Class Association Rules from Multi-dimensional Features

Hypertension is a common cardiovascular disease, which will lead to severe complications without timely treatment. Accurate hypertension identification is essential to preventing the condition deteriorated. However, the state of art hypertension identification methods only extract features from very few aspects, and hence have limited identification accuracy. Furthermore, they only can judge whether the subjects are hypertensive or not, more meaningful information (such as, why the subjects suffer from hypertension) that can help doctors to improve their diagnosis level are absent. In this paper, we propose a class association rules-based method to identify hypertension. Particularly, its key idea is to utilize the relationship existing in multi-dimensional features to characterize hypertension pattern more effectively, in order to improve the identification performance. In addition, it can also generate a set of class association rules (CARs), which can reflect the subjects' physiological status and are proved to be useful for doctors to analyze subject's condition deeply. Experiments based on 128 subjects (61 hypertension patients and 67 healthy subjects) shows that our method outperforms the baseline methods and the accuracy, precision and recall reach 85.2%, 85.0%, and 83.6%, respectively. Additionally, a user study based on five clinicians demonstrates the utility of the generated CARs.
Salgan360: Visual Saliency Prediction On 360 Degree Images With Generative Adversarial Networks

Understanding visual attention of observers on 360° images gains interest along with the booming trend of Virtual Reality applications. Extending existing saliency prediction methods from traditional 2D images to 360° images is not a direct approach due to the lack of a sufficient large 360° image saliency database. In this paper, we propose to extend the SalGAN, a 2D saliency model based on the generative adversarial network, to SalGAN360 by fine tuning the SalGAN with our new loss function to predict both global and local saliency maps. Our experiments show that the SalGAN360 outperforms the tested state-of-the-art methods.
Effect of moderate and regular consumption of Cinco Jotas acorn‐fed 100% Iberian ham on overall cardiovascular risk: A cohort study

Abstract Objective To evaluate the impact that the moderate and regular consumption of Cinco Jotas acorn‐fed 100% Iberian ham has on overall cardiovascular risk, lipid parameters, blood pressure, and weight. Methods A longitudinal, analytical, and quasi‐experimental clinical study with repeated measures was carried out with 100 randomly selected individuals in primary care. The sample population included men and women (64%) between the ages of 25 and 55 (42.08, SD 9.6) who were not diagnosed with any cardiovascular illness or diabetes, were not undergoing antihypertensive treatment, nor taking lipid‐lowering drugs. There were four visits during a 2‐week washout period for the first of three phases. Phases 2 and 3 included an 8‐week habitual diet phase followed by an 8‐week intervention phase when participants consumed 40 g daily of acorn‐fed 100% Cinco Jotas Iberian ham. Measurements of cardiovascular risk factors were taken following the SCORE table. These included total cholesterol, high‐density lipoproteins cholesterol (HDL‐c), low‐density lipoproteins cholesterol (LDL‐c), triglycerides (TG), and weight. Results The average vascular risk (SCORE) was 0.20 (SD 0.49) before the consumption phase and 0.18 (SD 0.48) at the end of the study (p > 0.05). An increase in HDL‐c of 5 mg/dl was observed while there was a decrease in LDL‐c and TG of 10 mg/dl (p < 0.05). There were no differences in total cholesterol levels, blood pressure, or weight; nor were differences observed in average consumption of calories, protein, lipids, carbohydrates, or alcohol (p > 0.05). Conclusions The daily consumption of 40 g of Cinco Jotas acorn‐fed 100% Iberian ham does not increase the risk of cardiovascular disease and has a favorable impact on lipid levels without affecting blood pressure or weight.
Cycle and Divergence of Performance on TCP BBR

A new high-performance TCP congestion control algorithm, called TCP BBR, was proposed in 2016. It achieves excellent performance especially on a network with high latency and high packet loss ratio. The interaction between TCP BBR and other TCP algorithms has not been discussed enough. In this paper, we focus on TCP BBR and CUBIC TCP, which is the most widely used algorithm in Linux operating system, and investigate the performance behaviors of TCP BBR and CUBIC TCP in a situation wherein these two TCP congestion control algorithms work concurrently. We then introduce three novel findings, which are extreme inter-protocol performance imbalance, intra-protocol performance balance, and cyclic performance switch. The first one means that the connections of a TCP algorithm consume almost all the bandwidth. The second one indicates that the performances of all the connections of a TCP algorithm increase and decrease as a group. The third one means that the extremely imbalanced performance switches periodically. We introduce a method for monitoring the parameters of TCP BBR and discuss the reason of and methods for relieving these features based on the monitored parameters. We postulate that difficulty of proving the bottleneck bandwidth and positive feedback mechanism on TCP BBR are important reasons. We assume that decreasing the buffer size of the bottleneck link can relieve these features.
Belief and Counterfactuality

The development and relation of counterfactual reasoning and false belief understanding were examined in 3- to 7-year-old children (N = 75) and adult controls (N = 14). The key question was whether false belief understanding engages counterfactual reasoning to infer what somebody else falsely believes. Findings revealed a strong correlation between false belief and counterfactual questions even in conditions in which children could commit errors other than the reality bias (rp = .51). The data suggest that mastery of belief attribution and counterfactual reasoning is not limited to one point in development but rather develops over a longer period. Moreover, the rare occurrence of reality errors calls into question whether young children’s errors in the classic false belief task are indeed the result of a failure to inhibit what they know to be actually the case. The data speak in favor of a teleological theory of belief attribution and challenges established theories of belief attribution.
Decentralized Sensor Localization by Decision Fusion of RSSI and Mobility in Indoor Environments

Localization of sensors has become an essential issue in wireless networks. This paper presents a decentralized approach to localize sensors in indoor environments. The targeted area is partitioned into several sectors, each of which having a local calculator capable of emitting, receiving, and processing data. Each calculator runs a local localization algorithm, by investigating the belief functions theory for decision fusion of radio fingerprints, to estimate the sensors zones. The fusion of all calculators estimates, is combined with a mobility model to yield a final zone decision. The decentralized algorithm is described and evaluated against the state-of-the-art. Experimental results show the effectiveness of the proposed method in terms of localization accuracy, processing time, and robustness.
Simulation of Near Field RCS to Reproduce Measurement Condition

Different approaches are presented to simulate the near field Radar Cross Section (RCS) of a perfectly conducting electrically large object. This type of environment is usually found in measurements at large anechoic chambers where the far field condition cannot be achieved, making it impossible to approximate the incident field by a planewave. Each approach will be implemented with Physical Optics (PO) for extremely large targets and an accelerated version of the Method of Moments for moderately large objects. The fast MoM method are either the Multilevel Adaptive Cross Approximation (MLACA) for an iterative solution or the Multiscale Compressed Block Decomposition (MSCBD) for a direct solution. This development and the performed simulations have been done with CAPITOLE-RCS commercial software developed by NEXIO.
Compressive Stress Strategies for Reduction of Cracked Cell Related Degradation Rates in New Solar Panels and Power Recovery in Damaged Solar Panels

Some crystalline silicon PV system underperformance is due to the presence of cracked solar cells, and this represents a financial risk for both already installed and future systems. This work describes compressive stress strategies and rear side pressure strategies that can be employed in new panel construction to prevent crack formation as front side mechanical loads from handling, wind, and snow are applied during shipping, installation and in the field. These strategies can also slow the opening of cracks and the related power loss for any cracks that do form. Furthermore, we present concepts for the retrofitting of older installed systems to close already open cracks and regain lost power, or to slow the future degradation of systems with panels that are sensitive to cracked cells.
Indoor Small-Scale Spatiotemporal Propagation Characteristics at Multiple Millimeter-Wave Bands

Indoor small-scale spatiotemporal propagation characteristics across multiple 5G millimeter-wave (mmWave) candidate bands are investigated. First, using a custom-designed channel sounder, channel measurement campaigns are conducted in indoor environments to capture the delay and angles of departure and arrival of the paths. Measurement results exhibit excellent agreements with physical scatterers and radio propagation environments. Next, the layout-related multipath propagation analyses are utilized to model frequency-dependent channel parameters for different polarimetric combinations, including root-mean-square delay and angular spread. Modeling results not only complete the 3GPP channel model above 6 GHz, but also help to understand the impacts of frequency and polarization on the radio wave propagation at the mmWave bands.
Dyadic Profiles of Health Behaviors Among Korean Baby Boomer Couples

Objectives: To address the issue of whether health behaviors are concordant within couples, this study identified dyadic profiles of health behaviors among Korean baby boomer couples (born 1955–1963). Methods: We analyzed a sample of 1,092 middle-aged couples from the Korean Baby Boomer Panel Study (2014). Latent class analysis and multinomial regression models were conducted to describe underlying health behavior profiles. Results: Four dyadic profiles of health behaviors emerged: (a) concordant—high health-promoting (26%), (b) moderate concordant—compensating (21%), (c) moderate concordant—low engaging (22%), and (d) less concordant—high health-promoting (31%). In addition, couples with higher levels of education, worse health, or better marital quality were more likely to belong to two high health-promoting profiles. Discussion: Encouraging husbands to engage in fewer health-compromising behaviors and acknowledging barriers to health-promoting behaviors for both spouses may contribute to healthier lifestyles among Korean couples.
Effects of Age, Race, and Ethnicity on the Optic Nerve and Peripapillary Region Using Spectral-Domain OCT 3D Volume Scans

Purpose To evaluate the effects of age, race, and ethnicity on the optic nerve and peripapillary retina using spectral-domain optical coherence tomography (SD-OCT) three-dimensional (3D) volume scans in normal subjects. Methods This is a cross-sectional study performed at a single institution in Boston. All patients received retinal nerve fiber layer (RNFL) scans and an optic nerve 3D volume scan. The SD-OCT software calculated peripapillary RNFL thickness, retinal thickness (RT), and retinal volume (RV). Custom-designed software calculated neuroretinal rim minimum distance band (MDB) thickness and area. Results There were 272 normal subjects, including 175 whites, 40 blacks, 40 Asians, and 17 Hispanics. Rates of age-related decline were 2.3%, 2.0%, 1.7%, 3.3%, and 4.3% per decade for RNFL, RT, RV, MDB neuroretinal rim thickness, and MDB area, respectively. The RNFL was most affected by racial and ethnic variations, with Asians having thicker global, superior, and inferior RNFL, Hispanics having thicker inferior RNFL, and blacks having thinner temporal RNFL, compared to whites. For MDB thickness and area, Asians had smaller nasal values and blacks had smaller temporal values. Peripapillary RT and RV parameters were not influenced by race and ethnicity. Conclusions All of the parameters exhibited age-related declines. RNFL, MDB thickness, and MDB area demonstrated racial and ethnic variations, while peripapillary RT and RV did not. Translational Relevance This study demonstrates that both normal aging and ethnicity affect several novel 3D OCT parameters used to diagnose and monitor glaucoma (i.e., RT, RV, and MDB), and this should be factored in when making clinical decisions based on these parameters.
Icme Grand Challenge Results on Heterogeneous Face Recognition: Polarimetric Thermal-to-Visible Matching

This paper describes the IEEE ICME Grand Challenge on Heterogeneous Face Recognition (Polarimetric Thermal to Visible Matching), presents the submitted face recognition algorithms, and details the evaluation results. The challenge problem, sponsored by ICME and Polaris Sensor Technologies, is motivated by nighttime face recognition and compares state-of-the-art domain adaptive algorithms for cross-spectrum face recognition. Using unique databases containing corresponding polarimetric thermal and visible facial imagery, the algorithms were developed and independently evaluated. A brief summary of each algorithm is described, and the face verification performances in term of equal error rate (EER) and area under the curve (AUC) are reported. The best performing algorithm was a GAN-based approach submitted by the Rutgers University Team.
Stability Analysis of Systems with Delay-Dependent Coefficients Subject to Some Particular Delay Structure

Stability of systems with commensurate delays and delay-dependent coefficients is studied along the line of the $\tau-$ decomposition approach. This particular delay structure allows the use of some sophisticated result from the matrix theory to generalize the stability analysis method developed for systems with a single delay. Criterion for determining cross directions of imaginary roots are presented, leading to a systematic stability analysis with the aid of the graphs of some functions.
Radiation Efficiency Cost of Resonance Tuning

Existing optimization methods are used to calculate the upper bounds on radiation efficiency with and without the constraint on self-resonance. These bounds are used for the design and assessment of small electric-dipole-type antennas. We demonstrate that the assumption of lossless, lumped, and external tuning skews the true nature of radiation efficiency bounds when practical material characteristics are used in the tuning network. A major result is that, when realistic (e.g., finite conductivity) materials are used, small antenna systems exhibit dissipation factors which scale as <inline-formula> <tex-math notation="LaTeX">$(ka)^{-4}$ </tex-math></inline-formula>, rather than <inline-formula> <tex-math notation="LaTeX">$({ka})^{-2}$ </tex-math></inline-formula> as previously predicted under the assumption of lossless external tuning.
Comparison of Severity on Mobile Government Application Mobile

At present E-Government in every country has been growing and experiencing stable growth, it is seen from many E-Governments which have started to provide services and information through an application especially with a mobile application. Through this development, we can get the benefit from user engagement in using a good E-Government app, usability, and credibility believed to be one of the factors affecting the developmental and growing participation. This research presents an empirical study in evaluating the usability of mobile E-Government mobile applications between two countries, namely BMKG INFO and MET Service Singapore. This study was conducted to determine the severity of the two applications. The results obtained show that by knowing the severity of the application, can be used for improvement on them.
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs). We introduce a regularized attention mechanism to GCNNs that not only improves performance on clean datasets, but also favorably accommodates noise in KGs, a pervasive issue in real-world applications. Further, we explore new visualization methods for interpretable modelling and to illustrate how the learned representation can be exploited to automate dataset denoising. The results are demonstrated on a synthetic dataset, the common benchmark dataset FB15k-237, and a large biomedical knowledge graph derived from a combination of noisy and clean data sources. Using these improvements, we visualize a learned model's representation of the disease cystic fibrosis and demonstrate how to interrogate a neural network to show the potential of PPARG as a candidate therapeutic target for rheumatoid arthritis.
Challenges and structural characterization of the solid solution Cu2Zn(Gex Si1-x) Se4

The quaternary chalcogenides Cu<inf>2</inf>ZnSiSe<inf>4</inf> and Cu<inf>2</inf>ZnGeSe<inf>4</inf>crystallize in the orthorhombic wurtz-stannite and tetragonal kesterite type structures, respectively. To investigate thestructural transformation within the solid solution series Cu<inf>2</inf> Zn(Ge<inf>x</inf> Si<inf>1-x</inf>) Se<inf>4</inf> a systematic structural study was performed. Polycrystalline samples, prepared by solid state reaction, show a single quaternary phase as analyzed by WDX spectroscopy. Structural parameters of the mixed crystals were obtained using powder X-ray diffraction. It can be shown that apart from Cu<inf>2</inf>ZnSiSe<inf>4</inf>, Si-rich mixed crystals of this series adopt the wurtzstannite type structure. The lattice parameters are in consistency with Vegard's law and increase with increasing Ge content.
Demand Effects of the Internet-of-Things Sales Channel: Evidence from Automating the Purchase Process

The internet of things (IoT) is rapidly becoming one of the most popular emerging technologies in business and society. One of the major verticals that has recently begun to effectively use IoT technologies is the retail industry. Given the unprecedented opportunities IoT generates for brands and retailers, it is important to glean timely insights regarding the business value of IoT and understand whether the introduction of an IoT technology as an alternative purchase channel for consumers affects the sales of physical products. Using empirical data from a multinational online retailer who adopted an IoT technology that largely automates the consumers’ purchases and employing a quasi-experimental framework, we study the effect of the introduction of IoT as an alternative sales channel on product sales. Our analyses reveal a statistically and economically significant increase in sales and demonstrate the business value of the IoT channel for retailers and brands. In addition, we conduct other analyses of IoT to delve into the effect of heterogeneity and empirically validate the underlying mechanisms by examining the impact of IoT for products in different price ranges, levels of substitutability, and product categories. For instance, our analyses reveal that less expensive and more differentiated products, as well as experience and utilitarian goods, can accrue higher benefits leveraging more effectively novel IoT technologies. This is the first paper to study the impact of an IoT technology on product sales, drawing important implications for devices and technologies largely automating the purchase process.
Location Verification based on Radio Irregularity: Sequential Evaluation and Performance Assessment

In this paper Location Verification System based on Radio Irregularity and Sequential Evaluation (LVS-RISE) is proposed as a novel system to improve the performance of the Location Verification System based on Radio Irregularity (LVS-RI). LVS-RISE utilizes a sliding window and voting scheme to detect the attack from malicious nodes at the same distance. Hence, LVS-RISE can limit the ambiguity of malicious nodes detection when compared with LVS-RI. To assess the performance of LVS-RISE in compared with LVS-RI both simulation and experimental results are presented. Our simulation results show that, with the same threshold of detection, LVS-RISE achieves better performance when compared with LVS-RI. Moreover, the experiments in real environment indicate that an assistant node is necessary for LVS-RI and LVS-RISE if the node to be detected is moving.