Search Results (465)

A large share of the global population now lives in urban areas, which creates growing challenges for city life. Local authorities are seeking ways to enhance urban livability, with transportation emerging as a major focus. Developing smart public transit systems is therefore a key priority. Accurately estimating travel times is essential for managing transport operations and supporting strategic decisions. Previous studies have used statistical, mathematical, or machine learning models to predict travel time, but most examined these approaches separately. This study introduces a hybrid framework that combines statistical regression models and machine learning algorithms to predict public bus travel times. The analysis is based on 1410 bus trips recorded between November 2021 and July 2022 in Kayseri, Turkey, including detailed meteorological and operational data. A distinctive aspect of this research is the inclusion of weather variables—temperature, humidity, precipitation, air pressure, and wind speed—which are often neglected in the literature. Additionally, sensitivity analyses are conducted by varying k values in the K-nearest neighbors (KNN) algorithm and threshold values for outlier detection to test model robustness. Among the tested models, CatBoost achieved the best performance with a mean squared error (MSE) of approximately 18.4, outperforming random forest (MSE = 25.3) and XGBoost (MSE = 23.9). The empirical results show that the CatBoost algorithm consistently achieves the lowest mean squared error across different preprocessing and parameter settings. Overall, this study presents a comprehensive and environmentally aware approach to travel time prediction, contributing to the advancement of intelligent and adaptive urban transportation systems. Full article

Spectroscopic technology offers the advantage of rapid online monitoring and has attracted significant attention in molecular detection. However, the complex optical spectroscopic structure results in a relatively complex structure for spectral detection systems, limiting their widespread application. In water spectral detection, in addition to ensuring the stability of the optical system, waterproofing is also crucial. Therefore, developing miniaturized spectral detection modules in water spectral detection can improve system stability and reduce the complexity of developing and maintaining underwater hardware. This work develops a compact multispectral detection system centered on a miniature multispectral sensor. The system, controlled by a microcontroller, detects eight spectral channels within the 400–700 nm range and transmits data via the I²C bus. The sensitivity and stability of the detection are sufficient for water reflectance spectral detection. Based on the reflectance spectrum obtained by the above module, this work develops a regression algorithm to estimate the chlorophyll concentration in water. By comparing with standard chlorophyll concentration detection instruments, the results demonstrate the effectiveness of the proposed system in accurately estimating chlorophyll concentration. Full article

This work presents a complete multi-serial adaptive bus interface system compatible with the most widely used industrial serial communications standards: RS-232, RS-485, RS-422, and CAN. The proposed system automatically detects the connected serial interface type through analog line sensors and dynamically redirects the bus to the appropriate transceiver using a logical multiplexer. This approach aims to simplify the configuration of heterogeneous serial devices in complex and modular integration scenarios, such as body builders in industrial or vehicular systems. The hardware is designed as a scalable PCIe card-based device, allowing multiple adaptive bus interfaces to be integrated within a rack-based modular architecture. In addition, a single 5-pin plug-and-play connector is proposed by unifying the different bus signals of the transceivers, thereby simplifying cabling and deployment. Complementary implemented capabilities include baud rate auto-detection and supervision, as well as automatic direction-control functionality for RS-485 communication. Experimental validation demonstrated that the proposed system successfully detected and redirected all supported interfaces, achieving reliable connection and disconnection within an average time of 2.5 s. Furthermore, the integrated baud rate auto-detection algorithm accurately identified transmission speeds up to 1 Mbps in under 80 ms, while the automatic direction-control capability operated reliably at speeds up to 576,000 bps. Full article

One of the main vulnerabilities in robotic systems lies in the communication buses that enable low-level controllers to interact with the actuators responsible for the robot’s movements. In this context, hardware attacks represent a significant threat; however, the hardware version of the man-in-the-middle attack, implemented by Trojan hardware, has not yet been extensively studied. This article examines the impact of such threats on robotic control systems, focusing on vulnerabilities in an asynchronous communication bus used to transmit commands to a digital servomotor. To explore this, Trojan hardware was implemented on an FPGA device (XC7A100T, AMD: Santa Clara, CA, USA). Furthermore, the article proposes and implements detection methods to identify this type of attack, integrating them into an FPGA device as part of the actuator. The method is based on measuring the answer time detecting the presence of a strange module due to an increase in this time considering an AX-12 servomotor (Robotis: Seoul, Republic of Korea), with a Dynamixel protocol. This approach has been validated through a series of experiments involving a large number of transmitted messages, resulting in a high rate of true positives and a low rate of false negatives. The main conclusion is that response time can be used to detect foreign modules in the system, even if the module is kept waiting to attack, as long as the condition that the servomotors have a low variation in their latency is met. Full article

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R² of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration. Full article

The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system targeting two high-priority IoV security pillars: availability (traffic overload) and integrity/authenticity (spoofing), with spoofing evaluated across multiple subclasses (GAS, RPM, SPEED, and steering wheel). In the offline phase, deep learning and hybrid models were benchmarked on the vehicular CAN bus dataset CICIoV2024, with the BiLSTM-XGBoost hybrid chosen for its balance between accuracy and inference speed. Real-time deployment uses a TinyML-distilled CNN on vehicles for ultra-lightweight, low-latency detection, while RSU-level BiLSTM-XGBoost performs a deeper temporal analysis. A Kafka–Spark Streaming pipeline supports localized classification, prevention, and dashboard-based monitoring. In baseline, stealth, and coordinated modes, the evaluation achieved accuracy, precision, recall, and F1-scores all above 97%. The mean end-to-end inference latency was 148.67 ms, and the resource usage was stable. The framework remains robust in both high-traffic and low-frequency attack scenarios, enhancing operator situational awareness through real-time visualizations. These results demonstrate a scalable, explainable, and operator-focused IDPS well suited for securing SC-IoV deployments against evolving threats. Full article

Breast cancer is the second leading cause of cancer-related mortality among women. Early detection enables timely treatment, improving survival outcomes. This paper presents a comparative evaluation of federated learning (FL) frameworks for multiclass breast cancer classification using ultrasound images drawn from three datasets: BUSI, BUS-UCLM, and BCMID, which include 600, 38, and 323 patients, respectively. Five state-of-the-art networks were tested, with MobileNet, ResNet and InceptionNet identified as the most effective for FL deployment. Two aggregation strategies, FedAvg and FedProx, were assessed under varying levels of data heterogeneity in two and three client settings. Results from experiments indicate that the FL models outperformed local and centralized training, bypassing the adverse impacts of data isolation and domain shift. In the two-client federations, FL achieving up to 8% higher accuracy and almost 6% higher macro-F1 scores on average that local and centralized training. FedProx on MobileNet maintained a stable performance in the three-client federation with best average accuracy of 73.31%, and macro-F1 of 67.3% despite stronger heterogeneity. Consequently, these results suggest that the proposed multiclass model has the potential to support clinical workflows by assisting in automated risk stratification. If deployed, such a system could allow radiologists to prioritize high-risk patients more effectively. The findings emphasize the potential of federated learning as a scalable, privacy-preserving infrastructure for collaborative medical imaging and breast cancer diagnosis. Full article

Vitamin B12 (B12) is a strong antioxidant and a cofactor for methionine synthase supporting DNA/RNA/protein methylation. We previously demonstrated that oral high-dose B12 supplement mitigates diabetic cardiomyopathy in Akita diabetic mice expressing twice the normal levels of Elmo1 (Engulfment and cell motility 1). To assess how B12 prevents early kidney damage, we treated Elmo1^HH mice and diabetic Elmo1^HH Ins2^Akita/+ mice with or without B12 in drinking water starting at 8 weeks of age. At 16 weeks, markedly reduced mesangial expansion was detected in the B12-treated diabetic kidneys (22% of glomeruli affected vs. 70% in the untreated diabetic kidneys). RNAseq analysis of the kidneys revealed that B12 suppressed expression of genes for adaptive immune response, while it upregulated those for solute carrier transporters and antioxidant genes. Strikingly, B12 treatment suppressed activators of circadian rhythm, Clock and Bmal1, and upregulated repressors like Cry1/2, Per1-3 and Dbp, suggesting a shift in their rhythmicity. B12 also upregulated linker histone H1 variants, and enhanced chromatin stability and cell cycle regulation. In BU.MPT proximal tubular cells in culture, B12 shifted forward the circadian expression phase of Bmal1 and Per1. Taken together, B12 supplement effectively mitigates early development of diabetic nephropathy in diabetic mice, potentially involving regulation of circadian rhythm. Full article

In this work, the sensor properties of multilayered Langmuir-Blodgett (LB) films of tetra-tert-butylphthalocyaninate zinc (tBuZnPc) were studied using an acoustoelectronic method. The morphology and optical properties of the fabricated films were characterized by atomic force microscopy and ultraviolet-visible spectroscopy, respectively. The LB films were deposited on surface acoustic wave (SAW) delay lines, and their gas-sensing properties were investigated. The films demonstrated high selectivity towards chloroform vapor compared to acetone, methanol, ethanol, and isopropanol. The highest selectivity was observed for the five-layer film, which can be attributed to the specific interaction of chloroform molecules with the hydrophobic cavities formed by the tert-butyl groups. Increasing the film thickness to 41 layers enhanced the absolute response to chloroform to 370 ppm; however, the selectivity decreased due to increased nonspecific adsorption. The results demonstrate the potential of using tBuZnPc LB films as sensitive coatings for the selective detection of chloroform in environmental and industrial monitoring applications. Full article

The Amaryllidaceae family represents a prolific source of pharmacologically active compounds, boasting over 700 diverse alkaloids identified to date. However, the genus Eustephia (Amaryllidoideae subfamily) remains largely unexplored. This study focused on the alkaloid profiles and pharmacological potential of bulb and leaves extracts from three Peruvian Eustephia species (E. coccinea, E. darwinii, and E. hugoei). The phenolic and flavonoid levels as well as the antioxidant activity of the methanolic extracts, were determined. Twenty-six alkaloids were identified in the alkaloid-enriched extracts (AEEs). Homolycorine-type alkaloids predominated in E. darwinii and E. hugoei, whereas E. coccinea displayed greater chemical diversity showing assoanine as the main detected alkaloid. In addition, candimine was widely distributed across species. AEEs showed stronger enzyme inhibition of acetylcholinesterase (AChE) compared to butyrylcholinesterase (BuChE). Notably, the AEE from E. coccinea leaves showed the highest AChE inhibition (IC₅₀ = 1.82 μg/mL), while the AEE from bulbs exhibited the strongest BuChE inhibitory activity (IC₅₀ = 61.22 μg/mL). Regarding anti-T. cruzi effect, the E. darwinii bulbs AEE was most potent and selective against amastigote forms (IC₅₀ = 2.1 μg/mL; SI = 8.83). These findings underscore the potential of Peruvian Eustephia species as promising sources of pharmacologically relevant alkaloids, with possible applications in neurodegenerative disorders and Chagas disease. Full article

The ring-assisted Mach–Zehnder interferometer (RA-MZI) has high sensitivity and fast optical response time, and it has been used as a label-free nanophotonic biosensor. Most RA-MZI-based biosensors, however, require chemical modification of the ring surface to immobilize biomolecules that can interact with target molecules for sensing. Here, we report a novel microfluidic-integrated RA-MZI (μFRA-MZI) where a microfluidic channel was fabricated right above the photonic ring resonator. μFRA-MZI allows for direct sample delivery to the RA-MZI without chemical modification of the ring surface and measures shifts in the resonance wavelength induced by the presence of target molecules, enabling label-free detection. In order to optimize the sensitivity of μFRA-MZI, seven devices were fabricated with varied design parameters, including the gap distance between the ring and the bus waveguide (G_ring), the length of the multi-mode interferometer (L_MMI), and the length of the directional coupler (L_DC). Photonic characterization showed that the device with G_ring = 1.2 μm, L_MMI = 15.5 μm, and L_DC = 13.5 μm exhibited the highest extinction ratio (ER) compared to the other six devices, consistent with the simulation-optimized design. Testing with NaCl solutions of varying concentrations yielded a bulk sensitivity of 11.48 nm/refractive index unit (RIU) and an ER of 0.41. With the potential to further improve the device’s sensitivity and the ability to detect samples directly in flow without chemical modifications of the ring resonator, μFRA-MZI will provide a robust and effective approach for label-free biosensing. Full article

Sensors in power systems utilize the detection results of fault signals to guide subsequent fault handling procedures. However, the traditional phase-shift restart strategy exhibits limitations such as power interruptions, reactive power redundancy, and intersystem fault clearance failures when addressing faults in the hybrid AC/DC transmission system. To address these shortcomings, a power compensation-based fault ride-through (FRT) scheme and a protection-control cooperation FRT scheme are proposed, taking into account the operational characteristics of the symmetric monopole LCC-HVDC (SM-LCC-HVDC). The power compensation-based FRT scheme actively compensates for power, mitigating the impact of reactive power redundancy on the AC-side bus during faults. The protection-control cooperation FRT scheme is activated after sufficient AC components are detected on the DC side. It leverages the adjustability of the DC system to proactively activate protection for AC transmission lines. An electromagnetic transient simulation model of the hybrid AC/DC same-tower transmission system was developed in PSCAD/EMTDC. Simulation results validate the effectiveness and superiority of the proposed methods. Full article

Identifying the activation status of advanced driver assistance systems (ADAS) in real-world driving environments is crucial for safety, responsibility attribution, and accident forensics. Unlike prior studies that primarily rely on simulation-based settings or unsynchronized data, we collected a multimodal dataset comprising synchronized controller area network (CAN)-bus and smartphone-based inertial measurement unit (IMU) signals from drivers on consistent highway sections under both ADAS-enabled and manual modes. Using these data, we developed lightweight classification pipelines based on statistical and deep learning approaches to explore the feasibility of distinguishing ADAS operation. Our analyses revealed systematic behavioral differences between modes, particularly in speed regulation and steering stability, highlighting how ADAS reduces steering variability and stabilizes speed control. Although classification accuracy was moderate, this study provides one of the first data-driven demonstrations of ADAS status detection under naturalistic conditions. Beyond classification, the released dataset enables systematic behavioral analysis and offers a valuable resource for advancing research on driver monitoring, adaptive ADAS algorithms, and accident forensics. Full article

Connected and Autonomous Vehicles are promising for advancing traffic safety and efficiency. However, the increased connectivity makes these vehicles vulnerable to a broad array of cyber threats. This paper presents a novel hybrid approach for intrusion detection in in-vehicle networks, specifically focusing on the Controller Area Network bus. Ensemble learning techniques are combined with sophisticated optimization techniques and dynamic adaptation mechanisms to develop a robust, accurate, and computationally efficient intrusion detection system. The proposed system is evaluated on real-world automotive network datasets that include various attack types (e.g., Denial of Service, fuzzy, and spoofing attacks). With these results, the proposed hybrid adaptive system achieves an unprecedented accuracy of 99.995% with a 0.00001% false positive rate, which is significantly more accurate than traditional methods. In addition, the system is very robust to novel attack patterns and is tolerant to varying computational constraints and is suitable for deployment on a real-time basis in various automotive platforms. As this research represents a significant advancement in automotive cybersecurity, a scalable and proactive defense mechanism is necessary to safely operate next-generation vehicles. Full article

Vision is a critical component of daily life, and its loss significantly hinders an individual’s ability to navigate, particularly when using public transportation systems. To address this challenge, this paper introduces a novel approach for accurately identifying bus route numbers and destinations, designed to assist visually impaired individuals in navigating urban transit networks. Our system integrates object detection, image enhancement, and Optical Character Recognition (OCR) technologies to achieve reliable and precise recognition of bus information. We employ a custom-trained You Only Look Once version 8 (YOLOv8) model to isolate the front portion of buses as the region of interest (ROI), effectively eliminating irrelevant text and advertisements that often lead to errors. To further enhance accuracy, we utilize the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to improve image resolution, significantly boosting the confidence of the OCR process. Additionally, a post-processing step involving a pre-defined list of bus routes and the Levenshtein algorithm corrects potential errors in text recognition, ensuring reliable identification of bus numbers and destinations. Tested on a dataset of 120 images featuring diverse bus routes and challenging conditions such as poor lighting, reflections, and motion blur, our system achieved an accuracy rate of 95%. This performance surpasses existing methods and demonstrates the system’s potential for real-world application. By providing a robust and adaptable solution, our work aims to enhance public transit accessibility, empowering visually impaired individuals to navigate cities with greater independence and confidence. Full article