This article presents a novel approach, employing an agent-oriented model. We scrutinize the preferences and decisions of numerous agents, motivated by utilities, in the context of a realistic urban environment (a metropolis). Our investigation focuses on modal selection, employing a multinomial logit model. Furthermore, we suggest certain methodological components for recognizing individual profiles from publicly available data sources, such as census information and travel surveys. This model's capability to mirror travel behaviors, combining private cars and public transport, is exhibited in a real-world application concerning Lille, France. Not only that, but we also focus on the role played by park-and-ride facilities in this context. In this manner, the simulation framework empowers a more comprehensive understanding of individual intermodal travel behaviors, facilitating the appraisal of development policies.

The Internet of Things (IoT) is a system where billions of daily objects are expected to share and communicate information. The ongoing development of new IoT devices, applications, and communication protocols necessitates a sophisticated evaluation, comparison, tuning, and optimization process, thereby emphasizing the importance of a proper benchmark. The distributed computing model of edge computing, in its goal of achieving network efficiency, is contrasted by this article's focus on the local processing efficiencies of IoT sensor nodes. A benchmark, IoTST, employing per-processor synchronized stack traces, is detailed, with its isolation and the exact quantification of its incurred overhead. Comparable detailed results are generated, helping to ascertain the processing operating point offering the highest energy efficiency, taking configuration into account. Benchmarking applications with network components often yields results that are contingent upon the ever-shifting network state. In order to circumvent these obstacles, diverse factors or postulates were taken into account during the generalisation experiments and in the comparative analysis of similar research. Using a readily available commercial device, we applied IoTST to assess the performance of a communication protocol, leading to comparable findings that were independent of network status. We undertook the evaluation of different Transport Layer Security (TLS) 1.3 handshake cipher suites using a spectrum of frequencies and different core counts. The choice of a specific suite, such as Curve25519 and RSA, can potentially reduce computation latency by as much as four times compared to the least performant suite, P-256 and ECDSA, even though both maintain a comparable security level of 128 bits.

Evaluating the condition of IGBT modules within traction converters is indispensable for ensuring the smooth running of urban rail vehicles. An effective and accurate simplified simulation approach, built on operating interval segmentation (OIS), is presented in this paper for evaluating IGBT conditions, considering the fixed line and the similar operating characteristics of contiguous stations. The proposed framework, detailed in this paper, evaluates conditions by segmenting operating intervals based on the similarity of average power loss between adjacent stations. learn more The framework facilitates a reduction in simulation counts, thereby minimizing simulation duration, while maintaining the accuracy of state trend estimation. This paper's second contribution is a fundamental interval segmentation model that takes operational conditions as input to delineate lines, thereby simplifying the operational parameters for the entirety of the line. The final stage of evaluating IGBT module condition involves simulations and analyses of temperature and stress fields segmented by intervals, effectively connecting predicted lifetimes to the module's real operational and internal stresses. To ascertain the method's validity, the interval segmentation simulation's results were contrasted with the observed findings from practical tests. Analysis of the results demonstrates that the method successfully captures the temperature and stress patterns of IGBT modules within the traction converter assembly, which provides valuable support for investigating IGBT module fatigue mechanisms and assessing their lifespan.

We propose a system with integrated active electrode (AE) and back-end (BE) components for improved electrocardiogram (ECG) and electrode-tissue impedance (ETI) data acquisition. The AE's structure includes a preamplifier and a balanced current driver. A matched current source and sink, operating under negative feedback, is employed by the current driver to augment output impedance. To extend the operational range within the linear region, a novel source degeneration method is introduced. The preamplifier's implementation employs a capacitively-coupled instrumentation amplifier (CCIA) augmented by a ripple-reduction loop (RRL). Active frequency feedback compensation (AFFC) surpasses traditional Miller compensation in bandwidth extension by utilizing a smaller compensation capacitor. Three signal types—ECG, band power (BP), and impedance (IMP)—are detected by the BE. Employing the BP channel, the ECG signal is analyzed to pinpoint the Q-, R-, and S-wave (QRS) complex. The IMP channel's role involves characterizing the resistance and reactance of the electrode-tissue system. The 180 nm CMOS process is employed to fabricate the integrated circuits used in the ECG/ETI system, which encompass a 126 mm2 area. Results of the measurements indicate that the driver provides a relatively high current level, more than 600 App, and exhibits a substantial output impedance, precisely 1 MΩ at a frequency of 500 kHz. Resistance and capacitance values within the 10 mΩ to 3 kΩ and 100 nF to 100 μF ranges, respectively, are detectable by the ETI system. The ECG/ETI system, sustained by a single 18-volt supply, consumes a power level of 36 milliwatts.

The precise measurement of phase shifts is facilitated by intracavity interferometry, a robust method utilizing two counter-propagating frequency combs (pulse series) emanating from a mode-locked laser. learn more Generating dual frequency combs synchronously at the same repetition rate in fiber lasers unveils a realm of previously unanticipated problems. Coupled with the exceptional intensity within the fiber core and the nonlinear index of refraction of the glass, a massive cumulative nonlinear index develops along the axis, rendering the signal being examined negligible in comparison. The laser's repetition rate is rendered erratic by the large saturable gain's fluctuating behavior, thereby preventing the construction of frequency combs with a consistent repetition rate. Due to the substantial phase coupling between pulses crossing the saturable absorber, the small-signal response (deadband) is completely eliminated. Although gyroscopic responses have been noted in earlier studies involving mode-locked ring lasers, our investigation, to the best of our understanding, signifies the pioneering implementation of orthogonally polarized pulses to effectively eliminate the deadband and achieve a beat note.

We develop a comprehensive super-resolution and frame interpolation system that concurrently addresses spatial and temporal image upscaling. We find performance changes correlated with the alteration of input permutations in video super-resolution and video frame interpolation. We believe that favorable characteristics extracted from various frames should be consistent, independent of the input order, if they are designed to be optimally complementary and frame-specific. Prompted by this motivation, we construct a permutation-invariant deep learning architecture that leverages multi-frame super-resolution principles through our order-invariant network design. learn more Using a permutation-invariant convolutional neural network module, our model extracts complementary feature representations from pairs of adjacent frames, thus enhancing the efficacy of both super-resolution and temporal interpolation processes. Against various combinations of the competing super-resolution and frame interpolation methods, our integrated end-to-end approach's efficacy is tested rigorously across demanding video datasets, thereby confirming the accuracy of our prediction.

It is essential to monitor the actions of elderly people living by themselves, as this enables the identification of critical events like falls. From this perspective, 2D light detection and ranging (LIDAR) has been studied, in addition to other methods, as a means of identifying these events. Continuous measurements from a 2D LiDAR, positioned close to the ground, are processed and classified by a computational device. However, within the confines of a real-world home environment and its associated furniture, the device's operation is hampered by the requirement of an unobstructed line of sight to its target. Monitored individuals can experience reduced sensor effectiveness due to furniture obstructing the infrared (IR) rays' reach. In spite of that, given their fixed position, a missed fall, at the time it occurs, cannot be identified subsequently. Cleaning robots, with their inherent autonomy, stand out as a superior alternative within this context. A 2D LIDAR, integrated onto a cleaning robot, forms the core of our proposed approach in this paper. With each ongoing movement, the robot's system is capable of continuously tracking and recording distance. Even with the same constraint, the robot's movement throughout the room can ascertain the presence of a person lying on the floor, a result of a fall, even after a considerable duration. Reaching this predefined goal necessitates the transformation, interpolation, and comparison of the measurements taken by the moving LIDAR sensor with a reference condition of the surrounding environment. A convolutional long short-term memory (LSTM) neural network is used to discern processed measurements, identifying instances of a fall event. Simulated tests show that the system attains an accuracy of 812% in fall recognition and 99% in detecting individuals lying down. Compared to the static LIDAR methodology, the accuracy for similar jobs increased by 694% and 886%, respectively.