Miracle-dz/UniFAR-FTUs huggingface dataset:
The AlN lattice-polarity inversion in a high-temperature-annealed c-oriented AlN/sapphire originated from the diffusion of Al and O atoms from sapphire

AlN films are widely used owing to their superior characteristics, including an ultra-wide bandgap, high breakdown field, and radiation resistance. High-temperature annealing (HTA) makes it easy to obtain high-quality AlN films, with the advantages of a simple process, good repeatability, and low cost. However, it is always found that there is a lattice-polarity inversion from a N-polarity near the sapphire to an Al-polarity in the HTA c-oriented AlN/sapphire. Currently, the formation mechanism is still unclear, which hinders its further wide applications. Therefore, the formation mechanism of the polarity inversion and its impacts on the quality and stress profile of the upper AlN in the HTA c-oriented AlN/sapphire were investigated. The results imply that the inversion originated from the diffusion of the Al and O atoms from the sapphire. Due to the presence of abundant Al vacancies (VAl) in the upper AlN, Al atoms in the sapphire diffuse into the upper AlN during the annealing to fill the VAl, resulting in the O-terminated sapphire, leading to the N-polar AlN. Meanwhile, O atoms in the sapphire also diffuse into the upper AlN during the annealing, forming an AlxOyNz layer and causing the inversion from N- to Al-polarity. The inversion has insignificant impacts on the quality and stress distribution of the upper AlN. Besides, this study predicts the presence of a two-dimensional electron gas at the inversion interface. However, the measured electron concentration is much lower than that predicted, which may be due to the defect compensation, low polarization level, and strong impurity scattering.
Research on Expressway Pavement Crack Detection based on Improved YOLOv5s

In order to address the issues of missed detection, false detection, and low accuracy of current road cracks, we propose a road crack recognition model based on improved YOLOv5. Firstly, add a CBAM attention module to the backbone network to enhance feature extraction capabilities; Then, a weighted bidirectional feature pyramid (BiFPN) is incorporated into the model for multi-scale feature fusion, replacing the traditional feature pyramid (FPN)+pixel aggregation network (PAN) structure to enhance feature fusion. The experimental results indicate that the improved model outperforms the traditional YOLOV5 model in terms of mAP@0.5 By 17.3%, the improved YOLOv5 algorithm performs well in detecting road cracks and can quickly and accurately identify and locate cracks on the road.
Eye Biometrics Combined with Periocular and Iris Recognition Using CNN

In this paper, we propose an eye biometric recognition method, which is combined with periocular and iris recognition. The proposed method extracts features using convolutional neural networks (CNNs) dedicated to periocular and iris recognition, respectively. The matching score is calculated by weighting scores from periocular and iris recognition. Through a set of experiments using public datasets, we demonstrate the effectiveness of the proposed method compared with conventional methods.
The Intuitive Eating Scale-2 Adapted for Mexican Pregnant Women: Psychometric Properties and Influence of Sociodemographic Variables

A weight-inclusive approach to health involves the promotion of intuitive eating, i.e., the individual’s ability to be aware of their physiological hunger and satiety cues to determine when and how much to eat, while paying attention to how certain foods affect their body. The second version of the Intuitive Eating Scale (IES-2) evaluates four interrelated traits of intuitive eating: Unconditional Permission to Eat (UPE), Eating for Physical rather than emotional Reasons (EPR), Reliance on internal Hunger/Satiety Cues (RHSC), and Body–Food Choice Congruence (BFCC). In this study, our aim was to evaluate the psychometric properties of a Mexican Spanish adaptation of the IES-2 for pregnant women and examine the relationship between intuitive eating traits and maternal sociodemographic characteristics. A sample of 514 pregnant women answered our IES-2 adaptation and a sociodemographic questionnaire. We determined the quality, validity, and reliability of our adaptation through descriptive measures, frequency distributions, intra-class correlations, and extreme answer group comparison for each item, eliminating those with weak technical properties. We then performed an exploratory principal component analysis and a confirmatory factor analysis. Last, we analyzed the association between intuitive eating and maternal sociodemographic and reproductive variables through correlation tests and multivariable linear regressions. Psychometric tests confirmed the validity and reliability of our IES-2 adaptation, which comprised 18 out of the 23 original items. Notably, both the exploratory and confirmatory factor analyses yielded not four but five factors, due to the EPR subscale splitting in two (the “emotional” and “physical” components of EPR). We attribute this novel finding to the emotional manifestations that naturally accompany pregnancy, which may incline pregnant women to base their eating behaviors more on the emotional than the physical component that would otherwise dominate their EPR trait. Further research is also needed about the UPE subscale during pregnancy, due to item removal and subtle changes in meaning. Finally, the influence of sociodemographic variables on the IES-2 score was extremely low, suggesting that other variables, possibly of a psychological nature, may have greater influence on a pregnant woman’s intuitive eating.
Anti-Hepatotoxic Effect of the Methanolic Anstatica Hierochuntica Extract In Ccl 4- Treated Rats

The study was investigated the hepatoprotective activity of methanolic extract of Anastatica hierochuntica using carbon tetra chloride (CCl 4 )-induced hepatotoxicity in rats. The levels of liver enzymes glutamate oxaloacetate transminase , glutamate pyruvate transminase ,alkaline phosphatase ,total Protein, total and direct bilirubin, in addition to Superoxide dismutase, glutathione peroxidase activities ,glutathione malondialdehyde and total antioxidant status levels were evaluated in experimental rats (with or without CCl 4 - inducedhepatotoxicity)following intake of 100 mg/kg p.o alcoholic Anastatica hierochuntica extract by using standard procedures compared with standard silymarin at a dose of 100 mg/kg p.o. Results showed that methanolic extract at a dose level of 100 mg/kg had a significant decrease(p<0.05) in activities of serum liver enzymes, glutamate oxaloacetate transminase , glutamate pyruvate transminase, alkaline phosphatase, total bilirubin and protein were significantly decreased in rats treated with CCl 4 after 4 weeks compared to that of silymarin group in addition to the methanolic extract had antioxidant activity through decreasing activities of superoxide dismutase and glutathione peroxidase to levels in control rats group. Histopathology of a liver tissue of the animals treated with the extract was also studied to monitor the liver status. The liver biopsy of all experimental rat groups treated with the methanolic Anastatica hierochuntica extract showed significant restoration of the
Likely ecological impacts of global warming and climatechange on the Great Barrier Reef by 2050 and beyond

This report considers the likely ecological impacts of global warming and climate change on the Great Barrier Reef, Australia, by 2050 and beyond. It has been prepared at the request of the Queensland Conservation Council (QCC) for use in an objections hearing before the Queensland Land and Resources Tribunal concerning a proposed large open-cut coal mine.
On Nontriviality of a Product in the Classical Adams Spectral Sequence

Let p ≥ 11 be an odd prime and q = 2(p − 1). Suppose that n ≥ 1 with n ≠ 5. Let 0 ≤ s < p − 4 and t = s + 2 + t = s + 2 + (s + 2)p + (s + 3)p2 + (s +4)p3 + pn . This paper shows that the product element δs+4h0bn−1 ∈ ExtAs+7,tq+s (Z/p,Z/p) is a nontrivial permanent cycle in the classical Adams spectral sequence, where δs+4 denotes the 4th Greek letter element.
Comparison of improved variational autoencoder models for human face generation

The variational autoencoder (VAE) model has evolved a number of VAE improved models in the past 10 years, including CVAE, WAE, NVAE, etc. These models have greatly improved the calculation speed of VAE and the resolution of generated images. The main goal of this paper is to compare the principles of these different models and the effect of generating images. The principle analysis method is mainly used to study the improvement direction of the VAE model in different papers. The main ideas for improving the VAE model include optimizing the loss function, optimizing the objective function, introducing other parameters, or improving code efficiency. Some models even add many algorithms in computer vision in for improving image effect. In the experiments in this paper, results show the image results processed by different models. In the current optimal model NVAE, this algorithm has solved most of the VAE image blur problems, and has achieved perfection in image details. The improvement of the VAE model in the future may require innovative ideas. Under the current principle and model structure, the room for improvement of the VAE model is relatively limited. The final experiment shows that to improve the quality of VAE generated images, it is required to optimize the objective function, optimize the algorithm and add image visual processing, and then the generated images will be significantly improved.
A study to develop a linear quadratic controller for a ship central cooling system

: In this study, a linear quadratic (LQ) controller was developed and applied to maintain the desired freshwater outlet temperature, efficiently operate the seawater (SW) pump rotational speed, and effectively regulate the three-way valve opening in response to step disturbances. A mathematical model of the central cooling system was established to integrate the two controllers. The state-space equation of the mathematical model was linearized to facilitate controller development. The voltage frequency of the SW pump motor and three-way valve opening degree were selected as the two primary inputs of the system control. In contrast, the heat exchanger (HEX) and three-way valve outlet temperatures were the primary outputs of the control system. To design the LQ controller, the input control vector that minimized the performance index by incorporating specific weighting matrices was determined. The resulting LQ controller with an integrator was developed and tested through control performance simulations. The simulation results revealed that the proposed controller controlled the freshwater outlet temperature with minimal overshoot, efficiently controlled the SW pump rotational speed, and effectively controlled the three-way valve opening against step disturbances. The proposed LQ controller for central cooling systems offers a refined approach for efficiently controlling ship central cooling systems.
Corals as proxy recorders of volcanic activity; evidence from Banda Api, Indonesia

Corals growing on the flanks of Banda Api, Indonesia, contain records of volcanic activity which occurred in May, 1988. Ashfall killed portions of some massive coral colonies (Porites lobata), resulting in the formation of death surfaces. These surfaces were preferential sites for extensive bioerosion and incorporation of volcanic ash into underlying skeletal pores. Subsequent coral regeneration resulted in the preservation of death/regrowth surfaces overlying trapped volcanic ash. An orange-colored iron-rich chemical precipitate is preserved in the skeletons of corals which survived the volcanic event. These distinct orange bands are contemporaneous with the death/ regrowth surfaces. The iron banding is interpreted as being a product of hydrothermal activity which accompanied volcanism. X-radiographs of coral skeletons confirm that the timing of formation of both the death surfaces and the orange banding is coincident with the 1988 eruption. The features preserved in these corals may be valuable proxy indicators of volcanic events in analogous recent and ancient environments. INTRODUCTION
FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively train models without exposing their raw data. In most cases, the data across devices are non-independently and identically distributed in FL. Thus, the local models trained over different data distributions will inevitably deviate from the global optima, which induces optimization inconsistency and even hurts global convergence. Moreover, the resource-constrained devices with heterogeneous training capacities (e.g., computing and communication) further slow down the convergence rate. To this end, we introduce an FL framework with adaptive data sampling and local training, namely FAST. Specifically, even without devices’ private data distributions, FAST enables each device to sample different rates of data points from each of its local classes to rebuild a dataset for training, thus adjusting the convergence direction of the aggregated global model to be closer to the global optima. The theoretical analysis shows that the convergence bound depends on the sampling rates as well as the number of local iterations executed on the sampled data. To achieve resource-effective and convergence-guaranteed FL, we then design an online learning algorithm that jointly optimizes the data sampling and local training strategies so as to encourage the decrease of global loss under the given time budget. Extensive experiments on physical and simulated environments show that, FAST improves the model accuracy by about 1.55%-6.78% given the same time budget, and accelerates training by about 1.39-5.89× with the same target accuracy, compared with the baselines.
Full-Band Quantum Simulations of Semiconductor Devices based on Empirical Pseudopotential Hamiltonians in the presence of Phonon Scattering and Non-Radiative Recombination

We present a full-band quantum transport model based on the Empirical Pseudopotential Method (EPM) that includes electron-phonon scattering and Shockley-Read-Hall (SRH) recombination via non-radiative multiphonon relaxation. The model has been used to simulate the current-voltage characteristics of two different Silicon-based devices and it shows that a) in a p-i-n diode, SRH recombination gives the dominant contribution to the OFF-state current of the junction, while phonon scattering is responsible for the degradation of the current in the ON-state; b) the inclusion of SRH recombination in the simulation of an Esaki tunneling diode predicts a reduction of the peak-to-valley current ratio, while phonon scattering enhances indirect tunneling transitions from the valence band in Γ to the conduction band minima in the Δ valleys that are not located along the transport direction.
Risk Division and Assessment of Typhoon Rainstorm Flooding Disasters in Hangzhou City

Based on the typhoon precipitation data from seven stations in Hangzhou City during the period from 1960 to 2009 and combined with the society economic and natural geographic factors,this study builds a set of typhoon rainstorm flooding disaster assessment model including hazard factors,hazard inducing environment,hazard-affected body and disaster prevention ability.And a map of Hangzhou's typhoon rainstorm flooding disaster risk division was drawn using 100 m X 100 m raster as basic assessment unit, by means of GIS spatial analysis technique for rasterizing evaluation indices and fuzzy comprehensive assessment methods.Finally,the five risk zones of Hangzhou,i.e.higher risk,sub-high risk,medium risk,inferior risk and lower risk,were mapped.Results show that the typhoon rainstorm flooding disaster risk in Hangzhou has an increasing tendency from southwest to northeast.The coastal plain,involved with Xiaoshan District,Yuhang City,main urban area of Hangzhou,and so on,has relative high risk,while the mid-west mountain and hilly area,involved with Jiande City,Chun'an County,and so on,has slightly lower risk.
Artificial neural network modelling of microbial acclimation periods in soil contaminated with petroleum hydrocarbons

Suchorski-Tremblay, A.M. and Kok,. R. 1997. Artificial neural network modelling of microbial acclimation periods in soil contaminated with petroleum hydrocarbons. Can. Agric. Eng. 39:123-130. Artificial neural networks (ANNs) were used to model the acclimation of indigenous microorganisms in soil contaminated with diesel fuel or creosote. Acclimation data were obtained by measuring the appearance of 14C (as C02) mineralized from radiolabelled tracers that were added to soil microcosms. The ANNs were trained and tested with the following inputs: incubation temperature, water content (as percentage of the soil's water holding capacity), addition or not of nitrogen and phosphorus fertilizers, and sampling time. The ANN output was directly related to the cumulative percent of 14C recovered (%L C). The resultant ANN models were incorporated into a user-friendly software package called AccliMat, written in QuickBASIC. With this package, the ANN models can be utilized to calculate the amount of %L14C that will be recovered after a given number of days, or the number of days required to reach a given %L C. Des reseaux de neurones artificiels (RNA) ont servi de modeles previsionnels pour l'acclimatation de microorganismes indigenes dans un sol contamine par Ie diesel ou Ie creosote. Cette acclimatation a ete mesuree en fonction de la presence de l'isotope 14C (sous forme de C02), provenant de la degradation des traceurs radioactifs ajoutes ades echantillons de sol. Les RNA ont ete soumis adifferentes conditions d'apprentissage suivant I'entree de cinq donnees: la temperature d'incubation, la teneur en eau du sol (par rapport a sa capacite de retention), l'ajout ou non d'engrais d'azote et de phosphore, et Ie temps d'echantillonnage. Les valeurs produites par les RNA etaient directement liees aux quantites de 14C recupcrces (%L C) dans les microcosmes pectologique. Nos modcles RNA ont ete incorpores aAccliMat, un progiciel relativement simple autiliser, ecrit en QuickBASIC. Dans Ie cadre de ce progiciel, les RNA peuvent servir aprevoir Ie pourcentage de 14C rccupere dans un sol apres un certain nombre de jours, ou encore, le.temps requis pour atteindre un niveau donne de 14C (%L C).
Automatic Segmentation of Finger Bone Regions from CR Image Using the Improved HRNet

In Japan, it is serious problems such as increasing nursing care of handicapped people requiring and the aging of caregivers which is caused in a super-aging society. One of the main causes of being classified as persons requiring nursing care include joint diseases such as rheumatoid arthritis and bone fractures due to osteoporosis. Early detection and treatment of these diseases are considered important. Rheumatoid arthritis and osteoporosis are generally diagnosed by simple X-ray examination. However, there are problems with radiographic diagnosis by physicians, such as lack of objectivity and reproducibility of diagnosis, and increased workload on the radiologists. To solve these problems, a Computer-Aided Diagnosis (CAD) system is being developed. Because the CAD system may use the results of quantitative computer analysis, it is expected to improve the reproducibility and accuracy of diagnosis and reduce the burden on physicians. Therefore, this paper proposes a segmentation method of phalanx region from CR images to develop a CAD system. The proposed method is based on HRNet + JPU + U-Net. The proposed method was applied to 101 cases of X-ray images, and mIoU=0.897 was obtained. Experiments for segmentation from images confirmed the usefulness of the proposed method by improving the extraction accuracy of the boundary of the phalanx region.
Attention-based CNN and Relative Phase Feature Modeling for Improved Imagined Speech Recognition

In EEG (Electroencephalography) based imagined speech recognition research, deep learning is gradually being applied, but has not yet achieved sufficient performance. The main reasons for this are the lack of ability to acquire generalized feature representations and the insufficient amount of training data. For the former, we propose two methods, one is a CNN-based feature extractor that introduces an attention mechanism, and the other is an improved feature extractor that introduces the inter-channel relative phase obtained from the multi-channel EEG signal as an additional spectral feature instead of a CNN-based feature extractor. For the latter, we propose a pre-training method using multi-subject EEG data to construct subject-adapted models for an imagined speech recognition task. The effectiveness of the proposed methods is verified by using an imagined speech recognition task for English phonemes, classification for English vowels and consonants. As a result, the CNN model with attention mechanism successfully improved the feature extraction capability of the CNN and achieved a classification accuracy rate of 71.9%, which are approximately 20% higher than that of the CNN only. On the other hand, a simple neural network based classifier approach using spectral features as input achieved 71.1% by using the proposed inter-channel relative phase features over the conventional relative phase features, and 75.0% by applying ensemble processing to the test data. Pre-training and subject adaptation also achieved a 6% improvement in accuracy, indicating that it is possible to mitigate the impact of insufficient training data.
Authentication Method for Spoofing Protection in Communication and Navigation Satellites: Utilizing Atmospheric Signature

The integration of satellite and terrestrial networks would generate more opportunities for various kinds of cyber attacks, such as spoofing. In this study, we propose a novel signal-processing algorithm for improving the physical layer security of integrated satellite-terrestrial networks (ISTN). The proposed algorithm is based on utilizing the atmospheric signature associated with the received signal at the ground station that causes rapid variations in the signal. We leverage the characteristics of spatial time-frequency signature caused by atmospheric channel associated with incoming signals at the ground station from different satellites to differentiate among the sources from which the signal has been transmitted. The various parameters extracted from the rapidly varying signal component are then provided as inputs to the machine learning algorithm for detecting the legitimate satellite. The algorithm is validated against the measured data obtained at a site located in Israel. The results shows very low false alarm (<inline-formula> <tex-math notation="LaTeX">$< 2 \%$ </tex-math></inline-formula>) and missed detection rate (<inline-formula> <tex-math notation="LaTeX">$< 1 \%$ </tex-math></inline-formula>) while the authentication rate is quite high (<inline-formula> <tex-math notation="LaTeX">$> 97 \%$ </tex-math></inline-formula>).