Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining Taiwanese university students’ multimodal multiple text comprehension: individual differences and epistemic prompting

ABSTRACT This study investigated multimodal multiple text comprehension (MMTC) among forty-eight Taiwanese university students, considering their cognitive and affective characteristics. A randomized trial with pre- and post-tests design was applied. The experiment group received a browser-embedded note-taking device along with epistemic prompting. The control group was provided with the browser-embedded note-taking device only. The results showed that situational interest, prior knowledge level, and cognitive load were associated with learners’ MMTC. Besides, prior knowledge and cognitive load moderated the effect of epistemic prompting on MMTC. Students with low prior knowledge scored significantly lower in MMTC without graphics than those with medium or high prior knowledge in the control group. Moreover, the cognitive load had a stronger negative influence on participants’ MMTC without graphics in the control group, but the negative impact was negligible on those in the experimental group. The contributions and implications of the findings were discussed regarding the MMTC intervention and instruction.
A rare case of colonic lipoma causing colo-colonic intussusecption

Gastrointestinal tract (GIT) lipomas are rare, benign mesenchymal neoplasm affecting all segments of the GIT and colon is affected most frequently. Reported incidence of colonic lipomas varies from 0.2% to 4.4%. Adult intussusception represents 1% of all bowel obstructions and 5% of all bowel intussuceptions. Gastrointestinal lipomas are rare benign tumors and intussusception due to a gastrointestinal lipoma constitute an infrequent clinical entity. Although intussusception is a common disease in children, intussusception caused by colonic lipoma in adults is a rare condition, and is usually caused by a large pedunculated lipoma. The majority are asymptomatic but may cause abdominal pain, obstruction or bleeding. The treatment for symptomatic or large lipomas is surgical excision. We would like to report a unique case of an elderly lady with colonic lipoma causing colo-colonic intussusception. After thorough investigations, exploratory laparotomy with colo-colonic anastomosis was done.
Impact of the Process Variables on the Yield of Mesenchymal Stromal Cells from Bone Marrow Aspirate Concentrate

Human bone marrow (BM) has been highlighted as a promising source of mesenchymal stromal cells (MSCs) containing various growth factors and cytokines that can be potentially utilized in regenerative procedures involving cartilage and bone. However, the proportion of MSCs in the nucleated cell population of BM is only around 0.001% to 0.01% thereby making the harvesting and processing technique crucial for obtaining optimal results upon its use in various regenerative processes. Although several studies in the literature have given encouraging results on the utility of BM aspiration concentrate (BMAC) in various regenerative procedures, there is a lack of consensus concerning the harvesting variables such as choice of anesthetic agent to be used, site of harvest, size of the syringe to be used, anticoagulant of choice, and processing variables such as centrifugation time, and speed. In this review article, we aim to discuss the variables in the harvesting and processing technique of BMAC and their impact on the yield of MSCs in the final concentrate obtained from them.
In vivo comparative study of the effects of using the enamel matrix derivative and/or photobiomodulation on the repair of bone defects

Background The repair of bone defects has been the subject of many studies that have shown inconclusive results as to what is the best bone substitute. Material and Methods Bone defects (Ø 2 mm) were induced on the tibia of seventy-two rats, which were distributed into the following four groups/treatments (n=18 each): Control: no treatment; EMD: enamel matrix derived protein; PBM: photobiomodulation therapy (660 nm, 0,035 W, 60 s); EMD + PBM: EMD and immediate treatment with PBM (660 nm, 0,035 W, 60 s). Six animals from each group were euthanized after 10, 30 and 60 days. Histological and immunohistochemistry analyses (osteocalcin - OCN and tartrate-resistant acid phosphatase - TRAP) were performed with scores for each of the biological events. Results All performed treatments resulted in an increased filling and maturation of bone tissue, being greater in the EMD and EMD + PBM groups in the 30 day period, compared to the Control group. The immunostaining of OCN was greater at 60 days in all treated groups than in the Control over the same period. TRAP immunostaining was higher at 30 days in all treated groups, and lower in groups EMD and PBM after 60 days, compared to the Control over the same period. There was greater immunostaining in the EMD + PBM group after 10 days than in the Control and EMD groups in the same period. Conclusions These results lead to the conclusion that treatments with EMD and PBM, both separate and in association were effective in filling and maturing bone tissue in tibial bone cavities, with greater effectiveness in the period of 30 days in the EMD and EMD + PBM groups. Key words:Enamel matrix proteins, low-level laser, bone, animal research.
Novel Methodology for Compact Ring Hybrids

A design method for miniaturization of microstrip rat-race couplers with good frequency bandwidth employing coupled line sections is reported in this paper. The footprint of the proposed design occupies 14.9% of the area of the conventional similar design. Moreover, the circuit is planar, without any high impedance section (fine line), multilayer substrates, plated thru holes, bonding wires, reactive components or artificial transmission lines. These lead to low insertion loss, high power handling and easy and low cost fabrication of the circuit. For experimental verification, one rat-race coupler is designed and fabricated with center frequency of 1 GHz. Simulation and measurement results verify that the frequency responses of the conventional coupler are remained unchanged during the miniaturization. Across the frequency bandwidth, the maximum amplitude imbalance is 0.5 dB, the phase variation is 5 o , the isolation and return loss are better than 20 dB.
A Postprocessing Method Based on Regions and Boundaries Using Convolutional Neural Networks and a New Dataset for Building Extraction

Deep convolutional neural network (DCNN)-based methods have shown great improvements in building extraction from high spatial resolution remote sensing images. In this paper, we propose a postprocessing method based on DCNNs for building extraction. Specifically, building regions and boundaries are learned simultaneously or separately by DCNNs. The predicted building regions and boundaries are then combined by the postprocessing method to produce the final building regions. In addition, we introduce a manually labeled dataset based on high spatial resolution images for building detection, the XIHU building dataset. This dataset is then used in the experiments to evaluate our methods. Besides the WHU building dataset, East Asia (WHUEA) is also included. Results demonstrate that our method that combines the results of DeepLab and BDCN shows the best performance on the XIHU building dataset, which achieves 0.78% and 23.30% F1 scores, and 1.13% and 28.45% intersection-over-union (IoU) improvements compared with DeepLab and BDCN, respectively. Additionally, our method that combines the results of Mask R-CNN and DexiNed performs best on the WHUEA dataset. Moreover, our methods outperform the state-of-the-art multitask learning network, PMNet, on both XIHU and WHUEA datasets, which indicates that the overall performance can be improved although building regions and boundaries are learned in the training stage.
The Comparison of Cybersecurity Datasets

Almost all industrial internet of things (IIoT) attacks happen at the data transmission layer according to a majority of the sources. In IIoT, different machine learning (ML) and deep learning (DL) techniques are used for building the intrusion detection system (IDS) and models to detect the attacks in any layer of its architecture. In this regard, minimizing the attacks could be the major objective of cybersecurity, while knowing that they cannot be fully avoided. The number of people resisting the attacks and protection system is less than those who prepare the attacks. Well-reasoned and learning-backed problems must be addressed by the cyber machine, using appropriate methods alongside quality datasets. The purpose of this paper is to describe the development of the cybersecurity datasets used to train the algorithms which are used for building IDS detection models, as well as analyzing and summarizing the different and famous internet of things (IoT) attacks. This is carried out by assessing the outlines of various studies presented in the literature and the many problems with IoT threat detection. Hybrid frameworks have shown good performance and high detection rates compared to standalone machine learning methods in a few experiments. It is the researchers’ recommendation to employ hybrid frameworks to identify IoT attacks for the foreseeable future.
Evaluation of credit risk using evolutionary-fuzzy logic scheme

Credit risk evaluation systems are playing an important role in the financial decisions making by enabling faster credit decisions and diminishing possible risks. Credit scoring is the most common used technique for evaluating the creditworthiness of the credit applicants. Its objective is to classify the credit applicants according to their likely payment behavior into good or bad customer groups. Accuracy and transparency are two important criteria that should be satisfied by any credit scoring system. Good accuracy enables correct assessment and avoids any heavy losses associated with wrong predictions while transparency is important for financial analysis and for understanding the decision process. In this research, the transparency and the accuracy of credit scoring model have been investigated and optimized using two different fuzzy model types, namely Takagi- Sugeno (TS) and Mamdani type. The former fuzzy credit scoring model is generated using a clustering method while the latter is extracted using neural networks learning technique. The transparency and accuracy of both the resulting fuzzy credit scoring models have been simultaneously optimized using two multi-objective evolutionary techniques. The potential and the main features of the proposed modelling approaches are illustrated using two benchmark real world data sets, namely German and Australian Credit Data Sets. Experimental results show that (TS fuzzy system) compares favorably with the other methods like NNs, RBF, â€œGA+SVMâ€ while it is superior to some methods like CART, Rough sets and the popular C4.5. Moreover, unlike black box methods like ANN and GA, this method has some levels of transparency, and can be useful to do some analysis like defining the customersâ€™ attributes that influence the system decision and approximate values of these attributes. For generating a completely transparent credit scoring model, Mamdani fuzzy system should be chosen. Finally, a generic software called EvoFNS (Evolutionary-Fuzzy-Neuro-System) was developed which can be used for fuzzy identification (generation and optimization), prediction or classification and knowledge extraction (data mining tool). As the TS fuzzy system is more accurate than Mamdani fuzzy system and it can be used for predicting the customersâ€™ reditworthiness while the latter, being more transparent,can be better utilized for data analysis and knowledge discovery.
Phenotypic Diversity of Cryptococcus neoformans var. neoformans Clinical Isolates from Localized and Disseminated Infections

Cryptococcus neoformans var. neoformans is the second most prevalent agent of cryptococcosis in central Europe. Infections mostly present with localized skin and disseminated infections. Previous studies did not find these presentations to be determined by the fungal genotype as detected by multilocus sequence typing (MLST). However, phenotypic fungal traits may impact clinical presentation. Here, we studied the growth and virulence factors of C. neoformans var. neoformans isolates from disseminated and localized infections and an environmental isolate. We used coincubation with Acanthamoeba castellanii and the Galleria mellonella infection model to identify phenotypic characteristics potentially associated with clinical presentation. Clinical isolates of C. neoformans var. neoformans present a substantial phenotypic variability. Median survival of G. mellonella varied between 6 and 14 days. C. neoformans var. neoformans isolates from disseminated infections showed stronger melanization and larger capsules. They demonstrated superior uptake into an amoeba and increased cytotoxicity for the amoeba. Differences of strains from localized and disseminated infections in coincubation with amoeba are in line with the importance of phagocytes in the pathogenesis of disseminated cryptococcosis. Phenotypic traits and non-vertebrate infection models may help understand the virulence potential of C. neoformans var. neoformans isolates.
Dynamic Performance Enhancement of a Renewable Energy System for Grid Connection and Stand-alone Operation with Battery Storage

This paper introduces a new formulated control scheme for enhancing the dynamic performance of a wind driven surface permanent magnet synchronous generator. The designed control scheme is based on predictive control theory, in which the shortcomings of previous predictive controllers are avoided. To visualize the effectiveness of the proposed control scheme, the performance of the generator was dynamically evaluated under two different operating regimes: grid connection and standalone operation in which a battery storage system was used to enhance the power delivery to the isolated loads. In addition, a detailed performance comparison between the proposed controller and traditional predictive controllers was carried out. The traditional control topologies used for comparison were the model predictive direct power control, model predictive direct torque control, and model predictive current control. A detailed description of each control scheme is introduced illustrating how it is configured to manage the generator operation. Furthermore, to achieve the optimal exploitation of the wind energy and limit the power in case of exceeding the nominal wind speed, maximum power point tracking and blade pitch angle controls were adopted. A detailed performance comparison effectively outlined the features of each controller, confirming the superiority of the proposed control scheme over other predictive controllers. This fact is illustrated through its simple structure, low ripples, low computation burdens and low current harmonics obtained with the proposed control scheme.
Effect of the use of augmented reality applications on academic achievement of student in science education: meta analysis review

ABSTRACT It was aimed to review the effect of the use of augmented reality applications on the academic achievement of students in science education in this study. In line with this aim, the experimental researches reviewing the effect of use of augmented reality applications within the scope of science course on student achievement were evaluated with meta-analysis method. 16 studies included into this research were accessed from ERIC, Google Scholar, ScienceDirect, SpringerLink, Taylor & Francis, Web of Science databases in line with certain criteria. In order to reach these studies from databases, the following key words were used; “‘augmented reality’ & ‘achievement’” and “‘augmented reality’ & ‘achievement & science’”. Funnel plot, Orwin’s Fail-Safe N and Egger tests were used for detecting the publication bias and it was determined that there is no publication bias. At the end of the meta-analysis, it was determined that the effect of the use of the augmented reality applications on the student achievement in the science course is moderate (d = 0,643) in favor of the experimental group. Thus, it was presented that the use of the augmented reality applications affects the student achievement positively in the science course.
Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning

An important question in planning and designing bike-sharing services is to support the user’s travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This study focuses on the short-term forecasting, 15 min ahead, of the shared bikes demand in Montreal using a deep learning approach. Having a set of bike trips, the study first identifies 6 communities in the bike-sharing network using the Louvain algorithm. Then, four groups of LSTM-based architectures are adopted to predict pickup demand in each community. A univariate ARIMA model is also used to compare results as a benchmark. The historical trip data from 2017 to 2021 are used in addition to the extra inputs of demand related engineered features, weather conditions, and temporal variables. The selected timespan allows predicting bike demand during the COVID-19 pandemic. Results show that the deep learning models significantly outperform the ARIMA one. The hybrid CNN-LSTM achieves the highest prediction accuracy. Furthermore, adding the extra variables improves the model performance regardless of its architecture. Thus, using the hybrid structure enriched with additional input features provides a better insight into the bike demand patterns, in support of bike-sharing operational management.
Fluoride Bio-Sorption Efficiency and Antimicrobial Potency of Macadamia Nut Shells

This paper presents the potential application of macadamia nut shells (MNS) in the bio-sorption of fluoride and its antimicrobial potency against common pathogens encountered in surface water resources. The efficiency of MNS in the sorption of fluoride was determined using batch mode experiments, while the antimicrobial potency was investigated using the well disc diffusion assay method. The maximum fluoride sorption capacity of 1.26 mg/g was recorded at an initial fluoride concentration of 5 mg/L, adsorbent dosage of 0.5 g/100 mL, contact time of 120 min and initial pH of 6. The adsorption kinetics data were better described with a pseudo second order model, indicating the dominance of the chemisorption mechanisms. The Langmuir adsorption isotherm model described the isotherm data suggesting a monolayered adsorption. The thermodynamic parameters, ∆Gº and ∆Hº, confirmed that F− sorption by MNS is a spontaneous and endothermic process. The bio-sorbent was regenerated for seven continuous cycles when HCl was used as regenerating solution. The antimicrobial studies revealed that MNS has minimal activity towards Escherichia coli, Staphylococcus aureus and Klebsiella pneumoniae. The MNS showed potentials for application in bio-sorption of fluoride. However, the doping of MNS with metal ions is recommended to enhance its anti-microbial potency.
Voter preferences as a source of descriptive (mis)representation by social class

Abstract This paper presents the results of a conjoint survey experiment in which Swiss citizens were asked to choose among parliamentary candidates with different class profiles determined by occupation, education and income. Existing survey‐experimental literature on this topic suggests that respondents are indifferent to the class profiles of candidates or biased against candidates with high‐status occupations and high incomes. We find that respondents are biased against upper middle‐class candidates as well as routine working‐class candidates. While the bias against upper middle‐class candidates is primarily a bias among working‐class individuals, the bias against routine working‐class candidates is most pronounced among middle‐class individuals. Our supplementary analysis of observational data confirms the bias against routine working‐class candidates, but not the bias against upper middle‐class candidates.
On the Exploration of Automatic Building Extraction from RGB Satellite Images Using Deep Learning Architectures Based on U-Net

Detecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks. In this work, we explore the effectiveness of the CNN-based architecture U-Net and its variations, namely, the Residual U-Net, the Attention U-Net, and the Attention Residual U-Net, in automatic building extraction. We showcase their robustness in feature extraction and information processing using exclusively RGB images, as they are a low-cost alternative to multi-spectral and LiDAR ones, selected from the SpaceNet 1 dataset. The experimental results show that U-Net achieves a 91.9% accuracy, whereas introducing residual blocks, attention gates, or a combination of both improves the accuracy of the vanilla U-Net to 93.6%, 94.0%, and 93.7%, respectively. Finally, the comparison between U-Net architectures and typical deep learning approaches from the literature highlights their increased performance in accurate building localization around corners and edges.
Desmoid Tumors Arising on the Mesenteric Surgical Scar of Abdominal Sarcomas

A sporadic desmoid tumor (DT) is a rare type of tumor of the mesenchymal connective tissues is now considered an intermediate disease or locally aggressive. They may develop on scars or after traumatism, favored by growth factors released during the initial phase of wound healing. Most of the abdominal DT arising on a scar is described on the wall incision. In this report, we describe two cases of DT arising on the intraperitoneal surgical scar, shortly after the resection of a low-grade retroperitoneal liposarcoma and a low-risk gastric gastrointestinal stromal tumor (GIST). Inconsistency between low risk according to the classification of the primary sarcoma and early local recurrence (LR) should raise the possibility of DT. Core needle biopsy (CNB) should be performed when it is feasible, including on local recurrences (LR). Surveillance has become the first-line treatment for DT. In case of progression between two imaging during the surveillance phase, surgery, when it's not mutilating, is indicated for selected cases as second-line treatment.