Miracle-dz/UniFAR-FTUs huggingface dataset:
Forensic Analysis of Synthetically Generated Western Blot Images

The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures. As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images. In this paper, we focus on the detection of synthetically generated western blot images. These images are largely explored in the biomedical literature and it has been already shown they can be easily counterfeited with few hopes to spot manipulations by visual inspection or by using standard forensics detectors. To overcome the absence of publicly available data for this task, we create a new dataset comprising more than 14K original western blot images and 24K synthetic western blot images, generated using four different state-of-the-art generation methods. We investigate different strategies to detect synthetic western blots, exploring binary classification methods as well as one-class detectors. In both scenarios, we never exploit synthetic western blot images at training stage. The achieved results show that synthetically generated western blot images can be spot with good accuracy, even though the exploited detectors are not optimized over synthetic versions of these scientific images. We also test the robustness of the developed detectors against post-processing operations commonly performed on scientific images, showing that we can be robust to JPEG compression and that some generative models are easily recognizable, despite the application of editing might alter the artifacts they leave.
Proposing Lane and Obstacle Detection Algorithm Using YOLO to Control Self-Driving Cars on Advanced Networks

Developing self-driving cars is an important foundation for the development of intelligent transportation systems with advanced telecommunications network infrastructure such as 6G networks. The paper mentions two main problems, namely, lane detection and obstacle detection (road signs, traffic lights, vehicles ahead, etc.) through image processing algorithms. To solve problems such as low detection accuracy of traditional image processing methods and poor real-time performance of methods based on deep learning methods, lane and object detection algorithm barriers for smart traffic are proposed. We first convert the distorting image caused by the camera and use a threshold algorithm for the lane detection algorithm. The image with a top-down view is then determined through the extraction of a region of interest and inverse perspective transform. Finally, we implement the sliding window method to determine pixels belonging to each lane and adapt it to a quadratic equation. YOLO algorithm is suitable for identifying many types of obstacles for identification problems. Finally, we use real-time videos and the TuSimple dataset to perform simulations for the proposed algorithm. The simulation results show that the accuracy of the proposal for detecting lanes is 97.91% and the processing time is 0.0021 seconds. The accuracy of the proposal for detecting obstacles is 81.90%, and the processing time is 0.022 seconds. Compared with the traditional image processing method, the average accuracy and execution time of the proposed method are 89.90% and 0.024 seconds, which is a strong antinoise ability. The results prove that the proposed algorithm can be deployed for self-driving car systems with a high processing speed of the advanced network.
MEASURING THE IMPACT OF COVID-19 PANDEMIC ON THE OIL PRICES VOLATILITY BY GARCH MODELING

The aim of this work is the study of crude oil price volatility, particularly during the Covid-19 pandemic, which changed the world scene and inflicted serious crises not only in the global health system, but also in the international financial markets and economy. This economic situation has many policy makers, financiers and portfolio managers worried about avoiding the risk of potential damage. Hedging strategies are based on the correct estimation of price volatility. For this aim, we use the $GARCH$ model to measure the impact of volatility and shocks. More precisely, the model used for predicting volatility associated with the price variable is the $GARCH(1,2)$ model, in through this analysis. Although the $EGARCH$ and $TGARCH$ models are better for their asymmetry property of volatility, but the $GARCH(1,2)$ model was adopted because it presents lower values of the forecasting criteria compared to the two other models. The forecast is made for the last thr\'{e}e months of 2021. The result concludes that the predicted values and the current values are very close. The oil price series that will be examined here is $WTI$ (West Texas Intermediate).
New therapy option: Maisonneuve fracture without transsyndesmotic fixation

Ankle fracture is one of the common injuries in the orthopedic department, the Maisonneuve fracture is a specific type of ankle injury. This fracture is usually caused by rotational force. According to the Lauge -Hansen classification, it is a pronation and external rotation type injury, often resulting in inferior tibiofibular injury. Because it is extremely unstable, it is usually treated surgically. Operative treatment includes medial malleolus fixation, reduction of the inferior tibiofibular joint and screw fixation. When the fibula fractured is without shortening or dislocation, it is still controversial if the inferior tibiofibular joint needs fixation. This study aims to introduce a new method-Maisonneuve without transsyndesmotic fixation and analysis the follow-up result.
Pioneer species of Cyanobacteria in hot springs and their role to travertine formation: The case of Aedipsos hot springs, Euboea (Evia), Greece

Cyanobacteria are considered to be among the first microorganisms to settle in hot springs where they form a favourable environment for further biological establishment. Nevertheless, the exact pioneer species and how early they start participating in the biomineralisation processes remain unknown. The aim of the present study was twofold, that is, to identify the pioneer Cyanobacteria in hot springs (i.e. Aedipsos area, Greece) and to record their early biomineralisation processes. The in situ experimental approach included the setup of sterile glass and/or plexiglass slides in several locations to facilitate colonisation by Cyanobacteria, and removal of slides for study after 48–202 h. Synechococcales (37%) and Oscillatoriales (33%) were the dominant orders, followed by Chroococcales (15%) and Spirulinales (11%); whereas Chroococcidiopsidales (4%) was found only in a few sites. The order Nostocales was not observed at the early stages of colonisation although it was present in mature stages. Forty‐three species of Cyanobacteria were identified as pioneer microorganisms, with Spirulina subtilissima being the most frequently found. The most common pioneers were multicellular filamentous Cyanobacteria, that is, organisms with a large surface area able to form significant amounts of extracellular polymeric substances. Among the pioneers, thermophilic species of Cyanobacteria were typical such as Chroococcidiopsis thermalis, Chroococcus thermalis, Leptolyngbya thermalis, S. subtilissima and Symploca thermalis, as well as typical limestone substrate species such as Chroococcus lithophilus and Leptolyngbya laminosa. Temperature seems to affect biodiversity. Also, pioneers were found to contribute to the biomineralisation processes from their first appearance. In the studied samples, three biomineralisation processes were identified, that is, (i) calcification of cyanobacterial sheaths, (ii) trapping of carbonate crystals on a crystal retention lattice formed by extracellular polymeric substances and filaments and (iii) trapping and confinement of carbonate crystals around filamentous Cyanobacteria.
An UWB Hemispherical Vivaldi Array

We report the first conformal ultra-wide band (UWB) array on a doubly curved surface for wide angle electronic scanning. We use a quadrilateral mesh as the basis for systematically arraying UWB radiators on arbitrary surfaces. A prototype consisting of a 52 element, dual-polarized Vivaldi array arranged over a 181 mm diameter hemisphere is developed. The antennas and SMP connectors are 3D printed out of titanium to allow for simple fabrication and assembly. We derive the theoretical gain of a hemispherical array based on the antenna size and number of elements. The measured realized gain of the prototype array is within 2 dB of the theoretical value from 2-18 GHz and scan angles out to 120° from the z-axis. This field of view is twice that of a planar array with the same diameter in agreement with theory. This work provides a baseline performance for larger conformal arrays that have more uniform meshes. Furthermore, the basic concept can be extended to other UWB radiating elements.
The Influence Leader-Member Exchange and Work Culture on Elementary School Teacher Performance

Teacher performance has a huge influence on the achievement of education. This study aims to determine the effect of leader-member exchange and work culture on teacher performance in elementary schools throughout the Neglasari District, Tangerang City. The research method used a survey. Participants involved in the study were 110 teachers drawn from 24 schools in Neglasari District. Data were taken using a questionnaire. Data analysis technique using regression. The results of this study indicate that: (1) there is a direct positive influence of leader-member exchange on teacher performance, (2) there is a direct positive influence of work culture on teacher performance, and (3) there is a direct positive influence of leader-member exchange on work culture. So, it can be concluded that the quality of teacher performance is influenced by internal and external factors, such as work culture and leader-member exchange. The study results have implications for the work system in schools that must be built by school principals that are relevant to teacher competence.
ROBUST APPROACH FOR URBAN ROAD SURFACE EXTRACTION USING MOBILE LASER SCANNING 3D POINT CLOUDS

Abstract. Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches that do not mitigate problems caused by the presence of noise and outliers. In practice, however, laser scanning point clouds are not free from noise and outliers, and it is apparent that the presence of a very small portion of outliers and noise can produce unreliable and non-robust results. A road surface usually consists of three key parts: road pavement, curb and roadside way. This paper investigates the problem of road surface extraction in the presence of noise and outliers, and proposes a robust algorithm for road pavement, curb, road divider/islands, and roadside way extraction using MLS point clouds. The proposed algorithm employs robust statistical approaches to remove the consequences of the presence of noise and outliers. It consists of five sequential steps for road ground and non-ground surface separation, and road related components determination. Demonstration on two different MLS data sets shows that the new algorithm is efficient for road surface extraction and for classifying road pavement, curb, road divider/island and roadside way. The success can be rated in one experiment in this paper, where we extract curb points; the results achieve 97.28%, 100% and 0.986 of precision, recall and Matthews correlation coefficient, respectively.
Facilitating a Hybrid College-level Course Using Microblogging: A Case Study

LUO, TIAN, Ph.D., August 2014, Curriculum and Instruction, Instructional Technology Facilitating a Hybrid college-level Course Using Microblogging: A Case Study (290 pp.) Director of Dissertation: David R. Moore Social media has become an increasingly growing phenomenon that arouses mounting interest as well as heated discussion concerning its varied definitions and practices for academic use. Recently, a common type of social media, microblogging tools such as Twitter, has garnered researchers and educators' mounting attention due to its prevailing usage among the younger generation. This study seeks to both examine student learning in the Twitter-supported learning environments and to understand the potential factors affecting student perceptions and participation. The researcher incorporated three major Twitter-based instructional activities into a hybrid course, namely, Exploring Hashtags, Discussion Topics, and Live Chats. Twitter was employed as a backchannel to enhance classroom interaction during lectures and student presentations in face-to-face settings. The participants were 24 college-level Teacher Education program students enrolled in a technology course at a large Midwestern University. Major data sources included student posts on Twitter, end-of-the course survey, and in-depth semi-structured interviews. The findings of the study suggest that Twitter, serving as an instructional technology platform to facilitate student learning, was able to engage student in reflective and meaningful knowledge creation. Student participation was active and relevant, especially in guided environments. Student perception on different activities was overall
BULLDOZER: AN AUTOMATIC SELF-DRIVEN LARGE SCALE DTM EXTRACTION METHOD FROM DIGITAL SURFACE MODEL

Abstract. This study presents a Digital Terrain Model extraction method called Bulldozer. The only required input of Bulldozer is a Digital Surface Model generated from any sensors (usually optical or LIDAR) with any kind of software. After reviewing both the initial DrapCloth algorithm (Zhang et al., 2016) and its multi scale implementation (Leotta et al., 2019), some issues have been highlighted when extracting DTM from stereo satellite images such as the lost of ground adhesion under rising terrain areas, the appearance of sinks due to correlation issues when computing the DSM and finally the lack of scalability when processing large input data. Bulldozer has been developed to tackle all these issues and proposes a full automatic scalable pipeline composed of a pre-processing step to clean noisy DSMs by detecting and smoothing disturbed areas, a DTM extraction step based on a modified DrapCloth algorithm to stick to the ground under rising terrain and a post-processing step to smooth sharp sinks. The scalability has been solved using a tiling strategy and the definition of a stability margin that ensures identical results to those obtained if the whole DSM would have been processed at once in memory. As a result, Bulldozer outperforms its concurrent with respect to runtime execution while providing high quality DTMs over various types of landscapes.
Plantar pressure profiles of athletes with and without ankle sprains while walking barefoot and in sport shoes

Plantar pressures have been investigated as contributors to the etiology of ankle sprains. Footwear has not been explored as a potential factor influencing plantar pressures in participants with previous ankle sprains. The aim of this study was to compare walking barefoot and shod plantar pressure distributions between participants with a recent ankle sprain and uninjured controls. Seventeen physically active individuals with a lateral ankle sprain sustained within the past two years, and 20 healthy controls walked at a self-selected speed across a RS-Footscan® pressure plate. Personal sports shoes were assessed for mediolateral asymmetry at the forefoot and heel. Seven walking trials were performed barefoot and shod. Mediolateral plantar pressure ratios for (1) the hindfoot and (2) the forefoot for the barefoot and the shoe conditions were processed. Two-way repeated measure ANOVAs (group × condition) were calculated for each of the two dependent variables and stance duration. Post-hoc testing with Bonferroni corrections was conducted when significant effects were found. Twelve (32%) of the thirty-seven shoes had neutral design and/or wear, twenty-four (65%) had lateral and one (3%) had medial asymmetry. There were no differences between the injured and control groups for forefoot and hindfoot plantar pressure ratios while walking barefoot or in shoes. However, wearing shoes increased medial forefoot pressure for the injured group and lateral hindfoot pressures in the controls compared to barefoot walking. Plantar pressure profiles were similar in participants with ankle sprains and controls walking barefoot. Walking in well-habituated footwear caused different adaptive strategies in the two groups. Footwear affects plantar pressures while walking, and the influence of mediolateral asymmetric footwear on lateral ankle sprains and recurrence needs further exploration.
Clinical attachment loss in the use of adjunctive antimicrobial photodynamic therapy in Stages II-IV Grade C molar-incisor periodontitis: A systematic review and meta-analysis

This systematic review and meta-analysis aimed to assess the extent of clinical attachment loss (CAL) as a clinical parameter in the efficacy of antimicrobial photodynamic therapy (aPDT) in non-surgical management of Stages II-IV Grade C molar-incisor pattern periodontitis. This review protocol was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis statements and is registered in PROSPERO (CRD42022321211). An electronic and manual search was conducted for relevant articles comparing the efficacy of aPDT versus scaling and root planning (SRP) alone or with amoxicillin/metronidazole (AMX/MET) published up until December 2021. The mean CAL, probing depth reduction, and BOP with a 95% confidence interval were pooled and compared between the two groups with CAL < and > 7 mm using a random-effect model after 3 and 6 months. To assess the heterogeneity of the findings, the I2 test was applied, and publication bias was evaluated by visual examination of the funnel plot symmetry. Analysis of nine studies indicated a significant difference in clinical attachment gain in patients with CAL > 7 mm between the aPDT group and the SRP alone (mean difference = 0.92, 95% CI = 0.01-1.84, p = 0.05) and SRP + AMX/MET (mean difference = 0.91, 95% CI = −0.14-1.68, p = 0.02) control groups. However, this difference was not significant in patients with CAL < 7 mm. Despite the limitations of the included studies, aPDT can be suggested to improve clinical parameters in Grade C molar-incisor pattern periodontitis with CAL > 7 mm. However, its application in milder cases requires further investigation.
Electromagnetic Methods for Production of Aluminium Metal Matrix Composites

Aluminium metal matrix composites (AMC) are perspective materials for a wide range of applications in automotive, aerospace and other industries where material mechanical properties and weight ratio is crucial. In AMC manufacturing through metallurgical process the main obstacle for particle introduction into the melt is poor particle wettability and their tendency to form agglomerates due to van der Waals and interfacial forces. Most of currently used AMC manufacturing methods through metallurgical route are effective only for small quantities or are time consuming, even though metallurgical AMC production route would promise significant cost savings. In this study we propose a permanent magnet stirring technology developed by IP UL as tool for alternative realization of stir- and compo-casting methods for AMC production. First results of contactless stirred semi-solid aluminium alloys show effective alloy stirring in melt volume and intense surface deformations that can break the oxide layer and stir in the reinforcement material from the melt surface.
COVID-19 Effect on Everyday Mobility of Different Demographic Groups of People with Blindness and Visual Impairment

Introduction: The COVID-19 pandemic imposed some challenges on the mobility of people with blindness and visual impairment (BVI). Compliance with social distance epidemiological guidelines proved to be especially troublesome for those with BVI. Objective: The goal of this research was to determine the differences between specific demographic groups of people with BVI before and during the COVID-19 pandemic lockdown in everyday mobility, to identify the groups most affected. Methods: Forty-five people with BVI members of the Croatian Blind Union completed the questionnaire describing their pre-pandemic and lockdown independent everyday mobility activity frequencies on a 5-point Likert scale along with demographic questions in May 2020. Wilcoxon signed ranks test and paired samples t-test were used to test differences before and during the pandemic lockdown on average scale results for demographic groups. Results: A decline in independent everyday mobility between before and during the COVID-19 pandemic lockdown was shown for the total sample of people with BVI. When considering demographic groups both those with blindness and low vision, non-married and married, females, participants living in apartments, employed and unemployed participants, and participants living in a household with two members showed a statistically significant decline in independence during the pandemic. Males, retired participants, and participants older than 40, living alone in a house did not show a statistically significant decline. Conclusion: The decline in mobility functioning implies possible long-term effects of the pandemic lockdown on specific groups of people with BVI and additional support needed after the lockdown and the COVID-19 pandemic.
Recognition of Visual Arabic Scripting News Ticker From Broadcast Stream

News ticker recognition is a vital area of research due to its applications such as information analysis, opinion mining and language translation for media regulatory authorities. Without automated systems, manual anatomizing is difficult. In this paper, we focus on the automatic Arabic and Urdu news ticker recognition system. It mainly consists of ticker segmentation and text recognition to generate textual data for various online services. Our work investigates character-wise explicit segmentation and syntactical models with Kufi and Nastaleeq fonts. Various network models anticipate learning of deep representations by homogenizing the classes regardless of inter-symbol correlations and linguistic taxonomy. The proposed learning model incorporates fairness by maximizing the balance among sensitive features of characters in a unified manner. Furthermore, we demonstrate the efficiency of the proposed model by carrying out experiments using customized news tickers datasets with accurate character-level and component-level labeling. Moreover, our method is evaluated on a challenging Urdu Printed Text Images (UPTI) dataset that only provides ligature based annotations. The proposed method attains 98.36%, outperforms the current state of the art method. Ablation investigations show that our technique enhances the performance of character classes with low symbol frequencies.