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We introduce a novel type of neutron interference mirrors based on a chemically homogeneous BxC (x>4) matrix with internal high precision 10B/11B isotope modulation. Simulations predict that these mirrors exhibit very high neutron reflectivities for a small number of bilayer periods. This is experimentally confirmed by neutron reflectivity measurements of mirrors synthesized by ion-assisted magnetron sputter deposition. For example, a 120 nm thick multilayer consisting of just 20 bilayer periods of 10B5.7C/11B5.7C, with a periodicity of 61.5 {\AA}, exhibits a neutron reflectivity of 13 percent at an incidence angle of 4.7 deg for neutrons with a wavelength of 4.825 {\AA}. This is attributed to a high scattering length density contrast between the layers with an interface width <5 {\AA}. Structural analyses by X-ray diffraction, X-ray reflectivity, and transmission electron microscopy demonstrate that the 10B5.7C/11B5.7C multilayer mirrors are composed of amorphous, chemically homogenous B5.7C, without any internal chemical modulation. The data show that 10BxC/11BxC multilayer mirrors have the potential for higher neutron reflectivities at higher q-values using fewer and thinner layers, compared to todays state-of-the-art chemically modulated neutron multilayer mirrors.

Polarized neutron scattering is a very important analysis technique for studies of magnetism, spintronics, and high-sensitivity measurements, among others, offering invaluable information. Yet, the efficiency of such experiments rely on the performance of the polarizing neutron optics to provide high reflectivity and polarization. Presently, state-of-the-art polarizers like Fe/Si supermirrors are not able to polarize neutrons at low scattering angles in a monochromatic beam or able to polarize neutrons with a variety of wavelengths as for a non-monochromatic beam. To overcome this limitation, it is suggested to use Co/Ti multilayers on Ti substrates owing to their favorable scattering length density characteristics. It is shown that this approach enables a wavelength bandwidth several times larger than achievable with state-of-the-art materials on Si or glass substrates. Consequently, enabling the possibility of polarizing neutrons across an extended wavelength range, including those with very high wavelengths.

The concept of scattering length density tuning for improved polarization is investigated for Fe/11B4CTi multilayers and compared to the commonly used Fe/Si system in polarizing multilayer neutron optics. X-ray and neutron reflectivity, magnetization, and neutron polarization have been measured on such multilayers, highlighting differences from conventional Fe/Si multilayers. The multilayer systems were deposited with 25 {\AA} period thickness, a layer thickness ratio of 0.35 and 20 periods, using ion-assisted DC magnetron sputtering. Replacing Si with 11B4CTi for these multilayers showed an increase in reflectivity due to a reduction in interface width. By tuning the ratio between 11B4C and Ti in the non-magnetic layers, a broad range of scattering length density values was achieved, facilitating scattering length density contrast matching between layers for spin-down neutrons, thereby enhancing polarization. These findings demonstrate the potential of Fe/11B4CTi multilayers as a promising option for polarizing neutron optics and highlight the concept of scattering length density tuning in a large range using 11B4CTi.

State-of-the-art Ni/Ti supermirror neutron optics have limited reflected intensity and a restricted neutron energy range due to the interface width. Incorporating low-neutron-absorbing 11B4C enhances reflectivity and allows for thinner layers to be deposited, with which more efficient supermirrors with higher m-values can be realized. However, incorporating 11B4C reduces the optical contrast, limiting the attainable reflectivity at low scattering vectors, making this approach infeasible. This study explores various approaches to optimize the material design of 11B4C-containing Ni/Ti supermirrors to maintain high reflectivity at low scattering vectors and achieve low interface widths at large scattering vectors. The scattering length density contrast versus interface width is investigated for multilayer periods of 30 {\AA}, 48 {\AA}, and 84 {\AA}, for designs involving pure Ni/Ti multilayers, multilayers with 11B4C co-deposited in Ni and Ti layers, multilayers with 11B4C co-deposited only in Ni layers, and multilayers with 11B4C as thin interlayers between Ni and Ti layers. Our results suggest that a depth-graded hybrid material design by incorporating 11B4C inside the Ni and Ti layers, below approximately 26 {\AA}, and introducing 1.5 {\AA} 11B4C interlayers between the thicker Ni and Ti layers can achieve a higher reflectivity than state-of-the-art Ni/Ti multilayers over the entire scattering vector range.

The utilization of polarized neutrons is of great importance in scientific disciplines spanning materials science, physics, biology, and chemistry. Polarization analysis offers insights into otherwise unattainable sample information such as magnetic domains and structures, protein crystallography, composition, orientation, ion-diffusion mechanisms, and relative location of molecules in multicomponent biological systems. State-of-the-art multilayer polarizing neutron optics have limitations, particularly low specular reflectivity and polarization at higher scattering vectors/angles, and the requirement of high external magnetic fields to saturate the polarizer magnetization. Here, we show that by incorporating 11B4C into Fe/Si multilayers, amorphization and smooth interfaces can be achieved, yielding higher neutron reflectivity, less diffuse scattering and higher polarization. Magnetic coercivity is eliminated, and magnetic saturation can be reached at low external fields (>2 mT). This approach offers prospects for significant improvement in polarizing neutron optics, enabling; nonintrusive positioning of the polarizer, enhanced flux, increased data accuracy, and further polarizing/analyzing methods at neutron scattering facilities.

The real-time crash likelihood prediction model is an essential component of the proactive traffic safety management system. Over the years, numerous studies have attempted to construct a crash likelihood prediction model in order to enhance traffic safety, but mostly on freeways. In the majority of the existing studies, researchers have primarily employed a deep learning-based framework to identify crash potential. Lately, Transformer has emerged as a potential deep neural network that fundamentally operates through attention-based mechanisms. Transformer has several functional benefits over extant deep learning models such as LSTM, CNN, etc. Firstly, Transformer can readily handle long-term dependencies in a data sequence. Secondly, Transformers can parallelly process all elements in a data sequence during training. Finally, a Transformer does not have the vanishing gradient issue. Realizing the immense possibility of Transformers, this paper proposes inTersection-Transformer (inTformer), a time-embedded attention-based Transformer model that can effectively predict intersection crash likelihood in real-time. The proposed model was evaluated using connected vehicle data extracted from Signal Analytics Platform. Acknowledging the complex traffic operation mechanism at intersection, this study developed zone-specific models by dividing the intersection region into two distinct zones: within-intersection and approach zone. The best inTformer models in 'within-intersection,' and 'approach' zone achieved a sensitivity of 73%, and 70%, respectively. The zone-level models were also compared to earlier studies on crash likelihood prediction at intersections and with several established deep learning models trained on the same connected vehicle dataset., Comment: 29 pages, 10 figures, 8 tables

Multilayer neutron optics require precise control of interface morphology for optimal performance. In this work, we investigate the effects of different growth conditions on the interface morphology of Ni/Ti based multilayers, with a focus on incorporating low-neutron-absorbing 11B4C and using different ion assistance schemes. Grazing incidence small angle X-ray scattering was used to probe the structural and morphological details of buried interfaces, revealing that the layers become more strongly correlated and the interfaces form mounds with increasing amounts of 11B4C. Applying high flux ion assistance during growth can reduce mound formation but lead to interface mixing, while a high flux modulated ion assistance scheme with an initial buffer layer grown at low ion energy and the top layer at higher ion energy prevents intermixing. The optimal condition was found to be adding 26.0 at.% 11B4C combined with high flux modulated ion assistance. A multilayer with a period of 48.2 {\AA} and 100 periods was grown under these conditions, and coupled fitting to neutron and X-ray reflectivity data revealed an average interface width of only 2.7 {\AA}, a significant improvement over the current state-of-the-art commercial Ni/Ti multilayers. Overall, our study demonstrates that the addition of 11B4C and the use of high flux modulated ion assistance during growth can significantly improve the interface morphology of Ni/Ti multilayers, leading to improved neutron optics performance., Comment: 11 pages

Speeding has been and continues to be a major contributing factor to traffic fatalities. Various transportation agencies have proposed speed management strategies to reduce the amount of speeding on arterials. While there have been various studies done on the analysis of speeding proportions above the speed limit, few studies have considered the effect on the individual's journey. Many studies utilized speed data from detectors, which is limited in that there is no information of the route that the driver took. This study aims to explore the effects of various roadway features an individual experiences for a given journey on speeding proportions. Connected vehicle trajectory data was utilized to identify the path that a driver took, along with the vehicle related variables. The level of speeding proportion is predicted using multiple learning models. The model with the best performance, Extreme Gradient Boosting, achieved an accuracy of 0.756. The proposed model can be used to understand how the environment and vehicle's path effects the drivers' speeding behavior, as well as predict the areas with high levels of speeding proportions. The results suggested that features related to an individual driver's trip, i.e., total travel time, has a significant contribution towards speeding. Features that are related to the environment of the individual driver's trip, i.e., proportion of residential area, also had a significant effect on reducing speeding proportions. It is expected that the findings could help inform transportation agencies more on the factors related to speeding for an individual driver's trip.

Digital Twin is an emerging technology that replicates real-world entities into a digital space. It has attracted increasing attention in the transportation field and many researchers are exploring its future applications in the development of Intelligent Transportation System (ITS) technologies. Connected vehicles (CVs) and pedestrians are among the major traffic participants in ITS. However, the usage of Digital Twin in research involving both CV and pedestrian remains largely unexplored. In this study, a Digital Twin framework for CV and pedestrian in-the-loop simulation is proposed. The proposed framework consists of the physical world, the digital world, and data transmission in between. The features for the entities (CV and pedestrian) that need digital twining are divided into external state and internal state, and the attributes in each state are described. We also demonstrate a sample architecture under the proposed Digital Twin framework, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). A case study that investigates Vehicle-Pedestrian (V2P) warning system is conducted to validate the effectiveness of the presented architecture. The proposed framework is expected to provide guidance to the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development of ITS applications on CV and pedestrians.

The mixed valence Cr compound NaCr$_2$O$_4$, synthesized using a high-pressure technique, offers a unique playground for investigating unconventional physical properties in condensed matter. In the present study, muon spin rotation/relaxation ($\mu^+$SR) and high-resolution neutron powder diffraction (NPD) measurements were carried out to clarify the true magnetic ground state of this interesting compound. Our detailed study brings new insight, allowing us to confirm the existence of a commensurate antiferromagnetic order (C-AFM) and to extract its ordered Cr moment $\mu^{\rm C}_{\rm Cr}=(4.30\pm0.01)\mu_B$. Such a value of the ordered moment is in fact compatible with the existence of high-spin Cr sites. Further, the value of the canting angle of the Cr spin axial vector is refined as $\theta_{\rm c}=(8.8\pm0.5)^{\circ}$. Employing high-quality samples in combination with time-of-flight NPD, a novel magnetic supercell was also revealed. Such supercell display an incommensurate (IC)-AFM propagation vector (0~0~${\textstyle \frac{1}{2}-}\delta$), having an ordered moment $\mu^{\rm IC}_{\rm Cr}=(2.20\pm0.03)\mu_B$. It is suggested that the C-AFM and IC-AFM modulations are due to itinerant and localized contributions to the magnetic moment, respectively. Finally, the direct measurement of the magnetic order parameter provided a value of the critical exponent $\beta = 0.245 \approx \frac{1}{4}$, suggesting a non conventional critical behavior for the magnetic phase transition in NaCr$_2$O$_4$.

In this work we present systematic set of measurements carried out by muon spin rotation/relaxation ($\mu^+$SR) and neutron powder diffraction (NPD) on the solid solution Na$_{\rm x}$Ca$_{1- \rm x}$Cr$_2$O$_4$. This study investigates Na-ion dynamics in the quasi-1D (Q1D) diffusion channels created by the honeycomb-like arrangement of CrO$_6$ octahedra, in the presence of defects introduced by Ca doping. With increasing Ca content, the size of the diffusion channels is enlarged, however, this effect does not enhance the Na ion mobility. Instead the overall diffusivity is hampered by the local defects and the Na hopping probability is lowered. The diffusion mechanism in Na$_{\rm x}$Ca$_{1- \rm x}$Cr$_2$O$_4$ was found to be interstitial and the activation energy as well as diffusion coefficient were determined for all the members of the solid solution.

Two$-$dimensional (2D) triangular lattices antiferromagnets (2D$-$TLA) often manifest intriguing physical and technological properties, due to the strong interplay between lattice geometry and electronic properties. The recently synthesized 2$-$dimensional transition metal dichalcogenide LiCrTe$_2$, being a 2D$-$TLA, enriched the range of materials which can present such properties. In this work, muon spin rotation ($\mu^+$SR) and neutron powder diffraction (NPD) have been utilized to reveal the true magnetic nature and ground state of LiCrTe$_2$. From high$-$resolution NPD the magnetic spin order at base$-$temperature is not, as previously suggested, helical, but rather collinear antiferromagnetic (AFM) with ferromagnetic (FM) spin coupling within the $ab-$plane and AFM coupling along the $c-$axis. The ordered magnetic Cr moment is established as $\mu_{\rm Cr}=$ 2.36 $\mu_{\rm B}$. From detailed $\mu^+$SR measurements we observe an AFM ordering temperature $T_{\rm N}\approx$ 125 K. This value is remarkably higher than the one previously reported by magnetic bulk measurements. From $\mu^+$SR we are able to extract the magnetic order parameter, whose critical exponent allows us to categorize LiCrTe$_2$ in the 3D Heisenberg AFM universality class. Finally, by combining our magnetic studies with high$-$resolution synchrotron X$-$ray diffraction (XRD), we find a clear coupling between the nuclear and magnetic spin lattices. This suggests the possibility for a strong magnon$-$phonon coupling, similar to what has been previously observed in the closely related compound LiCrO$_2$.

Intersection safety often relies on the correct modelling of signal phasing and timing parameters. A slight increase in yellow time or red time can have significant impact on the rear end crashes or conflicts. This paper aims to identify the relationship between surrogate safety measures and signal phasing. Unmanned Aerial Vehicle (UAV) video data has been used to study an intersection. Post Encroachment Time (PET) between vehicles was calculated from the video data as well as speed, heading and relevant signal timing parameters such as all red time, red clearance time, yellow time, etc. Random Parameter Ordered Logit Model was used to model the relationship between PET and these signal timing parameters. Overall, the results showed that yellow time and red clearance time is positively related to PETs. The model was also able to idendity certain signal phases that could be a potential safety hazard and would need to be retimed by considering the PETs. The odds ratios from the models also indicates that increasing the yellow and red clearance times by one second can improve the PET levels by 16% and 3% respectively.

Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system for regression analysis will be of great help for researchers as well as non-expert users. Thus, the objective of this research is to design and develop an automated curve fitting system. As outcome, a curve fitting system named "CurFi" was developed that uses linear regression models to fit a curve to a dataset and to find out the best fit model. The system facilitates to upload a dataset, split the dataset into training set and test set, select relevant features and label from the dataset; and the system will return the best fit linear regression model after training is completed. The developed tool would be a great resource for the users having limited technical knowledge who will also be able to find the best fit regression model for a dataset using the developed "CurFi" system.

Crash events identification and prediction plays a vital role in understanding safety conditions for transportation systems. While existing systems use traffic parameters correlated with crash data to classify and train these models, we propose the use of a novel sensory unit that can also accurately identify crash events: microphone. Audio events can be collected and analyzed to classify events such as crash. In this paper, we have demonstrated the use of a deep Convolutional Neural Network (CNN) for road event classification. Important audio parameters such as Mel Frequency Cepstral Coefficients (MFCC), log Mel-filterbank energy spectrum and Fourier Spectrum were used as feature set. Additionally, the dataset was augmented with more sample data by the use of audio augmentation techniques such as time and pitch shifting. Together with the feature extraction this data augmentation can achieve reasonable accuracy. Four events such as crash, tire skid, horn and siren sounds can be accurately identified giving indication of a road hazard that can be useful for traffic operators or paramedics. The proposed methodology can reach accuracy up to 94%. Such audio systems can be implemented as a part of an Internet of Things (IoT) platform that can complement video-based sensors without complete coverage.

In this work, we focus on detecting emergency vehicles using only audio data. Improved and quick detection can help in faster preemption of these vehicles at signalized intersections thereby reducing overall response time in case of emergencies. Important audio features were extracted from raw data and passed into extreme learning machines (ELM) for training. ELMs have been used in this work because of its simplicity and shorter run-time which can therefore be used for online learning. Recently, there have been many studies that focus on sound classification but most of the methods used are complex to train and implement. The results from this paper show that ELM can achieve similar performance with exceptionally shorter training times. The accuracy reported for ELM is about 97% for emergency vehicle detection (EVD).

The Muon Chamber (MuCh) is a sub-system of the Compressed Baryonic Matter (CBM) experiment for the detection of low-mass-vector mesons produced in high energy heavy-ion collisions at beam energies ranging from 2 AGeV to 11 AGeV and decaying in the di-muon channel. MuCh consists of a segmented absorber and four detector triplet stations sandwiched between the absorber segments. At the 3rd and 4th stations of MuCh, Resistive Plate Chambers (RPCs) have been conceived for muon tracking. We have tested the performance of a low resistivity bakelite RPC prototype equipped with self-triggered front end electronics (MuCh-XYTER) for the CBM Muon Chamber. A systematic study on the muon detection efficiency and time resolution has been carried out in a high-rate photon background at the Gamma Irradiation Facility (GIF++) at CERN. The details of the measurement setup and the results are presented here.

Robot arms have been using in different systems, which the control of designed in desired trajectory is the main task. Also, it is anticipated that while in operation the developed 2DoF robot arm will be constantly encountered with noises such as friction forces. A new integral sliding mode control (NISMC) is therefore being introduced to suppress noise due to its robustness. Then, New hybrid control system (NHISMC) is proposed, which constantly calculates an error value and applies a correction value to the system. This will enhance trajectory and minimize tracking error. In comparison with two other controllers, such as traditional sliding mode control (SMC) and NISMC, experimental results confirmed the efficacy of the proposed control method., Comment: 6 pages, 7 figures

In this work, a new SMS is proposed to achieve high tracking and suitable robustness. However, the chattering phenomenon should be regarded as the main drawback of the SMC. Therefore, a new compound control algorithm is used for reducing the chattering phenomenon. The applied compound control law constantly evaluates the error and send the correct value to the system. This significantly will reduce the chattering phenomenon. The performance of the control methods validated by applying on a robot arm experimentally., Comment: 17 pages, 9 figures