Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualizing the Interpretation of a Criteria-Driven System That Automatically Evaluates the Quality of Health News: Exploratory Study of 2 Approaches

Machine learning techniques have been shown to be efficient in identifying health misinformation, but the results may not be trusted unless they can be justified in a way that is understandable. This study aimed to provide a new criteria-based system to assess and justify health news quality. Using a subset of an existing set of criteria, this study compared the feasibility of 2 alternative methods for adding interpretability. Both methods used classification and highlighting to visualize sentence-level evidence. A total of 3 out of 10 well-established criteria were chosen for experimentation, namely whether the health news discussed the costs of the intervention (the cost criterion), explained or quantified the harms of the intervention (the harm criterion), and identified the conflicts of interest (the conflict criterion). The first step of the experiment was to automate the evaluation of the 3 criteria by developing a sentence-level classifier. We tested Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest algorithms. Next, we compared the 2 visualization approaches. For the first approach, we calculated word feature weights, which explained how classification models distill keywords that contribute to the prediction; then, using the local interpretable model-agnostic explanation framework, we selected keywords associated with the classified criterion at the document level; and finally, the system selected and highlighted sentences with keywords. For the second approach, we extracted sentences that provided evidence to support the evaluation result from 100 health news articles; based on these results, we trained a typology classification model at the sentence level; and then, the system highlighted a positive sentence instance for the result justification. The number of sentences to highlight was determined by a preset threshold empirically determined using the average accuracy. The automatic evaluation of health news on the cost, harm, and conflict criteria achieved average area under the curve scores of 0.88, 0.76, and 0.73, respectively, after 50 repetitions of 10-fold cross-validation. We found that both approaches could successfully visualize the interpretation of the system but that the performance of the 2 approaches varied by criterion and highlighting the accuracy decreased as the number of highlighted sentences increased. When the threshold accuracy was ≥75%, this resulted in a visualization with a variable length ranging from 1 to 6 sentences. We provided 2 approaches to interpret criteria-based health news evaluation models tested on 3 criteria. This method incorporated rule-based and statistical machine learning approaches. The results suggested that one might visually interpret an automatic criterion-based health news quality evaluation successfully using either approach; however, larger differences may arise when multiple quality-related criteria are considered. This study can increase public trust in computerized health information evaluation.
Physicochemical Transformations of Silver Nanoparticles in the Oro-Gastrointestinal Tract Mildly Affect Their Toxicity to Intestinal Cells In Vitro: An AOP-Oriented Testing Approach

The widespread use of silver nanoparticles (Ag NPs) in food and consumer products suggests the relevance of human oral exposure to these nanomaterials (NMs) and raises the possibility of adverse effects in the gastrointestinal tract. The aim of this study was to investigate the toxicity of Ag NPs in a human intestinal cell line, either uncoated or coated with polyvinylpyrrolidone (Ag PVP) or hydroxyethylcellulose (Ag HEC) and digested in simulated gastrointestinal fluids. Physicochemical transformations of Ag NPs during the different stages of in vitro digestion were identified prior to toxicity assessment. The strategy for evaluating toxicity was constructed on the basis of adverse outcome pathways (AOPs) showing Ag NPs as stressors. It consisted of assessing Ag NP cytotoxicity, oxidative stress, genotoxicity, perturbation of the cell cycle and apoptosis. Ag NPs caused a concentration-dependent loss of cell viability and increased the intracellular level of reactive oxygen species as well as DNA damage and perturbation of the cell cycle. In vitro digestion of Ag NPs did not significantly modulate their toxicological impact, except for their genotoxicity. Taken together, these results indicate the potential toxicity of ingested Ag NPs, which varied depending on their coating but did not differ from that of non-digested NPs.
New approach to refinery process simulation with adaptive composition representation

The established technique for simulation of refinery processes is the use of pseudocomponents. However, in order to increase the economical benefit of plant operation, it seems inevitable to include molecular information in petroleum mixtures characterization. This will lead to a strong increase of problem size. For this new class of models, there currently seems to be no special algorithms available. The classic pseudocomponent approach is compared with a newly developed solution strategy, which is explicitly developed to efficiently solve simulation problems with a high detail in composition representation. The new solution strategy is an adaptive multigrid method based on a wavelet–Galerkin discretization. With the wavelet–Galerkin discretization the model can easily be formulated on various levels of detail. In an iterative procedure the multigrid concept exploits these different formulations to construct correction-term approximations to the true solution. The discretization of these correction-term models is now done with a detail in composition representation that is determined by a residual-based adaptation strategy. The proposed method has been implemented for a simple 9-stage distillation column and tested for a variety of feed mixtures. In all investigated tests the proposed method proved to be superior to the conventional pseudocomponent approach in terms of accuracy and efficiency. © 2004 American Institute of Chemical Engineers AIChE J, 50: 633–645, 2004
Rounding Theorem the Possibility of Applying the Cryptosystems on the Decimal Numbers

The possibility of using the decimal numbers in cryptography as the base of the encryption and decryption is proposed by introducing the "Rounding Theorem". Until now, cryptosystems are used the integer numbers to avoid the fractions of the decimal numbers. That is to avoid losing the original plaintext after the decryption. This theorem is proved that there is no lost for any bit of information during communications when using a cryptosystem that is based on decimal numbers. The purpose of moving from integer numbers to decimal numbers is the decimal numbers are much faster during calculations than the integer numbers. We design a new function called "The Rounding off Function", which plays the primary role in proving this theorem. The security of using the decimal numbers in cryptosystems is studied and analyzed.
Trajectory tracking control design with command-filtered compensation for a quadrotor

The design of a flight control system capable of not only stabilising attitude but also tracking a trajectory accurately for an under-actuated quadrotor aircraft is quite challenging. This study constructs the relationship between the attitude and linear acceleration of a small quadrotor unmanned aircraft and proposes a trajectory tracking control design algorithm, based on the relationship, using a new command-filtered backstepping technique to stabilise the attitude and a linear tracking differentiator to eliminate the classical inner/outer-loop structure. Finally, the validity and the effectiveness of this algorithm are demonstrated by various numerical simulations under different conditions.
The Key Roles of Mycobacterium tuberculosis FadD23 C-terminal Domain in Catalytic Mechanisms

Sulfolipid-1 (SL-1) is located in the Mycobacterium tuberculosis (M. tb) cell wall, and is essential for pathogen virulence and intracellular growth. Multiple proteins (e.g., Pks2, FadD23, PapA1, and MmpL8) in the SL-1 synthesis pathway can be treated as drug targets, but, to date, their structures have not been solved. The crystal structures of FadD23 bound to ATP or hexadecanoyl adenylate was determined in this study. We have also investigated long-chain saturated fatty acids as biological substrates of FadD23 through structural, biological, and chemical analyses. The mutation at the active site of FadD23 greatly influences enzymatic activity. Meanwhile, the FadD23 N-terminal domain alone cannot bind palmitic acid without C-terminal domain facilitation since it is almost inactive after removing the C-terminal domain. FadD23 is the first protein in the SL-1 synthesis pathway whose structure has been solved. These results reveal the importance of the C-terminal domain in the catalytic mechanism.
Comparison of gradient descent and conjugate gradient learning algorithms for classification of electrogastrogram

Our previous study showed that the possibility of using an optical three-layer feedforward neural network employing the gradient descent learning algorithm for automated assessment of normality of the electrogastrogram. However, problems with this algorithm are slow convergence rate and critical user-dependent parameters. In the present study, two conjugate gradient learning algorithms (quasi-Newton and scaled conjugate algorithm) were introduced and compared with the gradient descent learning algorithm for the classification of the normal and abnormal electrogastrogram. Three indexes, the convergence rate, complexity per iteration and parameter robustness, were used to evaluate the performance of each algorithm. The results showed that the scaled conjugate gradient algorithm performed the best, which was robust and provided a super linear convergence rate.
Wearable Sensors for the Monitoring of Maternal Health—A Systematic Review

Maternal health includes health during pregnancy and childbirth. Each stage during pregnancy should be a positive experience, ensuring that women and their babies reach their full potential in health and well-being. However, this cannot always be achieved. According to UNFPA (United Nations Population Fund), approximately 800 women die every day from avoidable causes related to pregnancy and childbirth, so it is important to monitor mother and fetal health throughout the pregnancy. Many wearable sensors and devices have been developed to monitor both fetal and the mother’s health and physical activities and reduce risk during pregnancy. Some wearables monitor fetal ECG or heart rate and movement, while others focus on the mother’s health and physical activities. This study presents a systematic review of these analyses. Twelve scientific articles were reviewed to address three research questions oriented to (1) sensors and method of data acquisition; (2) processing methods of the acquired data; and (3) detection of the activities or movements of the fetus or the mother. Based on these findings, we discuss how sensors can help effectively monitor maternal and fetal health during pregnancy. We have observed that most of the wearable sensors were used in a controlled environment. These sensors need more testing in free-living conditions and to be employed for continuous monitoring before being recommended for mass implementation.
Neural Network Aided Path Splitting Strategy for Polar Successive Cancellation List Decoding

Combined with the path splitting selecting (PSS) strategy, the successive cancellation list (SCL) decoder achieves a lower decoding complexity without any performance loss. The main purpose of the PSS strategy is to select fewer non-frozen bits to perform the path splitting and pruning operations. To make the selection more efficient, a neural network aided path splitting selecting strategy is proposed in this paper. In the proposed method, the path splitting is seen as a classification problem. With the help of the neural network, the proposed method can precisely locate the non-frozen bits which are needed to be split. Simulation results verify that the proposed method can reduce the sorting complexity (which is equal to the number of path splitting) without any performance loss.
Diode array pumped kilowatt laser

The diode array pumped kilowatt laser (DAPKL) has demonstrated more than an order of magnitude increase in brightness and average power for short pulse diode-pumped solid-state lasers since its inception in 1991. Significant advances in component technology have been demonstrated, including: development of a diffusion bonding process for producing large slabs of Nd:YAG laser material. Phase conjugation by stimulated Brillouin scattering (SBS) has been demonstrated with high reflectivity and fidelity in a simple focused geometry with input powers of 100 W. Pulse energies at 1.06 /spl mu/m of 10 J have been demonstrated with a beam quality of 1.5 times diffraction limited at the 500-W level. An average power of 875 W at 100 Hz has been obtained. Efficient frequency doubling with a record power of 165 W has been demonstrated with 5 J per pulse at 0.53 /spl mu/m. Work is ongoing to enclose the system in a compact brassboard with improved performance and long term stability.
Thermometry of fermionic atoms in an optical lattice

Low temperatures are necessary for the observation of strongly correlated quantum phases of fermionic atoms in optical lattices. We analyze how the temperature of a Fermi gas is altered when the fermions are loaded into an optical lattice with an underlying harmonic confining potential, and show how the temperature can be measured. The temperature of the atoms in the optical lattice determines the fraction of doubly occupied lattice sites of a two-component Fermi gas. We analytically calculate this quantity and find a strong temperature dependence. This fraction can be measured by studying the production of molecules in the lattice using a Feshbach resonance, which allows for precise thermometry of atoms in an optical lattice.
Bulk micromachining of SOI wafers using double sided lithography and anisotropic wet etching

A novel method of manufacturing bulk micromachined components in SOI material with anisotropic wet etching is presented. The SOI material reduces the total chip size compared to standard bulk micromachining since the slopes are shorter. It also adds functionality to the components, such as inherent overload protection and squeezed air-film damping. The method is based on double sided lithography and anisotropic wet etch of the thin device layer of the SOI wafer. The lithography on the back of the buried oxide is carried out either by electro deposition of the photoresist PEPR 2400 or by spraying ma-P 215S. A highly symmetric three-axis accelerometer has successfully been manufactured in a 50 micrometers thick SOI film. © (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Quantum rates for a double well coupled to a dissipative bath: Accurate path integral results and comparison with approximate theories

We present accurate fully quantum calculations of thermal rate constants for a symmetric double well system coupled to a dissipative bath. The calculations are performed using the quasiadiabatic propagator path integral (QUAPI) methodology to evaluate the flux–flux correlation function whose time integral determines the rate coefficient. The discretized path integral converges very rapidly in the QUAPI representation, allowing efficient calculation of quantum correlation functions for sufficiently long times. No ad hoc assumption is introduced and thus these calculations yield the true quantum mechanical rate constants. The results presented in the paper demonstrate the applicability of the QUAPI methodology to practically all regimes of chemical interest, from thermal activation to deep tunneling, and the quantum transmission factor exhibits a Kramers turnover. Our calculations reveal an unusual step structure of the integrated reactive flux in the weak friction regime as well as quantum dynamical enhanc.
Nuclear Impurities in the Superfluid Crust of Neutron Stars: Quantum Calculation and Observable Effects on the Cooling

We perform a quantum mechanical calculation of the neutron-specific heat in the Wigner-Seitz cells that characterize the inner crust of a neutron star, where a Coulomb lattice of neutron-rich nuclei is permeated by a sea of unbound superfluid neutrons. We then evaluate the observable thermal properties associated with such a microscopic structure and compare them to the standard values obtained in a uniform density model of the crust superfluidity with no nuclear impurities. We first argue that the bottleneck for the diffusion of heat is represented by the outermost layers of the inner crust. We then show that the crust-cooling times obtained for the nonuniform system are significantly different from those obtained in the standard uniform model. We find that the sign and magnitude of this difference depend sensitively on the local matter temperature and the pairing potential. We conclude that in order to give predictive power to observations of the thermal emission from neutron stars, it is crucial to include in any realistic cooling code the effect of nuclear impurities on the neutron superfluid.
Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization

Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas.
Analysis and prediction of network connection behavior anomaly based on knowledge graph features

More and more complex and diverse network security problems bring great challenges to the analysis of abnormal network behavior. In order to detect the abnormal connection behavior of the network more accurately, this paper first uses the knowledge graph technology to extract the graph feature parameters that can reflect the node and the overall situation of the network, and then proposes a two-stage unsupervised anomaly analysis method for the abnormal changes of the feature parameters. In the first stage, the anomaly analysis of the whole network graph features is carried out based on clustering technology, so the rough positioning is carried out. In the second stage, the abnormal trend analysis is performed on the graph features of important nodes to determine the category of abnormal connection behavior. On this basis, the time series prediction method is used to predict the node graph features, so as to provide early warning for network security. The experimental results show that the method can effectively extract the network abnormal behavior and predict the development trend of the network in the future, and provide a good support for the understanding of network security situation.
A Reconfigurable Multiple Transform Selection Architecture for VVC

Video coding plays an important role in the highly information-based world as videos contribute the largest part of network traffic. The latest video coding standard Versatile Video Coding (VVC) introduces a new transform scheme multiple transform selection (MTS), which brings considerable coding gains at the expense of high coding complexity. In this article, we propose a reconfigurable MTS architecture that supports all transform types in VVC with square and rectangular sizes ranging from $4\times $ 4 to 32 $\times32$ . Firstly, we explore the features of three types of transform matrices and extract the features that are beneficial to designing a unified architecture. Then, we present an improved calculation scheme for general transforms, where the transform matrix is decomposed into two simpler matrices to increase the similarity and decrease the complexity of matrices involved in three types of transform operations. Thanks to the improved calculated scheme, a unified shift-adder unit (SAU) is designed and highly reused by different types. Moreover, we provide a twirling two-point splicing (T2S) scheme to improve reusability and deal with issues of data mismatch when conducting discrete cosine transform (DCT)-II of different sizes. As a consequence, an architecture with constant throughput of 32 pixels/cycle is implemented and specified in Verilog HDL. The synthesis results indicate that the application specific integrated circuit (ASIC)-based and field-programmable gate array (FPGA)-based hardware architectures achieve significant advantages both in area reduction and power consumption compared to existing methods in the literature.
The genomic and epigenetic footprint of local adaptation to variable climates in kiwifruit

Abstract A full understanding of adaptive genetic variation at the genomic level will help address questions of how organisms adapt to diverse climates. Actinidia eriantha is a shade-tolerant species, widely distributed in the southern tropical region of China, occurring in spatially heterogeneous environments. In the present study we combined population genomic, epigenomic, and environmental association analyses to infer population genetic structure and positive selection across a climatic gradient, and to assess genomic offset to climatic change for A. eriantha. The population structure is strongly shaped by geography and influenced by restricted gene flow resulting from isolation by distance due to habitat fragmentation. In total, we identified 102 outlier loci and annotated 455 candidate genes associated with the genomic basis of climate adaptation, which were enriched in functional categories related to development processes and stress response; both temperature and precipitation are important factors driving adaptive variation. In addition to single-nucleotide polymorphisms (SNPs), a total of 27 single-methylation variants (SMVs) had significant correlation with at least one of four climatic variables and 16 SMVs were located in or adjacent to genes, several of which were predicted to be involved in plant response to abiotic or biotic stress. Gradient forest analysis indicated that the central/east populations were predicted to be at higher risk of future population maladaptation under climate change. Our results demonstrate that local climate factors impose strong selection pressures and lead to local adaptation. Such information adds to our understanding of adaptive mechanisms to variable climates revealed by both population genome and epigenome analysis.