Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic Flow Prediction for UTM Application: A Deep Learning Approach

Over the past few years, the research community has focused greatly on predicting air traffic flows, yielding remarkable outcomes. We found that existing literature in the field mainly covers prediction of air traffic flows for conventional aircraft. However, there is limited research about prediction of air traffic flows for Uncrewed Aircraft Traffic Management (UTM). This research study proposes a deep learning-based approach to predict air traffic congestion in the context of UTM over a period of three minutes. The use of the model aims to address congestion considering air traffic uncertainties instead of addressing the conventional issues of trajectory prediction or conflict detection and resolution. Our model also considers the influence of recreational users who fly UAVs at random times, during the execution of the above essential missions. Further, the effects of airspace structure configurations like static No-Fly Zones (NFZ), airfields with variable availability for drone flights, recreational areas, emergency UTM operation and environmental factors such as weather conditions have also been studied. The proposed model shows better performance compared to other approaches such as the Shallow neural networks and regression models.
Safety Co-Pilot: A System for Autonomous Vehicle to Make Decision Safer and Smarter

The Autonomous Vehicle (AV) has become a hot research spot recently. Following the trend of auto driving (AD) development, making AV safer is evolving as a new direction of research. The decision system in AD can be regarded as the brain of the vehicle, which ensures safety and comfort in driving behaviors. However, technologies of decision systems, including algorithms and hardware, remain incomplete and could affect the safety of the AV. Hence, it is necessary to focus on the decision safety of AD systems before AVs are in widespread application. In this paper, the existing techniques for vehicle decision system safety real-time assessment and response are briefly reviewed first. Based on the current research, we propose the “safety co-pilot (SCP)” intelligent decision safety system to achieve better supervision of AD systems. To comply with the development tendency towards the V2X of AVs, the cooperation of the system with online cloud learning is fully expounded, and the outlook and challenges of this safety system are deeply analyzed. The proposed SCP system is supposed to not only increase the real-time driving safety of AV but through communicate with the decision system constantly to further improve the intelligence and reliability of the AD system.
Eliciting User Needs of Intelligent Transportation Systems in the Context of Autonomous Driving: An Application of Activity Theory

Properly elicit the user needs is of vital importance for the development of Intelligent Transportation System (ITS). This study proposed a theoretical framework for user needs elicitation based on the activity theory, which includes a three-level hierarchical structure to decompose the travel activity and a contradiction theory to explain the user need evolution. The proposed framework was applied to reconstruct the user needs in Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT). A total of 518 user needs were matched to the hierarchical model and the possible evolutions of user needs corresponding to different levels of driving automation were captured by the secondary, tertiary, and quaternary contradictions. Such a framework can help to elicit user needs for the next generation of ITS dominated by autonomous and connected vehicle technologies.
Knowledge Graph based Question Answering System for Remote School Education

An automated question-answering system aims to deliver answers to the questions based on an input text. Such systems are based on text processing and require extended processing time. Knowledge graphs for question answering have proven to be an efficient approach. The knowledge graphs can be applied in teaching-learning to make more efficient remote education. Developing a knowledge graph from unstructured text, processing and evaluating knowledge points, extracting knowledge entities, and integrating them are all focused. This article proposes a Question answering model incorporating a Knowledge graph and the pre-trained BERT(Bidirectional Encoder Representation from Transformers) for learning purposes. This model helps in assisting learners of all ages by providing immediate feedback. Hence it can be highly beneficial to students to obtain access to and continue remote learning.
The Vector Component Analyzer: A New Way to Characterize Distortions of Modulated Signals in High-Frequency Active Devices

Wireless telecommunication technology is evolving fast. Driven by an insatiable demand for higher data rates, the modulated signals that are being transmitted and received over the air and through space are growing in bandwidth and use ever higher carrier frequencies. The 6G visionaries are setting goals of 30 GHz of modulation bandwidth at a carrier frequency of 300 GHz and achieving a mind-boggling data rate of 11 Gb/s. At the heart of this technological evolution of the wireless telecommunications industry are billions of high-frequency active components: amplifiers, mixers, and frequency converters. These active components are necessary for the amplification and frequency conversion of the modulated signals. These components are not perfect, and the modulated signal deteriorates as it passes through the active component semiconductor core.
Assessment of implants installed in grafted maxillary sinuses after prosthetic rehabilitation

Purpose : To compare the stability and bone loss of implants installed in the maxillary sinus after sinus lift using Bio-oss  Small and Large, before functional loading (T1) and after it (T2). Methods : Ten patients received bone graft in the maxillary sinus in two different granulations: Bio-oss  Small and Large, with one granulation per sinus. After 8 months, the implants were installed. Prosthesis manufacturing and installation occurred after a six-month. Out of 10 patients, six (13 implants) were selected, with an average age of 53.4 years. The stability was measured with a resonance frequency and all implants installed showed high values in both groups. Results : The comparison between times showed a statistical difference with the Bio-oss  Large particles, in which T1 (64.21±7.41) was lower than T2 (69.96±4.95; P=0.003). However, the comparison between the different particle dimensions in the same period didn’t show a statistical difference. Regarding ma rginal bone loss, there wasn’t statistical difference between the particle dimensions. The re wasn’t correlation between stability and marginal bone loss and between the different biomaterial particle dimensions. Conclusion : The implants installed in grafted maxillary sinus with both Bio-Oss  particle dimensions showed similar behavior, allowing implant stability and functional loading. dimensiones de las partículas en el mismo período no mostró una diferencia estadística. En cuanto a la pérdida de hueso marginal, no hubo diferencia estadística entre las dimensiones de las partículas. No hubo correlación entre la estabilidad y la pérdida de hueso marginal y entre las diferentes dimensiones de las partículas del biomaterial. Conclusión : Los implantes instalados en seno maxilar injertado con ambas dimensiones de partículas Bio-Oss  mostraron un comportamiento similar, permitiendo la estabilidad del implante y la carga funcional. Palabras clave: Injerto óseo; Elevación del piso del seno maxilar; Implante dentales.
Domain-Compatible Synthetic Data Generation for Infrequent Objects Detection

The recent advances in generative models have resulted in massive progress in 1 the quality of the generated images to the point that in many cases they cannot be 2 easily distinguished from real images. Despite this quality improvement, using AI 3 generated images for the purpose of training robust down-steam computer vision 4 models for real-world applications has proven to be very challenging. The AI 5 generated images usually lack the required diversity and scene complexity that is 6 crucial for many real-world applications, specifically the ones with safety concerns. 7 The difficulty of this challenge grows significantly when the underlying application 8 involves detection of some specific objects that appear with critically low frequency 9 in the available real datasets. This paper studies a new approach for generating 10 diverse, complex and domain-compatible synthetic images for detecting infrequent 11 objects by employing a diffusion-based generative model pretrained on a generic 12 dataset. More specifically, the impact of using the generated synthetic images with 13 the proposed approach in solving the real world problem of detecting emergency 14 vehicles in road scenes is investigated. Furthermore, the challenges of generating 15 synthetic datasets with the proposed approach will be thoroughly discussed. 16
Digital Commons at Oberlin Digital Commons at Oberlin Initial Coin Offerings: The Role of Subjective Information in Initial Coin Offerings: The Role of Subjective Information in Whitepapers Whitepapers

Initial coin offerings (ICOs) are an unregulated form of financing that raises funds by issuing crypto tokens using blockchain technology. In this paper, I explore the relationship between the level of mispricing at ICOs and the subjectivity in whitepapers, which are documents released before ICOs that provide details on the features of the tokens. The subjective information is examined by indices of confidence, tone, and readability. The model in this paper analyzes a sample of 258 ICOs that ended during January to October 2018. I find that the confidence score of a whitepaper is negatively associated with the daily excess return of a token, and thus there is an inverse relationship between the confident whitepapers and the level of underpricing. Further analysis indicates that this relationship is more pronounced in countries with no crypto anti-money laundering laws. This study provides insights into the role of whitepapers in the emerging ICO markets. suggestions.
D EEP R EINFORCEMENT L EARNING FOR F URNITURE L AYOUT S IMULATION IN I NDOOR G RAPHICS S CENES

In the industrial interior design process, professional designers plan the size and position of furniture in a room to achieve a satisfactory design for selling. In this paper, we explore the interior graphics scenes design task as a Markov decision process (MDP), which is solved by deep reinforcement learning. The goal is to produce an accurate layout for the furniture in the indoor graphics scenes simulation. In particular, we first formulate the furniture layout task as a MDP problem by defining the state, action, and reward function. We then design the simulated environment and deploy a reinforcement learning agent that interacts with the environment to learn the optimal layout for the MDP. We conduct our experiments on a large-scale real-world interior layout dataset that contains industrial designs from professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts as compared with the state-of-art model.
Systemic Drugs Used to Treat American Tegumentary Leishmaniasis

American Tegumentary Leishmaniasis (ATL) is a complex and heterogeneous disease caused by many Leishmania species. ATL is a neglected disease and treatment is still nowadays a great challenge. Despite American Tegumentary Leishmaniasis (ATL) is a complex and heterogeneous disease caused by several Leishmania species. ATL has a diverse clinical picture including cutaneous, mucosal, disseminated and diffuse leishmaniasis. ATL is a neglected disease and therefore, treatment is still nowadays a great challenge. Despite the high number of medications described in the literature, only a few drugs has proven different degrees of efficacy in the several manifestations of ATL. Unfortunately, most drugs are used by parenteral route, with important systemic toxicity. Additionally, ATL therapy relies still in monotherapy with increasing Leishmania resistance and therapeutic failure. This mini review aim to discuss the pros and cons of the main systemic drugs used in the control of ATL, in a scenario where the development of safer and more efficient drugs, along with the production of robust and well-designed trials with combined therapy is of utmost importance.
in 365 between biomass burning and secondary formation in in Line

We are submitting a revised version of the manuscript (No.: acp-2022-321), entitled: “Measurement Report: Investigation of pHand particle size-dependent chemical and optical properties of water-soluble organic carbon: implications for its sources and aging processes”. We have carefully addressed all the comments provided by the reviewers. The details can be found in our enclosed “Response to Reviewers”. In the attachment, an item-by-item response to the comments of the reviewers is given below. All revisions are highlighted in blue color in the main text of the revised manuscript.
Along-strike spatial variation in shallow slow earthquake activity in Hyuga-nada, southwest

23 Hyuga-nada, off the Pacific coast of Kyushu along the Nankai Trough in southwest Japan, 24 is one of the most active slow earthquake regions around Japan. We estimated the energies of tremors 25 and moments of very low frequency earthquakes (VLFEs) in Hyuga-nada using data from a permanent 26 onshore broadband network and temporary ocean bottom seismometer observations. The energies and 27 moments of these slow earthquakes have a similar along-strike variation and are generally larger south 28 of the subducted Kyushu-Palau Ridge than near the top of the ridge. This spatial variation is also 29 related to the characteristics of slow earthquake migration. The along-strike migration speed was faster 30 at initiation in the south, where the moments of slow earthquakes are larger. The along-strike migration 31 of slow earthquake episodes can be characterized by deceleration with a parabolic pattern. Assuming 32 a constant patch size of slow earthquakes, we estimated the stress drops of VLFEs and found that the 33 stress drop in the south of the subducted ridge was approximately four times larger than that near the 34 top of the subducted ridge. This stress drop difference between adjacent regions can cause parabolic 35 migration. According to our observations and physical models, the difference in stress drop could be 36 one of the key factors for the spatial variation in slow earthquake activity associated with the subducted 37 ridge. 38 43
Closure of Constraints for Plane Gravity Waves

The metric for gravitational plane waves has very high symmetry (two spacelike commuting Killing vectors). For this high symmetry, a simple renormalization of the lapse function is found which allows the constraint algebra for canonical quantum gravity to close; also, the vector constraint has the correct form to generate spatial diffeomorphisms. A measure is constructed which respects the reality conditions, but does not yet respect the invariances of the theory.
Designing for effective online Teacher Professional Development (oTPD) in building education by supporting beliefs, communities, collaboration, and development

This article reports the initial research findings from a large project on online professional development and upskilling of in-service building teachers’ digital competencies. Based on the results from a preceding literature review and the pilot delivery of the course, three key factors for the design of effective online teacher professional development in building education — beliefs, communities and collaboration, and teaching development — are exemplified and discussed. The article concludes that the pilot was successful in developing the participants’ technical skills and competencies to introduce technology in building education; however, the pilot did not significantly influence the participants’ beliefs nor made them change practice within the duration of the course.
Comparison of Computational Notebook Platforms for Interactive Visual Analytics: Case Study of Andromeda Implementations

Existing notebook platforms have different capabilities for supporting visual analytics use. It is not clear which platform to choose for implementing visual analytics notebooks. In this work, we investigated the problem using Andromeda, an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python-based Jupyter Notebook, 2) JavaScript-based Observable Notebook, and 3) Jupyter Notebook embedding both Python (data science use) and JavaScript (visual analytics use). We also made comparisons for all the notebook platforms via a case study based on metrics such as programming difficulty, notebook organization, interactive performance, and UI design choice. Furthermore, guidelines are provided for data scientists to choose one notebook platform for implementing their visual analytics notebooks in various situations. Laying the groundwork for future developers, advice is also given on architecting better notebook platforms. Comparison of Computational Notebook Platforms for Interactive Visual Analytics: Case Study of Andromeda Implementations
341. Genomic prediction in multi-breed Irish beef cattle population using a breed origin of alleles model

The objective of this study was to evaluate genomic prediction using a breed origin of alleles (BOA) model for a multi-breed Irish beef cattle population. Our BOA model uses one matrix with allele counts for each breed to predict breed-specific marker effects, enabling to use crossbred and purebred animals in the reference population. Pedigree-based best linear unbiased prediction (PBLUP), a genomic model assuming a homogeneous population (SNP-BLUP) and BOA model were compared. Accuracy was estimated as the correlation between breeding values and corrected phenotype divided by the square root of heritability. The accuracy of predictions using BOA increased by 82% and 42% for crossbred and purebred animals, respectively, compared with PBLUP, and improvements of BOA over SNP-BLUP were of 6% for crossbred animals.
Organochlorine Pesticides Residues in Water, Sediment, and Various Species of Fish from Komadugu River Basin, Yobe State, Nigeria

Water, sediment and fish ( Claria sanguillaris, Tilapia zilli, Synodontisbudgetti, and Heterotisniloticus ) samples from Komadugu River basin of Yobe State, Nigeria were collected on a seasonal basis (rainy, dry seasons, and Harmattan period) for the determination of the levels of organochlorine pesticide residues (OCPs). Extraction and clean-up of water, sediment, and fish samples for OCPs was carried out using standard procedures. The concentrations of all the OCPs in water, sediment and fish samples were determined using GC/MS SHIMADZU (Agilent 7890A). The total concentration of OCPs in water samples from the study area ranged from 6.70E-01 µg/L to 2.55E+00 µg/L. The concentrations of OCPs in the different species of fish samples ranged from2.00E-02 to 1.42E+00 mg/kg. Alderin shows the highest concentration (1.42E+00 mg/kg) during the rainy season . The total OCPs (7.26E+00mg/kg) was obtained during the rainy season, while the lowest concentration of 5.68E+00 mg/kg was recorded during the dry season . The concentrations of OCPs residues in sediment samples studied ranged from 1.03E+00 to 1.20E+01 mg/kg, with dieldrin showing the highest total concentration of 1.20E+01 mg/kg during the rainy season. Dieldrin was the most abundant pesticide residues in the studied water, sediment and fish samples. The highest levels of all the studied pesticides were observed during the rainy season when compared to Harmattan period and the dry season. This study revealed that pesticide residues levels in the fish samples studied were above the maximum residue limits (MRLs) and acceptable dietary intake (ADI) and could be an important process of transferring pesticides to humans.
330. Including local genomic breed proportions in genomic predictions for crossbred

Genomic evaluations for crossbred animals introduce challenges not encountered for purebred animals. We propose to include effects of local breed proportions (LBP) based on detected breed of origin of alleles in a genomic model for crossbred dairy cows. We tested different segment length for modelling the LBP: 1, 20, 100 and 500 markers, for genomic prediction based on data of first lactation protein yields of 5,176 Danish crossbred dairy cows with contribution from three dairy breeds. The models that included LBP had slightly higher cross-validation predictive ability, 0.390 for all segment lengths compared with 0.385 for a model without LBP effects. The LBP models also had less dispersion bias. Inclusion of LBP in genomic predictions can therefore improve predictions even when considering the local effects in segments of hundreds of markers.
INVESTIGATING CONTRIBUTIONS OF GASES, METEOROLOGICAL PARAMETERS, AND AEROSOLS TOWARDS TROPOSPHERIC OZONE VARIABILITIES OVER MEGACITY LAHORE (PAKISTAN)

We investigated contributions of some tropospheric gases, meteorological parameters, and aerosol burden measured as aerosol optical depth (AOD) towards Tropospheric Ozone (TropoO 3 ) variabilities over Lahore by developing a stepwise multiple linear regression model. The tropospheric gases include; Methane (CH 4 ), Carbon Monoxide (CO), Carbon Dioxide (CO 2 ), and Sulphur Dioxide (SO 2 ), whereas the meteorological parameters include; cloud fraction (CLF), outgoing long-wave radiation (OLR), relative humidity (RH), temperature (Temp), and surface wind-speed (WS). Data products of these parameters have been retrieved from various satellite sensors during October 2004 – September 2015. Time series distributions of both observed and modeled TropoO 3 concentrations show similar increasing trends except for few months. Moreover, monthly-means of these observed and modeled TropoO 3 concentrations indicate similar decreasing trends with maximum during spring and summer (monsoon) seasons. Based upon weighted contributions of standardized regression coefficients, modeled TropoO 3 concentration was found dependent upon OLR[0.458], WS[0.262], CLF[0.256] TropoSO 2 [–0.097], TropoCO[–0.231], and RH[–0.320] out of all parameters used in this study. Modeled TropoO 3 concentrations were validated against an independent source of TropoO 3 concentrations retrieved from original subtracted product of OMI-MLS over Lahore and the results were found in good agreement. The study is unique and would be helpful for further research investigations.