Miracle-dz/UniFAR-FTUs huggingface dataset:
Selected Microstructural and Mechanical Properties of Open-Cell Metal Foams

Iron has been used as a basic material for everyday tools and weapons since prehistoric times. After the 19th century, which saw the emergence of new technologies for the production of iron-based alloys, steel has become ubiquitous, especially in construction. Now, with dynamic changes in technology, there is a need for new classes of materials that exhibit new characteristics. Looking for inspiration and solutions to difficult problems, engineers and scientists all over the world have often used nature, the best source of knowledge. For example, Ashby [1] wrote, “When modern man builds large load-bearing structures, he uses dense solids: steel, concrete, glass. When nature does the same, she generally uses cellular materials: wood, bone, coral”. Numerous experiments have been carried out to verify concepts concerning the fabrication and application of structural sponge-like materials. Some of the first successful attempts to produce foam materials for structural purposes led to the creation of porous polymer membranes [2] and porous polymer electrolytes [3]. Much of the research in this area has focused on the fabrication of advanced metal foams [4]. Several technologies have been invented to produce metal foams with closed or open porosity, with a crystalline or amorphous structure, and with pores ranging from a micrometre to several millimetres in size [5] and [6]. These efforts have resulted in many new applications, e.g. aluminium sound absorbers, copper heat exchangers, and nickel battery electrodes. However, Arwade et al. [7] complained, “Steel is one of the most widely used engineering materials, yet today no foam using steel as the base material is commercially available”. Further research is thus essential to develop efficient and cost-effective methods to produce Fe-based foam materials with desirable properties [8] and [9]. Sintering is one of the cheapest and most efficient methods to fabricate porous iron-based materials. It is not necessary to reach the melting point of iron to obtain a desired structure; porosity is achieved by using a space holder or a foaming agent. Bekoz and Oktay [10] fabricated sintered low-alloy steel foams using the space holder-water leaching technique. Their materials had porosity ranging from 47.8 % to 70.9 %, depending on the space holder size (500 μm to 1200 μm). Murakami et al. [11] produced iron foams using CO and CO2 as foaming gases; their maximum porosity was 55 %, and an average pore was 500 μm in size. These processes, however, involved powder compaction, which had a negative effect on the material structure. The sintered porous Selected Microstructural and Mechanical Properties of Open-Cell Metal Foams Depczyński, W. Wojciech Depczyński* Kielce University of Technology, Faculty of Mechatronics and Mechanical Engineering, Poland
Deep Clustering via Weighted $k$-Subspace Network

Subspace clustering aims to separate the data into clusters under the hypothesis that the samples within the same cluster will lie in the same low-dimensional subspace. Due to the tough pairwise constraints, <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-subspace clustering is sensitive to outliers and initialization. In this letter, we present a novel deep architecture for <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-subspace clustering to address this issue, called as Deep Weighted <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-Subspace Clustering (DWSC). Specifically, our framework consists of autoencoder and weighted <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-subsapce network. We first use the autoencoder to non-linearly compress the samples into the low-dimensional latent space. In the weighted <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-subspace network, we feed the latent representation into the assignment network to output soft assignments which represent the probability of data belonging to the according subspace. Subsequently, the optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> subspaces are identified by minimizing the projection residuals of the latent representations to all subspaces, using the learned soft assignments as a weighting vector. Finally, we jointly optimize the representation learning and clustering in a unified framework. Experimental results show that our approach outperforms the state-of-the-art subspace clustering methods on two benchmark datasets.
Differentially Inserted-Fed Compact Dual-Band Circularly Polarized Dielectric Resonator Antenna

In this letter, a differentially inserted-fed compact dual-band circularly polarized dielectric resonator antenna (DRA) is proposed. To realize a compact design, the inserted differential feeding structure composed of a pair of arc-shaped strips connecting with coaxial probes plays dual roles. It not only excites the fundamental mode HEM111 of the DRA, but also brings an additional resonant mode to form the second band. Besides, the lower and upper bands of the DRA are independently controlled by the DR and the strips, facilitating the design process of the dual-band DRA. For demonstration, a prototype is designed, fabricated, and measured. Measured results show that the proposed DRA with the profile of 0.34 $\lambda _{g}$ achieves impedance bandwidth (|Sdd11|<−10 dB) of 30.4% and 18.5% and 3 dB axial-ratio bandwidth of 8.06% and 6.38% with peak gain of 6.9 and 7.3 dBic for the lower and upper bands, respectively. Good agreement is observed between the simulated and measured results.
Wideband Four-Way Filtering Power Divider With Sharp Selectivity and High Isolation Using Coshared Multi-Mode Resonators

This letter presents an alternative fusion-design of a wideband four-way filtering power divider (FPD). A coshared coupling topology is proposed by embedding one pair of multi-mode resonators (MMRs) and three isolated resistors, bringing out wide power division band, decent frequency selectivity, and high in-band isolation. In comparison with other reported ones, the presented four-way FPD stands out not only by its favorable operation performance but also flexible topology that can also be easily extended to other multi-way multi-mode FPD designs and competitive size with a reduced number of resonators. A prototype is implemented to validate the design concept and method. Simulated results coincide well with the measured ones, verifying the theoretical design equations.
A Heterogeneous Integrated MEMS Inertial Switch With Compliant Cantilevers Fixed Electrode and Electrostatic Locking to Realize Stable On-State

A novel heterogeneous integrated inertial micro-switch has been designed with adjustable acceleration threshold and a stable ‘on’-state due to a predefined bias voltage. The bias voltage is applied onto the large-area parallel-plate electrodes, which endows the movable proof-mass with electrostatic forces. With an external excitation acceleration, the movable electrode moves to the fixed electrode and it can be locked by the electrostatic force onto the compliant electrical contacts, which are composed of micro-cantilever array to eliminate the contact rebound during electrostatic pull-in process. Both the dynamic response of the proof-mass and the relationship between the bias voltage and the inertial excitation acceleration were analyzed using theoretical model and finite element simulation. A unique heterogeneous integration process including both the silicon-based and non-silicon surface micromachining processes was adopted to fabricate the switch. The tests using a standard dropping hammer system demonstrated that the switch could keep a stable switch-on at the 57 g excitation acceleration and 38 V bias voltage. As wide as 52% adjustment range of the acceleration threshold was obtained when the applied bias voltage was from 38 V to 44 V. The tested relationship between the bias voltage and the external acceleration was very consistent with the simulated relationship. [2019-0038]
Snow water equivalent retrieval algorithm in Jilin Province of China based on multi-temporal Sentinel-1 data

Active microwave remote sensing is one of the important methods to monitor snow cover and retrieve snow parameters such as snow water equivalent (SWE), snow particle size and snow density. SWE provides useful information for hydrological, climatic and meteorological applications. Retrieving SWE from active microwave remote sensing data has attracted widespread attention in the academic community. This paper took Nongan County, Jilin Province, China as the study area, and selected two scenes of Sentinel-1 interference wide-mode (IW) SAR images to retrieve the SWE in the shallow dry snow area in winter. A mathematical expression that defines the relationship between the backscattering coefficient ratio and the thermal resistance calculated from the measured snow data was established. By using the relationship between the measured thermal resistance and the measured SWE, the value of SWE in the study area can be retrieved by the backscattering coefficient ratio. The result shows that the method used in this paper has high precision (MAE=0.198cm, RMSE=0.24cm). It is feasible to use the C-band Sentinel-1 SAR data to retrieve SWE.
Comparison of Tomographic SAR Reflectivity Reconstruction Algorithms for Forest Applications at L-band

Forest structure is a key parameter for forest applications, but it is difficult to be estimated at the required spatial and temporal scales. In this context, synthetic aperture radar Tomography (TomoSAR) that allows, at lower frequencies, the 3-D imaging of natural volume scatterers with high spatial and temporal resolution may be a game changer. The aim of this article is to evaluate three TomoSAR algorithms, Fourier beamforming (FB), Capon beamforming (CB), and compressive sensing (CS) with respect to their performance in the reconstruction of the 3-D forest reflectivity. The implications of volumetric forest scattering, as well as the temporal decorrelation of scatterers, are analyzed. The algorithms are compared on a set of simulated scenarios and then evaluated on an experimental L-band data set composed by four acquisition dates, each one consisting of five tomographic tracks. The data were acquired in 2014, within a time span of two months, over the Traunstein forest (Germany) using the F-SAR system. Additionally, discrete airborne Lidar has been used for a qualitative evaluation. The results indicate that the CS reconstruction is, for many practical cases, superior when compared to FB or CB reconstructions as they achieve higher vertical resolution, especially in cases with a lower number of acquisitions and complex forest scenarios. By combining acquisitions performed at different days, the effect of temporal decorrelation on each algorithm for two different tomographic implementations (repeat-pass vs. single-pass) has been assessed. The results indicate that simultaneously acquired image pairs allow a better reconstruction of the 3-D forest reflectivity.
Entrepreneurs in Saudi Arabia: Risk Attitude and Predisposition towards Risk Management

The success and survival of startup companies depend on the decision making of entrepreneurs. Entrepreneurs are individuals who are considered as risk takers. Recent academic literature supports a positive relationship between the decision to become an entrepreneur and their attitude towards risk taking. In the literature, entrepreneurs are also considered as a homogeneous group having a similar risk attitude. In this paper, we investigate the important characteristics of Saudi entrepreneurs with respect to risk perception and financial risk tolerance. We collected data from 132 entrepreneurs and analyzed it using Partial Least Square approach of Structural Equation Modeling. We found that entrepreneurs’ perception towards risk has a positive significant influence on financial risk tolerance, risk propensity, and entrepreneurial openness. In addition, findings of this study have shown that the relationship between entrepreneurs’ perception and financial risk tolerance is partially mediated by entrepreneurial openness and risk-taking propensity. The study contributes to the body of knowledge by being a pioneer in examining the relationship between risk perception and financial risk tolerance in the specific context of Saudi Arabia.
LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction

The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is focused on the procedure to follow for a correct LSTM data analysis by using artificial records (LSTM-AR-), improving the training dataset stability and test accuracy if compared with traditional MLP and LSTM approaches. The increase of the artificial data is important for all cases where only a few data of the training dataset are available, as for more practical cases. The paper represents a practical application about the LSTM approach into the decision support systems (DSSs) suitable for homecare assistance and for de-hospitalization processes. The paper goal is mainly to provide guidelines for the application of LSTM neural network in type I and II diabetes prediction adopting automatic procedures. A percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach, comparing results with up-to-date MLP works. The LSTM-AR- neural network can be applied as an alternative approach for all homecare platforms where not enough training sequential dataset is available.
Variational inference for varying-coefficient model

Abstract In this paper, we propose a variational Bayesian method for estimation of varying-coefficient model. Within the local likelihood framework, we develop variational updates for the approximated posterior and obtain variational lower bound. Mean-field assumption naturally simplifies the estimation procedure, and overcomes the computational burden of traditional Bayesian methods in nonparametric setting. We also propose a Metropolis-Hastings algorithm to select the bandwidth. We conduct simulation study to demonstrate proposed procedure, and apply the proposed estimation method in the analysis of stock return data.
Nitric oxide in acute and chronic inflammation.

Nitric oxide (NO) is a signalling molecule formed when L-arginine is converted to L-citrulline by the enzyme NO synthase (NOS). NOS exists as three isoforms, ecNOS is constitutively expressed in endothelial cells and nNOS in neuronal cells, while a third isoform (iNOS) is induced in response to inflammatory stimuli and is capable of sustained production of high levels of NO. NO produced in response to an inflammatory insult, has been shown by use of NOS inhibitors to be detrimental during inflammation by producing the potent oxidising agent peroxynitrite. However, iNOS knockout animals generally produce a similar inflammatory profile to wild type controls. Hence, there is a discrepancy between the effects of phamacological inhibition and gene deletion of iNOS in vivo. Therefore, the aim of this thesis is to use a number of approaches to modulate NO production in acute and chronic inflammatory models and to assess the effects on the NOS pathway and other markers of the inflammatory response. In this thesis, it was established that iNOS protein expression and nitrite formation was significantly elevated after injection of the inflammatory stimulus in the carrageenin-induced pleurisy (RCIP), bovine serum albumin (BSA)-induced pleurisy and methylated BSA-induced pleurisy in the rat, and a murine models of croton oil-induced chronic granulomatous tissue of the air pouch (MCGTAP). The majority of immunostaining was associated with migrating inflammatory cells. NO production was modulated in acute and chronic models of inflammation using NOS inhibitors and NSAIDs. Local injection of NOS inhibitors in the RCIP model caused an increase in pro-inflammatory mediators, including superoxide, histamine and PMN chemoattractants that resulted in an exacerbation of inflammation. This was a result of inhibition of both eNOS and NOS at the inflammatory site. In contrast, systemic inhibition of NOS reduced both inflammatory cell influx and exudation into the pleural cavity. A similar inhibitory
Evaluation of Dosimetric Performance and Global Uncertainty of the Harshaw 6600 Plus System Used to Staff Monitoring in Côte d’Ivoire

The objective of this work is to check the dosimetric performances of the TLD-100 as stated by the manufacturer as well as the technical standards of radiation protection. The purpose of the performance audit is to assess the inhomogeneity of TLD sensitivity, repeatability and reproducibility, linearity, energy dependence, angular dependence, and fading. All tests were performed under the conditions of ambient temperature and relative humidity recommended by the manufacturer. We began the study by calibrating the Harshaw 6600 Plus, and checking its performance. The TLD-100 performance verification results were all acceptable and in accordance with the manufacturer’s advertised values and the radiation protection technical standards. However the performance of the TLD-100 that we have evaluated may have some limitations; these limits, which are sources of uncertainty, have been taken into account in this work by evaluating the overall uncertainty of the Hp (10) dose in the uncertainty range 9.45% to 15.80% by simple formulas. The TLD-100 personal dosimeters and the 6600 Plus reader system indicate that the calculated values of the overall uncertainty Hp (10) are well below the allowable values of 21% to 42% suggested for personal dosimetry services. The obtained data encourage the use of the system for the routine evaluation of the external exposure of workers under ionizing radiation in our laboratory.
Theorizing Territorial Withdrawal: The Need to Think Strategically

Abstract This article examines what factors cause states to withdraw from foreign territorial interventions. Scholarly analyses of withdrawal are rare, whilst within the broader research area of territorial conflict, studies are often dichotomized into neorealist or constructivist-inspired works, emphasizing a select few variables and one level of analysis alone. We argue these excessive simplifications of international politics lack utility for understanding territorial withdrawal. Instead, we employ the principles of strategic theory informed by a Clausewitzian paradigm, and construct a framework of three “arenas of bargaining,” spanning multiple variable-types and levels of analysis, to explain territorial withdrawal. In so doing, the analysis delineates a comprehensible and novel theoretical framework for understanding an under-researched policy problem.
Spectrum Occupancy Measurements and Analysis in 2.4 GHz WLAN

High time resolution spectrum occupancy measurements and analysis are presented for 2.4 GHz WLAN signals. A custom-designed wideband sensing engine records the received power of signals, and its performance is presented to select the decision threshold required to define the channel state (busy/idle). Two sets of measurements are presented where data were collected using an omni-directional and directional antenna in an indoor environment. Statistics of the idle time windows in the 2.4 GHz WLAN are analyzed using a wider set of distributions, which require fewer parameters to compute and are more practical for implementation compared to the widely-used phase type or Gaussian mixture distributions. For the omni-directional antenna, it was found that the lognormal and gamma distributions can be used to model the behavior of the idle time windows under different network traffic loads. In addition, the measurements show that the low time resolution and angle of arrival affect the statistics of the idle time windows.
How shared ideas may hinder lobbying success: British preference formation in the G20

Abstract Much existing work on ideas in policymaking and lobbying has focused on how shared ideas can facilitate achievement of policy preferences. However, interviews with lobbyists and recent research indicate shared ideas may also hinder lobbying success—a topic largely ignored to date. I begin to address this gap by mapping two roles for shared ideas in lobbying processes: as icebreakers to facilitate communication with politicians and as instruments in pursuit of specific interests. I subsequently propose pathways via which shared ideas may facilitate or hinder lobbying success and illustrate these mechanisms in case studies examining British preference formation during G20 negotiations over tax reform. The analysis underlines the crucial role of strategic agency and the interaction of actor’s communication strategies when seeking to understand how ideas affect lobbying success. Likewise, it highlights the value of explicitly incorporating ideas’ multivocality into studies of ideas’ role(s) in politics.
The orbitofrontal cortex as a negative feedback control system: computational modeling and fMRI

In this work we address two inter-related issues. First, the computational roles of the orbitofrontal cortex (OFC) and hippocampus in value-based decision-making have been unclear, with various proposed roles in value representation, cognitive maps, and prospection. Second, reinforcement learning models have been slow to adapt to more general problems in which the reward values of states may change over time, thus requiring different Q values for a given state at different times. We have developed a model of artificial general intelligence that treats much of the brain as a high dimensional control system in the framework of control theory. We show with computational modeling and combined fMRI and representational similarity analysis (RSA) that the model can autonomously learn to solve problems and provides a clear computational account of how a number of brain regions, particularly the OFC, interact to guide behavior to achieve arbitrary goals.
A business frame perspective on why perceptions of top management's bottom‐line mentality result in employees’ good and bad behaviors

Emerging research suggests that bottom‐line mentalities (BLMs) (i.e., a sole focus on bottom‐line outcomes to the exclusion of other considerations) can have dysfunctional consequences within the workplace. However, research has yet to consider how and why BLMs may result in both beneficial and dysfunctional organizational outcomes. In the present research, we examine employees’ perceptions of top management's BLM as a type of business frame that results in two cognitive states. Under the influence of this business frame, employees may adopt a mental preoccupation with work (i.e., a state of ongoing work‐related cognitions) that propels beneficial employee outcomes by reducing customer incivility and enhancing customer service performance. Yet, also in response to top management's high BLM as a business frame, employees may adopt self‐interest cognitions (i.e., a cognitive state of self‐interest) that instigate customer‐directed unethical conduct. Across two field studies, we found general support for our hypotheses. Taken together, our findings suggest that perceptions of top management's high BLM can be a mixed blessing in that it may drive employees to adopt focused work efforts (mental preoccupation with work), but also self‐interest cognitions, with each cognitive state predicting beneficial or dysfunctional behaviors. We discuss the implications of these findings and directions for future research.