Miracle-dz/UniFAR-FTUs huggingface dataset:
AURORA: an air quality model for urban regions using an optimal resolution approach

For the assessment of air quality in cities, we developed an integrated model system, known as AURORA (Air quality modelling in Urban Regions using an Optimal Resolution Approach). This urban air quality management system has been designed for urban and regional policy support and reflects the state-of-the-art in air quality modelling, using fast and advanced numerical techniques. Modules for meteorological input data, emissions, advection, diffusion and chemistry have been designed, tested and coupled through a user interface. The model system is implemented in the cities Antwerp and Hasselt (B) and is being applied in various EU 5th framework projects (BUGS, DECADE).
Research on Decoding Algorithm of Split-Type Magnetic Encoder

Magnetic encoders are widely used in aerospace technology, robot joints, machine tool turntables, and other equipment applications. Interval misjudgment is a problem that occurs during synthesis of multiple interval angles because of the effects of the working environment and the encoder’s power supply. This article proposes an approximate arctangent algorithm based on a combination of table lookup and interpolation to improve the speed at which the arctangent solution is calculated. Finally, a unique angle interval translation decoding method is proposed that can implement mechanical angle synthesis for split magnetic encoders. The algorithm’s performance is then confirmed experimentally.
Robust Land Cover Classification With Multimodal Knowledge Distillation

In recent years, enormous studies have been conducted to improve the land cover (LC) classification performance of multimodal remote sensing (RS) data, which outperforms single-modal-based methods by a large margin due to information diversity. To go a step further, we develop a two-branch patch-based convolutional neural network (CNN) with an encoder–decoder (ED) module to fuse multimodal RS data information. A knowledge distillation in model (DIM) module is proposed to guild per-modality encoder learning with the final fused information to enable multimodal data fusion more effectively. Moreover, utilizing multimodal information to guide single-modal learning still remains to be explored. To this end, a knowledge distillation cross-model (DCM) module is designed to improve single-modal LC classification with multimodal knowledge distillation, which bridges the gap between single-modal-based and multimodal-based methods. In particular, the multimodal-based method is taken as a teacher to transfer knowledge to single-modal-based methods. Extensive experiments are carried out on two multimodal RS datasets, including hyperspectral (HS) and light detection and ranging (LiDAR) data, i.e., the Houston2013 dataset, and HS and synthetic aperture radar (SAR) data, i.e., the Berlin dataset. The results demonstrate the effectiveness and superiority of the proposed multimodal fusion strategy in comparison with several state-of-the-art multimodal RS data classification methods. Also, the proposed DCM module improves the LC classification performance of single-modal methods by a large margin.
Convolutional sparse coding for compressed sensing photoacoustic CT reconstruction with partially known support

In photoacoustic tomography (PAT), imaging speed is an essential metric that is restricted by the pulse laser repetition rate and the number of channels on the data acquisition card (DAQ). Reconstructing the initial sound pressure distribution with fewer elements can significantly reduce hardware costs and back-end acquisition pressure. However, undersampling will result in artefacts in the photoacoustic image, degrading its quality. Dictionary learning (DL) has been utilised for various image reconstruction techniques, but they disregard the uniformity of pixels in overlapping blocks. Therefore, we propose a compressive sensing (CS) reconstruction algorithm for circular array PAT based on gradient domain convolutional sparse coding (CSCGR). A small number of non-zero signal positions in the sparsely encoded feature map are used as partially known support (PKS) in the reconstruction procedure. The CS-CSCGR-PKS-based reconstruction algorithm can use fewer ultrasound transducers for signal acquisition while maintaining image fidelity. We demonstrated the effectiveness of this algorithm in sparse imaging through imaging experiments on the mouse torso, brain, and human fingers. Reducing the number of array elements while ensuring imaging quality effectively reduces equipment hardware costs and improves imaging speed.
Shared Mental Models and Coordination in Large-Scale

A fusible electrode is provided, which consists of ledeburitic tool steel and is suitable for use in a remelting process. Said electrode is melted. A slab ingot is built up by solidifying the resulting melt in a water-cooled slab ingot mold. Said slab ingot is upset by a succession of forging steps in the direction of the longitudinal axis of said slab ingot to provide a semifinished product. Said semifinished product is subjected to a stretching deformation to an extent of at least two times, based on the cross-section of said semi-finished product, to form a stretched product having the cross-section desired for said rod. From said stretched product, a rod is obtained which has a fairly homogeneous carbide distribution throughout its cross-section and length.
Travellers’ Attitude and Intention towards post Covid-19 Travel: The Moderating Role of Traveller Personality Type

COVID-19 has crucially changed the motivations, attitudes, and behaviours of travellers. This study explores the shifts in travel attitudes after the COVID-19 pandemic and how these changes affect the intention for upcoming travels. It investigates the moderating role of the traveller personality in forming the relationships between travel attitude’s antecedents and future travel intention. The study adopts the deductive approach and employs the quantitative method to achieve its aim. It compares the perceptions collected via a questionnaire from random travellers from three countries: UAE, Egypt, and Jordan. PLS-SEM is used for data analysis. It is revealed that protection motivation intentions, destination selection factors, and travel patterns/ arrangements predict travel attitudes post the pandemic. Cautious travellers are highly concerned with protection intentions and destination selection post-epidemic compared with normal travellers. Findings help us understand the shifts in travel attitudes and intentions after the pandemic. Understating the influence of the epidemic on tourists’ attitudes and intention to travel post-pandemic is strongly needed to accelerate tourism recovery and ensure a safe travel environment for tourists. Theoretically, the study responds to the research calls for examining the changes in travel motivations, attitudes, and behaviours. Practically, the study profiles travellers based on their travel personality (i.e., cautious versus normal travellers) and identifies the characteristics of each category. This will help destination marketers and service providers to adopt the relevant strategies to meet the tourists’ needs, expectations and fears in the post-pandemic new normal.
Effect of an Alliaceae Encapsulated Extract on Growth Performance, Gut Health, and Intestinal Microbiota in Broiler Chickens Challenged with Eimeria spp.

Simple Summary Coccidiosis is caused by an intracellular parasite that damages the intestinal integrity, negatively affecting the digestion and absorption of nutrients and consequently worsening weight gain, feed efficiency, and pigmentation of birds, even causing mortality. Therefore, it has a negative impact on the economy of the poultry industry. Currently, the disease is mainly treated by using anticoccidials drugs added to the diet. The drug resistance, as well as the residue of drugs in the meat, has prompted the development of natural alternatives to combat coccidiosis. The purpose of this research was to determine whether an Alliaceae encapsulated extract added to the broiler chickens diet decreased the number of oocysts excreted in feces and the harm caused to the intestinal mucosa, consequently improving the productive performance of broiler chickens challenged with Eimeria spp. Under our experimental conditions, both the inclusion of Alliaceae extract, as well as the use of conventional anticoccidials (nicarbazin/narasin/salinomycin), diminished the detrimental effect of Eimeria spp. Moreover, the Alliaceae extract favored the abundance of acid butyric bacteria (Ruminococcus spp. and Intestinimonas spp.) in the cecum, related to intestinal health. Based on the current findings, the Alliaceae extract could be a natural additive used to lessen the effects of coccidiosis infections. Abstract This study analyzed the effects of an Alliaceae encapsulated extract (AE-e) on daily gain (ADG), feed intake (ADFI), feed conversion ratio (FCR), oocysts per gram of feces (OPG), intestinal lesion (LS), and microbiota composition in broilers challenged with Eimeria spp. A total of 4800 one day Cobb-500 were allotted into 10 treatment groups with 12 replicates of 40 birds in a 2 × 4 + 2 factorial arrangement. The first factor was non-challenged (NC) or challenged (C), the second was four levels of AE-e added in the basal diet, 0 (AE0), 250 (AE250), 500 (AE500), and 750 mg·kg−1 (AE750), plus two ionophore controls, non-challenged (NC-Ion) and challenged (C-Ion). No interactions were observed between factors (NC0, NC250, NC500, NC750, C0, C250, C500, and C750), while C-Ion improved FCR at 21 d. The challenge affected negatively ADG and FCR and promoted enteropathogens in cecum. AE750 improved FCR in the finisher and cumulative phases, while C-Ion had fewer total OPG than C0 and C250. Likewise, at 21d, C250, C500, and C-Ion had fewer LS than C0, while at 28 d, C750 showed lower than C-Ion. In the cecum microbiota, C500 had more Ruminococcus, Firmicutes b, and Intestinimonas than C-Ion. In summary, AE-e showed beneficial results in broilers infected with Eimeria spp.
Particulate Matter Phytoremediation Effectiveness of Japanese Prunus × Yedoensis Tree Through Spring and Summer Season

Using green biofilters can help eliminate air pollutants and improve urban air quality. In the current study, Prunus × Yedoensis capacity of phytoremediation was investigated as a bio filter of Particulate matter (PM) in spring and summer seasons in Japan. Two samples were analysed to extract three fractions of PM (coarse, fine and ultra-fine). Results showed lower deposition of PM in spring season with total of 20.2 μg. cm-2 and high proportion for fine fraction (2.5-10μm), comparing with summer season which showed a higher deposition of PM with total of 31.9 μg. cm-2. Ultra-fine fraction (0.2–2.5μm) recorded the highest values with a percentage of 23.9% of the total PM deposition. Leaf micromorphological characteristics such as, rough surface and serrated margin can enhance the Prunus × Yedoensis ability of particulate matter accretion. This study confirms that vegetation has an efficient role in improving the quality of urban air. Further structural analysis should be included to deepen the understanding of phytoremediation phenomena in plants.
Structural, Geomorphic, and Depositional Characteristics of Contiguous and Broken Foreland Basins: Examples from the Eastern Flanks of the Central Andes in Bolivia and NW Argentina

In this chapter, we contrast the elements of a typical, contiguous foreland basin system in theBolivianAndeswith the broken foreland farther south innorthwesternArgentina. We illustrate differences in deposition and geomorphic shape that arise from the structural conditions towhich these two systems are subjected. Generally, the principal elements of foreland-basin systems resultmainly from accommodation space created by the flexural response of the crust to the topographic load of a fold-and-thrust belt. This leads to the formation of four distinct depozones: the wedge-top, foredeep, forebulge, and backbulge. In contrast, broken foreland basins are formed in areas where retroarc convergence is accommodated primarily along re-activated, high-angle structures. Rather than creating a broad area of consistently sloping mean topography, rock uplift along these structures is often disparate in space and time, leading to the formation of discrete ranges of limited along-strike extent that occur far inboard of the main topographic front of the orogen. The potentially high rock-uplift rates accommodated by steep, reactivated reverse faults favors isolation of the headwater basins of such systems from the downstream fluvial network, leading to sediment ponding behind the rising mountain ranges. In addition, the limited flexural response associated with short wavelength, laterally restricted topography may fail to create large amounts of accommodation space seen in the foredeep depozone that typifies foreland basin systems. Thus, instead of the generally continuous, laterally extensive foreland basin system associated with continental-scale crustal flexure, broken foreland basins, such as in the northwesternArgentineAndes, consist of a set of variably connected, laterally restricted depocenters. These evolve behind actively rising topography or small basins that form within the limited accommodation space created around individual uplifted mountain ranges. These types of depositional systems lackmany of the elements that are typical of recent foreland basins, including well-developed foredeep, forebulge, and backbulge depositional systems. Instead of applying foreland basin models to broken foreland basin systems, it is important to view these systems in the context of their distinct structural, topographic, and geodynamic circumstances.
Sharp decline of dust events induces regional wetting over arid and semi-arid Northwest China in the NCAR Community atmosphere model

Multiple lines of observational evidence have indicated a significant wetting over the arid and semi-arid Northwest China (NWC) during recent decades, coinciding with a simultaneous sharp decline of dust events. Although recent studies have attributed NWC wetting to different anthropogenic and natural forcings, the mechanisms are not definitive and the regional wetting has been greatly underestimated in the Coupled Model Intercomparison Project historical simulations. Based on sensitivity experiments with different dust emission amounts using the NCAR Community Atmospheric Model version 5 (CAM5), here we find that decreasing dusts exert significant impacts on mixed-phase clouds through reducing the concentration of ice nucleating particles, increase the NWC precipitation and thus induce regional wetting through enhancing convection precipitation. A possible convection invigoration mechanism whereby the atmospheric vertical temperature gradient and convective instability are strengthened by reduced dusts, leading to convection invigoration and increased precipitation. These results are reinforced by simulations over the dust region in North Africa where mixed-phase and ice clouds are rare and reduced dusts do not increase precipitation. This study highlights the possible mechanism of dust-ice cloud interactions in recent NWC wetting and future regional climate change.
Physical-Simulation-Based Dynamic Template Matching Method for Remote Sensing Small Object Detection

Object detection is a fundamental challenge encountered in understanding and analyzing remote sensing images. Many current detection methods struggle to identify objects in remote sensing images due to poor feature saliency, diverse directions, and unique viewing angles of small remote sensing targets. As most remote sensing images are captured through overhead photography, the structural contour features of objects in these images remain relatively stable, such as the cross-shaped structure of the aircraft and the rectangular structure of the vehicle. Therefore, we utilize the physical simulation images as prior knowledge to supplement the invariant structural features of the target, extract the target’s key geometric features through the dynamic matching method, and fuse it with the features extracted by the neural network to detect the remote sensing small target more effectively in this article. We propose a template matching method based on physical simulation images (TMSI) and add it to the modified Darknet-53 (named TMSI-Net) for small target detection. Based on this, we perform specific transformations on existing templates in terms of target scale and direction to achieve better adaptation to the corresponding goals and propose DTMSI-Net with a dynamic template library (Dynamic TMSI-Net). Experiments on several datasets demonstrate that the DTMSI-Net exhibits higher detection accuracy and more stable performance compared with the state-of-the-art methods, 4% and 2.7% higher than the second-ranked model on the VEDAI and NWPU datasets, respectively.
A Domain-Adversarial Wide-Kernel Convolutional Neural Network for Noisy Domain Adaptive Diesel Engine Misfire Diagnosis

In practical application scenarios, it is significant to take corresponding measures and diagnose diesel engine misfire faults. However, since the diesel engine noise is changeable and random, the existing methods obtain limited performance for diesel engine misfire fault diagnosis between different noise domains. In order to handle the above problems, this article proposes a domain-adversarial wide-kernel convolutional neural network (DAWDCNN) for noisy domain adaptive diesel engine misfire diagnosis. First, we utilize five 1-D convolution-pooling layers as feature extractor, of which the first layer contains a wide convolutional kernel. Then, extracted deep features from source domain data are fed into the fault classifier. Similarly, the extracted deep features from source and target domains are fed into the domain discriminator. Moreover, domain-adversarial training is employed as a training method of DAWDCNN. Also, we introduce a gradient reversal layer (GRL) to the backpropagation of the network in order to speed up the model training. Compared with the conventional phased domain-adversarial training method, GRL effectively improved the training speed by up to nearly 50%. It is also shown that DAWDCNN converges the fastest and has the most stable accuracy after convergence by comparing the accuracy–epoch curves of the test phases. Finally, for 11 noisy domain adaptability tasks from both one- and hybrid-cylinder diesel engine misfire datasets, DAWDCNN obtained better generalization performance of noisy domain adaptability than other existing methods. For example, on the four datasets, general average accuracies for other existing methods, such as random forest (RF), long short term memory (LSTM), deep neural networks (DNNs), convolutional neural network (CNN)_1d, CNN_1d-2d, DNN_maximum mean discrepancy (MMD), CNN_1d_MMD, and WDCNN_MMD, were 45.785%, 47.339%, 96.152%, 95.423%, 96.366%, 63.678%, 92.151%, and 97.519%, respectively, while DAWDCNN obtained 99.900% general average accuracy.
Speech Language Pathology interventions in the areas of breathing, chewing, swallowing and speaking: a scoping review

ABSTRACT There are several types of approaches that can be used to achieve therapeutic goals in disorders related to the functions of mastication, swallowing, speech, and breathing. However, the literature lacks evidence to support their use in speech-language clinical practice. The objective of this review was to map the syntheses of evidence on speech-language pathology intervention in the areas of breathing, mastication, swallowing and speech in adults and the elderly. Only studies classified by their authors as a systematic review, studies that addressed therapy for orofacial disorders in individuals over 18 years of age were included. The procedures performed included: electronic and manual search; selection of studies; data extraction; evaluation of the quality of studies and data analysis. It was possible to observe different types of interventions mainly aimed at the swallowing function, ranging from traditional therapy to the use of devices. However, due to the limitations of the studies, the data must be interpreted with caution.
On-Chip Acceleration of RF Signal Modulation Classification With Short-Time Fourier Transform and Convolutional Neural Network

Automatic Modulation Classification (AMC) is a technique used in wireless communication systems to identify the modulation type of received signals at the receiver. Improving spectrum utilization efficiency is essential for AMC and is carried out by fundamental signal processing methods within the physical layer. Convolutional Neural Networks (CNN) based deep-learning models have recently been employed in AMC systems, demonstrating superior performance. However, the large size of CNN models, floating-point weights, and activations make deploying such systems with limited hardware resources quite complex. In this paper, we propose a hybrid Radio Frequency-based Machine Learning (RFML) model that combines Short-time Fourier Transform and Convolutional Neural Network (STFT-CNN) for AMC. Simulations on RadioML2016.10a demonstrate an average recognition accuracy of 79% for a Signal-to-Noise Ratio of 0dB or higher when using 16-bit fixed-point operations. An on-chip accelerator for the STFT-CNN, designed and synthesized in 28nm CMOS, offers 19 times lower power consumption, 34 times smaller area, 1.3 times higher bandwidth, and 50 times less memory than a time-domain CNN accelerator utilizing 32-bit floating-point operations.
inhibits colorectal cancer progression by directly targeting KRAS and suppressing the AKT and ERK pathways

Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of cancer-related death worldwide. Tumour progression and development in CRC is a multi-step process involving a large number of genetic and epigenetic alterations. Previous studies indicated that abnormally expressed microRNAs play critical roles in CRC through regulation of oncogenic and tumour-suppressor genes. Hence, determination of the function of microRNAs may provide novel therapeutic targets for CRC diagnosis and treatments. MicroRNA-337 (miR-337) has been reported to be downregulated in several cancer types. However, the expression, function and underlying mechanisms of miR-337 in CRC have not been clearly elucidated. In this study, miR-337 was significantly decreased in CRC tissues and cell lines. Low miR-337 expression level was correlated with lymph node metastasis, distant metastasis and TNM stage of CRC patients. In
Study of Fuzzy Control for Pitch and Yaw Angles of Two-Degrees-of-Freedom Helicopter System

In this paper, PID (Proportional-Integral-Derivative), cascade, and fuzzy logic controller (FLC) controllers are developed for the Pitch and Yaw angles of a two-degrees-of-freedom helicopter system (2-DOF HS). Two different FLCs are used for Pitch and yaw angles in order to provide the output speed. A Mamdani-type FLC is implemented for the 2-DOF HS. The inputs of FLC are position error and rate of errors. The FLC is developed to give accurate results in terms of time domain specifications compared to PID and cascade controllers. All controllers have been established in MATLAB/ SIMULINK to test their control performance. According to the obtained simulation results, the FLC returns accurate control results, and robustness to eliminate the external disturbances.