Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Characteristics of an Active Coastal Spreading Area Combining Geophysical Data with Satellite, Aerial, and Unmanned Aerial Vehicles Images

The northern region of the Maltese archipelago is experiencing lateral spreading landslide processes. This region is characterized by cliffs with a hard coralline limestone outcropping layer sitting on a thick layer of clay. Such a geological configuration causes coastal instability that results in lateral spreading which predispose to rockfalls and topplings all over the cliff slopes. The aim of this research was to develop a methodology for evaluating cliff erosion/retreat using the integration of geomatics and geophysical techniques. Starting from a 3D digital model of the Selmun promontory, generated by unmanned aerial vehicle (UAV) photogrammetry, it was possible to map the fractures and conduct geophysical measurements such as electrical resistivity tomography and ground penetrating radar for the identification and mapping of vertical fractures affecting the hard coralline limestone plateau, and to create a 3D geological model of the study area. In addition to this, high-accuracy orthophotos from UAV that were captured between 1957 and 2021 were georeferenced into a GIS and compared to aerial and satellite images. The movement and evolution of boulders and cracks in rocks were then vectorized to highlight, track and quantify the phenomenon through time. The results were used to derive a qualitative assessment of the coastal variations in the geometric properties of the exposed discontinuity surfaces to evaluate the volumes and the stop points of the observed rockfalls. The outcomes of this research were finally imported in a GIS which offers an easy approach for the collection and processing of coastal monitoring data. In principle, such a system could help local authorities to address social, economic and environmental issues of pressing importance as well as facilitate effective planning in view of a risk mitigation strategy.
Inductance Analysis of Transverse Flux Linear Synchronous Motor for Maglev Trains Considering Three-Dimensional Operating Conditions

Despite the availability of powerful finite element method (FEM) tools, an accurate and fast analysis method is needed in the early design stage to find an initial motor design structure with the expected performance. When it is applied to maglev trains, inductance of the transverse flux linear synchronous motor (TFLSM) is affected by the cogging structure, the change of air gap length, and the variation of lateral offset. Therefore, the analytical method of inductance in the TFLSM is different compared to that in the conventional rotating field winding motors. The three-dimensional analysis models for obtaining inductance parameters are comprehensively introduced in this article. The results of the inductance analytical calculation are verified and evaluated in light of the FEM simulation results and the prototype measurements.
Erythemato-Squamous Disease Detection using Best Optimized Estimators of ANN

—Medical area focused on automating skin cancer detection after the pandemic era of "Monkey Pox". Previous works proposed ANN mechanisms to classify the type of skin cancer. However, all those models implement layers of ANN with standard estimator components like hidden layers implemented using the ReLu activation function, several neurons are generally a power of two and others, but these values are not always perfect. Few researchers implemented optimization techniques for tuning the estimators of A.I. algorithms, but all those mechanisms require more resources and don't guarantee the best values for each estimator. The proposed method analyzes all the essential estimators of every possible neural network layer. Then it applies a modified version of Bayesian optimization because it avoids the disadvantages of Grid and Random optimization techniques. It picks the best estimator by using the conditional probability of naive Bayesian for every combination.
The stress analysis of thin contact layers: a singular perturbation method for integral equations

In this paper, a contact problem is considered with a circular indenter pressed normally against a semi-infinite elastic composite that consists of a contact layer with uniform thickness welded together with another dissimilar medium. The indentation is modelled by means of a real continuous function g(t), and an integral equation representation for the displacement is derived in terms of g(t) on the contact boundary. The integral equation is evaluated numerically for g(t) when the contact layer is finite. In the case when the thickness of the contact layer becomes small compared with the radius of the contact region, a singular perturbation technique is used to derive an asymptotic expansion for g(t). This leads to a Wiener–Hopf equation formulation for the semi-infinite geometry in the Fourier-transformed domain of the inner coordinates. Subsequently, Van Dyke's matching principle is used to match the inner with the outer solution of g(t). The results are illustrated and verified with a simple limit solution derived using an integral argument.
Notch-Jagged1 signaling and response to bevacizumab therapy in advanced colorectal cancer: A glance to radiomics or back to physiopathology?

Introduction The Notch intracellular domain (NICD) and its ligands Jagged-1(Jag1), Delta-like ligand (DLL-3) and DLL4 play an important role in neoangiogenesis. Previous studies suggest a correlation between the tissue levels of NICD and response to therapy with bevacizumab in colorectal cancer (CRC). Another marker that may predict outcome in CRC is radiomics of liver metastases. The aim of this study was to investigate the expression of NICD and its ligands and the role of radiomics in the selection of treatment-naive metastatic CRC patients receiving bevacizumab. Methods Immunohistochemistry (IHC) for NICD, Jag1 and E-cadherin was performed on the tissue microarrays (TMAs) of 111 patients with metastatic CRC treated with bevacizumab and chemotherapy. Both the intensity and the percentage of stained cells were evaluated. The absolute number of CD4+ and CD8+ lymphocytes was counted in three different high-power fields and the mean values obtained were used to determine the CD4/CD8 ratio. The positivity of tumor cells to DLL3 and DLL4 was studied. The microvascular density (MVD) was assessed in fifteen cases by counting the microvessels at 20x magnification and expressed as MVD score. Abdominal CT scans were retrieved and imported into a dedicated workstation for radiomic analysis. Manually drawn regions of interest (ROI) allowed the extraction of radiomic features (RFs) from the tumor. Results A positive association was found between NICD and Jag1 expression (p < 0.001). Median PFS was significantly shorter in patients whose tumors expressed high NICD and Jag1 (6.43 months vs 11.53 months for negative cases; p = 0.001). Those with an MVD score ≥5 (CD31-high, NICD/Jag1 positive) experienced significantly poorer survival. The radiomic model developed to predict short and long-term survival and PFS yielded a ROC-AUC of 0.709; when integrated with clinical and histopathological data, the integrated model improved the predictive score (ROC-AUC of 0.823). Discussion These results show that high NICD and Jag1 expression are associated with progressive disease and early disease progression to anti VEGF-based therapy; the preliminary radiomic analyses show that the integration of quantitative information with clinical and histological data display the highest performance in predicting the outcome of CRC patients.
IoT and Computer Vision Based Aquaponics System

A farming method known as aquaponics aims to be a more effective response to the world's worries about food production and scarcity. In order to produce plants or vegetables in addition to fish to meet the rising global demand for food, this technique combines aquaculture (fish farming) and aquaponics (plants grown without soil). This technique or system promises to utilize less water, fertilizer, pesticides, and space while yet producing as much as conventional farming. The challenge is making it more usable and scalable for commercial and public use. To overcome such challenges, this technique must be intelligent and automated by rigorous sensing, monitoring, and system control. Our current research aims to develop a practical smart IoT-based aquaponics system. It offers a workable solution and ideas for further investigation while also looking for gaps in the earlier investigations.
THE APPROXIMATE SLOPES OF ECONOMETRIC TESTS

In this paper the concept of approximate slope, introduced by R. R. Bahadur, is used to make asymptotic global power comparisons of econometric tests. The approximate slope of a test is the rate at which the logarithm of the asymptotic marginal significance level of the test decreases as sample size increases, under a given alternative. A test with greater approximate slope may therefore be expected to reject the null hypothesis more frequently under that alternative than one with smaller approximate slope. Two theorems, which facilitate the computation and interpretation of the approximate slopes of most econometric tests, are established. These results are used to undertake some illustrative comparisons. Sampling experiments and an empirical illustration suggest that the comparison of approximate slopes may provide an adequate basis for evaluating the actual performance of alternative tests of the same hypothesis.
Enhanced Optimized Classification Model of Chronic Kidney Disease

—Chronic kidney disease (CKD) is one of the leading causes of death across the globe, affecting about 10% of the world's adult population. Kidney disease affects the proper function of the kidneys. As the number of people with chronic kidney disease (CKD) rises, it is becoming increasingly important to have accurate methods for detecting CKD at an early stage. Developing a mechanism for detecting chronic kidney disease is the study's main contribution to knowledge. In this study, preventive interventions for CKD can be explored using machine learning techniques (ML). The Optimized deep belief network (DBN) based on Grasshopper's Optimization Algorithm (GOA) classifier with prior Density-based Feature Selection (DFS) algorithm for chronic kidney disease is described in this study, which is called "DFS-ODBN. " Prior to the DBN classifier, whose parameters are optimized using GOA, the proposed method eliminates redundant or irrelevant dimensions using DFS. The proposed DFS-ODBN framework consists of three phases, preprocessing, feature selection, and classification phases. Using CKD datasets, the suggested approach is also tested, and the performance is evaluated using several assessment metrics. Optimized-DBN achieves its maximum performance in terms of sensitivity, accuracy, and specificity, the proposed DFS-ODBN demonstrated accuracy of 99.75 percent using fewer features comparing with other techniques.
Abnormal Behavior Monitoring Method of Larimichthys crocea in Recirculating Aquaculture System Based on Computer Vision

It is crucial to monitor the status of aquaculture objects in recirculating aquaculture systems (RASs). Due to their high density and a high degree of intensification, aquaculture objects in such systems need to be monitored for a long time period to prevent losses caused by various factors. Object detection algorithms are gradually being used in the aquaculture industry, but it is difficult to achieve good results for scenes with high density and complex environments. This paper proposes a monitoring method for Larimichthys crocea in a RAS, which includes the detection and tracking of abnormal behavior. The improved YOLOX-S is used to detect Larimichthys crocea with abnormal behavior in real time. Aiming to solve the problems of stacking, deformation, occlusion, and too-small objects in a fishpond, the object detection algorithm used is improved by modifying the CSP module, adding coordinate attention, and modifying the part of the structure of the neck. After improvement, the AP50 reaches 98.4% and AP50:95 is also 16.2% higher than the original algorithm. In terms of tracking, due to the similarity in the fish’s appearance, Bytetrack is used to track the detected objects, avoiding the ID switching caused by re-identification using appearance features. In the actual RAS environment, both MOTA and IDF1 can reach more than 95% under the premise of fully meeting real-time tracking, and the ID of the tracked Larimichthys crocea with abnormal behavior can be maintained stably. Our work can identify and track the abnormal behavior of fish efficiently, and this will provide data support for subsequent automatic treatment, thus avoiding loss expansion and improving the production efficiency of RASs.
Machine learning-based prediction of diabetes risk by combining exposome and electrocardiographic predictors

Diabetes is a high-burden non-communicable disease affecting more than 532 million people worldwide and resulting in a range of life-threatening comorbidities. Pre-identifying high-risk individuals and applying preventive actions will likely reduce the prevalence and health consequences of diabetes. Under this context, we developed and evaluated the first predictive model of diabetes risk that combines both electrocardiography (ECG) and exposome predictors. A comprehensive list of ECG signals and exposome variables were extracted from the UK Biobank, then used to build and compare a set of machine learning models for diabetes risk prediction. Random Forest combining ECGs and exposome variables achieved an 0.82 ± 0.03 AUC when predicting diabetes risk. This integrative model outperformed separate models based on exposome factors or ECG signals alone. These preliminary results indicate the potential of low-cost machine learning models trained from ECG and exposome data to predict diabetes years before its onset.
Fully relativistic two-component-spinor approach in the ultrasoft-pseudopotential plane-wave method

We have introduced effects of spin-orbit interaction nonperturbatively to the ultrasoft-pseudopotential scheme accompanied by two component spinor wave functions. Application to the electronic structure calculations of some heavy elements successfully reproduced results of the all electron approaches. The magnetic anisotropy energy and the orbital magnetic moment for alloys, which have shown a good agreement with the previous results, demonstrated broad capabilities of the approach.
Characterization and role exploration of ferroptosis-related genes in osteoarthritis

Osteoarthritis (OA), viewing as a degenerative aseptic inflammatory disease, is characterized by joint pain and inflammation that significantly affects the quality of patients’ life, especially for the elder. Although rapid progress has been achieved in elucidating the underlying mechanisms of OA occurrence and progression, there is still a lack of effective clinical therapeutics for OA patients. Currently the most common treatments including drug therapy and surgical operations are not very satisfactory in majority of cases, so it is worthy to explore new remedies. During the past few decades, a number of novel forms of regulated cell death have been reported widely, typified by ferroptosis, with its prominent features including reactive oxygen species (ROS) elevation, lipid peroxidation, iron accumulation and glutathione deprivation. Our study was designed to identify the functional roles of differentially expressed ferroptosis-related genes in OA, which were screened out by referring to GEO database via bioinformatics analyses. Human chondrocytes were applied to validate the above findings in the scenario of ferroptosis inhibitors administration. Results partially proved the consistency with bioinformatics analyses that ATF3 and TFRC were highly expressed in interleukin-1β (IL-1β)-stimulated chondrocytes whereas CXCL2 and JUN were downregulated. Besides, TFRC was firstly validated to be upregulated in IL-1β-stimulated chondrocytes, which could be reversed by ferroptosis inhibitors. In conclusion, our study reported two prominent ferroptosis-related genes, ATF3 and TFRC are upregulated in IL-1β-stimulated chondrocytes while CXCL2 and JUN are downregulated. And preliminary results demonstrated that TFRC might serve as an accomplice of ferroptosis process in IL-1β-stimulated chondrocytes and ferroptosis inhibitors have the potential to inhibit ROS in IL-1β-stimulated chondrocytes.
Motion Estimation Based on an Improved Optical Flow Method Using PIV for VSLAM

As an important research topic in the field of robotics, the optical flow method is widely used for motion estimation in visual simultaneous location and mapping (VSLAM). However, the optical flow method is based on the gray-invariant assumption, which restricts its application in the case of drastic luminosity variation. Moreover, the optical flow method can speed up the processing speed in motion estimation, but it cannot work effectively in scenarios with missing features. As a velocity measurement technology in the field of flow field and fluid, the particle image velocimetry (PIV) can overcome the aforementioned disadvantages of the optical flow method, and achieve motion estimation since it considers points in an image uniformly and can achieve sub-pixel accuracy for positional estimation. To this end, an improved optical flow method based on PIV is proposed by adopting the FFT cross-correlation matching algorithm and the sub-pixel displacement matching algorithm to estimate the image pixel displacement in the field of missing features. Experiments on the EUROC data-set show that the proposed method can not only track the motion of more pixels compared with that the multi-layer optical flow method, but also run in higher accuracy in the areas with missing features.
Hydrodynamics of a multistage wet scrubber incineration conditions

The objective of the study was to determine the hydrodynamics of the two stage counter-current cascade wet scrubbers used during incineration of medical waste. The dependence of the hydrodynamics on two main variables was studied: Inlet air flow rate and inlet liquid flow rate. This study introduces a new wet scrubber operating features, which are liquid accumulation, together with a detailed analysis of liquid-to-gas ratio and pressure drops. Liquid accumulation at the base of each scrubber helps to prevent the gas from short circuiting to the scrubber solution feed tanks. It was observed that liquid accumulation increases linearly with inlet scrubbing liquid flow rate beyond 0.2 L/s in the absence of flue gas flow. When flue gas is flowing into multistage wet scrubber the accumulation level increases abruptly starting from inlet scrubbing liquid of 0.45 L/s and stabilizes at accumulation level of 0.1 m beyond 0.75 L/s. Increasing the flue gas flow rate increases the minimum inlet scrubbing liquid flow rate at which initial accumulation was observed. The outlet scrubbing solution flow rate and liquid accumulation in the bottom of the scrubber increase as the inlet liquid flow rate increases. Beyond inlet liquid flow rate of 0.75 L/s, the outlet liquid flow rate does not increase any more, the wet scrubber enters flooding range, whereby, the volume of accumulated liquid increases faster with inlet scrubbing liquid flow rate. The maximum liquid-to-gas ratio was observed to be 1.9 L/m3 and the minimum liquid-to-gas ratio was 0.1 L/m3. Based on liquid accumulation and minimum flooding conditions, the operating liquid-to-gas ratio was recommended to be 0.6 to 1.9 L/m3, while the allowable inlet liquid flow rate ranged between 0.45 and 0.75 L/s, giving a constant accumulation of scrubbing liquid in the wet scrubber. The pressure drop across the wet scrubber remained constant at lower flow rate and increased linearly beyond Ug = 5 m/s. The maximum pressure drop of the multistage wet scrubber was 75 kPa. Key words: Wet scrubber, liquid accumulation, flooding, liquid-to-gas ratio, incineration, pressure drop
Increased Pre-Boundary Lengthening Does Not Enhance Implicit Intonational Phrase Perception in European Portuguese: An EEG Study

Prosodic phrasing is the segmentation of utterances into prosodic words, phonological phrases (smaller units) and intonational phrases (larger units) based on acoustic cues—pauses, pitch changes and pre-boundary lengthening. The perception of prosodic boundaries is characterized by a positive event-related potential (ERP) component, temporally aligned with phrase boundaries—the Closure Positive Shift (CPS). The role of pre-boundary lengthening in boundary perception is still a matter of debate: while studies on phonological phrase boundaries indicate that all three cues contribute equally, approaches to intonational phrase boundaries highlight the pause as the most powerful cue. Moreover, all studies used explicit boundary recognition tasks, and it is unknown how pre-boundary lengthening works in implicit prosodic processing tasks, characteristic of real-life contexts. In this study, we examined the effects of pre-boundary lengthening (original, short, and long) on the EEG responses to intonational phrase boundaries (CPS effect) in European Portuguese, using an implicit task. Both original and short versions showed equivalent CPS effects, while the long set did not elicit the effect. This suggests that pre-boundary lengthening does not contribute to improved perception of boundaries in intonational phrases (longer units), possibly due to memory and attention-related constraints.
Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory

Spaced repetition, namely, learners review items in a given schedule, has been proven powerful for memorization and practice of skills. Most current spaced repetition methods focus on either predicting student recall or designing an optimal review schedule, thus omitting the integrity of the spaced repetition system. In this work, we propose a novel spaced repetition schedule framework by capturing the dynamics of memory, which alternates memory prediction and schedule optimization to improve the efficiency of learners’ reviews. First, the framework collects logs from students’ reviews and builds memory models with Markov property to capture the dynamics of memory. Then, the spaced repetition optimization is transformed a stochastic shortest path problem and solved via the value iteration method. We also construct a new benchmark dataset for spaced repetition, which is the first to contain time-series information during learners’ memorization. Experimental results on the collected data from the real world and the simulated environment demonstrate that the proposed approach reduces 64% error and 17% cost in predicting recall rates and optimizing schedules compared to several baselines. We have publicly released the dataset containing 220 million rows and codes used in this paper at: https://github.com/maimemo/SSP-MMC-Plus.
A method for the critical analysis of science communication texts

ABSTRACT We present a novel method for the critical analysis of professional scientists’ communications with the public. The method blends Entman’s (2007) [Framing bias: Media in the distribution of power. Journal of Communication, 57(1), 163–173)] framing theory and van Leeuwen’s (1996) [The representation of social actors. In C. R. Caldas-Coulthard, & M. Coulthard (Eds.), Texts and practices readings in critical discourse analysis (pp. 32–104). Routledge] sociosemantic analysis to distill the relationship between written texts and larger political discourses circulating in society. We demonstrate our novel method by comparing two articles from a popular science publication, both of which discuss issues of gender equity in the professional STEM community. We close by discussing the affordances and limitations of our novel method and offering possible extensions of our method for future research.