Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing x-ray mirror thermal performance using variable length cooling for second generation FELs

The success of the LCLS led to an interest across a number of disciplines in the scientific community including physics, chemistry, biology, and material science. Fueled by this success, SLAC National Accelerator Laboratory is developing a new high repetition rate free electron laser, LCLS-II, a superconducting linear accelerator capable of a repetition rate up to 1 MHz. Undulators will be optimized for 200 to 1300 eV soft X-rays, and for 1000 to 5000 eV hard X-rays. To absorb spontaneous radiation, higher harmonic energies and deflect the x-ray beam to various end stations, the transport and diagnostics system includes grazing incidence plane mirrors on both the soft and Hard X-ray beamline. To deliver the FEL beam with minimal power loss and wavefront distortion, we need mirrors of height errors below 1nm rms in operational conditions. We need to mitigate the thermal load effects due to the high repetition rate. The absorbed thermal profile is highly dependent on the beam divergence, and this is a function of the photon energy. To address this complexity, we developed a mirror cradle with variable length cooling and first order curve correction. Mirror figure error is minimized using variable length water-cooling through a gallium-indium eutectic bath. Curve correction is achieved with an off-axis bender that will be described in details. We present the design features, mechanical analysis and results from optical and mechanical tests of a prototype assembly, with particular regards to the figure sensitivity to bender corrections.
Validation of 5G Use Case Solutions – Simultaneous Assessment of Business Value and Social Acceptance in Early Stages of the Research and Innovation Projects

This article recommends a lean approach when validating next generation mobile technology (5G) use case solutions in research and innovations projects (R&I). This validation should include a simultaneous and iterative assessment of business value and social acceptance beyond the traditional assessment of technological performance. The context studied in the article is R&I projects funded by the European Commission. Here a consortium of academic and enterprise partners jointly develops 5G product and service solutions for different industry verticals. From the literature review we learn that partnerships and ecosystems accelerate innovations as well as the significance of performing the validation already in the initial steps of the innovation projects. Based on a case study of seven completed 5G R&I projects we revealed that lean methodology for development of use case solutions were not applied. Furthermore, the assessment of potential business values from the 5G use case solutions, was postponed to the later stages of the R&I projects, and that assessing the social acceptance of these solutions were absent. After piloting the lean start-up methodology in an ongoing 5G R&I project (5G SOLUTIONS) we found that the partners advocated technological performance, business value and social acceptance to be ranked equally important to successfully validate the 5G use case solutions in the early steps in the innovation process. The two pilots we executed was performed as online workshops with partners from the 5G SOLUTIONS consortium and the media and entertainment vertical. The prototype was presented and feedback from the stakeholders were collected using surveys and interactive software tools. We recommend further studies and pilots of our lean start-up validation methodology in forthcoming 5G R&I projects funded by Horizon Europe for confirmation of our findings and further refinement.
Comparison of Similarity Measures for Trajectory Clustering - Aviation Use Case

—Various distance-based clustering algorithms have been reported, but the core component of all of them is a similarity or distance measure for classification of data. Rather than setting the priority to comparison of the performance of different clustering algorithms, it may be worthy to analyze the influence of different similarity measures on the results of clustering algorithms. The main contribution of this work is a comparative study of the impact of 9 similarity measures on similarity-based trajectory clustering using DBSCAN algorithm for commercial flight dataset. The novelty in this comparison is exploring the robustness of the clustering algorithm with respect to algorithm parameter. We evaluate the accuracy of clustering, accuracy of anomaly detection, algorithmic efficiency, and we determine the behavior profile for each measure. We show that DTW and Frechet distance lead to the best clustering results, while LCSS and Hausdorff Cosine should be avoided for this task.
Comparative Analysis of Machine Learning-based Forest Fire Characteristics in Sumatra and Borneo

Sumatra and Borneo are areas consisting of rainforests with a high vulnerability to fire. Both areas are in the tropics which experience rainy and dry seasons annually. The long dry season such as in 2019 triggered forest and land fires in Borneo and Sumatra, causing haze disasters in the exposed areas. This indicates that climate variables play a role in burning forests and land in Borneo and Sumatra, but how climate affects the fires in both areas is still questionable. This study investigates the climate variables: temperature, humidity, precipitation, and wind speed in relation to the fire’s characteristics in Borneo and Sumatra. We use the Random Forest model to determine the characteristics of forest fires in Sumatra and Borneo based on the climate variables and carbon emission levels. According to the model, the fire event in Sumatra is slightly better predicted than in Borneo, indicating a climate-fire dependence is more prominent in Sumatra. Nevertheless, a maximum temperature variable is seemingly an important indicator for forest and land fire in both domains as it gives the largest contribution to the carbon emission.
A Practical Non-Linear Parameterization of the BRDF Manifold

Real-world reflectance data can be used to improve the realism of synthesized images, albeit with many challenges: memory footprints can be large, profiles are limited to a finite (usually small) set of materials and rendering with measured data can be costly. Since the observation space (number of reflectance measurements) is usually much larger than the underlying space of real-world reflectance profiles, a typical optimisation strategy identifies principal components in the data to directly render from compressed representations of the measurements. We directly learn an underlying low-dimensional non-linear reflectance manifold amenable to rapid exploration and rendering of the space of real-world materials. We show that interpolated materials can be expressed as linear combinations of the measured data, despite lying on a non-linear manifold. This allows us to efficiently interpolate, extrapolate and render directly from the manifold. We apply a Gaussian process latent variable model to represent the reflectance manifold, demonstrating its utility in the context of high-performance and realistic rendering with materials that are interpolations of acquired BRDFs (from the popular MERL dataset).
Association between Dietary Inflammatory Index and Gastric Adenocarcinoma: A Multicenter Case-Control Study in Brazil

Background: Few studies have evaluated the association between diet-related inflammation and gastric adenocarcinoma (GA) and evidence is scarce in Brazil. This study evaluated the association between a pro-inflammatory diet and GA. Methods: A multicenter case–control study was conducted in Brazil. A total of 1645 participants—492 cases, 377 endoscopy controls, and 776 hospital controls—were included. Energy-adjusted Dietary Inflammatory Index (E-DIITM) scores were derived from a validated food frequency questionnaire. We used binary and multinomial logistic regression models for the analysis of total GA, and its subtypes (cardia and non-cardia, intestinal, and diffuse histological subtypes). Results: In cases versus endoscopy controls, a pro-inflammatory diet, estimated by higher E-DII scores, was associated with a higher risk GA (ORQ4vsQ1: 2.60, 1.16–5.70), of non-cardia GA (OR: 2.90, 1.06–7.82), and diffuse subtype (OR: 3.93, 1.59–9.70). In cases versus hospital controls, higher E-DII scores were associated with a higher risk of GA (OR: 2.70, 1.60–4.54), of cardia GA (OR: 3.31, 1.32–8.24), non-cardia GA (OR: 2.97, 1.64–5.39), and both intestinal (OR: 2.82, 1.38–5.74) and diffuse GA (OR: 2.50, 1.54–5.11) subtypes. Conclusions: This study provides evidence that a pro-inflammatory diet is associated with an increased risk of GA in Brazil. E-DII requires the inclusion of sodium due to its importance in carcinogenesis.
Indonesian Bebras Challenge 2021 Exploratory
Data Analysis

Indonesia is a full member of International Bebras Community and regularly perform the annual Bebras Challenge since 2016. The tasks for the Indonesian Bebras Challenge are taken and adapted from International Bebras Task pool, and each of the tasks is related to informatics and Computational Thinking (CT). We explore and analyze the Indonesian Bebras Challenge 2021 data to find interesting insight into Indonesian student’s competencies in informatics and computational thinking, the suitability of the selected Bebras Tasks difficulty level for each age group, and the relation between participants’ score and time duration in completing Bebras Challenge. The data exploratory has been done with statistics and data visualizations. We found that Indonesian students need to learn more about informatics and CT. The difficulty level of the Indonesian Bebras Challenge for elementary school students is still in accordance with Indonesian students’ competency. In contrast, the Bebras Challenge difficulty level for junior and senior high school is higher than the students’ competency. By analyzing the time duration of each participant completing the challenge, we also found some dishonest attitude presumptions during the online Bebras Challenge. We discover some suggestions for the improvement of Indonesian Bebras Challenge event as a means to improve Indonesian students’ informatics and CT skills.
Electromagnetic band gap coupled tri‐notched miniaturized ultra‐wideband antenna loaded with slot and parasitic strip

An electromagnetic band gap (EBG) coupled miniaturized tri‐notched printed ultra‐wideband (UWB) monopole microstrip antenna having dimensions of 22 mm × 26 mm × 1.6 mm loaded with a slot in radiating patch and a parasitic strip in the ground plane has been presented. The proposed structure incorporates a square‐shaped metallic radiating patch with a square EBG structure adjacent to the microstrip feed line, a U‐shaped meandered slot over the radiating element, and a U‐shaped parasitic resonator at the ground plane beneath the radiating element, to reject the C‐band satellite downlink (3.7 to 4.2 GHz), WLAN frequency band (5.15 to 5.85 GHz), and X‐band satellite downlink (7.25 to 7.75 GHz) frequency bands, respectively. The designed antenna operates in the frequency range from 3 to 11.1 GHz, with an impedance bandwidth of 8.1 GHz and a percentage bandwidth of 114%. Modification steps incorporating into the reference antenna to achieve the desired design objectives have been discussed, along with parametric studies. The proposed design has been simulated using Ansys HFSS, and measurement has been taken using standard measurement technique and compared with the simulated results.
Thermodynamic Consideration of the Solid Saponin Extract Drop–Air System

The aim of this research was to elucidate the surface active properties of Saponaria officinalis (soapwort) extract containing the plant surfactants saponins. To this end, the advancing contact angle (θ) of water, formamide and diiodomethane on the glass, as well as θ of the aqueous solution of S. officinalis extract fractions on PTFE, PMMA and glass, were studied. Based on the obtained results, the wetting behaviour of saponins was considered with regard to the surface tension components and parameters of the solutions and solids. The investigations also involved the description of the θ isotherms, the dependences between the cosine of contact angle and/or the adhesion of the solution to the solid surfaces and solution surface tension, as well as the critical surface tension of PTFE, PMMA and glass wetting. These dependences were studied based on the saponin adsorption at the different interfaces, which was deduced from the dependence between the adhesion and surface tension of the solution, as well as using the Gibbs and Frumkin isotherm equations. This proved that the saponins are poor wetting agents and that the contact angle isotherm can be described by the exponential function of the second order as well as the Szyszkowski equation, but only for PTFE.
Climate mitigation through soil amendments: quantification, evidence, and uncertainty

Abstract Soil amendments are a broad class of materials that enhance physical, chemical or biological characteristics in croplands, pastures, or rangelands. While organic soil amendments such as manure, mulch and seaweed have well established agronomic benefits, there has been renewed private and governmental interest in quantifying and incentivizing their role in mitigating climate change. Likewise, biostimulants and biopesticides, which are intended to target specific plant or microbial processes, are emerging with claims of improved soil health, crop yields, soil organic carbon sequestration, and greenhouse gas emission reductions. We conducted a literature review to address the climate mitigation potential of organic soil amendments, including biostimulants and biopesticides. In doing so, we identify three elements of climate mitigation through the use of soil amendments: soil organic carbon sequestration, soil greenhouse gas emission reductions, and life cycle emission reductions. We review common soil amendment classes in detail, addressing the empirical evidence (or lack thereof) in which they meet these three elements of climate mitigation. We conclude by suggesting priorities for government and private investment.
Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients’ quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.
Outlier Detection Based on Group Behavior in Trajectory Data

In recent years, with the rising popularity of GPS, and sensor network, the behavior of the trajectory data is collected and stored in the application server. There has been lots of research achievements at present in the application scenario. The uncertainty of the eventis not completely suitable for outlier detection algorithm of various kinds of application scenarios. In this paper, we study the behavior of the trajectory data, which is based on the behavior of the event and the group relation between objects, which based on the above two points. This paper introduce the object trajectory similarity computing the exception object detection algorithm.The experimental results on real datasets show the effectiveness and efficiency of proposed algorithms.
A 194–214-GHz Single-Sideband Mixer With Vertical and Inline Transitions Suitable for Linear Array Applications

A 194–214-GHz single-sideband (SSB) Schottky diode-based subharmonic mixer is newly presented for linear array applications. To realize 3-D integration, a vertical transition is adopted, which also acts as an IF-LO duplexer and an impedance transformer. A waveguide filter is integrated into the mixer for high image rejection, and an inline transition performs efficient mode conversion and impedance matching between the waveguide filter and the diode. The proposed mixer is more suitable for repeated arrangement in a linear array than conventional mixers with orthogonal configuration. A four-element mixer array is fabricated and measured. The array’s minimum SSB conversion loss (CL) is 8.1–10.4 dB from 194 to 214 GHz, the CL difference between elements is less than 1.3 dB, and the sideband rejection ratio is better than 30 dBc.
The Sphere Motion via SLERP

In this paper, we investigate a brief survey on the three-sphere motion by using the quaternion interpolasyon SLERP. Firstly, we consider the moving and fixed quaternion frames for three-sphere motion onto a unit quaternionic sphere. Then we calculate the equations of the velocity and we investigate some properties of the canonical relative system. Finaly we give some examples for these equations.
Rayleigh-Bénard turbulence modified by two-way coupled inertial, nonisothermal particles

Direct numerical simulation (DNS) combined with the Lagrangian point particle model is used to study Rayleigh-Bénard convection in order to understand modifications due to the interaction of inertial, non-isothermal particles with buoyancy-driven turbulence. In this system, turbulence can be altered through direct momentum coupling, as well as through buoyancy modification via thermal coupling between phases. We quantify the effect of the dispersed phase by changes to the total integrated turbulent kinetic energy (TKE) and Nusselt number (Nu). The dispersed particles experience gravitational settling, and are introduced at the lower wall so that turbulence must overcome the settling velocity for the particles to vertically distribute throughout the domain. We focus primarily on particle inertia, settling velocity, mass fraction, and the ratio of the particle to fluid specific heat. Furthermore, individual contributions by the momentum coupling and thermal coupling are studied to see which most significantly changes Nu and TKE. Our results show that particles with Stokes number of order unity maximize Nu, corresponding to a peak of clustering and attenuation of TKE. Increased mass fractions lead to a linear increase of Nu and decrease of TKE. With varying specific heat ratio, Nu and TKE exhibit monotonic behaviors, where in the high limit particles become isothermal and depend upon the initialized particle temperature. It is also shown that particles two-way coupled only through momentum attenuate Nu and weakens TKE while thermal-only coupling also weakens TKE but enhances Nu. When both couplings are present, however, thermal coupling overwhelms the momentum coupling attenuation and the net result is an enhancement of Nu.
Exponential Synchronization of Dynamical Network with Distributed Delays Via Intermittent Control

In this paper, the exponential synchronization of dynamical network with distributed time delays is investigated. The intermittent controllers are designed to achieve the exponential synchronization. Based on the Lyapunov function method and the mathematical analysis technique, some synchronization criteria with respect to the system parameters, control gain and control rate are presented. From the criteria, for any given dynamical network, the needed values of control gains and rate can be easily estimated. Finally, two numerical simulations are performed to verify the derived theoretical results.
An Analysis of Causal Relationship between Remittances and Imports in Nepal

The study aims to evaluate the casual relationship between remittances and imports in Nepal. Remittances are considered to be the most significant source of foreign currency that makes strong position of balance of payments, whereas the volume of imports is one of the most significant elements that lowers the volume of foreign currency reserve of the nation. It is generally believed that remittances have positive effects on volume of imports. On the contrary, if there is more imports than exports, it may run out trade deficit. It does make negative impact on the exchange rate of the home country. A weaker currency can raise the value of remittances sent back to the country, encouraging migrants to send more money home that influenced in growth of remittance. To analyze the relationship between remittance and imports, the study used annual data for 46 years from 1975 to 2020 A.D. The study used Vector Autoregressive model as there is no co-integration between the variables as per Johansen's technique. The variance decomposition test is carried out in a similar manner. The results demonstrate that there is a close connection between remittances and imports. But surprisingly, the result of VAR Granger causality test reveals that imports cause remittance rather than the other way around. Since import influences remittances, policymakers should consider enacting policies that discourage import and encourage export.
Cell motility in confinement: a computational model for the shape of the cell

While cells typically tend to spread their cytoplasm in a flat and thin lamellipodium when moving on a flat substrate, it is widely observed that the cytoplasm has a compact shape in micro-channels, tending to fulfill the cross-section of the microchannel. We propose a minimal mathematical model for a 2D test case which describes the cell lamellipodium deformations when confined in a channel. We then go through a numerical investigation of this mathematical model and show that it allows to recover qualitatively the physiological characteristics of the confined cell.
Mechatronic Device Control by Artificial Intelligence

Nowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article presents the use of artificial intelligence (prediction) for the control of three motors used for effector control in a spherical parallel kinematic structure of a designed device. The kinematic model used was the “Agile eye” which can achieve high dynamics and has three degrees of freedom. A prototype of this device was designed and built, on which experiments were carried out in the framework of motor control. As the prototype was created through the means of the available equipment (3D printing and lathe), the clearances of the kinematic mechanism were made and then calibrated through prediction. The paper also presents a method for motor control calibration. On the one hand, using AI is an efficient way to achieve higher precision in positioning the optical axis of the effector. On the other hand, such calibration would be rendered unnecessary if the clearances and inaccuracies in the mechanism could be eliminated mechanically. The device was designed with imperfections such as clearances in mind so the effectiveness of the calibration could be tested and evaluated. The resulting control of the achieved movements of the axis of the device (effector) took place when obtaining the exact location of the tracked point. There are several methods for controlling the motors of mechatronic devices (e.g., Matlab-Simscape). This paper presents an experiment performed to verify the possibility of controlling the kinematic mechanism through neural networks and eliminating inaccuracies caused by imprecisely produced mechanical parts.