Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of CPW-Fed Dual-Band Four-Element MIMO Microstrip Patch Antenna for WLAN/WiMAX Applications

the goal of our study is to design, fabricate, and verify a dual band 4-element MIMO antenna that can address both the WLAN and the WiMAX bands. The suggested antenna is made up of two radiating patches that work together to operate in the 5.8 GHz WLAN and 3.5/5.5 GHz WiMAX bands. The four element MIMO antenna with size of 64×64 mm2 is fabricated on a 1.6 mm in thickness FR-4 sheet with a dielectric constant of 4.4 and a loss tangent of 0.02. To boost the performance of the structure in respect to operating bandwidth; a parametric study was done based on FDTD technique resulting in an optimized dual band antenna. Moreover, a four element MIMO configuration separately 50Ω-fed is considered, and the prototype is fabricated and measured using VNA. Spatial diversity is introduced in the MIMO configuration resulting in good isolation without employing the known decoupling structures and techniques. The measurements reveal that the system operates on two desired bands with a coupling of less than -15 dB between the different elements. In addition, the MIMO metrics are analyzed and found to meet the practical standards of: ECC < 0.04, DG > 9.80, -12 dB < MEG < -3 dB, TARC < -10 dB, and CCL < 0.4 bits/s/Hz over the concerned bands.
Dietary Patterns of Off-Reserve Indigenous Peoples in Canada and Their Association with Chronic Conditions

Nationally representative nutrition surveys (Canadian Community Health Survey (CCHS) Cycle 2.2, Nutrition 2004 and 2015) were used to examine dietary patterns and their association with socioeconomic/sociodemographic factors and chronic conditions in off-reserve Indigenous population in Canada. A cluster analysis was used to identify dietary patterns (DPs), and the Nutrient Rich Food Index (NRF 9.3) was used as the diet quality score and stratified by age/gender groups. In 2004 (n = 1528), the dominant DPs among Indigenous adults (age = 41 ± 2.3) were “Mixed” (mean NRF = 450 ± 12) and “Unhealthy” among men (mean NRF = 426 ± 18), “Fruits” among women (mean NRF = 526 ± 29), and “High-Fat/High-Sugar” among children (age = 10.2 ± 0.5) (mean NRF = 457 ± 12). In 2015 (n = 950), the dominant DPs were “Unhealthy” (mean NRF = 466 ± 6), “Mixed” (mean NRF = 485 ± 21), Healthy-Like (mean NRF = 568 ± 37), and “Mixed” (mean NRF = 510 ± 9) among adults (age = 45.6 ± 2.2), men, women, and children (age = 10.9 ± 0.3) respectively. The majority of Indigenous peoples had the “Unhealthy” DP with a low diet quality, which may contribute to a high prevalence of obesity and chronic diseases. The income level and smoking status among adults and physical inactivity among children were recognized as important factors that may be associated with the dietary intake of off-reserve Indigenous population.
Joint distribution of successive wave periods revisited

A joint distribution of successive wave periods is presented. First, the analysis includes comparisons between observed wave data and an empirical as well as theoretical distributions of wave period. Secondly, a joint distribution of successive wave periods based on a best fit of a Weibull distribution to the wave period distribution is developed. Comparisons are made with observed wave data as well as with the previous approach of Myrhaug & Rue (1993). It appears that the revised approach is in better agreement with the data.
A3MR: Attentive Auto-encoder for Acoustic-assisted Music Recommendation

In the era of big data, users often seem to be unable to make choices in the face of such a huge range of options. This is especially the case with music; consequently, personalized music recommendation has attracted much interest. Many effective strategies have been offered by researchers, nevertheless, they still face two challenging problems: (1) how to model the complex usermusic relationships from sparse implicit feedback data, and (2) how to introduce auxiliary information to improve the recom-mendation effect. To copy with these challenges, we propose an Attentive Auto-encoder for Acoustic-assisted Music Recommen-dation (A3MR), which takes user historical behaviors, music acoustic features, and similar music to the objective music into account. Especially, we design a multi-attention layer to learn complex user-music relationships in order to gain the hidden representation of the objective music based on uses' behaviors. Besides, we use an embedding layer to generate music repre-sentation based on acoustic features, and cluster the similar music to objective music in order to predict users' preference. We conduct a series of experiments on the real-world dataset to evaluate the proposed model, and the results indicate that it is effective.
Unpolarized calibration and nonuniformity correction for long-wave infrared microgrid imaging polarimeters

Recent developments for long-wave infrared (LWIR) imaging polarimeters include incorporating a microgrid polarizer array onto the focal plane array. Inherent advantages over other classes of polarimeters include rugged packaging, inherent alignment of the optomechanical system, and temporal synchronization that facilitates instantaneous acquisition of both thermal and polarimetric information. On the other hand, the pixel-to-pixel instantaneous field-of-view error that is inherent in the microgrid strategy leads to false polarization signatures. Because of this error, residual pixel-to-pixel variations in the gain-corrected responsivity, the noise-equivalent input, and variations in the pixel-to-pixel micropolarizer performance are extremely important. The degree of linear polarization is highly sensitive to these parameters and is consequently used as a metric to explore instrument sensitivities. We explore the unpolarized calibration issues associated with this class of LWIR polarimeters and discuss the resulting false polarization signature for thermally flat test scenes.
From Passive to Active: Near-optimal DNS-based Data Exfiltration Defense Method Based on Sticky Mechanism

DNS-based data exfiltration has become increasingly popular among advanced persistent threat (APT) attackers owing to the ubiquity and penetrability of the DNS protocol. AI-powered methods solve the defect that attackers can easily bypass because of the fixed threshold and weight in rule matching while still suffer from several issues. Such as the lack of malware samples for training, the amplified impact of even low FPR present enormous obstacles to applying the model in real-world detection.We present a method to generate malicious traffic covering an extensive sample space based on Tactics, Techniques, and Procedures (TTPs). We then propose a sticky mechanism, which transforms certain decision-making into dynamic human-computer interaction decision-making, to verify the suspicious hosts recognized by the AI model. The experimental results demonstrate the superiority of our model by identifying eight kinds of real attacks precisely. The good performance on real-world traffic shows our method is a solid foothold for applying AI-powered detection to practical applications.
Components of computational thinking in citizen science games and its contribution to reasoning for complexity through digital game-based learning: A framework proposal

Abstract Education has undergone many changes in teaching and learning, intensified by the significant technological developments that have responded to the fourth industrial revolution and other emergent situations. In this context, developing information and communication technologies has become vital in supporting new ways and learning models in the various educational levels to address a complicated environment where individuals must have complex and computational skills to respond to challenges. This study proposes a complex thinking framework that links citizen science and digital game-based learning to develop university students’ computational thinking skills. The results indicate that (a) it is possible to consider the sub-competencies of complex thinking in the design of a digital citizen-science game to develop computational thinking, and (b) the digital game-based learning framework for citizen science topics can potentially increase students’ engagement and teamwork in data collection and analysis while building their knowledge and computational thinking skills, and their complex thinking competency and sub-competencies.
Resistance in pea (Pisum sativum) genetic resources to the pea aphid, Acyrthosiphon pisum

The pea aphid (PA), Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae), can cause significant damage to dry and green peas, Pisum sativum L. (Fabaceae). Identifying pea lines with resistance to PA is important to advance available control measures. This research identified lines and putative genes with resistance to PA in the USDA Pisum sativum Collection located in Pullman, WA, USA, consisting of 301 lines collected worldwide. Eight commercial pea cultivars were also evaluated. A cluster analysis based on the mean number of adults, nymphs, and total aphids that developed on lines 10 days after the start of infestation by a single adult aphid per plant identified six clusters. Cluster 2, consisting of 48 lines, had the lowest aphid fecundity of all clusters with 2.1 adults, 7.3 nymphs, and 9.4 total aphids produced on average. ‘Lifter’ was the most resistant line based on low aphid fecundity. Lifter is a white‐flowered green dry pea cultivar that can advance PA resistance in food‐type peas. Genotyping by sequencing based on single‐nucleotide polymorphisms (SNPs) were used in genome‐wide association mapping to locate genes or quantitative trait loci (QTL) linked to PA resistance in which three, nine, and six SNPs were associated with resistance to adult, nymph, and total number of PAs that developed on lines, respectively. Seventeen candidate genes were identified in response to PA resistance. The results can effectively be used in breeding programs to target specific genomic regions with PA resistance and in traditional breeding using resistant lines as parents.
Myopia Is Suppressed by Digested Lactoferrin or Holo-Lactoferrin Administration

Myopia is becoming a leading cause of vision impairment. An effective intervention is needed. Lactoferrin (LF) is a protein that has been reported to inhibit myopia progression when taken orally. This study looked at the effects of different forms of LF, such as native LF and digested LF, on myopia in mice. Mice were given different forms of LF from 3 weeks of age, and myopia was induced with minus lenses from 4 weeks of age. Results showed that mice given digested LF or holo-LF had a less elongated axial length and thinned choroid, compared to those given native-LF. Gene expression analysis also showed that the groups given native-LF and its derivatives had lower levels of certain cytokines and growth factors associated with myopia. These results suggest that myopia can be more effectively suppressed by digested LF or holo-LF than native-LF.
Micro-benchmarks Performance Analysis of Fujitsu ARM A64Fx and Intel Cascadelake Processor Nodes

The A64FX processor is the most powerful Armbased processor to date. Despite the fact that it is a cache-based multiple cores CPU, its peak performance and memory bandwidth are comparable to the modern High Performance Computing architectures like Accelerators. In this paper, It is tried to understand and explore the Fujitsu's A64FX processor architecture with different micro-benchmarks available in LIKWID tool and also tried to compare it's peak flop performance and memory bandwidth with Intel Cascadelake architecture.
A Novel Two-Stage Framework for Mid-Term Electric Load Forecasting

Electric utilities and planners rely heavily on accurate mid-term load projections to effectively schedule maintenance, coordinate load dispatch, and manage fuel reserves. Current approaches that attempt to forecast electric load for multiple horizons in a single stage, however, often result in inaccurate forecasts due to the lack of information for predicting the data at the farthest horizon and propagation of large errors. To address this issue, this article presents a two-step mechanism for forecasting electric load that utilizes the benefits of both backpropagation neural networks (BPNNs) and radial basis function neural networks (RBFNNs). The first stage utilizes a pool of BPNNs to make partial predictions. BPNNs are used as they are well-known for their capability of fast and accurate predictions. In the second stage, RBFNNs are used to make complete forecasts. RBFNNs are known for their ability to model nonlinear relationships in the data which is crucial in this stage of the forecast process. Additionally, this article also proposes a unique method for generating additional training samples by incorporating stacking noise, which adds more variability to the data, increasing the generalization capability of the models, resulting in better forecasting performance. The performance of the proposed framework has been investigated with simulation studies and has been validated on actual load data of the Madhya Pradesh Power Transmission System, India. The performance comparison with the existing methods in terms of both accuracy and reliability shows that the proposed forecasting framework provides more accurate results.
ON THE THERMODYNAMICS AND OTHER CONSTITUTIVE PROPERTIES OF A CLASS OF STRONGLY MAGNETIZED MATTER OBSERVED IN ASTROPHYSICS

It is shown that the occurrence of magnetization work is a consistent thermodynamic explanation of the property of anti-correlation between temperature and density of the electrons gas in a class of magnetic-field-dominated structures observed in the interplanetary medium. In this model, a 7/4 scaling ratio for magnetization work to electron-gas work explains the often observed anomalous adiabatic polytropic exponent γ a = 1 / 2 ? > . This interpretation is built on the theoretical conjecture of a matter state having spatial confinement of most hadronic elements of matter, i.e., matter held in place by the action of what is here denominated as a “super-strong” magnetic field, which together with the plasma it contains satisfies—on medium to large spatial-temporal scales—ideal magnetohydrodynamics. Several elements of the interpretation are tested for a case study, the flux-rope (FR) structure passing Wind SC on 1998 June 2. This allows us to extract, for a 185 s sample interval inside the FR, the following constitutive properties of this diamagnetic state of matter: (i) sound speed, (ii) thermal temperature, (iii) magnetic permeability, and (iv) a low limit to its dielectric permittivity. The intervals of coherence, i.e., thermodynamic homogeneity, extend from a few to many 104 km for plasma and magnetic field average with a sampling rate of 3s per value. We point out that this state of matter, which we identify to be an amorphous three-dimensional Langmuir lattice, differs from other materials studied in the laboratory at extreme low temperatures and is well described as BCS-superconductors because in our case we understand that (a) the magnetic permeability is non-zero, and (b) substantial field-aligned, convected-current density exists.
Using optical flow techniques to electronically stabilize scene motion

A major consideration in designing imaging systems that will be placed on moving platforms is the stability of the image. For example, the tracking or cuing of small objects requires a very steady image sequence. To provide the necessary stabilized platform typically requires the use of an inertial stabilized gimbaling system. Such gimbaling systems are both expensive and bulky. These factors are becoming increasingly important to the military community. The cost of a gimbal system can preclude its use on disposable ordinance systems and can be a significant portion of the cost of such systems. A low cost and low bulk alternative is needed. This paper describes a method for performing electronic stabilization using optical flow techniques. Electronic stabilization allows either the elimination of the inertial stabilized platform or the use of a low cost gimbal. The operating scenario available to electronic stabilization is examined and the resulting basic system requirements are derived. A model- based approach for deciding sensor motion is described. The utilization of the motion information for performing motion compensation is presented. Finally, preliminary results are given.
Experimental investigation of homogeneity, isotropy, and circulation of the velocity field in buoyancy-driven turbulence

We present direct multipoint velocity measurements of the two-dimensional velocity field in the central region of turbulent Rayleigh–Bénard convection. The local homogeneity and isotropy of the velocity field are tested using a number of criteria and are found to hold to an excellent degree. The properties of velocity circulation Γr are also studied. The results show that the circulation appears to be more effective in capturing the effect of local anisotropy than the velocity field itself. The distribution of Γr is found to depend on the scale r, reflecting strong intermittency. It is further found that the velocity circulation has the same anomalous scaling exponents as the longitudinal and transverse structure functions for low-order moments (p≲5), whereas, for high-order moments (p≳5), the anomalous scaling exponents for circulation are found to be systematically smaller than the scaling exponents of the longitudinal and transverse structure functions.
Characterization of Planar Ring-shaped Array Antenna Fed by Differentially Proximity Coupling Method

This paper presents the characterization of an array antenna composed of 2×5 ring-shaped patch elements arranged in a planar manner. Each radiating patch element is differentially fed by a proximity coupling method at the opposite sides. This method aims to produce symmetrical radiation pattern with proper impedance matching and adequate impedance bandwidth. The proposed array antenna is fabricated using two layers of FR4 epoxy dielectric substrate with the total dimension of 0. 805λo × 0. 417λo × 0. 014λo. From the experimental characterization, the result shows that the proposed array antenna operated at the center frequency of 1.275 GHz with the –10 dB impedance bandwidth of 27 MHz. Moreover, the fabricated array antenna produced a symmetrical radiation pattern with the front-to-back ratio of 6.41 dB, in which this result is comparable to the simulated one with the front-to-back ratio of 10.45 dB.
Trust-Based Certificate Management for Industrial IoT Networks

The Industrial Internet of Things (IIoT) network is composed of devices that contain sensitive data, which makes them vulnerable to various security threats. Digital Certificates can be used to reinforce the security of the IIoT network, however, their management remains a major issue. Hence, in this article, we rely on trust management to deal with the whole certificate management process in IIoT networks, from revocation to verification. For this purpose, we organize the IIoT network into a clustering architecture where each cluster head (CH) hosts an agent, called CH-UR agent, that renews/revokes the certificates of its cluster member nodes. We apply signaling game theory to build a Certificate Revocation Game modeling the interactions between a member IIoT node and the CH-UR agent. Thus, upon the belief on the member node, updated by using the Bayesian rules, the best response strategy for the CH-UR agent can be obtained. Further, we propose a new efficient certificate verification scheme based on short-lived certificates (SLCs) and suitable for IIoT network requirements. The performance evaluation of our framework proves, first, the accuracy and convergence speed of our revocation mechanism to detect untrusted devices and on-off attacks. Second, the effectiveness of our clustering architecture to reduce the resource consumption resulting from the management of SLCs to 60% even with the increase of network density. Third, the effectiveness of the proposed certificate verification scheme to reduce the time needed to obtain the revocation information as well as the resulting storage and communication overhead to achieve this purpose.
Retrospective Analysis of Vaccinated and Unvaccinated COVID-19 Patients Treated with Monoclonal Antibodies (mAb) and Their Emergent Needs (RAVEN)

Strategies to combat COVID-19 include vaccines and Monoclonal Antibody Therapy. While vaccines aim to prevent development of symptoms, Monoclonal Antibody Therapy aims to prevent the progression of mild to severe disease. An increasing number of COVID-19 infections in vaccinated patients raised the question of whether vaccinated and unvaccinated COVID-19 positive patients respond differently to Monoclonal Antibody Therapy. The answer can help prioritize patients if resources are scarce. We performed a retrospective study to evaluate and compare the outcomes and risks for disease progression between vaccinated and unvaccinated COVID-19 patients treated with Monoclonal Antibody Therapy by measuring the number of Emergency Department visits and hospitalizations within 14 days as well as the progression to severe disease, defined through the Intensive Care Unit admissions within 14 days, and death within 28 days from the Monoclonal Antibody infusion. From 3898 included patients, 2009 (51.5%) were unvaccinated at the time of Monoclonal Antibody infusion. Unvaccinated patients had more Emergency Department visits (217 vs. 79, p < 0.0001), hospitalizations (116 vs. 38, p < 0.0001), and progression to severe disease (25 vs. 19, p = 0.016) following treatment with Monoclonal Antibody Therapy. After adjustment for demographics and comorbidities, unvaccinated patients were 2.45 times more likely to seek help in the Emergency Department and 2.70 times more likely to be hospitalized. Our data suggest the added benefit between the COVID-19 vaccine and Monoclonal Antibody Therapy.
Hepatitis E Virus in Finland: Epidemiology and Risk in Blood Donors and in the General Population

Autochthonous hepatitis E (HEV) cases have been increasingly recognized and reported in Europe, caused predominantly by the zoonotic HEV genotype 3. The clinical picture is highly variable, from asymptomatic to acute severe or prolonged hepatitis in immunocompromised patients. The main route of transmission to humans in Europe is the ingestion of undercooked pork meat. Transfusion-transmitted HEV infections have also been reported. The aim of the study was to determine the HEV epidemiology and risk in the Finnish blood donor population. A total of 23,137 samples from Finnish blood donors were screened for HEV RNA from individual samples and 1012 samples for HEV antibodies. Additionally, laboratory-confirmed hepatitis E cases in 2016–2022 were extracted from national surveillance data. The HEV RNA prevalence data was used to estimate the risk of transfusion transmission of HEV in the Finnish blood transfusion setting. Four HEV RNA-positive were found, resulting in 1:5784 (0.02%) RNA prevalence. All HEV RNA-positive samples were IgM-negative, and genotyped samples represented genotype HEV 3c. HEV IgG seroprevalence was 7.4%. From the HEV RNA rate found in this study and data on blood component usage in Finland in 2020, the risk estimate for a severe transfusion-transmitted HEV infection is 1:1,377,000 components or one in every 6–7 years. In conclusion, the results indicate that the risk of transfusion-transmitted HEV (HEV TTI) in Finland is low. However, continuous follow-up of the HEV epidemiology in relation to the transfusion risk landscape in Finland is necessary, as well as promoting awareness in the medical community of the small risk for HEV TTI, especially for immunocompromised patients.