Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decentralized and Coordinated Routing Algorithm for Connected and Autonomous Vehicles

Aiming for better mobility and more efficient utilization of transportation networks, emergent connected and autonomous vehicle (CAV) technologies, and the resulting communication capabilities can produce more coordinated and efficient routing behavior. Current routing strategies either rely on a centralized control system which can fail in scaling, or employ decentralized schemes that yield sub-optimal coordination and accordingly poor system performance. This paper presents a Decentralized Collaborative Time-dependent Shortest Path Algorithm (Dec-CTDSP) with which the CAVs optimize their routes according to the communicated mobility messages from the other CAVs within their connected cluster. These messages are assumed to carry information regarding the vehicles’ location, speed, and preferred path to their destination. We analyzed the impacts of this optimization scheme under various levels of CAV market penetration and communication radius. The results of this study reveal (1) Up to 40% improvement in mean system travel time and speed; (2) Up to 45% increase in travel time and speed prediction reliability; (3) A strong correlation between mean system travel time and network usage distribution; and (4) significant improvements in network utilization as a result of Dec-CTDSP. The performance of Dec-CTDSP, in terms of runtime, convergence, and mobility improvements, is further benchmarked against other random and Dijkstra-based algorithms. The findings of this work will help steer further research on the implementation of coordinated and decentralized multiagent routing optimization in the context of connected and autonomous vehicles.
Forecasting drought probabilities for streams in the northeastern United States

Maximum likelihood logistic regression (MLLR) models for the northeastern United States forecast drought probability estimates for water flowing in rivers and streams using methods previously identified and developed. Streamflow data from winter months are used to estimate chances of hydrological drought during summer months. Daily streamflow data collected from 1,143 streamgages from April 1, 1877, through October 31, 2018, are used to provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February. This allows estimates of outcomes from 5 to 11 months ahead of their occurrence. Models specific to the northeastern United States were inves tigated and updated. The MLLR models of drought stream flow probabilities utilize the explanatory power of temporally linked water flows. Models with strong drought streamflow probability correct-classification rates were produced for streams throughout the northeastern United States. A test of northeastern United States drought streamflow probability predictions found that overall correct-classification rates for drought streamflow probabilities in the northeastern United States exceeded 97 percent when predicting July 2019 drought probability using February 2019 monthly mean streamflow data. Using hydrological drought probability estimates in a water-management context informs understandings of possible future streamflow drought conditions in the northeastern United States, provides warnings of potential future drought conditions, and aids water-management decision making and responses to changing circumstances.
Robust Reconstruction Method Based on QCQP Optimization for Multichannel SAR With Closely Singular Sampling

The conventional reconstruction algorithm allows for unambiguous recovery of the Doppler spectrum of multichannel synthetic aperture radar (SAR) system in azimuth with an accurately known channel response matrix. However, the available knowledge of the actual channel response matrix is imprecise due to channel errors. In this case, the SAR image will suffer from severe error-induced azimuth ambiguities if reconstructed by the conventional reconstruction algorithm, especially for the case of closely singular sampling. To this end, a novel method based on a quadratically constrained quadratic program (QCQP) optimization is proposed to increase the robustness to the channel errors. Accordingly, the error-induced azimuth ambiguities can be greatly suppressed by the proposed method. Furthermore, performance comparisons of the conventional reconstruction algorithm and the proposed method via numerical simulation are presented.
Effect of Tetragonula laeviceps propolis from Thailand on periodontal ligament fibroblasts

Periodontal diseases are highly prevalent and can affect up to 90% of the world population. They have attracted more interest in recent year, since it is becoming evident that such infection can influence systemic health in many ways e.g. heart disease (Beck et al., 2005), stroke (Hozomi et al., 2012) and pneumonia in the elderly (Sharma et al., 2011). It is a chronic infection caused by bacterial infection of the periodontium, resulting in degeneration of tissue. This disease is characterized by an inflammatory reaction in gingival tissue and subsequently destroys periodontal structures including cementum, periodontal ligament and alveolar bone. An important aspect of periodontal therapies is to slow down or to arrest the progression of the disease by scaling and root planning of the tooth. Medications for example tetracycline, macrolides, quinolone, metronidazole in combination with tetracycline may also be used, and sometimes surgery may be needed. Therapeutic methods may involve tissue engineering to regenerate the alveolar bone, cementum, periodontal ligament and finally prevent recurrence of the disease. Periodontal ligament cells play an important role in regeneration of the periodontium and of the bone, since they possess
Use of the REML/BLUP methodology for the selection of sweet orange genotypes

Abstract: The objective of this work was to select superior sweet orange (Citrus sinensis) genotypes with higher yield potential based on data from eight harvests, using the residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology. The experiment was carried out from 2002 to 2008 and in 2010 in the municipality of Rio Branco, in the state of Acre, Brazil. Analyzes of deviance were performed to test the significance of the components of variance according to the random effects of the used model, and parameters were estimated from individual genotypic and phenotypic variances. A selection intensity of 20% was adopted regarding genotypic selection, i.e., only the best 11 of the 55 genotypes tested were selected. The estimates of the genetic parameters show the existence of genetic variability and the selection potential of the studied sweet orange genotypes. The genotypic correlation between harvests is of low magnitude, except for the variable average fruit mass, and, as a reflex, there is a change in the ordering of the genotypes. Genotypes 5, 48, 19, 14, and 47 stand out as being the most productive, and, therefore, are the most suitable for selection purposes. Genotypes 14 and 47 show superior performance for the character set evaluated.
Digital Cartographic Generalization in Spatial Databases: application issues in Power Grids CAD tools

This work presents the results of applying several digital cartographic generalization techniques to improve the performance of an Electrical Power Grids Computer-Aided-Design application. The performance increase is attained by adjusting the level of detail of the grid topology being visualized in the CAD application. This adjustment takes place at the database level using a multi-scale architecture and the available spatial extensions of Geographic Information Systems databases. The data volume is minimized to ﬁt the exact requirements of the scale being used in the visualization so that no processing time is wasted on representing irrelevant elements. Results show that up to a 90 % of the data can be skipped and thus, a 84 % of the time required to render the visualization can be saved on average, enabling the CAD application to render it in real-time. Furthermore, the optical legibility of the grid visualization is enhanced as a consequence of applying cartographic generalization.
High-Speed Synchronous Reluctance Machines: Materials Selection and Performance Boundaries

This article presents a comprehensive comparative design exercise of synchronous reluctance (SyR) machines considering different soft magnetic materials and a wide range of speeds. First, a general design methodology able to consider all the consequences of selecting different materials is presented. In fact, magnetic nonlinearities, rotor structural limitations, and the rise of both stator and rotor iron losses are all considered. The adopted design approach allows achieving optimal stator and rotor geometries balancing all these competitive multiphysics aspects and keeping constant the cooling system capability. Both silicon–iron (SiFe) and cobalt–iron (CoFe) alloys with optimized magnetic and mechanical performance are examined to assess the maximum capabilities achievable with an SyR machine technology. The adoption of CoFe alloys leads to machines that outperform the SiFe counterparts up to a certain speed, above which, machines with SiFe provide better performance. Indeed, in the lower speed range, the effect of the higher saturation flux density of the CoFe materials is dominant, while for higher design speeds, their higher iron losses and lower yield strength, with respect to the SiFe ones, make the latter more convenient. All the design considerations are finally validated by comparing the predicted performance with the experimental test results on a 6.5-kW, 80-kr/min SyR machine prototype.
Centralized Robust Multi-Sensor Chandrasekhar-Type Recursive Least-Squares Wiener Filter in Linear Discrete-Time Stochastic Systems with Uncertain Parameters

— In the centralized robust multi-sensor recursive least-square (RLS) Wiener filtering algorithm, the number of recursive equations increases compared to that of the centralized multi-sensor RLS Wiener filter in linear discrete-time stationary stochastic systems with uncertain parameters. Due to the increase in the number of recursive Riccati-type algebraic equations, the accumulation of round-off errors is not negligible. The round-off errors cause unstable numerical characteristics of the filter, especially for the small variance of the observation noise. To reduce the round-off errors - as the first attempt in the research field of centralized robust multi-sensor estimation - this paper designs the Chandrasekhar-type centralized robust multi-sensor RLS Wiener filter, which updates the filter gains recursively. To verify the effectiveness of the proposed filter, a numerical simulation example is demonstrated and its estimation accuracy is compared with the centralized robust multi-sensor RLS Wiener filter and the centralized multi-sensor RLS-Wiener filter. The obtained results show that the proposed filter exhibits better stability.
Recommended System for Enhancing Tag Popularity in a Question Answering Community
through Splay-net Techniques

Collaborative filtering filters information by using the recommendations of peer participants. The long tail problem states users with higher points obtain a high reputation compared to less scored users. In popular community question answering websites, like stack exchange network sites, users with unanswered or ignored questions for a long time get a tumbleweed badge without considering their past history. This deteriorates their further contribution to the website. Mostly new or low-reputation people ask the tumbleweed questions. The popularity of the tags follows a long tail theory. The focus of this research work is to design a recommendation system that prevents participants from tumbleweed badge with tag suggestion method to add new or non-popular tags to the existing popular tag list. The splay-net has a self-balancing graph which brings the recently accessed item to the top of the tree. In this paper, we use the splay-net technique to represent users’ reputation along with their tags.
Generating airshower images for the VERITAS telescopes with conditional Generative Adversarial Network

VERITAS (Very Energetic Radiation Imaging Telescope Array System) is the current-generation array comprising four 12-meter optical ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs). Its primary goal is to indirectly observe gamma-ray emissions from the most violent astrophysical sources in the universe. Recent advancements in Machine Learning (ML) have sparked interest in utilizing neural networks (NNs) to directly infer properties from IACT images. However, the current training data for these NNs is generated through computationally expensive Monte Carlo (MC) simulation methods. This study presents a simulation method that employs conditional Generative Adversarial Networks (cGANs) to synthesize additional VERITAS data to facilitate training future NNs. In this test-of-concept study, we condition the GANs on five classes of simulated camera images consisting of circular muon showers and gamma-ray shower images in the first, second, third, and fourth quadrants of the camera. Our results demonstrate that by casting training data as time series, cGANs can 1) replicate shower morphologies based on the input class vectors and 2) generalize additional signals through interpolation in both the class and latent spaces. Leveraging GPUs strength, our method can synthesize novel signals at an impressive speed, generating over $10^6$ shower events in less than a minute.
Effectiveness of water adaptation responses in reducing climate related risks: A meta review

co-authors/ contributors/ collaborators Holly M. Berman; Angelica Katharina Casparina Brackel; Martina Angela Caretta, Calynn Dowler; Valeria Fanghella; Sharlene Liane Gomes; Benjamin Jerome Gray; Greeshma Hegde; Jonathan Lautze; Ashwina Mahanti; Mamta Mehar; Megan Mills-Novoa; Sanchari Mukhopadhyay; Lakshmikantha NR; Prajjwal Kumar Panday; Jagadish Parajuli; Ritu Priya; E.B. Uday Bhaskar Reddy; Pamela Rittelmeyer; Cydney Kate Seigerman; Rinan Shah; Gitta Shrestha; Balsher Singh Sidhu; Aprajita Singh; Jaishri Srinivasan; Brock Ternes; William Rigoberto Delgado Thompson; Shuchi Kirit Vora; Yashoda Yashoda
On-Line English Learning in The Perception of Twelfth Grade Students at SMA Negeri 3 Palu

The study discusses students’ perception over on-line English Learning from the twelfth grade students at SMA Negeri 3 Palu. This study uses qualitative research approach with online survey method through Kobbo Collect for collecting the data. 86 twelfth grade students were chosen as the sample from 431 population by using the convenience sampling technique. Two English teachers were involved in this study as secondary data.The data was collected using a 25 items close-ended questionnaire and 25 questions open-ended statement for teachers interview question items divided into 6 variables,The general perception on on-line learning, Students’ achievement, Teachers’ Competence, Learning Media and Tools, Parental Support and Financial. The findings of the study were suggested that on-line English learning based on students’ perception generally still considered far from expected. On-line learning causes a decrease on students’ English grades. The burdensome of homework and project from the English teacher in on-line English learning do not accompanied with good material and media that used by the English teacher. The teacher implies their bad performance due to the fact that there was no training session regarding on-line learning before. The lack of proper electronic devices and powerless internet from the network provider also had both teacher and students. Parents were still lack of understanding regarding the on-line learning process that cause interference during the learning session. Although on-line learning using digital media but somehow, students still have to pay for English books, including a way to provide a good internet connection to successfully conducting on-line learning.
Instantaneous Momentum-Based Control of Floating Base Systems

In the last two decades a growing number of robotic applications such as autonomous drones, wheeled robots and industrial manipulators started to be employed in several human environments. However, these machines often possess limited locomotion and/or manipulation capabilities, thus reducing the number of achievable tasks and increasing the complexity of robot-environment interaction. Augmenting robots locomotion and manipulation abilities is a fundamental research topic, with a view to enhance robots participation in complex tasks involving safe interaction and cooperation with humans. To this purpose, humanoid robots, aerial manipulators and the novel design of flying humanoid robots are among the most promising platforms researchers are studying in the attempt to remove the existing technological barriers. These robots are often modeled as floating base systems, and have lost the assumption – typical of fixed base robots – of having one link always attached to the ground. From the robot control side, contact forces regulation revealed to be fundamental for the execution of interaction tasks. Contact forces can be influenced by directly controlling the robot’s momentum rate of change, and this fact gives rise to several momentum-based control strategies. Nevertheless, effective design of force and torque controllers still remains a complex challenge. The variability of sensor load during interaction, the inaccuracy of the force/torque sensing technology and the inherent nonlinearities of robot models are only a few complexities impairing efficient robot force control. This research project focuses on the design of balancing and flight controllers for floating base robots interacting with the surrounding environment. More specifically, the research is built upon the state-of-the-art of momentum-based controllers and applied to three robotic platforms: the humanoid robot iCub, the aerial manipulator OTHex and the jet-powered humanoid robot iRonCub. The project enforces the existing literature with both theoretical and experimental results, aimed at achieving high robot performances and improved stability and robustness, in presence of different physical robot-environment interactions.
Robust Adaptive Trajectory Tracking Control Based on Sliding Mode of Electrical Wheeled Mobile Robot

In this paper, a robust adaptive trajectory tracking controller is proposed for an electrical wheeled mobile robot in the presence of dynamic disturbances. This method, based on the nonlinear dynamic model of the robot and its actuators, guarantees the stability and the convergence of the closed-loop system. Moreover, the developed controller ensures the robustness of the system against the bounded dynamic disturbances, the smoothness of the computing voltage against the chattering phenomenon, and the optimal convergence of the velocity and posture errors.  The Lyapunov theory is used to analyze the full stability of the control scheme. The simulation results further illustrate the effectiveness of the developed strategy.
Routing-Oblivious Network-Wide Measurements

The recent introduction of SDN allows deploying new centralized network algorithms that dramatically improve network operations. In such algorithms, the centralized controller obtains a network-wide view by merging measurement data from Network Measurement Points (NMPs). A fundamental challenge is that several NMPs may count the same packet, reducing the accuracy of the measurement. Existing solutions circumvent this problem by assuming that each packet traverses a single NMP or that the routing is fixed and known. This work suggests novel algorithms for three fundamental network-wide measurement problems without making any assumptions on the topology and routing and without modifying the underlying traffic. Specifically, this work introduces two algorithms for estimating the number of (distinct) packets or byte volume in the measurement, estimating per-flow packet and byte counts, and finding the heavy hitter flows. Our work includes formal accuracy guarantees and an extensive evaluation consisting of the realistic fat-tree topology and three real network traces. Our evaluation shows that our algorithms outperform existing works and provide accurate measurements within reasonable space parameters.
A Conceptual Framework for an Information Behavior Model Based on the Collaboration Perspective between User and System for Information Retrieval

This research aimed (1) to study and analyze the ability of current information retrieval (IR) systems based on views of information behavior (IB), and (2) to propose a conceptual framework for an IB model based on the collaboration between the system and user, with the intent of developing an IR system that can apply intelligent techniques to enhance system efficiency. The methods in this study consisted of (1) document analysis which included studying the characteristics and efficiencies of the current IR systems and studying the IB models in the digital environment, and (2) implementation of the Delphi technique through an in-depth interview method with experts. The research results were presented in three main parts. First, the IB model was categorized into eight stages, different from traditional IB, in the digital environment, which can correspond to all behaviors and be applied to with an IR system. Second, insufficient functions and log file storage hinder the system from effectively understanding and accommodating user behavior in the digital environment. Last, the proposed conceptual framework illustrated that there are stages that can add intelligent techniques to the IR system based on the collaboration perspective between the user and system to boost the users’ cognitive ability and make the IR system more user-friendly. Importantly, the conceptual framework for the IB model based on the collaboration perspective between the user and system for IR assisted the ability of information systems to learn, recognize, and comprehend human IB according to individual characteristics, leading to enhancement of interaction between the system and users.
TEICORPO: A Conversion Tool for Spoken Language Transcription with a Pivot File in TEI

CORLI is a consortium of Huma-Num, the French national infrastructure dedicated to the technical support and promotion of digital humanities. The goal of CORLI is to promote and provide tools and information for good and efficient research practices in corpus linguistics and especially spoken language corpora. Because of the time required to collect and transcribe spoken language resources, their number is limited and thus corpora need to be interoperable and reusable in order to improve research on various themes (phonology, prosody, interaction, syntax, textometry…). To help researchers reach this goal, CORLI has designed a set of tools: TEICORPO to assist in the conversion and use of spoken language corpora, and TEIMETA for metadata purposes. TEICORPO is based on the principle of an underlying common format, namely the TEI as described in its specification for spoken language use (ISO/TEI 24624:2016). This tool enables the conversion of transcriptions created with alignment software such as CLAN, Transcriber, Praat or ELAN as well as common file formats (csv, xlsx, txt or docx) and the TEI format, which plays the role of a pivot format, without losing information. Backward conversion is possible in many cases, with limitations inherent to the destination target format. TEICORPO can run the Treetagger Part of Speech tagger and the Stanford CoreNLP tools on TEI files and can export the resulting files to textometric tools such as TXM, Le Trameur, or Iramuteq, making it a tool dedicated to spoken language corpora editing as well as to various research purposes.