Miracle-dz/UniFAR-FTUs huggingface dataset:
MICRORHEOLOGICAL MODELING OF WEAKLY AGGREGATED DISPERSIONS

A microrheological model of aggregating dispersions is proposed in which the shear stress is estimated as the sum of hydrodynamic and structural parts. The former is attributed to the hydrodynamic cores of fractal aggregates, which behave as a suspension of impermeable spheres. The latter accounts for the forces transmitted by chains of particles linking neighboring aggregates into a transient network. To calculate the structural part the concept of fractal aggregation is incorporated into a transient network theory, to account for the creation and breakup of chains of colloidal particles connecting the aggregates. Rigid and soft chains are distinguished. The former have multiply connected backbones which deform as contorted elastic rods, while the latter have at least one soft junction and deform without elastic resistance until fully loaded. The contribution of the soft chains to the stress tensor is neglected. The calculations treat two different mechanisms for the evolution of rigid chains: a purely m.
Suppression of Gate Leakage Current in Ka-Band AlGaN/GaN HEMT With 5-nm SiN Gate Dielectric Grown by Plasma-Enhanced ALD

A <italic>Ka</italic>-band AlGaN/GaN metal–insulator–semiconductor high-electron-mobility transistor (MIS-HEMT) with SiN gate dielectric grown by plasma-enhanced atomic layer deposition (PEALD) is demonstrated. The gate reverse leakage current in a controlled HEMT is reduced by about three orders of magnitude by the PEALD-SiN, contributing to an improved breakdown voltage of 92.5 V (source–drain separation of <inline-formula> <tex-math notation="LaTeX">$2.0~\mu \text{m}$ </tex-math></inline-formula>) in the fabricated MIS-HEMTs. The MIS-HEMTs also feature small threshold voltage hysteresis in dc transfer and capacitance–voltage characterizations, suggesting a good PEALD-SiN/AlGaN interface. The fabricated MIS-HEMTs deliver a high output power density of 7.16 W/mm and a peak power-added efficiency of 60.3%, respectively, at <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}} =30$ </tex-math></inline-formula> V and <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}} =10$ </tex-math></inline-formula> V in pulse mode at 28 GHz. PEALD-SiN could be a compelling gate dielectric for fabrication of high-power and high-efficiency MIS-HEMTs.
Uplink Performance of Cell-Free Massive MIMO Over Spatially Correlated Rician Fading Channels

We consider a practical cell-free massive multiple-input-multiple-output (MIMO) system with multi-antenna access points (APs) and spatially correlated Rician fading channels. The significant phase-shift of the line-of-sight component induced by the user equipment movement is modeled randomly. Furthermore, we investigate the uplink spectral efficiency (SE) with maximum ratio (MR)/local minimum mean squared error (L-MMSE) combining and optimal large-scale fading decoding based on the phase-aware MMSE, phase-aware element-wise MMSE and linear MMSE (LMMSE) estimators. Then new closed-form SE expressions with MR combining are derived. Numerical results validate our derived expressions and show that the SE benefits from the spatial correlation. It is important to observe that the performance gap between L-MMSE and MR combining increases with the number of antennas per AP and the SE of the LMMSE estimator is lower than that of other estimators due to the lack of phase-shifts knowledge.
A Thesaural Interface for the Basic Concepts Classification

: We describe a thesaural interface that is being developed for the Basic Concepts Classification. This interface is particularly well-suited to the synthetic phenomenon-based approach to classification pursued by the BCC. We describe how the thesaural interface works, our plans to develop it further, and the advantages of this interface for both classifier and user.
Intelligent recognition of acoustic emission signals from damage of glass fiber-reinforced plastics

Glass fiber-reinforced plastics (GFRP) is widely used in many industrial fields. When acoustic emission (AE) technology is applied for dynamic monitoring, the interfering signals often affect the damage evaluation results, which significantly influences industrial production safety. In this work, an effective intelligent recognition method for AE signals from the GFRP damage is proposed. Firstly, the wavelet packet analysis method is used to study the characteristic difference in frequency domain between the interfering and AE signals, which can be characterized by feature vector. Then, the model of back-propagation neural network (BPNN) is constructed. The number of nodes in the input layer is determined according to the feature vector, and the feature vectors from different types of signals are input into the BPNN for training. Finally, the wavelet packet feature vectors of the signals collected from the experiment are input into the trained BPNN for intelligent recognition. The accuracy rate of the proposed method reaches to 97.5%, which implies that the proposed method can be used for dynamic and accurate monitoring of GFRP structures.
Radar Inertial Odometry With Online Calibration

Accurate localization in visually degraded and GNSS denied environments is key for autonomous robotics. Vision based approaches usually fail in challenging conditions like smoke, dusk, direct sunlight or darkness. Our approach uses an Inertial Measurement Unit (IMU) and a millimeter wave FMCW radar sensor as both sensors are not affected by such conditions. A filter based 3D Radar Inertial Odometry (RIO) approach is presented which enables the online estimation of the extrinsic calibration of the radar sensor. Consequently, tedious calibration is not required any more. The application of stochastic cloning enables to process delayed radar measurements properly which enables to apply the navigation filter for online navigation. The proposed system is evaluated in simulation which proves a consistent estimation. Real world experiments with carried datasets and UAV flights prove the improvement by online calibration resulting in reduced position errors.
Transforming Work Organization with Internal Crowds: a Process Theory

Internal crowdsourcing showed a substantial increase of use in recent years, since it describes a promising alternative to traditional orchestration of employees in today’s digital era. However, literature falls short in explaining the transformation process that is en-acted by such approaches of platform-based work organization. We apply a process ontology on internal crowdsourcing as platform-based mode of work organization, following two organizations employing internal crowdsourcing in a case study approach for over four years. On a macro level, our theory describes the transformation process enacted by internal crowdsourcing as three-phased process. On the micro-level, we illustrate that this transformation process is driven by specific design choices on single elements. In so doing, our process theory contributes to a better understanding of internal crowdsourcing as means for transformation work organization and to STS theory by showing that the emergence and constitution of STS is mainly driven by processes on a micro-level.
THE SYNTHESIS AND THE CHARACTERIZATION OF THE MONOMETHYL- AND THE DIMETHYL-QUINOLINES

All 7 monomethyl- and all 21 dimethyl-quinolines were synthesized by unambiguous methods and those solid above room temperature were purified to their maximum melting points. The picrates, styphnates, and trinitro-m-cresolates of each quinoline were prepared and their properties recorded. The picrates in particular were purified to their ultimate melting points. Incidentally, 7-ethyl-quinoline and a number of trimethyl-quinolines were also prepared. It is concluded that the melting points previously given in the literature for most of the picrates are of little use for identification purposes.
Resina de Protium heptaphyllum: isolamento, caracterização estrutural e avaliação das propriedades térmicas

Three mixtures of triterpenes (maniladiol and breine; a and b-amyrin; lupenone, a and b-amyrinone) were isolated from Protium heptaphyllum March resin. The structural identification was based on NMR and mass spectrometry data. Lupenone, and a and b-amyrinone were not reported before as constituents of this resin. The resin was submitted to methylation and acetylation reactions. The pure and derivatized resins and the mixtures (maniladiol and breine; a and b-amyrin) were analyzed by TG and DSC. The TG curves revealed that the derivatization decreases the thermal stability of the resin. The DSC curves showed peaks that can be assigned to evaporation and phase transitions processes.
The Use of Information and Communication Technology Through Website and Social Media as Public Relations Information Media

The purpose of this study was to determine the use of communication and information technology through websites and social media as public relations information. Public relations are an effort to build and maintain a good reputation, image and communication between the organization and the public. School public relations as a forum for schools in conveying information to the public about everything related to the formation of the school image, programs, profiles and school promotion. In the current era of globalization, the development of technology is increasing rapidly so that information can be obtained more quickly by the public. School public relations are required to follow technological developments by utilizing online media, namely websites and social media as school information media to the wider public. Data collection techniques were carried out by observation, interviews, and documentation. The research subjects consisted of the vice principal in the field of public relations, the coordinator of the public relations section of the website and social media by determining the sample using purposive sampling technique. The results showed the role of communication and information technology through websites and social media at As-Salam Islamic Elementary School, Malang, Indonesia as a medium for delivering school information and a means of communication between schools and parents and public.
Molecular reactivity in the ground and excited electronic states through density-dependent local and global reactivity parameters

Molecular hardness values have been calculated for a few selected diatomics,viz., H2, HF, N2, BF, CO, and F2 in their ground and first excited electronic configurations using 4-31G double ζ type basis functions. The excited electronic configurations are so chosen that they happen to have the lowest energy for a particular symmetry, keeping in mind the validity of the excited state density functional theory for such systems. It is observed for all the molecules studied that hardness values decrease with electronic excitation. Surface plots of different local quantities like the charge density, the laplacian of the charge density, the quantum potential, the molecular electrostatic potential, and the Fukui function reveal an increase in the molecular reactivity with excitation.
Flame Front Geometry and Stretch During Interactions of Premixed Flames with Vortices

Abstract Flame front geometry and flame stretch during interactions of premixed flames with vortices at various configurations are computationally investigated using a kinematical relationship between vortex-induced velocity and flame propagation velocity, in conjunction with Lagrangian coordinates to represent the flame front. The maximum flame stretch that can occur during an interaction with a single vortex is found to be very close to Uθ/rm + SL/2rm, while the mean flame stretch has a maximum that asymptotes to 18-23% of this value for vortex radii between 1.25 and 5.0 mm as UθSL is increased to 2. For flames interacting with a counter-rotating vortex pair, the flame stretch near the flow symmetry axis increases when the spacing between the vortices increase to align the velocity vectors with the flame front; while the flame stretch away from this region is relatively independent of the action of the opposite vortex and is determined primarily by the vortex velocity and length scale similar to the sin.
Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients

A recently developed algorithm for 3D analysis based on machine learning (ML) principles detects left ventricular (LV) mass without any human interaction. We retrospectively studied the correlation between 2D-derived linear dimensions using the ASE/EACVI-recommended formula and 3D automated, ML-based methods (Philips HeartModel) regarding LV mass quantification in unselected patients undergoing echocardiography. We included 130 patients (mean age 60 ± 18 years; 45% women). There was only discrete agreement between 2D and 3D measurements of LV mass (r = 0.662, r2 = 0.348, p < 0.001). The automated algorithm yielded an overestimation of LV mass compared to the linear method (Bland–Altman positive bias of 13.1 g with 95% limits of the agreement at 4.5 to 21.6 g, p = 0.003, ICC 0.78 (95%CI 0.68−8.4). There was a significant proportional bias (Beta −0.22, t = −2.9) p = 0.005, the variance of the difference varied across the range of LV mass. When the published cut-offs for LV mass abnormality were used, the observed proportion of overall agreement was 77% (kappa = 0.32, p < 0.001). In consecutive patients undergoing echocardiography for any indications, LV mass assessment by 3D analysis using a novel ML-based algorithm showed systematic differences and wide limits of agreements compared with quantification by ASE/EACVI- recommended formula when the current cut-offs and partition values were applied.
Study on Talent Identification of Soccer For Young Indian Male Players

Background:The identification of talents is a crucial field in the development of sports. In India, most of the coaches, scout players evaluating their current skill, not considering their future development. The scouts and coaches need a systematic and multidimensional approach in identifying talent. Methods: The article reviews the various factors considered for the identification process that affect the success of young soccer talents (age under 14). It includes physiological, sociological, psychological, and technical/tactical factors. It also considers the relative age effect and training history of the player. Conclusion:the study emphasis the need to consider all these variables and their relationships between them by the scouts and coaches. The scouting sheet simplifies this process and the checklist for the same is demonstrated in this paper.
Perceptual Quality Assessment of Digital Images Using Deep Features

Perceptual quality assessment is a tough task especially in the absence of reference information. No-reference image quality assessment is more challenging than full-reference or reduced reference methods, as the system has to model the diﬀerent image distortions in the form of a quality score. Most of the approaches are based on handcrafted features which are based on natural scene statistics and are speciﬁc to some distortion types. These approaches provide high correlation with human opinion score for datasets containing speciﬁc distortions, but they fail to generalize well in scenarios were multiple distortions or real-time distortions are present in images. Deep learning algorithms, on the other hand, demonstrated their abilities to learn expert features with better discriminatory power for various classi-ﬁcation and regression tasks. It is a big challenge to use those deep learning methods for image quality assessment as the image datasets with human opinion score are very small and cannot be used eﬀectively to train a deep learning algorithm. We experimented with activations of diﬀerent deep layers of thirteen pre-trained models and checked for their suitability for the task of no-reference quality assessment. Fine-tuning of these models on quality assessment datasets provided even better performance. A Gaussian process regression model is trained on these activations to perform the quality assessment and it provided state-of-the-art performance. Cross-dataset validation demonstrated its performance further and also provided further prospects of research in this direction.
Ultralow-Latency VLSI Architecture Based on a Linear Approximation Method for Computing Nth Roots of Floating-Point Numbers

State-of-the-art approaches that perform root computations based on the COordinate Rotation Digital Computer (CORDIC) algorithm suffer from high latency in performing multiple iterations. Therefore, root computations based on the CORDIC algorithm cannot meet the strict latency requirements of some applications. In this paper, we propose a methodology for performing <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>th root computations on floating-point numbers based on the piecewise linear (PWL) approximation method. The proposed method divides an <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>th root computation into several subtasks approximated by the PWL algorithm. It determines the widest segments of the subtasks and the smallest fractional width needed to satisfy the predefined maximum relative error <inline-formula> <tex-math notation="LaTeX">$Max\_{}Err_{r}$ </tex-math></inline-formula>. Our design is coded in Verilog HDL and synthesized under TSMC 40 nm CMOS technology. The synthesized results show that our design can reach the highest frequency of 2.703 GHz with an area consumption of <inline-formula> <tex-math notation="LaTeX">$2608.84~\mu ~\text{m}^{2}$ </tex-math></inline-formula> and a power consumption of 2.4476 mW. Compared with one state-of-the-art architecture, our design saves 91.60%, 89.84%, and 63.33% of the area, power, and latency @1.89GHz frequency, respectively, while reducing <inline-formula> <tex-math notation="LaTeX">$Max\_{}Err_{r}$ </tex-math></inline-formula> by 57.30%. In addition, it saves 94.52%, 92.68%, and 73.17% of the area, power, and delay @1.89GHz frequency, respectively, and reduces <inline-formula> <tex-math notation="LaTeX">$Max\_{}Err_{r}$ </tex-math></inline-formula> by 1.65% when compared with the other state-of-the-art design.
Challenges to Evidence Synthesis and Identification of Data Gaps in Human Biomonitoring

The increasing number of human biomonitoring (HBM) studies undertaken in recent decades has brought to light the need to harmonise procedures along all phases of the study, including sampling, data collection and analytical methods to allow data comparability. The first steps towards harmonisation are the identification and collation of HBM methodological information of existing studies and data gaps. Systematic literature reviews and meta-analyses have been traditionally put at the top of the hierarchy of evidence, being increasingly applied to map available evidence on health risks linked to exposure to chemicals. However, these methods mainly capture peer-reviewed articles, failing to comprehensively identify other important, unpublished sources of information that are pivotal to gather a complete map of the produced evidence in the area of HBM. Within the framework of the European Human Biomonitoring Initiative (HBM4EU) initiative—a project that joins 30 countries, 29 from Europe plus Israel, the European Environment Agency and the European Commission—a comprehensive work of data triangulation has been made to identify existing HBM studies and data gaps across countries within the consortium. The use of documentary analysis together with an up-to-date platform to fulfil this need and its implications for research and practice are discussed.
Indwelling Device-Associated Biofilms in Critically Ill Cancer Patients—Study Protocol

Health care-associated infections are a leading cause of inpatient complications. Rapid pathogen detection/identification is a major challenge in sepsis management that highly influences the successful outcome. The current standard of microorganism identification relies on bacterial growth in culture, which has several limitations. Gene sequencing research has developed culture-independent techniques for microorganism identification, with the aim to improve etiological diagnosis and, therefore, to change sepsis outcome. A prospective, observational, non-interventional, single-center study was designed that assesses biofilm-associated pathogens in a specific subpopulation of septic critically ill cancer patients. Indwelling device samples will be collected in septic patients at the moment of the removal of the arterial catheter, central venous catheter, endotracheal tube and urinary catheter. Concomitantly, clinical data regarding 4 sites (nasal, pharyngeal, rectal and skin) of pathogen colonization at the time of hospital/intensive care admission will be collected. The present study aims to offer new insights into biofilm-associated infections and to evaluate the infection caused by catheter-specific and patient-specific biofilm-associated pathogens in association with the extent of colonization. The analysis relies on the two following detection/identification techniques: standard microbiological method and next generation sequencing (NGS). Retrospectively, the study will estimate the clinical value of the NGS-based detection and its virtual potential in changing patient management and outcome, notably in the subjects with missing sepsis source or lack of response to anti-infective treatment.
Automatic Permission Optimization Framework for Privacy Enhancement of Mobile Applications

Mobile applications play a crucial role in the IoT system, which is experiencing unprecedented growth. However, users possessing little knowledge of permission configurations often accept app permission requests without reading them, which opens a backdoor for the potential adversaries to launch the future attacks. Proposing an automatic permission management scheme is an attractive solution to solve this issue, but since users have varying attitudes toward privacy, such a scheme would be neither straightforward nor user friendly. In this study, an automatic permission optimization framework, Permizer, is proposed to recommend different app permission configurations to users with different privacy preferences. Permizer estimates the permission risks and builds the permission-functionality mapping to each app, then regulates the relationship between permission and app functionality. Permizer is the first module to achieve a balance between privacy protection and app functionality under the personal privacy preference condition. Finally, we develop Permizer as a one-button service on the real-world Android OS with 58 apps. Case studies conducted on TikTok and Amazon Alexa also demonstrate its practicability and effectiveness.