Miracle-dz/UniFAR-FTUs huggingface dataset:
An investigation into road trees’ root systems through geostatistical analysis of GPR data

Street trees are a critical asset for the urban environment due to the variety of environmental and social benefits provided [1]. However, the conflicting coexistence of tree root systems with the built environment, especially with road infrastructure, frequently results in extensive damage, such as the uplifting and cracking of sidewalks and curbs, endangering pedestrians, cyclists, and road drivers’ safety.
Black hole complementarity with the generalized uncertainty principle in Gravity's Rainbow

When gravitation is combined with quantum theory, the Heisenberg uncertainty principle could be extended to the generalized uncertainty principle accompanying a minimal length. To see how the generalized uncertainty principle works in the context of black hole complementarity, we calculate the required energy to duplicate information for the Schwarzschild black hole. It shows that the duplication of information is not allowed and black hole complementarity is still valid even assuming the generalized uncertainty principle. On the other hand, the generalized uncertainty principle with the minimal length could lead to a modification of the conventional dispersion relation in light of Gravity's Rainbow, where the minimal length is also invariant as well as the speed of light. Revisiting the gedanken experiment, we show that the no-cloning theorem for black hole complementarity can be made valid in the regime of Gravity's Rainbow on a certain combination of parameters.
The Lorentz-Dirac and Dirac Equations

It is well known that the Classical theory of the electron reached the limits of its description at time intervals of the order of 10 − 23 secs , that is the Compton time. It is widely believed that below these time intervals Classical Electrodynamics doesn’t work and that a Quantum description is required. Using the Lorentz Dirac and the Dirac equations of the electron, we point out that in fact there is a convergence of the two descriptions at the Compton scale.
Risk Factors for Adjacent Segment Disease Following Anterior Cervical Discectomy and Fusion with Plate Fixation

Background: Adjacent segment disease (ASD) following anterior cervical discectomy and fusion with plating (ACDF-P) may yield a poor prognosis or reoperation. This review aimed to summarize risk factors for radiographic ASD (RASD) and clinical ASD (CASD) after ACDF-P. Methods: Six electronic databases were searched from inception to October 30, 2021. Four reviewers independently screened titles, abstracts, and full-text articles to identify relevant studies. Methodological quality of the included studies was evaluated. Meta-analyses for risk factors were conducted, if possible. Results: Sixteen cohort and 3 case-control studies (3,563 participants) were included. These studies showed low (n = 2), moderate (n = 9), and high (n = 8) risk of bias. One risk factor for RASD was pooled for 2 meta-analyses based on the follow-up period. Four different risk factors for CASD at ≥4 years were pooled for meta-analyses. Limited evidence showed that multi-level fusion, greater asymmetry in total or functional cross-sectional area of the cervical paraspinal muscle, and preoperative degeneration in a greater number of segments were associated with a higher RASD incidence <4 years after ACDF-P. In contrast, no significant risk factors were identified for CASD <4 years after ACDF-P. At ≥4 years after ACDF-P, limited evidence supported that both cephalad and caudal plate-to-disc distances of <5 mm were associated with a higher RASD incidence, and very limited evidence supported that developmental canal stenosis, preoperative RASD, unfused C5-C6 or C6-C7 adjacent segments, use of autogenous bone graft, and spondylosis-related ACDF-P were associated with a higher CASD incidence. Conclusions: Although several risk factors for RASD and CASD development after ACDF-P were identified, the supporting evidence was very limited to limited. Future prospective studies should extend the existing knowledge by more robustly identifying risk factors for RASD and CASD after ACDF-P to inform clinical practice. Level of Evidence: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Hydroxychloroquine reduces hypertension and soluble fms-like kinase-1 in a Nω-nitro-l-arginine methyl ester-induced preeclampsia rat model

Objective: Hydroxychloroquine, a drug used for malaria and autoimmune diseases reportedly has beneficial effects against preeclampsia in pregnant women with lupus. However, its mechanism against preeclampsia remains unclear. We investigated the effect of hydroxychloroquine on an Nω-nitro-l-arginine methyl ester-induced preeclampsia rat model. Methods: Pregnant Sprague–Dawley rats were divided into four groups based on treatment (administered on gestational days 7–18): control, Nω-nitro-l-arginine methyl ester, hydroxychloroquine, and Nω-nitro-l-arginine methyl ester plus hydroxychloroquine. All animals were sacrificed on gestational day 19. We assayed tube formation and determined reactive oxygen species levels using human umbilical vein endothelial cells. Results: Results showed that hydroxychloroquine significantly lowered mean systolic blood pressure (P < 0.05) in Nω-nitro-l-arginine methyl ester-treated rats. Hydroxychloroquine did not affect their fetal and placental weights. Hydroxychloroquine mitigated Nω-nitro-l-arginine methyl ester-associated changes in proteinuria (P < 0.05). It normalized plasma soluble fms-like kinase-1 (P < 0.05) and endothelin-1 (P < 0.01) levels. In the tube formation assay, hydroxychloroquine increased the total meshes area (P < 0.05) and mitigated Nω-nitro-l-arginine methyl ester–induced reactive oxygen species formation (P < 0.05) in human umbilical vein endothelial cells. Conclusion: We conclude that hydroxychloroquine alleviated hypertension, proteinuria, and normalized soluble fms-like kinase-1 and endothelin-1 levels in our preeclampsia model and that these changes may involve the restoration of endothelial dysfunction; thus, hydroxychloroquine could potentially be used for preventing preeclampsia, even in the absence of lupus.
Model predictive control of bus platoons in a circular route considering dispatching, holding and boarding limits

Public transport forms the backbone of the city's operation. Proper planning and investment of public transport can create additional jobs to revitalize and recover cities from covid-19. In this paper, we propose a combined dispatching-operation bus model predictive control strategy, where a rolling horizon mechanism is adopted to control the bus system in a real-time manner. Either a bus platoon or a single bus is allowed to be dispatched in each trip, and bus re-dispatching is captured in the system to realistically reflect the real-world. Also, the additional bus initial constraints allow control to be applied at any time when buses are either driving on the road or loading at the stop. Model complexity is investigated by solving the optimization problem under various prediction horizons, number of buses and bus stops. Furthermore, the comparison experiment with a high-frequency fixed dispatching method is performed on the Singapore bus line 179A developed in SUMO simulator to illustrate the effectiveness of the proposed method.
Region of Interest and Redundancy Problem in Migratory Birds Wild Life Surveillance

Wetlands or humid zones are one of the most vital areas where many species of birds that maintain the balance of ecological systems. Due to global warming and change climate, rare species are threatened with extinction. It is important to preserve track and monitor these species. Most wild life monitoring systems are expensive or sub-optimal in terms of performance, deployment and network overloading. In this paper, we explore the possibility of using image processing techniques to reduce the large amount of data transmitted in traditional audio/video streaming monitoring systems. We used the region of interest technique to convey only the occurrence of a moving object. Feature extraction and matching techniques are used to deal with the redundancy and counting problem. We believe that our results show the viability of employing and using these techniques to reduce the amount of data transmitted in wild life monitoring systems.
An Eco-Friendly Fight Against Thaumetopoea Pityocampa Infestations in Pine Forests Using Deep Learning on UAV Imagery

Thaumetopoea Pityocampa (TP), or Pine Processionary Moth, is one of the most destructive species in the Mediterranean, causing defoliation of pine forests. TP infestations have a considerable economic impact in primary sectors, particularly forestry. They also have a negative impact on human health, the environment, and society. TPs make silken, tent-like nests on the tops of pine trees. The detection and mapping of Thaumetopoea Pityocampa Tents (TPTs) in aerial imagery taken from Unmanned Aerial Vehicles (UAVs) flying over TP-infested pine forest zones are presented in this research. Deep learning-based image segmentation has been used to detect TPTs in the aerial imagery. The geolocations of the centroids of the detected TPTs are calculated and converted to spatiotemporal maps using navigation logs and the camera parameters of the UAV. These maps are a valuable resource for biologists, ecologists, and forestry agencies' pre- and post-management plans. The system's effectiveness has been demonstrated in the pine forests surrounding the Çakilarasi Mathematics Village, Saricakaya region of Eskişehir, Türkiye.
Characteristics of Human-Autonomy Teaming for Future Aerospace Operations

Two use cases in future aerospace operations were compared to better understand how an autonomous system could be perceived as a teammate. This comparison aims to identify the generalized characteristics and requirements of Human-Autonomy Teaming (HAT) in future single pilot operations and long-distance space operations. In many future operations, autonomous systems will have significant roles in complex decision-making tasks to support humans and to perform certain tasks without human direction. However, designing an autonomous system that acts as a teammate can be challenging for future concepts of operations that are not yet well-defined. Developing these systems requires careful human factors analysis to successfully integrate humans and autonomous systems in the work domain. A five-step systematic approach was developed to evaluate HAT in future aerospace operations: (1) eliciting knowledge, (2) extrapolating domain knowledge models, (3) defining requirements for HAT in future aerospace operations (Single Pilot Operations in commercial aviation, and long-distance human space exploration), (4) evaluating hypotheses and requirements, (5) synthesizing lessons learned and defining HAT characteristics and requirements. Emerging themes included design of autonomous system, interaction paradigms, communication and feedback modalities, teaming roles, and expected teammate characteristics and processes. Thirty-nine requirements were derived from comparison of the two case studies. The five-step approach can inform future HAT system development from a human factors perspective.
MuscleNET: Smart Predictive Analysis for Muscular Activity Using Wearable Sensors

Doing weightlifting training at home has become more popular during the pandemic. Unfortunately, exercising without professional help can lead to dangerous injuries such as muscle tearing. It is possible to create a smart system with machine learning to overcome muscle injuries and suggest an appropriate training program. The use of suitable algorithms enables us to develop programs that can perform predictions based on sEMG (Surface Electromyography) signals. In this study, sEMG signals are collected from the skin surface and features are extracted to be used in deep learning networks. A wearable hardware collects sEMG signals and transfers them to our mobile application via Bluetooth. The mobile application transfers data to the cloud to make predictions based on sEMG signals. We developed MuscleNET for training monitoring, injury prediction/detection, and training quality prediction. Initial measurements indicate that MuscleNET can be used effectively for training quality prediction and real time training monitoring.
Comparison of Deep Reinforcement Learning Methods for Safe and Efficient Autonomous Vehicles at Pedestrian Crossings

These past years, the domain of Connected and Autonomous Vehicles (CAV) has been extremely flourishing, with fully autonomous self-driving cars being an active research area. The most challenging aspect of the driving task is the interaction between the driver agent and the environment, especially to handle conflicts with the other road users. The most vulnerable ones are the pedestrians. To ensure better safety at crosswalks, this paper focuses on planning a vehicle trajectory, to pass without risks, while cooperating with the pedestrian. According to recent works, Deep Reinforcement Learning can be used to efficiently control the vehicle in a complex environment. This paper assesses both widely held continuous action space approaches for controlling the vehicle: Deep Deterministic Policy Gradient and Proximal Policy Optimization. Both approaches are used to allow the vehicle to safely and efficiently interact with a pedestrian willing to cross the road at the crosswalk. The main contributions of the paper are the design and parameterization of these approaches for this single conflict use-case, the feasibility study of both approaches, and the detailed comparison showing their benefits and limitations.
3D-MBNet: Intention Based Multimodal Vehicle Trajectory Prediction with 3D Social Convolution

Predicting vehicle trajectories in traffic scenes is an important issue for autonomous driving and advanced driver assistance systems. The spatial and temporal interactions between the predicted vehicle and its surrounding vehicles are commonly used for trajectory prediction. However, most existing methods usually deal with the two interactions separately. In this paper, we propose a multimodal vehicle trajectory prediction model, which includes a 3D social convolution module to jointly model spatial and temporal interactions. Furthermore, to make interpretable multimodal predictions, we define several types of driving intentions (namely lane changes, acceleration, deceleration and driving with uniform velocity) and introduce multi-branch decoders to decode different intentions. The two-stage training strategy is introduced to guarantee the recall rates of the intentions with long-tailed distributions. Our model outperforms several state-of-the-art methods on the NGSIM and highD benchmark datasets. In addition, the ablation experiments show that our method can improve the prediction accuracy of each specified modality.
Route Planning by Merging Local Edges into Domains with LSTM

With the development of positioning technology, massive trajectories are recorded. How to effectively use hidden features in massive trajectories for route planning has attracted widespread attention. Based on the distribution characteristics of the trajectories and the modeling ability of the RNN for variable long-term sequences, we summarize the trajectory distribution through the deep learning model. However, due to the excessive length of the original trajectory sequence, the direct use of the original trajectory to train the RNN causes large search space. In this paper, the sequences in the original trajectory are merged according to the road name, which effectively reduces the search space and speeds up inference. At the same time, the source and destination direction, grid information, attributes of road, and other information are added to improve the inference effect. Finally, the possible routes between the source and destination (SD pair) are output by beam search, which can be ranked according to the distance and travel time to achieve multi-route recommendations. Our experiment based on real travel trajectories shows that our approach outperforms the existing approaches.
Cooperative Bidirectional Mixed-Traffic Overtaking

Safe overtaking, especially in a bidirectional mixed-traffic setting, remains a key challenge for Connected Autonomous Vehicles (CAVs). The presence of human-driven vehicles (HDVs), behavior unpredictability, and blind spots resulting from sensor occlusion make this a challenging control problem. To overcome these difficulties, we propose a cooperative communication-based approach that utilizes the information shared between CAVs to reduce the effects of sensor occlusion while benefiting from the local velocity prediction based on past tracking data. Our control framework aims to perform overtaking maneuvers with the objective of maximizing velocity while prioritizing safety and passenger comfort. Our method is also capable of reactively adjusting its plan to dynamic changes in the environment. The performance of the proposed approach is verified using realistic traffic simulations.
Spectral observations of X Persei: Connection between Hα and X-ray emission

We present spectroscopic observations of the Be/X-ray binary X Per obtained during the period 1999–2018. Using new and published data, we found that during “disc-rise” the expansion velocity of the circumstellar disc is 0.4–0.7 km s−1. Our results suggest that the disc radius in recent decades show evidence of resonant truncation of the disc by resonances 10:1, 3:1, and 2:1, while the maximum disc size is larger than the Roche lobe of the primary and smaller than the closest approach of the neutron star. We find correlation between equivalent width of Hα emission line (Wα) and the X-ray flux, which is visible when 15 Å < Wα ≤ 40 Å. The correlation is probably due to wind Roche lobe overflow.
Linear vs. Non-Linear Embedding Methods in Recommendation Systems

Predicting customer interest in items is very crucial in direct marketing as it can potentially boost sales. Data mining techniques are developed to predict which items a particular user might be interested in based on their purchase history or explicit feedback in form of ratings or comments. Recently, non-linear and linear methods have been developed for this purpose. In this study, we applied Neighborhood based Collaborative Filtering (CF), Matrix Factorization (MF), Singular Value Decomposition (SVD), Neural Graph CF (NGCF) and Light Graph Convolutional Network (LightGCN) on explicit user product rating data which is acquired from the online gaming and mobile entertainment platform called HADI. We compared the results of node embedding methods in terms of Precision @ k, Recall@k and NDCG@k values. SVD and LightGCN showed the best test performance and SVD was significantly superior to LightGCN in terms of training speed. To further increase predictive performance of SVD, we have applied classification with Logistic Regression and Deep Random Forest on user and item embeddings created by the SVD.
AN APPLICATION OF THE STRING AVERAGING METHOD TO ONE-SIDED BEST SIMULTANEOUS APPROXIMATION

For (equation omitted) be an ordered (t)-tuple of numbers in｛1,2, …,｝and let Tt be chosen from a finite composition of orthogonal projections (equation omitted) acting on the normed linear space (X) to closed convex subset respectively. In this paper, we study the convergence of the sequence (equation omitted) where (equation omitted).
A*-Guided Scenario-based Stochastic Model Predictive Control for an Autonomous Vehicle in Simulated Pedestrian Environments

An autonomous vehicle must behave reasonably in a complex dynamic environment, especially crowded with pedestrians, in order to reach its goal robustly and safely. Our approach, considering the uncertainties induced by the pedestrian behavior as well as by localizing the vehicle itself, is introduced in this paper. The interacting multiple model Kalman filter (IMM-KF) is applied to detect the most likely behavior of surrounding pedestrians and the generation of their trajectories is executed. The A* algorithm is utilized to find a rough path with sudden changes in path orientation. A set of Dubins curves are used to smooth out the calculated path and a reference trajectory is finally calculated. A scenario-based stochastic model predictive control (SSMPC), considering all of the uncertainties and collision constraints, calculates a reasonable longitudinal acceleration by solving the quadratic programming of the trajectory around the current pose. Furthermore, a pedestrian obstacle reciprocal collision avoidance (PORCA) is used for modelling a simulated, more realistic interaction of the pedestrians to the vehicle. We evaluate the approach in MATLAB for both unstructured and semi-structured environments while considering the real-time capability of the guidance layer within 1 second.
A Wideband Sensor for Complex Permittivity of Carasau Bread Dough Based on a Double Ridge Waveguide

This work aims at the preliminary design of a microwave devices to perform indirect measurements of the complex permittivity of the Carasau bread, a traditional food product from Sardinia (Italy). To this purpose, the complex dielectric permittivity of doughs is measured with open-ended coaxial probe in the range 0.5-8.5 GHz and the spectra fitted to a third-order Cole-Cole model. The acquired data are then used to numerically study the microwave signal propagation in the designed a double ridge waveguide and investigate different sample configuration in order to obtain a wideband transmission performance.
Non-magnetic semiconductor spin transistor

We propose a spin transistor using only non-magnetic materials that exploits the characteristics of bulk inversion asymmetry (BIA) in (110) symmetric quantum wells. We show that extremely large spin splittings due to BIA are possible in (110) InAs/GaSb/AlSb heterostructures, which together with the enhanced spin decay times in (110) quantum wells demonstrates the potential for exploitation of BIA eﬀects in semiconductor spintronics devices. Spin injection and detection is achieved using spin-dependent resonant interband tunneling and spin transistor action is realized through control of the electron spin lifetime in an InAs lateral transport channel using an applied electric ﬁeld (Rashba eﬀect). This device may also be used as a spin valve, or a magnetic ﬁeld sensor. The electronic structure and spin relaxation times for the spin transistor proposed here are calculated using a nonperturbative 14-band k · p nanostructure model.