Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstacle Location and Identification using Time Reversal and Deep Learning

: Finding unknown obstacles in a medium using numerical methods is an important task, with many applications. It is also very challenging, since the properties of the obstacle often have many degrees of freedom. Most existing methods utilize prior knowledge of the obstacles they aim to recover, and use optimizers that take long time to extract the obstacle location, shape and size. We propose a method based on time reversal and deep learning, for locating obstacles and finding their shape and size. We test the method on synthetic data that was generated to mimic the physical experiment of acoustic waves propagating in an underwater medium. In addition, we use a physically-informed neural network to get even more accurate results.
AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5?

Timely formative feedback is considered as one of the most important drivers for effective learning. Delivering timely and individualized feedback is particularly challenging in large classes in higher education. Recently Large Language Models such as GPT-3 became available to the public that showed promising results on various tasks such as code generation and code explanation. This paper investigates the potential of AI in providing personalized code correction and generating feedback. Based on existing student submissions of two different real-world assignments, the correctness of the AI-aided e-assessment as well as the characteristics such as fault localization, correctness of hints, and code style suggestions of the generated feedback are investigated. The results show that 73% of the submissions were correctly identified as either correct or incorrect. In 59% of these cases, GPT-3.5 also successfully generated effective and high-quality feedback. Additionally, GPT-3.5 exhibited weaknesses in its evaluation, including localization of errors that were not the actual errors, or even hallucinated errors. Implications and potential new usage scenarios are discussed.
Population Based Structural Health Monitoring: Homogeneous Offshore Wind Model Development

This is a development of the preceding paper that introduced the idea and methodology of population-based structural health monitoring (PBSHM). PBSHM involves transferring knowledge from one structure to a different structure so that predictions about the structural health on each of the members in the population can be inferred. One of the most important aspects of PBSHM involves using the information on the source domain structure and the target domain structure to create an effective classifier. Domain adaptation is a subcategory of transfer learning that can create a general classifier using both the 5 source and target domain structures to create an enhanced overall classifier of the entire population. This paper presents a novel domain adaptation model for PBSHM in offshore wind.
Classification of chronic pain using fMRI data: Unveiling brain Classification of chronic pain using fMRI data: Unveiling brain activity patterns for diagnosis activity patterns for diagnosis

: Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep and then parcellated using five various atlases such as MSDL, Yeo’17, Harvard, Schaefer, and Pauli. Then the functional connectivity between the parcellated Region of Interests (ROIs) has been taken as features for the machine learning classifier models using the Blood Oxygenation Level Dependent (BOLD) signals. To distinguish between those with chronic pain and healthy controls, this study used Support Vector Machines (SVM), Boosting, Bagging, convolutional neural network (CNN), XGboost, and Stochastic Gradient Descent (SDG) classifiers. The classification models use stratified shuffle split sampling to fragment the training and testing dataset during various iterations. Hyperparameter tuning was used to get the best classifier model across several combinations of parameters. The best parameters for the classifier were measured by the accuracy, sensitivity, and specificity of the model. Finally, to identify the top ROIs involved in chronic pain was unveiled by the probability-based feature importance method. The result shows that Pauli (subcortical atlas) and MSDL (cortical atlas) worked well for this chronic pain fMRI data. Boosting algorithm classified chronic pain and healthy controls with 94.35% accuracy on the data parcellated with the Pauli atlas. The top four regions contributing to this classifier model were the extended Amygdala, the Subthalamic nucleus, the Hypothalamus, and the Caudate Nucleus. Also, the fMRI data parcellated using a cortical MSDL atlas was classified using the XGboost model with an accuracy of 87.5%. Left Frontal Pole, Medial Default mode Network, right pars opercularis, dorsal anterior cingulate cortex (dACC), and Front Default mode network are the top five regions that contributed to classify the participants. These findings demonstrate that patterns of brain activity in areas associated with pain processing can be used to categorize individuals as chronic pain patients or healthy controls reliably. These discoveries may help with the identification and management of chronic pain and may pave the way for the creation of more potent tailored medicines for those who suffer from it.
Asking the right questions about leadership

Five questions prompted by the articles in the American Psychologist special issue on leadership (January 2007, Vol. 62, No. 1) suggest some new directions for leadership research: (1) Not do leaders make a difference, but under what conditions does leadership matter? (2) Not what are the traits of leaders, but how do leaders’ personal attributes interact with situational properties to shape outcomes? (3) Not do there exist common dimensions on which all leaders can be arrayed, but are good and poor leadership qualitatively different phenomena? (4) Not how do leaders and followers differ, but how can leadership models be reformulated so they treat all system members as both leaders and followers? (5) Not what should be taught in leadership courses, but how can leaders be helped to learn?
Beneﬁts of a robotic chamber system for determining evapotranspiration in an erosion-affected, heterogeneous cropland

In the light of the ongoing global climate crisis and the related increases in extreme hydrological events, it is crucial to assess ecosystem resilience and – in agricultural systems – to ensure sustainable management and food security. For this purpose, a comprehensive understanding of ecosystem water cycle budgets and spatiotemporal dynamics is indispensable. Evapotranspiration (ET) plays a pivotal role in returning up to 90 % of incoming precipitation back to the atmosphere. Here, we studied the impacts of soil types and management on an agroecosystem’s seasonal cumulative ET (ET sum ) and agronomic water use efﬁciency (WUE agro , the dry matter per unit of water used by the crop). To do so, a plot experiment with winter rye (17 September 2020 to 30 June 2021) was conducted in an eroded cropland which is located in the hilly and dry ground moraine
E-marketing

The 5th edition of E-Marketing treats the subject as traditional marketing with a twist: the Internet and other technologies have had a profound effect on the way we do business. This transformation has resulted in new business techniques that add customer value, build customer relationships, and increase company profitability. Stressing product, pricing, distribution, and promotion, the authors use a strategic perspective and give many important practices not covered in previous editions: namely, blogs, social networking, online branding, and search marketing. Point-of-purchase scanning devices, databases, and other offline technologies are discussed. For anyone interested in learning more about electronic marketing, this is an excellent handbook; its comprehensive glossary makes this a must-have reference.
Li DAR Points Cloud Filtering Method Based On Adaptive Morphological

Mathematical morphology is one of the primary method in Li DAR point cloud filtering. Traditional mathematical morphology algorithm has great blindness on the selection of iteration number and elevation threshold,which usually rely on experience to set. Depending on the analysis of number change characteristic of point cloud before and after filtering by using different iteration number, and the relationship of point cloud density and elevation threshold, an adaptive mathematical morphology filtering method was put forward in this paper. The experiments using different types of Li DAR data show that the method is feasible.
The role of hypothalamic pathways in the metabolic side effects of Olanzapine

Atypical antipsychotic drugs such as Olanzapine (Ola) induce weight gain and metabolic changes associated with the development of type 2 diabetes. The mechanisms underlying these undesired side effects are currently unknown. It has been shown that peripheral injections of Ola activate neurons in the lateral hypothalamus/perifornical area and that a large part of these neurons are orexin (Ox) A-positive. We investigated further the possible involvement of the central Ox system in the metabolic side effects of Ola by comparing the hyperglycemic effects of an intragastric (IG) Ola infusion between animals treated intracerebroventricularly (ICV) with an Ox-1 receptor antagonist (SB-408124) or vehicle. As observed in previous studies IG Ola caused an increase in blood glucose, endogenous glucose production and plasma glucagon levels. ICV pre-treatment with the Ox-1 receptor antagonist did not affect the Ola-induced hyperglycemia or increased plasma glucagon concentrations, but the increased endogenous glucose production was blunted by the ICV SB-408124 treatment. From these results, we conclude that the metabolic side effects of Ola are partly mediated by the hypothalamic Ox system.
Working Paper in Islamic Economics and Finance

One o f the primary objectives of the shariah concerning the community’s wealth is to ensure its preservation and growth. At the same time, the maqasid al shari’ah is also aimed to make the distribution of economic resources, wealth and income fair and equitable. Thus it is the utmost important that the Islamic banking system could bridge the gap between the rich and the poor by modifying the distribution of wealth and economic resources. Theoretically, the money invested by customer (depositors), or money invested by banks or money (as capital) utilized by customers (or economic agents like individual, firm and government) can generate profit and then can be distributed to Islamic banks and depositors and hence it could be used to preserve the wealth (mal) of economic agents. Does it true
Automatic Georeferencing of Aerial Images Using Stereo High-Resolution Satellite Images

For airborne surveys, the GPS/INS system has become the primary source for aerial image georeferencing. However, alternative automated georeferencing is required to serve as a backup for georeferencing when GPS/INS-based georeferencing is not feasible. High-resolution satellite images (HRSI) have been globally available with better spatial resolution and increasing positional accuracy. Therefore, HRSI has high potential as a ground control source for aerial image georeferencing which usually requires accurate 3D ground control points. Unfortunately, single imagery, which is often used as a reference, contains relief displacement due to objects on the ground introducing positional errors, unless it is not true ortho-rectified which is costly and time consuming. Therefore, in this study, a stereo HRSI-based automated georeferencing approach is proposed. The use of stereo images can avoid the impact of relief displacement and requires no external height information. The proposed method is based on a multi-scale image matching approach utilizing a combination of SIFT (Scale-Invariant Feature Transform) and RANSAC (RANdom SAmple Consensus). In the georeferencing step, the bundle adjustment with outlier removal of Baarda's data snooping was utilized. Experimental results for a strip of aerial images with stereo Ikonos images showed its potential as a backup system for automated georeferencing.
Effects of biochar applications on growth, nutrient content and biochemical properties of Ocimum basilicum L.

This study investigated the effects of biochar treatments on the growth, nutrient content and some biochemical properties of basil. Biochars obtained from two different biomasses, rice husk (RBC) and tomato harvest waste (TBC), were applied at a dose of 2% to the growing medium consisting of a 1 : 1 soil and peat mixture. No biochar-added medium (1 : 1 soil and peat) was used as a control. The experiment was established in a completely randomized design with six replications for each treatment. At the end of the study, the height, number of lateral branches, total herb weight, and leaf weight of the plants were measured. In addition, chlorophyll contents in SPAD (Soil Plant Analysis Development), different nutrients and total phenolics contents, and antioxidant activities were analyzed. As a result of the study, the effects of biochar treatments differed depending on the biomass source. RBC significantly increased plant height, total herb weight, and leaf weight while negatively affecting the number of lateral branches. TCB did not cause any significant variation in plant height and number of lateral branches. While RBC provided a slight increase in leaf weight compared to the control, it did not cause a significant change in plant height, total herb weight, and the number of lateral branches. Except for a slight increase in K content due to RBC application, both biochar treatments did not cause a significant increase in leaf nutrient content. While RBC treatment did not cause a significant change in total phenol, it caused an increase in antioxidant activity. TBC application decreased the SPAD value from 22.4 in the control to 20.4.
An Adjustable P-Region Potential SiC Trench MOSFET With Improved High-Frequency and Short-Circuit Performance

A silicon carbide (SiC) trench MOSFET featuring an adjustable P-region potential (AP-TMOS) is proposed and investigated to improve high frequency and short-circuit performance in this article. A P/N-base layer is embedded in the trench bottom to introduce a P-region portion at the trench corner. The P-region is at a floating potential due to the hole barrier formed in the fully depleted P-base during ON-state conduction of AP-TMOS, and P-region is grounded by the undepleted region of the P-base during OFF-state operation. Compared with the conventional SiC trench MOSFET (C-TMOS), AP-TMOS shows about 23% reduction of the specific ON-resistance because of the shrinkage of JFET depletion layer caused by the floating P-region. Furthermore, the Miller capacitance and gate–drain charge of AP-TMOS are 4.7 pF/cm2 and 51.1 nC/cm2, which are improved by about 2.4 and 1.7 times compared with C-TMOS owing to the reduced overlapping area between gate and drain contact. The short-circuit withstand time of the proposed TMOS with the adjustable P-region potential increases from 4 to $6~\mu \text{s}$ under 800-V dc bus voltage in comparison with C-TMOS. Finally, an analytical model for the hole barrier height has been discussed for AP-TMOS.
Simulações da SAR na cabeça e antenas planares para telefones móveis

The thermal and the non-thermal effects of the Non-Ionizing Radiation (NIR) are briefly described. The Specific Absorption Rate (SAR) in the head of mobile phone users is simulated using the Finite Difference Time Domain (FDTD) method in three dimensions (3D) when conventional monopole and planar antennas are employed. The performance simulation of microstrip rectangular patch antennas, as well as its predicted and measured results are presented. The software Ensemble is used to estimate the antenna voltage standing wave ratio (VSWR) and its bandwidth. An algorithm based in 3D FDTD is developed to model the radiation pattern in the horizontal plane and the electric field in the Fresnel region. Measured VSWR as well as horizontal radiation pattern are shown. It can be seen that there is a strong correlation between the measured and the FDTD simulated results when the ground plane is finite and when it is not effectively grounded. This type of antenna may be of great interest in a new generation of mobile phones, improving its performance, reducing the battery drain and the SAR in the user’s head.
Effect of Silybum marianum (L.) Gaertn. seeds hydrostatic pressure pretreatment on biological properties of the extract by Drosophila melanogaster example

The article analyzes the feeding intensity, chemotaxis of larvae and changes in the expression of the sqh gene in D. melanogaster individuals cultivated on a nutrient medium with the introduction of ethanol extract from S. marianum seeds under pretreatment with hydrostatic pressure of 5, 10, 15 and 20 MPa. The preferred chemotaxis of larvae with respect to seed extract without prior pressure exposure was revealed. Changes in the intensity of nutrition in individuals of the experimental groups were not registered. A hypo-expressive direction of changes in the activity of the sqh gene was found when growing individuals on a medium with the introduction of an extract from seeds treated with pressure levels of 15 and 20 MPa. Variants of further experiments are proposed.
circMSH3 is a potential biomarker for the diagnosis of colorectal cancer and affects the distant metastasis of colorectal cancer

Objectives To identify the most significantly differentially expressed circular RNAs (circRNAs) in colorectal cancer (CRC) tissues in terms of their expression levels and circularity, and to analyze the relationship between their expression levels and the clinical characteristics of patients. Methods circRNA RNA-seq technology was used to screen differentially expressed circRNAs in CRC. Sanger sequencing was used to identify circRNA back-splice junction sites. The relative expression levels of hsa_circ_0003761 (circMSH3) in CRC tissues and cell lines were detected by quantitative real-time fluorescence PCR technology. An RNA-protein pull-down assay was used to detect protein binding to circRNAs. Dual-luciferase reporter gene vectors were constructed to verify that circRNAs bind to microRNAs. Results Four hundred twenty circRNAs were found to be upregulated, and 616 circRNAs were downregulated. circMSH3 was derived from the MutS homolog 3 (MSH3) gene and was formed by a loop of exons 9, 10, 11, and 12. In 110 pairs of CRC and adjacent tissues, circMSH3 expression was 4.487-fold higher in CRC tissues. circMSH3 was also highly expressed in the HT-29 and LOVO CRC cell lines. The expression level of circMSH3 was associated with distant metastasis in CRC patients (P = 0.043); the area under the curve (AUC) of circMSH3 for CRC diagnosis was 0.75, with a sensitivity and specificity of 70.9% and 66.4%, respectively. circMSH3 could bind to a variety of proteins, mainly those involved in RNA transcription, splicing, cell cycle, and cell junctions. Furthermore, circMSH3 could bind to miR-1276, miR-942-5p, and miR-409-3p. Conclusion circMSH3 is a potential biomarker for the diagnosis of CRC and affects the distant metastasis of CRC. Multiple RNA-binding protein binds to circMSH3, and circMSH3 binds to miR-1276, miR-942-5p, and miR-409-3p, thereby affecting the expression of circMSH3.
Cellulose-Based Metallogels—Part 3: Multifunctional Materials

The incorporation of the metal phase into cellulose hydrogels, resulting in the formation of metallogels, greatly expands their application potential by introducing new functionalities and improving their performance in various fields. The unique antiviral, antibacterial, antifungal, and anticancer properties of metal and metal oxide nanoparticles (Ag, Au, Cu, CuxOy, ZnO, Al2O3, TiO2, etc.), coupled with the biocompatibility of cellulose, allow the development of composite hydrogels with multifunctional therapeutic potential. These materials can serve as efficient carriers for controlled drug delivery, targeting specific cells or pathogens, as well as for the design of artificial tissues or wound and burn dressings. Cellulose-based metallogels can be used in the food packaging industry to provide biodegradable and biocidal materials to extend the shelf life of the goods. Metal and bimetallic nanoparticles (Au, Cu, Ni, AuAg, and AuPt) can catalyze chemical reactions, enabling composite cellulose hydrogels to be used as efficient catalysts in organic synthesis. In addition, metal-loaded hydrogels (with ZnO, TiO2, Ag, and Fe3O4 nanoparticles) can exhibit enhanced adsorption capacities for pollutants, such as dyes, heavy metal ions, and pharmaceuticals, making them valuable materials for water purification and environmental remediation. Magnetic properties imparted to metallogels by iron oxides (Fe2O3 and Fe3O4) simplify the wastewater treatment process, making it more cost-effective and environmentally friendly. The conductivity of metallogels due to Ag, TiO2, ZnO, and Al2O3 is useful for the design of various sensors. The integration of metal nanoparticles also allows the development of responsive materials, where changes in metal properties can be exploited for stimuli-responsive applications, such as controlled release systems. Overall, the introduction of metal phases augments the functionality of cellulose hydrogels, expanding their versatility for diverse applications across a broad spectrum of industries not envisaged during the initial research stages.
SASiamNet: Self-Adaptive Siamese Network for Change Detection of Remote Sensing Image

With increasingly rapid development of convolutional neural networks, the field of remote sensing has experienced a significant revitalization. However, understanding and detecting surface changes, which necessitate the identification of high-resolution remote sensing images, remain substantial challenges in achieving precise change detection. Excited deep learning-based change detection techniques often exhibit limitations and lack the necessary precision to detect edge details or other nuanced information in remote sensing images. To address these limitations, we propose a unique semantic segmentation deep learning network, the self-adaptive Siamese network (SASiamNet), specifically devised for enhancing change detection in remote sensing images. The SASiamNet excels in real-time land cover segmentation, adeptly extracting local and global information from images via the backbone residual network. Furthermore, it incorporates a primary feature fusion module to extract and fuse the primary stage feature map, and a high-level information refinement module to refine the resultant feature map. This methodology effectively transmutes low-level semantic information into high-level semantic information, thereby improving the overall detection process. Aimed at empirically testing the effectiveness of the SASiamNet, we utilize two distinct datasets: the public dataset, LEVIR-CD, and a challenging dataset, CDD. The latter is composed of bitemporal images sourced from Google Earth, spanning various regions across China. The experiment results unequivocally demonstrate that our approach outperforms traditional methodologies as well as contemporary state-of-the-art change detection techniques, hence underscoring the efficacy of the SASiamNet in the context of remote sensing image change detection.