Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a generative adversarial network to generate synthetic MRI images for multi-class automatic segmentation of brain tumors

Challenging tasks such as lesion segmentation, classification, and analysis for the assessment of disease progression can be automatically achieved using deep learning (DL)-based algorithms. DL techniques such as 3D convolutional neural networks are trained using heterogeneous volumetric imaging data such as MRI, CT, and PET, among others. However, DL-based methods are usually only applicable in the presence of the desired number of inputs. In the absence of one of the required inputs, the method cannot be used. By implementing a generative adversarial network (GAN), we aim to apply multi-label automatic segmentation of brain tumors to synthetic images when not all inputs are present. The implemented GAN is based on the Pix2Pix architecture and has been extended to a 3D framework named Pix2PixNIfTI. For this study, 1,251 patients of the BraTS2021 dataset comprising sequences such as T1w, T2w, T1CE, and FLAIR images equipped with respective multi-label segmentation were used. This dataset was used for training the Pix2PixNIfTI model for generating synthetic MRI images of all the image contrasts. The segmentation model, namely DeepMedic, was trained in a five-fold cross-validation manner for brain tumor segmentation and tested using the original inputs as the gold standard. The inference of trained segmentation models was later applied to synthetic images replacing missing input, in combination with other original images to identify the efficacy of generated images in achieving multi-class segmentation. For the multi-class segmentation using synthetic data or lesser inputs, the dice scores were observed to be significantly reduced but remained similar in range for the whole tumor when compared with evaluated original image segmentation (e.g. mean dice of synthetic T2w prediction NC, 0.74 ± 0.30; ED, 0.81 ± 0.15; CET, 0.84 ± 0.21; WT, 0.90 ± 0.08). A standard paired t-tests with multiple comparison correction were performed to assess the difference between all regions (p < 0.05). The study concludes that the use of Pix2PixNIfTI allows us to segment brain tumors when one input image is missing.
GaN Transistors’ Radiated Switching Noise Source Evidenced by Hall Sensor Experiments Toward Integration

Wide bandgap Gallium Nitride (GaN) technology promises to deliver the next generation of power transistors capable of high energy density and compact design integration however, without active monitoring high failing rates are recorded due to its instability to design parameter variations. Moreover, the electromagnetic (EM) radiofrequency (RF) emissions due to GaN power switching require extra design resources. Considering the extensive research area dedicated to galvanic isolated magnetic sensors for GaN wafer monolithic integration with usage in power monitoring, this study investigates the conditions that a Hall sensor is required to meet when operating in close proximity of a GaN transistor. Through considerable experimental testing, it was determined that the sensor requires a magnetic field starting from ±1 mT when interfaced with a microcontroller. Additionally, since the GaN transistor’s EM RF switching noise was one of the most monitored parameters during the experiments, it was discovered that it is proportional to the transistor’s current transfer area whereas its magnitude is due to electrical current required by the load. As a result of these findings, the EM radiated switching noise may apply to all electrical switches and provide a significant advantage when designing for EM compatibility (EMC).
Poverty Estimation Using a ConvLSTM-Based Model With Multisource Remote Sensing Data: A Case Study in Nigeria

Poverty is a global challenge, the effects of which are felt on the individual to national scale. To develop effective support policies to reduce poverty, local governments require precise poverty distribution data, which are lacking in many areas. In this study, we proposed a model to estimate poverty on a spatial scale of 10 × 10 km by combining features extracted from multiple data sources, including nighttime light remote sensing data, normalized difference vegetation index, surface reflectance, land cover type, and slope data, and applied the model to Nigeria. Considering that the trends of environmental factors contain valid information related to poverty, time-series features were extracted through convolutional long short-term memory and used for the assessment. The poverty level is represented by the wealth index derived from the Demographic and Health Survey Program. The model exhibited good ability to estimate poverty, with an R2 of 0.73 between the actual and estimated wealth index in Nigeria in 2018. Applying the proposed model to poverty estimation for Nigeria in 2021 yielded an R2 value of 0.69, indicating good generalization ability. To further validate model reliability, we compared the assessment results with high-resolution satellite imagery and a state-level multidimensional poverty index. We also investigated the impact of incorporating time-series features on the accuracy of poverty assessment. Results showed that the addition of time-series features increased the accuracy of poverty estimation from 0.64 to 0.73. The proposed method has valuable applications for estimating poverty at the grid scale in countries without such data.
Three-dimensional profile reconstruction based on infrared multi-view vision

Multi-view vision technology is use of multiple images to reconstruct three-dimensional (3D) information of the research object, and the images were captured with more than two cameras from different angles. This technology has the advantages of high efficiency, simple structure and low cast. It is very suitable for on line noncontact product test and quality control. The existent multi-view vision technology is focus on the visible information, thus it is easy to be influenced by the testing environment (weather, background, light etc.), so it’s application field has some limitations. However, in the field of medical diagnostic technology, infrared vision technology reflects its advantages, like determining whether the tissue lesions by observe certain parts temperature distribution of the body. This paper is studied on infrared Multi-vision which is based on visible binocular vision theory. Firstly, obtained the intrinsic parameter and external parameter of each infrared thermal imager by Zhang’s calibration method; Secondly, taken infrared images from different angles, then combined the infrared images two by two to do feature point detecting and matching in order to find the points to be reconstructed; Finally, reconstructing 3D profile information based on calculating point clouds of the spatial coordinates.
Understanding Crowdfunding via Social Media: A Review of the Relevancy of Previous Studies

Crowdfunding has emerged as an alternative financing method, enabling individuals and organizations to raise funds through small contributions from a large number of people. Social media platforms such as Facebook, Twitter, and Instagram have played a pivotal role in the success of crowdfunding initiatives. Utilizing social media, creators can reach a wider audience, share their campaigns, and connect with potential supporters who share similar interests. As a result, crowdfunding via social media has gained significant popularity in recent years. This study aims to examine the relevance and rigour of current research on crowdfunding via social media through a comprehensive review of past studies. By addressing the existing literature gaps, we seek to contribute to a better understanding of this phenomenon. Specifically, we will expand on prior research and identify key areas that require further exploration. The findings of this study will provide valuable insights for researchers, enabling them to generate more meaningful and comprehensive studies that contribute to the growth of crowdfunding via social media. By exploring these aspects, this study aims to advance knowledge and address the existing gaps in the literature.
Suitability of soils derived from sandstone and beach sands for cashew (Anacardium occidentalis) and coconut (Cocos nucifera) cultivation in the Niger Delta Area, Nigeria

Land evaluation was carried out to determine the suitability of soils derived from the sandstone (SST) and beach ridge sands (BRS) parent materials for cashew (Anacardium occidentalis) and coconut (Cocos nucifera) cultivation in the Niger Delta area of Akwa Ibom State, Nigeria. Data were obtained through field soil survey of areas underlain by the BRS and SST parent materials. Nine pedons represented the BRS while six pedons represented the SST. The evaluation was done following the conventional method of FAO framework as well as the parametric method. The result showed that by the conventional method all the nine pedons of the BRS were moderately suitable (S2) for cashew cultivation, whereas by the parametric method all the pedons were marginal (S3). The major constraints for cashew cultivation were soil physical characteristics and drainage. For coconut cultivation, evaluation by the conventional method, showed that seven of the nine pedons (77.78%) were highly suitable (SI) for coconut while two pedons (22.22%) were moderately suitable (S2). But by the parametric method, the seven pedons were moderately suitable while the two were marginal, with topography as moderate constraint. For the six pedons from SST, the result further showed that both by the conventional and parametric methods of evaluation, all the six pedons were highly suitable (S1) for cashew cultivation except one pedon, which was moderately suitable (S2). The result of this study showed that land evaluation can assist in identifying suitable land for optimum production of specific crops, even in an agro-ecological zone which may be considered marginal land for most kinds of agricultural land use.
Construction of a molecular inflammatory predictive model with histone modification-related genes and identification of CAMK2D as a potential response signature to infliximab in ulcerative colitis

Background Ulcerative colitis (UC) is a lifelong inflammatory disease affecting the rectum and colon with numerous treatment options that require an individualized treatment plan. Histone modifications regulate chromosome structure and gene expression, resulting in effects on inflammatory and immune responses. However, the relationship between histone modification-related genes and UC remains unclear. Methods Transcriptomic data from GSE59071 and GSE66407 were obtained from the Gene Expression Omnibus (GEO), encompassing colonic biopsy expression profiles of UC patients in inflamed and non-inflamed status. Differentially expressed gene (DEG) analyses, functional enrichment analyses, weighted gene co-expression network analysis (WGCNA), and random forest were performed to identify histone modification-related core genes associated with UC inflammation. Features were screened through the least absolute shrinkage and selection operator (LASSO) and support vector machine‐recursive feature elimination (SVM‐RFE), establishing a molecular inflammatory predictive model using logistic regression. The model was validated in the GSE107499 dataset, and the performance of the features was assessed using receiver operating characteristic (ROC) and calibration curves. Immunohistochemistry (IHC) staining of colonic biopsy tissues from UC patients treated with infliximab was used to further confirm the clinical application value. Univariate logistic regression on GSE14580 highlighted features linked to infliximab response. Results A total of 253 histone modification-related DEGs were identified between inflammatory and non-inflammatory patients with UC. Seven key genes (IL-1β, MSL3, HDAC7, IRF4, CAMK2D, AUTS2, and PADI2) were selected using WGCNA and random forest. Through univariate logistic regression, three core genes (CAMK2D, AUTS2, and IL-1β) were further incorporated to construct the molecular inflammatory predictive model. The area under the curve (AUC) of the model was 0.943 in the independent validation dataset. A significant association between CAMK2D protein expression and infliximab response was observed, which was validated in another independent verification set of GSE14580 from the GEO database. Conclusion The molecular inflammatory predictive model based on CAMK2D, AUTS2, and IL-1β could reliably distinguish the mucosal inflammatory status of UC patients. We further revealed that CAMK2D was a predictive marker of infliximab response. These findings are expected to provide a new evidence base for personalized treatment and management strategies for UC patients.
Comparative Study for Postoperative Initial Fixation Patterns of Two Different Types of Cementless Short Stem Using Three-Dimensional Templating Software

Background While cementless short stems have become popular in total hip arthroplasty (THA), Metha is a relatively recent development that differs from other short stems in its initial fixation concept of partial collum-sparing metaphyseal anchorage. The purpose of this study was to quantify the contact state between Metha and the femur. Additionally, we investigated the difference in contact points between Meta and Fitmore, which is one of the more popular curved short stems. Methods We conducted a retrospective review of 42 hips that underwent THA using Metha and 41 hips using Fitmore. Stem-to-femur contact was evaluated by density mapping using a three-dimensional digital template system to quantify the contact condition according to the modified Gruen zone. The criterion for the stem-to-bone contact boundary was defined as a computed tomography value of 543 Hounsfield. Results Quantitative evaluation of Metha according to the modified Gruen zones showed the ratio of surface area with high cortical contact in each zone. The results were 4.6% ± 5.7% in zone 1, 0.9% ± 2.3% in zone 2, 19.1% ± 12.9% in zone 3, 1.4% ± 3.2% in zone 5, 29.6% ± 16.4% in zone 6, and 25.1% ± 17.7% in zones 7. Evaluation of Fitmore for the same zones was as follows: 1.6% ± 2.4%, 18.5% ± 16.9%, 20.8% ± 17.4%, 12.7% ± 12.8%, 3.7% ± 5.8%, and 13.3% ± 10.3%. Comparing the two groups, the contact area was significantly greater for Metha in zones 1, 6 and 7 and Fitmore in zones 2 and 5 (p < 0.05). Conclusions It is possible for Metha to achieve metaphyseal anchoring by contacting the cortical bone at the proximal femur, thus avoiding proximal offloading. To the best of our knowledge, no previous studies have quantitatively reported stem-to-cortical bone contact conditions in curved short stems.
Learning analytics in the EFL classroom: Technology as a forecasting tool

Ideal e-learning management systems (eLMSs) offer many applications given their huge dataset. They are a repository of information needed for the smooth functioning of the teaching-learning process in tech-enabled spaces. Databases created in these analytical systems permit not only the retrieval of data but also the updation of changing learning and pedagogical needs. This study aims to provide insights into predictive educational benefits to EFL classrooms as a result of the application of analytic methods by using grades in final exam scores of 25 EFL female learners of an undergraduate course at Prince Sattam bin Abdulaziz University (PSAU), Saudi Arabia. The study also analyzed the learning behaviours on Blackboard. Results show that lesson videos have the highest hit out of the three parameters (lesson videos, textual notes, practice exercises) offered on the e-learning platform (BlackBoard), followed by practice questions, and the least visitations were registered for the textual notes. Correlation between the learning videos (related to the three literature lessons taught) and final grades showed statistical significance at 95% confidence level, in addition to the factor of topic completion by the learners being a highly influencing factor in final grades. As a predictive model, Learning Analytics (LA) established that videos are a preferred way of learning English in the Saudi EFL context. The results of the study are likely to be of significance to course developers, teachers, and learners of EFL.
Physical Property Variation of Ferrite Nanoparticles under Heavy Ion Irradiation

The size-dependent behavior of nanostructured ferrite is well established. Various physical properties such as magnetic, optical and electrical exhibit strong size dependence. Thus, any treatment which causes a change in size is able to modify the characteristics of ferrites. The result of this effect can be seen when these ferrites are subjected to an intense heavy ion beam which modifies the physical properties of ferrites. This modification is related to cation redistribution owing to size change in most of the cases under heavy ion irradiation. However, few recent studies show that cation redistribution may occur under ion irradiation even though no size change is observed. The objective of this review is to highlight this effect in ferrite systems which ultimately may provide ample opportunity for its potential applications.
Removal of organic matter during adaptation of Nannochloropsis oculata in livestock waste

Microalgae, like plants, contribute significantly to the development of the oxygen biogeochemical cycle due to their high photosynthetic efficiency. In addition, they provide high yields of polyunsaturated fatty acids, sterols, proteins, terpenoids, and pigments, among others. Therefore, different species of microalgae have been studied and used on a laboratory scale to carry out processes such as wastewater treatment or aerobic bioconversion, which are presented as sustainable and viable alternatives for the treatment and recovery of organic waste (OW), usually rich in carbohydrates, lipids and proteins. In the present investigation, the removal of organic matter was evaluated during the adaptation of Nannochloropsis oculata in residues of poultry wastewater and swine origin, obtained from technical and semi-technical plants, respectively, located in the high mountain zone of the state of Veracruz, Mexico. The experiment was carried out in 250 mL discontinuous photobioreactors with a working volume of 200 mL, where 3 inoculum-substrate ratios were studied for each organic residue: 10, 15 and 20% inoculum in poultry wastewater (PWW) and 30, 50 and 70% inoculum in pig manure (PM). In addition, the conditions of temperature (20 ± 2 °C), illumination (2000 lx), photoperiod of 12/12 (light/dark) and continuous aeration were controlled. It was shown that N. oculata can tolerate alkaline conditions of pH ≥ 10 and contributes to the reduction of soluble organic matter in OW. PWW and PM were found to be viable media for the survival of N. oculata. Finally, regarding the inoculum concentrations studied, the most appropriate were 10% for PWW and 70% for PM.
MULTIPLE CONTROLLING FACTORS OF THE ENRICHMENT OF ORGANIC MATTER IN THE UPPER CRETACEOUS OIL SHALE SEQUENCES OF THE SONGLIAO BASIN, NE CHINA: IMPLICATIONS FROM GEOCHEMICAL ANALYSES

Two oil shale sequences in the Upper Cretaceous Qingshankou and Nenjiang Formations of the Songliao Basin, NE China, representing organic matter (OM)-rich hydrocarbon source rocks, are investigated. According to bulk and inorganic geochemical data, there are significant differences in OM enrichment conditions between the first member of the Qingshankou Formation (K2qn1) and the second member of the Nenjiang Formation (K2n2). The oil shale in K2qn1 was deposited in a warm-humid paleoclimate, with strong water salinity stratification and dysoxic conditions, combined with high bioproductivity and low detrital matter input. In contrast, the oil shale in K2n2 was accumulated in a warm semi-humid paleoclimate, with weak water salinity stratification and partially oxic conditions, when the bioproductivity and detrital matter input were medium. In addition, the origin of OM influenced its enrichment. In summary, high bioproductivity and strong water salinity stratification were the major controlling factors for OM enrichment in K2qn1, while the origin of OM and dysoxic conditions were the major governing factors for OM enrichment in K2n2. Thus, the OM enrichment models of these two members are established.
Securing Mobile Edge Computing Using Hybrid Deep Learning Method

In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques.
Aperture Efficiency of Non-Uniform Antenna Arrays With Controlled Sidelobe Level

The non-uniform distribution of the weight vector of the elements in an antenna array has attracted much attention in order to obtain such as the desired beam pattern, null locations, and performance objectives. This topic has been of interest for many decades. Several mathematical models and optimization algorithms have been developed to achieve a controllable sidelobe level. However, few researchers have considered the effect of the non-uniform distribution on the aperture efficiency. This paper presents a comprehensive study and proposes accurate mathematical modelling of the aperture efficiency of an antenna array which is divided into two components: power-loss efficiency (which dominates total aperture efficiency) and power-distribution efficiency to gain a better understanding. The paper also shows how the sidelobe level of an antenna array is linked to these efficiencies and how they can be improved. It is proved that the aperture efficiency exponentially decays when lowering the sidelobe level. However, it converges to a certain value when the sidelobes vanish. Moreover, a validation example through simulations is presented to verify the mathematical modelling of the aperture efficiency.