Signal Time Research Articles

Rapid, non-contact identification of organic solvents: Monolithic GaN chips incorporating PDMS/PS photonic crystals

The prompt identification of organic solvents is essential in many practical scenarios. This work introduces a compact optical device for rapid, non-contact detection of organic solvents. The fabrication of the GaN chip employs a monolithic integration approach, enabling the simultaneous formation of on-chip components responsible for light emission and light detection. The self-assembled polystyrene (PS) spheres embedded in a polydimethylsiloxane (PDMS) layer as photonic crystals can effectively induce distinct optical changes when exposed to organic solvents. By analyzing the magnitude and recovery time of the photocurrent signals, up to seven types of organic samples can be distinguished. Different from traditional optical sensing systems, the proposed chip-level integration strategy eliminates the need for external assembly of optical components, resulting in an ultracompact millimeter-sized sensing unit. The developed device also offers features of simplicity of use and a short measurement time of less than 10 s, making it a feasible solution for quick detection in emergency scenarios.

An efficient hybrid bat sand cat swarm optimization‐based node localization for data quality improvement in wireless sensor networks

SummaryNode localization in wireless sensor networks (WSNs) ensures that the collected data is contextually accurate, enabling effective monitoring and management of various applications. Recently, there has been a surge in research focused on addressing node localization within WSNs. Emerging trends in this field involve the application of metaheuristic optimization techniques to refine node location determination accuracy. However, existing techniques often struggle with balancing accuracy, energy consumption, network lifetime, and computational efficiency, particularly in challenging WSN environments. Therefore, this research introduces a novel approach called efficient hybrid bat sand cat swarm optimization (EHBSCSO) to address node localization within WSNs. The hybrid method leverages the exploration capabilities of the bat optimization algorithm and the exploitation strengths of the sand cat swarm optimization algorithm. This combination allows for efficient determination of node positions, significantly improving localization accuracy while minimizing energy consumption. The EHBSCSO utilizes the received signal strength indicator (RSSI) and time of flight (ToF) approaches to assess distances among nodes accurately. Accurate node localization directly improves data quality by ensuring spatially precise data collection, reducing communication overhead, and enhancing the overall reliability of the collected data. Compared to conventional methods, the proposed EHBSCSO algorithm demonstrates superior performance, with a mean localization error of 0.18%, energy consumption of 7.2 J, computational time of 8.9 s, and localization time of 0.19 s. These metrics underscore its efficiency and precision. The research indicates that EHBSCSO not only optimizes localization accuracy but also contributes to energy efficiency and faster computational times, addressing key challenges in WSN node localization.

Measurement dynamics and data treatment in fire testing

Fire testing is and will remain of paramount importance in helping industry, regulators and all other fire safety stakeholders to achieve a safer world. For this to happen, test data must be stable and reliable, regardless of the test facility that produced the data, its various operators, the measurement equipment used, and so on. The instruments and sensors used to measure heat release, smoke production and mass loss rate are susceptible to mechanical and/or electrical disturbances and all have their own dynamic behaviour. This leads to a variation in the test results. A scatter that is only due to the measurement.This paper examines the time and frequency domain behaviour of various measurement components and hardware & software filters in use today. It proposes an instrument-independent method of eliminating noise and other interference, describes a method of adjusting the response time of the various input signals to a common value applicable to all test institutes, and proposes a method of accurately synchronising these signals in time.The result is unambiguous qualitative measurement data that can be confidently compared with data from other sample sets or between different laboratories and researchers. It improves the stability and reproducibility of test results leading to a more stable classification of materials.

Square Wave Electrogravimetry Monitors Transient Submonolayer Adsorption of Redox-Active Molecules with a Precision of Less Than 1% of a Monolayer

Square wave voltammetry on electrolytes containing the reversible redox pairs flavin adenine dinucleotide (FAD) and methyl viologen (MV) was complemented by a new form of acoustic microgravimetry. The analytes FAD and MV have promising applications in bacterial fuel cells and redox flow batteries. The instrument operates similarly to a quartz crystal microbalance with dissipation monitoring (EQCM-D). It reports shifts in resonance frequency and half bandwidth on several overtones. Compared to the existing instruments, the time resolution was improved by a factor of 40 down to 2.5 ms by multifrequency lockin amplification, which is important for the analysis of the transients in electrochemistry. The frequency noise after accumulation was reduced below 10 mHz ≙ 0.12 ng/cm2 ≙ 1.2 pm (assuming ρ = 1 g/cm3) by employing modulation and averaging. Square wave modulation of the electrode potential is superior to linear ramps (cyclic voltammetry) because it reduces the contribution of non-Faraday currents and, also, improves sensitivity. The shifts of frequency,Δf, and half bandwidth, ΔΓ, are in line with the Sauerbrey prediction (Δf/n ≈const. and ΔΓ/n < -Δf/nwith n the overtone order). Small deviations (Figure B and D) presumably are caused by the layer’s softness. The apparent shear modulus, G app, of this layer is in the GPa range. The frequency difference, Δf/n|CT (Figure E), amounts to an apparent mass transfer rate. Plots of Δf/n|CT versus potential sometimes show curves similar to the electric current as observed for e. g. MV. For FAD, the results obtained depended on pH and the apparent mass transfer rates are much different from the current (Figure C and E). This difference is most likely caused by adsorbed molecules that are intermediates of the underlying two-electron process. The explanation is further corroborated by the response time of the QCM signals being much larger than the RC time of double layer recharging as determined with EIS. An interpretation in terms of adsorption/desorption is more plausible than an interpretation in terms of changed viscosity in the diffuse double layer.[1] Caption: Currents and frequency shifts obtained on a solution containing FAD. Df/n|av evidences the preferred adsorption of the intermediate state (FADH).

Fatigue Crack Monitoring Method Based on the Lamb Wave Damage Index.

For practical engineering structures, fatigue is one of the main factors affecting their safety and durability. Under long-term service conditions, the minor damage will be affected by fatigue loading and expand to macroscopic cracks, affecting the structure's service performance. Based on the sensitivity of Lamb waves to minor and initial damage, a damage monitoring method for fatigue crack propagation is proposed. By carrying out fatigue crack propagation tests under constant amplitude loading, the Paris equation of 316L steel and damage signals at different crack growth stages were obtained. Combined with damage monitoring tests and finite element analysis, the relationship between the phase damage index (PDI), amplitude damage index (ADI), signal correlation coefficient, and fatigue crack propagation length was studied. Compared with PDI and ADI, the signal correlation coefficient is more sensitive to crack initiation, which can be selected as the damage monitoring index in the initial stage of crack growth. With the increase of fatigue crack propagation length, the peak time of the direct wave signal gradually moves backward, which shows an obvious phase change. In the whole fatigue crack growth stage, PDI and crack length show a monotonically changing trend. By using the stress intensity factor as the conversion parameter, a prediction model of the fatigue crack propagation rate based on PDI was established. Compared to the fatigue crack propagation rate measured by experiments, the relative error of the predicted results is 10%, which verifies the accuracy of the proposed damage monitoring method.

Design and characterization of the time-resolved opacity spectrometer (OpSpecTR) for the NIF iron opacity campaign.

A new time-resolved opacity spectrometer (OpSpecTR) is currently under development for the National Ignition Facility (NIF) opacity campaign. The spectrometer utilizes Icarus version 2 (IV2) hybridized complementary metal-oxide-semiconductor sensors to collect gated data at the time of the opacity transmission signal, unlocking the ability to collect higher-temperature measurements on NIF. Experimental conditions to achieve higher temperatures are feasible; however, backgrounds will dominate the data collected by the current time-integrating opacity spectrometer. The shortest available OpSpecTR integration time of ∼2ns is predicted to reduce self-emission and other late-time backgrounds by up to 80%. Initially, three Icarus sensors will be used to collect data in the self-emission, backlighter, and absorption regions of the transmission spectrum, with plans to upgrade to five Daedalus sensors in future implementations with integration times of ∼1.3ns. We present the details of the diagnostic design along with recent characterization results of the IV2 sensors.

CATM: A Multi-Feature-Based Cross-Scale Attentional Convolutional EEG Emotion Recognition Model.

Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency-space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency-space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence-arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence-arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments.

Research on the Inhibition and Transmission Properties of Photonic Spiking Dynamics in Semiconductor Ring Lasers

Significant progress has been made in the research of all-optical neural networks in recent years. In this paper, we theoretically explore the properties of a neural system composed of semiconductor ring lasers (SRLs). Our study demonstrates that external optical signals generated by a tunable laser (TL) are injected into the first semiconductor ring laser photonic neuron (SRL1). Subsequently, the responses of SRL1 in the clockwise (CW) and counterclockwise (CCW) directions are unidirectionally injected into the CW and CCW directions of the second semiconductor ring laser photonic neuron (SRL2), respectively, which then exhibits similar spiking inhibition behaviors. Numerical simulations reveal that the spiking inhibition behavior of the SRL response can be precisely controlled by adjusting the perturbation time and intensity of the external injection signal, and this behavior is highly repeatable. Most importantly, we successfully achieve the stable transmission of these responses between the two SRL photonic neurons. These inhibition behaviors are analogous to those of biological neurons, but with a response speed reaching the sub-nanosecond level. Additionally, we indicate that SRL photonic neurons undergo a refractory-period-like phenomenon when subjected to two consecutive perturbations. These findings highlight the immense potential for the design and implementation of future all-optical neural networks, providing critical theoretical foundations and support for them.

The Evaluation of Interface Quality in HfO2 Films Probed by Time-Dependent Second-Harmonic Generation.

Time-dependent second-harmonic generation (TD-SHG) is an emerging sensitive and fast method to qualitatively evaluate the interface quality of the oxide/Si heterostructures, which is closely related to the interfacial electric field. Here, the TD-SHG is used to explore the interface quality of atomic layer deposited HfO2 films on Si substrates. The critical SHG parameters, such as the initial SHG signal and characteristic time constant, are compared with the fixed charge density (Qox) and the interface state density (Dit) extracted from the conventional electrical characterization method. It reveals that the initial SHG signal linearly decreases with the increase in Qox, while Dit is linearly correlated to the characteristic time constant. It verifies that the TD-SHG is a sensitive and fast method, as well as simple and noncontact, for evaluating the interface quality of oxide/Si heterostructures, which may facilitate the in-line semiconductor test.

FlowOptix : A Traffic Signal Control and Management System

The innovation in the field of vehicles has led to the access of vehicles in high reach, thus increasing the number of vehicle counts. But this increase count has created a need of managing the congestion of vehicles on road in a more efficient way. The traditional system and approach of the traffic signal systems isn’t adequate enough to tackle the increasing congestion. The traditional system tends to be efficient where the count is sparse, as the density of vehicles on a particular side of road increases or if the traffic is comparatively larger on one side than other side in such case the approach fails. Hence, the introduced system redesigns the traffic signal system that is static switching to signal switching based on real-time traffic management. So, in this project the switching time of signal will be decided based on real time image detection with good accuracy in dense traffic. This practice can prove its most effectiveness in releasing the congested traffic at an efficient and faster rate.

Enhanced ambient-stability and charge transport property of the CsPbBr3@Polyaniline

Although all-inorganic lead halide perovskites (CsPbX3 (X = Cl, Br, I)) have received widespread attention due to excellent optoelectronic properties, their low carrier mobility and poor stability have limited their application in photodetection. In this study, to alleviate these problems, CsPbBr3@Polyaniline composite is fabricated by passivating CsPbBr3 quantum dots (QDs) into a CSA-doped polyaniline conducting polymer. Notably, polyaniline is employed as a protective layer to block the structure damage of CsPbBr3 QDs against water, light and heat. The contact between polyaniline and CsPbBr3 QDs forms a type II heterojunction for charge transfer, which greatly reduces the recombination rate of charge carriers compared to pristine CsPbBr3 QDs. Benefiting from the high stability and enhanced charge mobility, CsPbBr3@Polyaniline-based photodetector achieves improved photocurrent signal and optical response time. The results demonstrate that the surface passivation could resolve the crucial stability problem and simultaneously facilitate the superior performance of perovskite optoelectronic devices.

Traffic State Comparison of Signalized Intersections Using SIDRA and SYNCHRO Software

The number of shopping malls and vehicles in Baghdad has quickly increased along with the city's population. This has resulted in an increase in daily trips, which affects the roadway network's traffic flow and causes congestion, particularly on Al-Mansour Street and Al-Rowad Street near the city center. To determine the best signal time, the 14th Ramadan signalized intersection was evaluated and analyzed using the SIDRA 8.0 and SYNCHRO 11.0 software. First, the findings revealed that the chosen traffic facility is presently experiencing a substantial reduction in service level that is leading to forced conditions (LOS "F"). It is suggested that the cycle time at the intersection of the 14th Ramadan be changed from 240 seconds to 160 seconds and 125 seconds in SIDRA and SYNCHRO, respectively. Signal timings have reduced the delay for the 14th Ramadan intersection (76.8%), thus improving LOS from (F) to (D) for the intersection. The best delays after optimization in SYNCHRO 11.0, however, were 31% reduction with LOS of (E and D). Cycle times have also been lowered for the intersection to 25% as part of the optimum signal timing optimization. In SYNCHRO 11.0, which is better than SIDRA 8.0, the back of the queue has improved at the 14th Ramadan intersection by 33%. However, SYNCHRO has a better representation than SIDRA for current traffic saturation

Dynamic flexural strength of Aluminosilicate glass with a perspective of impulsive and quasi-impulsive responses: An experimental–numerical coupled evaluation

The flexural strength of glass is a critical design parameter for applications encountering impact loadings. However, the micro defects, specimen geometry, loading rate, and load transformation from a quasi-dynamic to quasi-impulsive state may influence the measurement accuracy. Due to the stochastic and amorphous nature of the material, an accurate determination of the flexural strength remains a challenge. In this two-fold study, a coupled experimental–numerical strategy was devised to evaluate the dynamic flexural strength. In the first phase, three-point bending experiments were conducted on a novel “Electromagnetic Split Hopkinson Pressure Bar (ESHPB)”. The incident stress signal and fracture time were recorded from experimental data, while the flexural strength was indirectly computed from a numerical algorithm. A quantitative comparison of the flexural strength with those in existing literature established the accuracy of the proposed methodology. Results of the study indicate that the specimen response became independent of the support conditions under impulsive loading. That being said, the specimen behaved like it had an infinite span length, and the measured flexural strength remained the same whether the specimen was supported or not. Besides, the specimen also maintained contact at the interfaces of the incident bar and fixture supports for the entire loading duration. In the second part of this study, the computed flexural strength was used to calibrate the existing JH-2 model. Numerical prediction of the damage propagation corroborated with that obtained from reprography images, though qualitatively. This work presents a precise and robust methodology to determine the dynamic flexural strength of brittle ceramics like Aluminosilicate glass over traditional experimental procedures to facilitate its adoption.

A Hybrid Localization Algorithm for Enhanced Accuracy and Robustness in Healthcare Systems

This paper presents a novel hybrid localization algorithm designed for healthcare systems, integrating Received Signal Strength Indicator (RSSI) and Time of Arrival (ToA) measurements with machine learning techniques. The algorithm aims to enhance the accuracy, robustness, and computational efficiency of sensor localization in dynamic healthcare environments. Experimental results demonstrate that the hybrid algorithm achieves a significantly lower localization error, averaging 0.5 meters, compared to traditional RSSI-only and ToA-only methods. The algorithm's rapid convergence and low computational time make it suitable for real-time applications. Additionally, its robustness to measurement noise, a common challenge in healthcare settings, underscores its reliability. This research underscores the potential of advanced localization technologies to improve patient monitoring, safety, and overall healthcare delivery, with future work poised to further enhance performance and adaptability.

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression with a likelihood of suicidal ideation. Early detection of MDD is thus necessary to reduce the impact, however, it requires monitoring vitals in daily living conditions. EEG is generally multi-channel and due to difficulty in signal acquisition, it is unsuitable for home-based monitoring, whereas, wearable sensors can collect single-channel ECG. Classical machine-learning based MDD detection studies commonly use various heart rate variability features. Feature generation, which requires domain knowledge, is often challenging, and requires computation power, often unsuitable for real time processing, MDDBranchNet is a proposed parallel-branch deep learning model for MDD binary classification from a single channel ECG which uses additional ECG-derived signals such as R-R signal and degree distribution time series of horizontal visibility graph. The use of derived branches was able to increase the model's accuracy by around 7%. An optimal 20-second overlapped segmentation of ECG recording was found to be beneficial with a 70% prediction threshold for maximum MDD detection with a minimum false positive rate. The proposed model evaluated MDD prediction from signal excerpts, irrespective of location (first, middle or last one-third of the recording), instead of considering the entire ECG signal with minimal performance variation stressing the idea that MDD phenomena are likely to manifest uniformly throughout the recording.

An intelligent security mechanism in mobile Ad-Hoc networks using precision probability genetic algorithms (PPGA) and deep learning technique (Stacked LSTM)

The Mobile Ad-hoc Networks (MANETs) have gained a significant attention in the recent years due to their proliferation and huge application purposes. To defend from many types of modern cyber dangers like Distributed Denial of Service (DDoS) attacks, Advanced Persistent Threats (APTs), Insider Threats, Ransomware, Zero-Day Exploits, Social Engineering tactics, and etc is not easy when it comes to keeping MANETs security. These complex assaults focus on network infrastructure, take advantage of weaknesses in communication protocols and control user actions. Although there have been improvements in intrusion detection systems (IDS), it is still difficult to fully safeguard MANETs. The purpose of this research is to create advanced methods that can accurately find and decrease attacks inside MANETs. Applying Sensor-based Feature Extraction (SFE) to extract useful network features such as Received Signal Strength Indication (RSSI) and Time of Travel (TOT) from datasets NSL-KDD and CICIDS-2017. Utilizing the fresh method of Precise Probability Genetic Algorithm (PPGA) optimization for removing unrelated details, which enhances precision in detecting attacks. Predicting normal and attacking labels by applying Stacked Recurrent Long Short Term Memory (SRLSTM) method, fine-tuning classifier's parameters in every layer to improve outcomes. In order to authenticate and compare the suggested methods with current attack detection tactics, this study will make use of various evaluation measurements. The NSL-KDD, which is a benchmark dataset in network intrusion detection research, has a wide variety of network traffic data with instances that are labeled as normal and different attacks. CICIDS-2017 is similar because it contains an extensive dataset too - this includes real-world traces from network traffic where there's both regular activity and harmful actions. The purpose is to enhance the existing status of MANET security so as it can withstand more strongly against cyber dangers. According to the outcomes, it is analyzed that the attack detection accuracy has improved greatly 99 % when compared to other methods, as shown by the detailed assessment measurements. Better handling of big datasets with top detection accuracy reduces the time needed 8.9 s for training and testing models. Decrease in misclassification results and better ability to differentiate normal network actions from harmful intrusions. Improved resistance of MANETs to different cyber dangers, guaranteeing the safety and dependability of network communication in changing and non-centralized settings.

Phase retrieval for radar waveform design

Abstract The ability of a radar to discriminate in both range and Doppler velocity is completely characterized by the ambiguity function (AF) of its transmit waveform. Mathematically, it is obtained by correlating the waveform with its Doppler-shifted and delayed replicas. We consider the inverse problem of designing a radar transmit waveform that satisfies the specified AF magnitude. This process may be viewed as a signal reconstruction with some variation of phase retrieval methods. We provide a trust-region algorithm that minimizes a smoothed non-convex least-squares objective function to iteratively recover the underlying signal-of-interest for either time- or band-limited support. The method first approximates the signal using an iterative spectral algorithm and then refines the attained initialization based on a sequence of gradient iterations. Our theoretical analysis shows that unique signal reconstruction is possible using signal samples no more than thrice the number of signal frequencies or time samples. Numerical experiments demonstrate that our method recovers both time- and band-limited signals from sparsely and randomly sampled, noisy and noiseless AFs.

Hybrid passive vibration control of lightweight manipulators

Passive vibration control methods in the literature are known to be highly sensitive to dynamic system parameters and are generally applied one by one. Therefore, their effectiveness in suppressing vibration control is limited. In this work, hybrid passive control (HPC) by integrating the Posicast control (PC) and the motion parameters-based control (MPC) is developed to suppress the during-motion vibrations (DMV) and residual vibrations (RV) of a single-link lightweight manipulator with a payload. PC is designed as a three-step cycle using the first natural frequency and damping ratio of the manipulator. MPC is designed by determining the time parameters of the motion profiles based on the first natural frequency of the manipulator. HPC simulations are performed on MATLAB, creating a Simscape model of the manipulator. Then, the proposed control method is tested in experiments. The prepared hybrid motion inputs actuate the servomotor while the displacements are measured, using a laser sensor at the tip of the manipulator. DMV and RV responses and their RMS values reveal that suppressing the vibration amplitudes efficiently. MPC is mostly effective in eliminating RV, whereas PC is sensitive to DMV as well. HPC combines these methods by eliminating their disadvantages and highlighting their advantages. Moreover, it is also successful in cases where the deceleration time of the trapezoidal input signal is single times half of the natural period, unlike MPC.

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

Abstract Current self-attention based Transformer models in the field of fault diagnosis are limited to identifying correlation information within a single sequence and are unable to capture both time and frequency domain fault characteristics of the original signal. To address these limitations, this research introduces a two-channel Transformer fault diagnosis model that integrates time and frequency domain features through a cross-attention mechanism. Initially, the original time-domain fault signal is converted to the frequency domain using the Fast Fourier Transform, followed by global and local feature extraction via a Convolutional Neural Network. Next, through the self-attention mechanism on the two-channel Transformer, separate fault features associated with long distances within each sequence are modeled and then fed into the feature fusion module of the cross-attention mechanism. During the fusion process, frequency domain features serve as the query sequence Q, and time domain features as the key-value pairs K. By calculating the attention weights between Q and K, the model excavates deeper fault features of the original signal. Besides preserving the intrinsic associative information within sequences learned via the self-attention mechanism, the Twins Transformer also models the degree of association between different sequence features using the cross-attention mechanism. Finally, the proposed model's performance was validated using four different experiments on four bearing datasets, achieving average accuracy rates of 99.67%, 98.76%, 98.47%, and 99.41%. These results confirm the model's effective extraction of time and frequency domain correlation features, demonstrating fast convergence, superior performance, and high accuracy.&amp;#xD;

Young novice drivers’ road crash injuries and contributing factors: A crash data investigation

Objective Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers’ crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures. Methods Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022. Results The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level). Conclusions The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.