Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Evidence for Asymmetry in ENSO–IOD Interactions

Abstract The interaction between the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) has been noted in many previous studies. However, the interaction is often presumed to exist in a robust manner. This paper questions this assumption and looks for statistical evidence for the robust existence of the ENSO–IOD interaction rather than chance co-occurrences. Consistent definitions of ENSO and IOD events were developed so that ENSO and IOD events identified are comparable in nature, strength, persistence, and time of occurrence during the year, relative to their natural variability. We applied a combinatorial probability test to the probability tables of ENSO and IOD events to test for robustness of our results and those computed from two published works. Our findings show that there is an asymmetry in the interaction between ENSO and IOD. The interaction between El Niño (EN) and the positive and negative phases of the IOD (pIOD and nIOD, respectively) is more robust, enhancing the EN–pIOD co-occurrence and suppressing the EN–nIOD co-occurrence. On the other hand, the interaction between La Niña (LN) and either phase of the IOD cannot be said to exist with the same robustness; the interaction suppresses LN–pIOD co-occurrence, but the occurrence of LN–nIOD might be coincidental. Further research is needed to determine the cause and mechanisms for the asymmetry in the interaction between LN and pIOD/nIOD.
Current Browning of Surface Waters Will Be Further Promoted by Wetter Climate

Browning of surface waters because of increasing terrestrial dissolved organic carbon (OC) concentrations is a concern for drinking water providers and can impact land carbon storage. We show that positive trends in OC in 474 streams, lakes, and rivers in boreal and subarctic ecosystems in Norway, Sweden, and Finland between 1990 and 2013 are surprisingly constant across climatic gradients and catchment sizes (median, +1.4% year–1; interquartile range, +0.8–2.0% year–1), implying that water bodies across the entire landscape are browning. The largest trends (median, +1.7% year–1) were found in regions impacted by strong reductions in sulfur deposition, while subarctic regions showed the least browning (median, +0.8% year–1). In dry regions, precipitation was a strong and positive driver of OC concentrations, declining in strength moving toward high rainfall sites. We estimate that a 10% increase in precipitation will increase mobilization of OC from soils to freshwaters by at least 30%, demonstrating the .
Analysis of different machine learning approaches in the context of urban mobility: a systematic review

In the urban planning of cities, with population growth and expansion of infrastructure demands leading to the displacement of people, the theme of urban mobility is highlighted in social agendas. As a result, in recent years, there has been an increase in research in the academic environment aimed at minimizing adversities, such as slow traffic flow, lack of accessibility in public and private equipment, reduction of long routes, and provision of means of transport. With the addition of large volumes of data, machine learning has contributed positively to solutions to combating these problems. From this perspective, this study sought to systematically review works in the academic literature that used machine learning in urban mobility, seeking to understand the main objects of study, data collection, learning techniques and algorithms, and evaluation metrics. With this work, after filtering and selecting studies, a continuous uniform exploration of the theme in the last four years was noticed. In addition, in each analyzed research, specific peculiarities were found in the intended objects of study. However, quantitatively, there was a greater preference for classification techniques, with the most commonly used algorithms being random forest, SVM, Decision Tree, convolutional neural network, KNN, and Naive Bayes. In this context, a small number of algorithms and metrics were identified in most studies, which may suggest a tendency.
PANLS: Performance Analysis of Node Localization Scheme in WSNs

Node localization (NL) in wireless sensor networks (WSN) has numerous applications, such as maritime rescue, agriculture, and hazardous environments, that heavily rely on the node position. In WSN, node localization is crucial since GPS-enabled sensors are not economical or energy-efficient. Therefore, GPS-enabled sensors are not appropriate for many WSN applications. The exact positioning of sensor nodes has a considerable impact on WSN performance. NL is the process of locating a target node (an unknown node). Gaussian noise and RSSI are practical methods for locating a target node's position. The seagull optimization algorithm and its improved versions are used in this research to improve the localization accuracy of nodes. To enhance the accuracy of NL and improve the randomness of the seagull optimization algorithm a chaotic map is employed in this work to enhance the seagull optimization algorithm (SOA). The performance evaluation of C-SOA is far better than the SOA.
Brain Tumour Detection Using Deep Learning

A brain tumour is an ailment resulting from the formation of aberrant brain cells. There exist two elementary varieties of brain neoplasms: malignant (cancerous) brain neoplasms and benign (non-cancerous) brain neoplasms. Because brain tumours are rare and come in various kinds, it is challenging to forecast the survival rate of a patient predisposed to them. The type of brain tumour, the degree of cellular abnormality, the part of the brain where the tumour is located and other variables all affect how it is treated. As artificial intelligence advances, deep learning algorithms are utilized for the verdict of brain tumours using magnetic resonance imaging data. Magnetic Resonance Imaging (MRI) scanning technique utilizes Strong electromagnetic waves to provide accurate and sharp images of the interior body. This research paper focuses on providing a comparative analysis of several algorithms which can be used for image segmentation and detecting tumours.
Single-Modality Supervised Joint PET-MR Image Reconstruction

We present a new approach for deep learned joint PET-MR image reconstruction inspired by conventional synergistic methods using a joint regularizer. The maximum a posteriori expectation–maximization algorithm for PET and the Landweber algorithm for MR are unrolled and interconnected through a deep learned joint regularization step. The parameters of the joint U-Net regularizer and the respective regularization strengths are learned and shared across all the iterations. Along with introducing this framework, we propose an investigation of the impact of the loss function selection on network performance. We explored how the network performs when trained with a single or a joint-modality loss. Finally, we explored under which settings a joint reconstruction was beneficial for MR reconstruction by using various undersampling factors. The results obtained on 2-D simulated data show that the joint networks outperform conventional synergistic methods and independent deep learned reconstruction methods. For PET, the network trained with only a PET loss achieves a better global reconstruction accuracy than the version trained with a weighted sum of PET and MR loss terms. More importantly, the former further improves the reconstruction of PET-specific features where MR-guided methods show their limit. Therefore, using a single-modality loss to supervise the training while still reconstructing the two modalities in parallel leads to better reconstructions and improved modality-unique lesion recovery in our proposed framework. For MR, while the same effect is observed, joint reconstruction gains only occur in the presence of highly undersampled data. Single-modality loss joint reconstruction results are also demonstrated on 3-D clinical PET-MR datasets.
A novel feature extraction approach for skin cancer screening using active thermography

Skin cancer is one of the most common types of cancer, whose number of cases is constantly increasing. The most used method to detect skin cancer is the biopsy. It is relevant to reduce the number of biopsies, since it is an invasive and expensive procedure, and it has limited availability in some locations. Among the most successful approaches that aim to improve skin cancer detection are the algorithms that process active infrared thermography.Here, a skin cancer detection scheme is proposed, which extracts key features from active thermography videos, and uses them in the following five classifiers: K-Nearest Neighbors, Decision tree, Random forest, Support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost). Under a minimization error design criteria, the best result was performed by a SVM classifier, reaching 84.14% of accuracy and 78.92% of precision. Modifying the classifiers to ensure that all the malignant cases are detected, the best performance was also achieved by the SVM classifier, with 72.85% of accuracy and 63.95% of presicion.The proposed scheme is 15% less accurate than the best detection algorithm. However, it is easier to implement and deploy and provides a framework with key preprocessing aspects to address this detection problem using active thermography. As future work, a further exploration of features will be carried out, with the aim of improving the performance of the classifier.
On the Design of Accessories for HVDC Cables: Complexity of the Field Distribution

With progresses achieved in power converter technologies, HVDC is becoming now a mature technology for energy transmission. Cable systems and cable accessories constitute important parts of the transmission systems. The question of optimal design of these systems is still a complicated issue as the field distribution is intimately linked to the electrical conductivity of insulating materials, which is indeed a substantially variable property according to temperature, field and considered material. In this work, we consider the association of different materials constituting accessories and compute the field distribution in a non-stationary state. We show that there are indeed solutions to smooth the field, but defining the right properties of the joint material is still not an easy task as it depends on the electrical and thermal service conditions.
Scaffolding Critical Reflection Writing Across Programs Using a Blended Learning Approach

Students need to be taught intentionally how to reflect critically. Findings from co-assessments in class revealed that students’ reflections were uncritical, disorganized, and unconnected to their lives and studies. The aim of this study was to examine the impact of a critical thinking framework and a critical reflection micromodule taught in a blended mode, towards students’ ability to reflect critically. Two groups of freshmen students from varied disciplines participated in the study: Computer Science and Physiotherapy. The students filled in a survey that compared their current thinking process to the critical thinking framework, and their pre- and post-learning reflections were analysed. The findings show that the framework might have raised students’ awareness about what reflecting critically means, and the average scores for their post-learning reflections improved. There was no clear indication of differences between these two groups of students. The results from this pilot study are promising and suggest that the teaching of critical reflection can be scaled and taught in an intentional manner.
Electriﬁcation and Lightning in Idealized Simulations of a Hurricane-Like Vortex Subject to Wind Shear and Sea Surface Temperature Cooling

Relationships between intensity ﬂuctuations, cloud microphysics, lightning variations, and electrical structureswithinidealizedtropicalcyclonesareinvestigatedwiththecloud-resolvingCollaborativeModelfor Multiscale Atmospheric Simulation (COMMAS). An initial strong tropical cyclone is subjected to either steady-state control conditions (CTRL), increased wind shear (SHEAR), or a reduction in sea surface temperature (SST). In CTRL, nearly all the lightning ( . 95%) occurred in the outer region (100 , r # 300km) and was overall very episodic in the inner core ( r # 100km), consistent with observations. The inner-core updrafts were weaker and experienced greater depletion of cloud water by warm rain processes, which, in contrast to the deeper updrafts in the rainband convection, reduced the mixed-phase cloud depth and conﬁned the bulk of the charging and lightning initiations to lower levels. Notably, larger ﬂash rates were produced in the asymmetric inner core of the SHEAR case, with the majority of the ﬂashes located in the downshear left quadrant, consistent with prior observational works. In contrast to CTRL, the more vigorous inner-core convection in SHEAR resulted in the formation of a prominent negative charge region and enhanced production of negative ground ﬂashes. With a nearly identicalﬁlling rate as SHEAR,theintroduction of cooler seasurface temperature in the SST case caused lightning activity to fade rapidly in both the inner core and rainbands.
Pattern of liver enzymes alteration in new corona virus Pattern of liver enzymes alteration in new corona virus disease(covid-19) disease(covid-19)

Background : Despite the fact that corona virus disease-19 (COVID-19) mostly damages the lower respiratory system and presents as pneumonia in humans, several people with COVID-19 also show variable degrees of liver impairment. There are numerous factors that lead to liver damage in the setting of COVID-19. Aim : The aim of this study it was important to ﬁ nd out relation to liver function to explore the pattern of liver enzymes alteration in new corona virus disease and monitoring this alteration along the disease course. Patients and methods : This prospective study included patients with Covid-19 infection. All procedures followed Al-Azhar University Ethics Committee regulations. Results : There was signi ﬁ cant decrease (improvement) in aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, direct bilirubin and gamma-glutamyltransferase (GGT) 2 weeks after admission and 2 weeks after discharge compared with their levels at admission ( P < 0.001), while, no signi ﬁ cant differences were found in ALP and serum albumin throughout follow-up time ( P > 0.05). Conclusion : Liver dysfunction was present in a high proportion of hospitalised COVID-19 patients.
Quatemion CNN-Based Diagnosis of Potato Leaf Diseases in India: Enhancing Accuracy and Reducing Crop Production Costs

Potato is one of India's main agricultural crops. In recent years, potato farms have become increasingly popular in India. However, a variety of ailments are driving up farmers' costs in the production of potatoes. Nevertheless, a number of diseases that are harming the crop are mostly to blame for the high cost of potato production. What is disrupting the farmer's schedule? Automation has been used to modernize the potato business and expedite illness diagnosis. Despite assertions, but potato leaf disease is a severe problem that has the potential to significantly lower crop production. Infected potato plants will exhibit on their leaves, the diseases early blight, Septoria blight, late blight, and others can be seen. If such outbreaks are detected early and sufficient intervention is carried out, farmers won't be at risk of suffering significant financial losses. A new model is released for precisely recognizing and diagnosing diseases in potato leaf stands in light of the experiment's findings. Making use of quaternion neural networks. While a variety of techniques, including the CNN model may be used in machine learning. For the purpose of identifying the disease in photographs of potato leaves, we developed an improved CNN model. In order to forecast the illness of potato leaves, this study adopts a sequential model based on Quaternion CNN. On this model, this study's model accuracy was 96.2%. To differentiate between the two, the proposed model was checked on both healthy and sick potato leaves. The potato tree leaf is categorized as healthy and sick after the algorithm has been applied to the photos.
Highly Skilled Migration and Knowledge Diffusion

This paper documents the influence of diaspora networks of highly-skilled individuals on international knowledge diffusion. It adds to the growing literature on highly-skilled interna tional migration and its contribution to knowledge and so economic growth in migrants' home countries. In particular, it explores knowledge feedbacks to home countries generated by migrant inventors, a representa tive category of high-skilled migrants, most of them scientists and engineers. In line with a recent strand of literature on migration and innovation studies, we make use of patent and inventor data to measure mi gration flows between countries, and test our hypothesis in a country-pair gravity model fra mework, for the period 1990-2010, using patent citations across countries as a measure of international knowledge diffusion. Our results confirm our initial assumption on the positive impact of highly sk illed migrants on knowledge diffusion to their homelands. We find a 10% increase in the number of inventors of a gi ven nationality at a destination country, leads to 1- percentage point knowledge diffusion to their h ome economy from that same host land. And these results are not driven neither by the U.S. as a tradit ional country of highly skilled immigration, nor by India and China as the biggest highly skilled sending countries, since we obtain similar results even after having dropped these three countries.
On the Relationship between Mw and ML for Small Earthquakes

Abstract Estimating the moment magnitudes ( M w ) of a small earthquake is a challenging task. One viable option to measure its size is to calculate its local magnitude ( M L ) and convert it to the physically based M w . Unfortunately, to correctly perform such a conversion is not easy; moreover, even though many studies demonstrate that the equivalence between M L and M w is incorrect for small events, these two parameters are sometimes thought to be strictly equivalent, regardless of the earthquake’s size. Using random vibration theory, we show that, below M w ∼4, the M L of a small earthquake is proportional to the logarithm of its seismic moment, and the following relationship holds: We test our findings on a high‐quality data set in the Upper Tiber Valley (northern Apennines, Italy), composed of events in the range of 0≤ M L ≤3.8, for which we compute accurate estimates of M L and M w . Online Material: Details of the processing procedure, figures of the empirical regional attenuation functional, and source terms of 1191 events from the Alto Tiberina fault (ATF) data set and earthquake catalog.
Pedestrian Trajectory Prediction for Autonomous Vehicle by Integrating IA-LSTM and MSFM

In this paper, pedestrian trajectory prediction is systematically explored by integrating the Intention-Attention-Long Short Term Memory Network (IA-LSTM) and Modified Social Force Model (MSFM). Firstly, a novel IA-LSTM is developed for pedestrian trajectory prediction, pedestrian intention (waiting/crossing), pedestrian-pedestrian interactions and pedestrian-vehicle interactions are considered. Secondly, a MSFM is proposed for pedestrian trajectory prediction, the influences of other pedestrians, vehicles and crosswalk boundary are taken into account. Finally, an integrated model based on the IA-LSTM and MSFM is developed. Moreover, traffic data is collected at an un- signalized crosswalk, and the parameters of the MSFM are calibrated by proposing the use of Maximum Likelihood Estimation (MLE). The experimental results indicate that the integrated model surpasses the existing methods, and the prediction accuracy is improved by more than 19%, which inspires confidence in the application of the integrated model in the autonomous vehicle field to enhance the safety of pedestrians.
Strategies to Enhance Educators' Soft Skills: The Role of Managers

This study explores the role of managers at New Zealand's Institutes of Technology and Polytechnics (ITPs) in developing their educators' soft skills. Soft skills are interpersonal skills that are essential for enhancing communication and collaboration between educators and learners. From an employability perspective, managers want to hire educators with strong knowledge of pedagogy and subject matter, as well as strong interpersonal skills. The modern education environment requires educators to invest in developing their soft skills to succeed in their careers. However, there is a need to understand how managers can contribute to the development and utilization of these skills among educators.This study will provide strategies, challenges, and considerations for enhancing various soft skills among educators. It will also explore how managers can create a supportive environment for developing the soft skills of educators. By understanding the managerial perspective and the challenges faced by educators in developing and utilizing soft skills, this research aims to contribute to the enhancement of educational quality within ITPs.The findings can inform the design of professional development programmes, recruitment processes, and performance evaluations aligned with identified soft skills, ultimately enhancing educational quality within ITPs, and benefiting educators and students alike.
Improving Immersive Virtual Reality Training of Bioreactor Operations using Gamification

The effectiveness of virtual reality (VR) for learning and training is rooted in its ability to create immersive and interactive environments that simulate real-world experiences. VR was used to complement practical laboratory learning objectives for an industrial single-use bioreactor (SUB) for bio-pharmaceutical undergraduate students In this study, a fictional futuristic VR laboratory was designed with emphasis on the integration of game elements, widely referred to as gamification. The simulation facilitates an immersive learning environment through the introduction of a storyline, hints, guides, varying degrees of challenges, and a scoreboard in a safe and contextualised setting. The impact of gamification is then evaluated via a mixed-methods user study, to assess if gamification improved students’ theoretical and practical understanding. Overall, the gamified immersive VR laboratory captured higher interest and enhanced learning outcomes and motivation, with resultant test scores comparable to traditional laboratory sessions. Therefore, there is future potential to improve learning and training by providing such immersive and experiential educational experiences for students.
Emotions Recognition Based on Physiological Signals Using Machine Learning Techniques

Emotion recognition through Electroence-phalogram (EEG) involves identifying and understanding emotions by analysing the brain's electrical activity. Electroencephalogram, electrocardiogram (ECG), respiration (RSP), Galvanic Skin Response (GSR), blood volume pulse (BVP), and electromyogram (EMG), are the example of physiological signals. In this paper, emotions are categorised using EEG signals and calculate the accuracy of the classifier with F1-score. The results show that the proposed method is more accurate, has better accuracy, a manageable F1 score, and has a lower standard deviation than existing methods for the two-class problem. The proposed method is compared to some recent other studies. Finally, the performance of our suggested technique on DEAP dataset (a public dataset) is evaluated. All of the results were implemented in MATLAB R2017a.