Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Evaluation of Service Composition in Commercial Multiclouds Using Timed Colored Petri Nets

The increasing demand for Web services encourages commercial cloud service providers to publish their own services with various functional and nonfunctional capabilities in different cloud platforms. The aggregation of atomic services from multiple service repositories is the main idea of the service composition concept in multiclouds. The cloud Web service composition is a suitable way for satisfying users’ complex requests by integrating services from different clouds in order to create a new value-added composite service. The time required to serve a composite service by a multicloud environment is an important parameter, which depends on different factors, ranging from the service composition and selection algorithm to the number of atomic services published in the clouds. In this paper, a model based on timed colored Petri nets (TCPNs) is proposed to evaluate the service composition in multicloud environments while minimizing the number of clouds involved in serving a composite service request. The proposed TCPN graphically models the process of request submission, composite service analysis, service selection, and service provisioning in a multicloud environment. It also assesses both mean response time of the environment and probability of dropping composite requests. The verification of the accuracy of the proposed model is done by comparing the results obtained from the TCPN model, in two different scenarios, with the results from the CloudSim framework. These results confirm that our proposed TCPN model can appropriately model the system and evaluate its performance more efficiently than the CloudSim.
Trajectory Planning of Automated Vehicles Using Real-Time Map Updates

The development of connected and automated vehicles (CAVs) presents a great opportunity to extend the current range of vehicle vision, by gathering information outside of its sensors. Two main sources could be aggregated for this extended perception; vehicles making use of vehicle-to-vehicle communication (V2V), and infrastructure using vehicle-to-infrastructure communication (V2I). In this paper, we focus on the infrastructure side and make the case for low-latency obstacle mapping using V2I communication. A map management framework is proposed, which allows vehicles to broadcast and subscribe to traffic information-related messages using the Message Queuing Telemetry Transport (MQTT) protocol. This framework makes use of our novel candidate/employed map (C/EM) model for the real-time updating of obstacles broadcast by individual vehicles. This solution has been implemented and tested using a scenario that contains real and simulated CAVs tasked with doing lane change and braking maneuvers. As a result, the simulated vehicle can optimize its trajectory planning based on information which could not be observed by its sensor suite but is instead received from the presented map-management module, while remaining capable of performing the maneuvers in an automated manner.
Isolation and Selection of Antagonistic Bacteria against Cercospora arachidicola Causing Brown Spot on Peanut

This study was conducted with the aim to isolation bacterial strains capable of antagonism to Cercospora arachidicola (Passalora arachidicola) causing brown spot in peanut. From 3 soil samples collected in the rhizosphere of peanut grown in Tay Ninh province, Vietnam, 10 bacterial strains were found having antagonistic action with Cercospora arachidicola. After 5 days of testing, the antagonistic efficiency of all isolates ranged from 33.33±1.28 to 60.183±0.927%. Studying the antagonistic mechanism showed that 5/10 isolates produced siderophore, 5/10 isolates were capable of decomposing chitin, 7/10 isolates capable of decomposing cellulase and 8/10 isolates proteolytic. Basing on the results of 16S rRNA gene sequence combined with morphological and biochemical characteristics, TN-TB 4, TN-TB 6 and TN-TB 12 were identified as Bacillus, representing the genera Bacillus amyloliquefaciens, Bacillus pasterurii, and Bacillus velezensis, respectively. These strains of antagonistic bacteria have the potential to produce probiotics.
Moving Vehicle Detection with Real Time Speed Estimation Using Deep Learning Techniques

Moving Vehicle Detection with Real-Time Speed Estimation and Number Plate Detection using OpenCV and YOLO is a system that can be used to automatically detect vehicles in a video stream, estimate their speed in real-time, and detect their number plates using computer vision techniques. The system uses the YOLO algorithm to detect and localize vehicles in the video frames. Once the vehicles are detected, their positions are tracked across multiple frames using object tracking algorithms such as Kalman filters or MOSSE. This allows the system to estimate the speed of the vehicles by measuring the distance they travel between consecutive frames and dividing by the time interval. If number plate detection is required, the system uses OCR algorithms to detect the characters on the number plates of the vehicles. This information can be used to identify and track specific vehicles, or to trigger an alert if a vehicle's number plate is associated with a specific event or situation. The system is typically implemented using OpenCV, an open-source computer vision library, and can run on a variety of hardware platforms, including desktop computers, embedded devices, or cloud servers. It can be used in a variety of applications, such as traffic monitoring, parking management, or law enforcement, to improve the efficiency and accuracy of vehicle detection, speed estimation, and number plate recognition. In this study, we offer a model for speed estimates and vehicle identification that uses yolo and OpenCV. The green box indicates speeds of 40 KM per hour or less, while the red box indicates speeds of 40 kilometers per hour or greater. Both the vehicle's speed and license plate will be determined by our model.
Positionnement multidimensionnel d'un tableau à 3 voies

Nous proposons une methode nouvelle de positionnement multidimensionnel permettant de representer graphiquement un tableau a 3 voies ou les trois voies jouent des roles symetriques et ou la donnee est exprimee en termes de difference ou de ressemblance. Nous presentons un exemple de donnees reelles illustrant la methode proposee.
Impact of Gut Recolonization on Liver Regeneration: Hepatic Matrisome Gene Expression after Partial Hepatectomy in Mice

The hepatic matrisome is involved in the remodeling phase of liver regeneration. As the gut microbiota has been implicated in liver regeneration, we investigated its role in liver regeneration focusing on gene expression of the hepatic matrisome after partial hepatectomy (PHx) in germ-free (GF) mice, and in GF mice reconstituted with normal gut microbiota (XGF). Liver mass restoration, hepatocyte proliferation, and immune response were assessed following 70% PHx. Hepatic matrisome and collagen gene expression were also analyzed. Reduced liver weight/body weight ratio, mitotic count, and hepatocyte proliferative index at 72 h post PHx in GF mice were preceded by reduced expression of cytokine receptor genes Tnfrsf1a and Il6ra, and Hgf gene at 3 h post PHx. In XGF mice, these indices were significantly higher than in GF mice, and similar to that of control mice, indicating normal liver regeneration. Differentially expressed genes (DEGs) of the matrisome were lower in GF compared to XGF mice at both 3 h and 72 h post PHx. GF mice also demonstrated lower collagen expression, with significantly lower expression of Col1a1, Col1a2, Col5a1, and Col6a2 compared to WT mice at 72 h post PHx. In conclusion, enhanced liver regeneration and matrisome expression in XGF mice suggests that interaction of the gut microbiota and matrisome may play a significant role in the regulation of hepatic remodeling during the regenerative process.
Impact of income inequality on carbon-intensive extractivism

Abstract In recent decades, lower emissions have been driven by structural changes such as the shift from manufacturing to the service sector and investment in energy efficiency. However, many developing countries extract fossil fuels from the Earth’s surface, to export raw materials such as coal, oil, and natural gas in the global market. “Extractivism” is a mode of economic growth currently practiced by many developing countries. This way of economic growth can influence the income distribution and composition of activities that contribute to environmental degradation. Therefore, rent-seeking on fossil fuel export can lead to income inequality and a carbon-intensive economy. The relationship between carbon intensity and fossil fuel rent, which is the main concern of this paper, was first studied by Friedrichs and Inderwildi (2013) using the concept of the carbon curse. This paper follows similar model to carbon curse theory; however, it presents a new approach by examining the augmented the carbon curse theory through the lens of income inequality. Accordingly, this paper examines how extractivism affects the relationship between income distribution and carbon intensity for 137 countries from 1990 to 2014. Our findings show that countries that are highly dependent on oil rents have a higher degree of unequal income distribution and carbon-intensive developmental pathways. Additionally, there exists a unidirectional causality relationship among oil rents, income inequality, and carbon intensity. Finally, the short-run effect of oil fuel rents on income inequality typically continues in the long run but the effects changes for carbon intensity.
Predictive Analytics: How to Improve Availability of Manufacturing Equipment in Automotive Firms

In the current production scenario, asset management and performance are necessities. Predictive and prescriptive maintenance permits various industries to analyze historical data in real time for the purpose of optimization of industrial operations, such as production, manufacturing, supplantation, etc., to increase productivity and cumulative outcome. The data-driven Industry 4.0 paradigm provides various competitive advantages affecting productivity, quality, and key performance indicators (KPIs). It considers three essential indicators of availability, quality, and performance. Overall equipment efficiency has become the target KPI for most manufacturing companies. This article presents the unique condition-monitoring-based predictive maintenance framework incorporated into the modern world to create a machine-learning-based predictive maintenance approach for automotive industries. We also provide insights into the various methods utilized for data acquisition for a condition-based predictive maintenance framework. The proposed framework has been validated by collecting the raw data from the water pump machine through sensors to preprocess and analyze the performance indicators. The equipment's remaining useful lifetime was calculated based on the data points acquired in real time by calculating the adjacent variation. The developed dashboard has allowed the visible monitoring of all possible anomalies and the remaining useful life of equipment while the machine runs in real time.
NIH/3T3 Fibroblasts Selectively Activate T Cells Specific for Posttranslationally Modified Collagen Type II

It has been shown that synovial fibroblasts (SF) play a key role in the initiation of inflammation and joint destruction, leading to arthritis progression. Fibroblasts may express major histocompatibility complex class II region (MHCII) molecules, and thus, they could be able to process and present antigens to immunocompetent cells. Here we examine whether different types of fibroblasts (synovial, dermal, and thymic murine fibroblasts, destructive LS48 fibroblasts, and noninvasive NIH/3T3 fibroblasts) may be involved in the initiation of rheumatoid arthritis (RA) pathogenesis and can process and present type II collagen (COL2)—an autoantigen associated with RA. Using a panel of MHCII/Aq-restricted T-cell hybridoma lines that specifically recognize an immunodominant COL2 epitope (COL2259–273), we found that NIH/3T3 fibroblasts activate several T-cell clones that recognize the posttranslationally glycosylated or hydroxylated COL2259–273 epitope. The HCQ.3 hybridoma, which is specific for the glycosylated immunodominant COL2 epitope 259–273 (Gal264), showed the strongest response. Interestingly, NIH/3T3 cells, but not destructive LS48 fibroblasts, synovial, dermal, or thymic fibroblasts, were able to stimulate the HCQ.3 hybridoma and other COL2-specific T-cell hybridomas. Our experiments revealed that NIH/3T3 fibroblasts are able to activate COL2-specific T-cell hybridomas even in the absence of COL2 or a posttranslationally modified COL2 peptide. The mechanism of this unusual activation is contact-dependent and involves the T-cell receptor (TCR) complex.
Rheological master curves for modified asphalt binders

Modified bitumen enhances the performance properties of asphalt mixtures. A number of studies in the last decade have evaluated the performance properties of modified binders at different service temperatures. It is however possible to evaluate some modified binder time-temperature characteristics and performance properties through their rheological master curves. In this research, styrene butadiene styrene, ethylene vinyl acetate, polyphosphoric acid and crumb rubber were used to modify a PG58-22 base binder. Rheological master curves for |G*| and phase angles of these binders were generated using data from the dynamic shear rheometerand bending beam rheometerat temperatures ranging from -30℃to about 88℃. The rheological interpretation of the results indicated improved high and low temperature properties and reduced temperature susceptibility for most of the modified binders. A comparison of the modified binders was done based on the visual inspection of master curves for different temperature ranges.
Comparison of Biological and Genetic Characteristics between Two Most Common Broad-Leaved Weeds in Paddy Fields: Ammannia arenaria and A. multiflora (Lythraceae)

Simple Summary Ammannia arenaria and A. multifloras, the most common broad-leaved weeds in rice paddy fields in China, are morphologically similar at the seedling stage. However, their degree of damage to rice may vary. Furthermore, the sensitivity of two species to the constantly emerging new herbicides is also unknown. This study conducted field investigations, indoor biological experiments, and chloroplast genome construction and analysis to clarify the differences in biological characteristics, herbicide sensitivity, and chloroplast genetics between A. arenaria and A. multifloras. Our research results may provide theoretical basis for weed occurrence prediction, selection of herbicides, and Ammannia classification and distinction. Furthermore, the results provided valuable biological information on cp genomes of Ammannia that will be useful to identify and classify Ammannia, and study their phylogenetic relationships and evolution. Abstract Ammannia arenaria and A. multifloras, morphologically similar at the seedling stage, are the most common broad-leaved weeds in paddy fields. Our study showed that A. arenaria occupied more space than A. multifloras when competing with rice. However, A. multifloras germination has lower temperature adaptability. No difference in sensitivity to common herbicides between two Ammannia species was observed. Chloroplast (cp) genomes could be conducive to clarify their genetic relationship. The complete cp genome sequences of A. arenaria (158,401 bp) and A. multiflora (157,900 bp) were assembled for the first time. In A. arenaria, there were 91 simple sequence repeats, 115 long repeats, and 86 protein-encoding genes, one, sixteen, and thirty more than those in A. multiflora. Inverted repeats regions expansion and contraction and the phylogenetic tree based on cp genomes demonstrated the closely relationship between the two species. However, in A. arenaria, 20 single nucleotide polymorphisms in the CDS region were detected compared to A. multiflora, which can be used to distinguish the two species. Moreover, there was one unique gene, infA, only in A. arenaria. This study provides reliable molecular resources for future research focusing on the infrageneric taxa identification, phylogenetic resolution, population structure, and biodiversity of Ammannia species.
Forward Kinematics Algorithm in Dual Quaternion Space Based on Denavit-Hartenberg Convention

: Forward kinematics is fundamental to robot design, control, and simulation. Different forward kinematics algorithms have been developed to deal with the complex geometry of a robot. This paper presents a robot forward kinematics algorithm in dual quaternion space. The presented method uses Denavit-Hartenberg (DH) convention for uniform definition of successive rotational and translational transformations in joints along the robot’s kinematic chain. This research aims to utilize the advantages of dual quaternions and DH convection for forward kinematics computation and make the algorithm, which is compact, intuitive, numerically robust, and computationally efficient as it uses the minimal number of parameters required for the computation, suitable for implementation in ROS and similar software. The algorithm is verified on the 6DoF industrial robot RL15, with the symbolic equations and numerical simulation presented.
Optimal Execution via Multi-Objective Multi-Armed Bandits (Student Abstract)

When trying to liquidate a large quantity of a particular stock, the price of that stock is likely to be affected by trades, thus leading to a reduced expected return if we were to sell the entire quantity at once. This leads to the problem of optimal execution, where the aim is to split the sell order into several smaller sell orders over the course of a period of time, to optimally balance stock price with market risk. This problem can be defined in terms of difference equations. Here, we show how we can reformulate this as a multi-objective problem, which we solve with a novel multi-armed bandit algorithm.
On A Characterization Of Cm-Homomorphisms

Terms and notation not defined here can be found in Adu, [1] and Clifford and Preston, [4]. A topological space X is a Magill space (Adu, [2]) if it is T^ and compositions of closed relations on X are closed. Such a space was referred to by Magill and Yamamuro [6] as a C-spare. It will be assumed that a Magill space is first countable and has more than one point. The symbol M[X] denotes the semigroup, under composition, of all closed binary relations on a Magill space X. Let X and Y be Magill spaces, A CM-homomorphism from M[X] into M[Y] is one which is non-constant, preserves unions and carries symmetric relations onto symmetric relations. This notion was introduced by A.H. Clifford and D.D. Miller [3] and put in topological setting by K.D. Magill and S. Yamamuro [6], We denote by H0MCM(M[X],M[Y]) the set of all CMhomomorphisms from M[X] into M[Y]. In this paper, our main result is that if 0 : M[X] —• M[Y] is a union preserving homomorphism from M[X] into M[Y], then the requirement that 0 be symmetry preserving is equivalent to the apparently weaker condition that both tfi(0) and 0(XxX) be symmetric, where 0 is the empty relation.
Using Scottish School inspection documentation as a primary source for research dissertations

Documentary research in education can often be overlooked as an approach to exploring policy and practice issues, particularly for dissertation research projects. However, a range of documents can offer valuable data. In particular, school inspection documentation can be an illuminating and accessible source. Building on my recent research and teaching experiences, this paper provides a guide to utilising school inspection documentation from the Scottish context as primary sources for research dissertations in education and other disciplines. The intention is to support students and researchers undertaking research projects to navigate these sources confidently and to enable them to develop insightful analyses and discussions.
A Low-Cost Measurement Methodology for LiDAR Receiver Integrated Circuits

This paper presents a test methodology to facilitate the measuring processes of LiDAR receiver ICs by avoiding the inherent walk error issue. In a typical LiDAR system, a costly laser diode driver emits narrow light pulses with fast rising edges, and the reflected pulses from targets enter an optical detector followed by an analog front-end (AFE) circuit. Then, the received signals pass through the cascaded amplifiers down to the time-to-digital converter (TDC) that can estimate the detection range. However, this relatively long signal journey leads to the significant decline of rising-edge slopes and the output pulse spreading, thus producing inherent walk errors in LiDAR receiver ICs. Compensation methods requiring complex algorithms and extra chip area have frequently been exploited to lessen the walk errors. In this paper, however, a simpler and lower-cost methodology is proposed to test LiDAR receiver ICs by employing a high-speed buffer and variable delay cells right before the TDC. With these circuits, both START and STOP pulses show very similar pulse shapes, thus effectively avoiding the walk error issue. Additionally, the time interval between two pulses is easily determined by varying the number of the delay cells. Test chips of the proposed receiver IC implemented in a 180-nm CMOS process successfully demonstrate easier and more accurate measurement results.
Detecting Laryngeal Cancer Lesions From Endoscopy Images Using Deep Ensemble Model

To improve the chances of survival for a patient with laryngeal cancer, early detection is crucial. Currently, the standard diagnostic method involves an endoscopic examination of the larynx, followed by a biopsy and histological analysis by an oncologist, which can be subject to variability due to subjective evaluation. Therefore, there is a need for a faster and more accurate detection system that can replace the current manual examination. Recent research has shown that Deep Learning technology can assist in identifying laryngeal cancer, including precancerous and cancerous tumors, from endoscopic pictures. However, endoscopic image processing is a challenging task due to the highly dynamic nature of the endoscopic video, spectrum fluctuations, and numerous image interferences. To address this challenge, a Deep Ensemble Learning approach using convolutional neural networks (CNNs) and an effective image segmentation technique has been proposed. The suggested model has an overall accuracy of 98.12%.