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This study experimentally investigates the performance, combustion, and emission characteristics of a Reactivity-Controlled Compression Ignition (RCCI) engine fueled with diesel and the high-viscosity oxygenated alcohol 2-Methyl-1-butanol. Experiments were conducted on a single-cylinder common-rail direct injection engine operating at brake mean effective pressures of 3 and 5 bar and fuel injection pressures of 400, 600, and 800 bar. Diesel was directly injected as the high-reactivity fuel, while 2-Methyl-1-butanol was port-injected to establish RCCI combustion. Fuel blends containing 10%, 20%, and 30% alcohol were evaluated and compared with neat diesel operation. Results indicate that increasing injection pressure improves fuel atomization, advances combustion phasing, and enhances heat release characteristics. At full load and 800 bar injection pressure, the D70MB30 blend achieved the highest brake thermal efficiency of 37.4%, compared to 26.0% for neat diesel. Significant emission reductions were also observed, with NO_x decreasing from 4.5 ppm (diesel) to 3.1 ppm and smoke opacity showing a consistent declining trend due to improved charge homogeneity and oxygen availability. However, higher alcohol content resulted in increased CO and HC emissions at part-load conditions because of low-temperature combustion and evaporative cooling effects. These penalties were substantially mitigated at higher injection pressures. Overall, the D70MB30 blend at 800 bar provided the best trade-off between performance and emissions, demonstrating the potential of 2-Methyl-1-butanol as a sustainable alternative fuel for advanced RCCI engine operation.

With the rapid development of technologies such as cloud computing and the Internet of Things, organizations face the thorny reality that network attacks are becoming increasingly diverse, covert, and intelligent. Traditional signature-based intrusion detection systems (IDSs) struggle to address zero-day attacks and advanced persistent threats (APTs), often resulting in low detection rates and high false-positive rates. To address this, this paper proposes an adaptive network intrusion detection system that integrates random forest (RF) and real-time reputation evaluation. The system first preprocesses and normalizes the original network traffic and behavior logs, and then uses a random forest to perform preliminary multi-category classification. It then introduces a historical behavior risk metric, weighting the error rate of the current detection with the device's historical risk profile using exponential decay. A comprehensive reputation score is generated using a continuously differentiable “four-stage” smoothing function: sigmoid in the low-confidence zone, cosine in the medium-low zone, inverse sigmoid in the medium-high zone, and exponential decay in the extremely high zone. Finally, RRF-IPS's reputation scoring system executes automated policies such as bandwidth throttling, warning notifications, and session isolation or blocking, forming a closed “detect-assess-respond-archive” loop. Experimental results demonstrate that, on CICIDS2017, our system improves accuracy by 0.6% and F1 score by 5.9% compared to state-of-the-art methods.

Malware remains a significant concern for modern digital systems, increasing the need for reliable and scalable detection methods. This work proposes an ensemble method that combines a random forest (RF) with a vision transformer (ViT). The approach exploits complementary feature spaces, including bag-of-words (BoW) and image representations, to enhance multi-class malware classification. We also evaluate traditional machine learning models (Naïve Bayes, Support Vector Machine, and RF) and deep learning (DL) models (ResNet50 and ViT) using the Microsoft Malware and Dike datasets. The proposed ensemble model achieves 99.32% accuracy and 98.11% F1 score on the Malware dataset, outperforming individual models and recent state-of-the-art studies. While ViT captures spatial and sequence dependencies via attention mechanisms, RF captures textual and byte-level frequency patterns. Their combination, through a product rule, enhances robustness and reliability in multi-class cybersecurity tasks.

This paper addresses the issue of alarm-based hybrid fault-tolerant control for Markovian jump systems with uncertainty, in which there may exist actuator and structural failures. Through the integration of passive, observer-based active, and adaptive fault-tolerant control techniques, the effect of actuator partial failure, actuator bias failure, and structural failure can be separately attenuated. Moreover, an alarm-based multi-threshold system is designed to invoke the suitable control strategy. Finally, to prove the practical applicability of the given method, a single-link robot arm system is presented, with results confirming the theoretical findings.

Dynamic pricing has become a core approach for optimizing data value in both personal and enterprise contexts. However, accurately determining data prices across multiple dimensions remains a key research challenge. This study proposes a novel dynamic pricing mechanism based on a multi-dimensional dynamic model to enhance pricing accuracy. The model analyzes pricing factors across various dimensions and dynamically adjusts prices according to real-time data attributes and usage scenarios. Experimental results show that the proposed model achieves a pricing deviation of only 129 yuan from actual values (approximately 2.5%), significantly outperforming the traditional equal pricing model. The proposed model reduces the maximum error by 2.5 and the root mean square error by 2.2 in comparison. In addition, it demonstrates improved computational efficiency, with a runtime reduction of 296.10 milliseconds, and achieves an absolute increase of 14.29 percentage points in the F1 score and 14.90 percentage points in recall rate. These results indicate that the multi-dimensional dynamic pricing model offers superior performance in both pricing precision and operational efficiency. The findings provide valuable insights for developing more accurate and adaptable data pricing strategies in real-world applications.

This study focuses on urban railway train parameters and train speeds up to 120 km/h, which examines how curve alignment parameters affect train dynamic responses and structural vibrations in curved floating slab tracks, combining theoretical analysis with simulation, translating nonlinear physical mechanisms into computable engineering formulas. A coupled train–track dynamic simulation model and an environmental vibration simulation model are established. Key findings show that vertical loads on inner rail fasteners increase with higher unbalanced superelevation, while those on outer rail fasteners decrease. Lateral loads and resultant lateral/vertical loads on fasteners rise linearly with the absolute value of unbalanced superelevation. The peak frequency of fastener loads under unbalanced superelevation primarily falls within the ranges of 1.6–2.5 and 3.15–10 Hz. In contrast, straight sections or sections with balanced superelevation exhibit nearly linear load spectra. Compared to straight sections, curved sections show increases of up to 210% in peak fastener loads. The peak values of fastener loads, wheel–rail contact forces, vehicle accelerations, derailment coefficients, and wheel load reduction rates are mainly influenced by train speed, curve radius, and superelevation. Rail and slab displacements are additionally affected by spring stiffness. Fitting formulas derived for these variables provide a theoretical basis for optimizing the relationship among curve radius, train speed, superelevation, and steel-spring stiffness, with the aim of improving both driving safety and vibration mitigation in railway systems. The peak vertical vibration acceleration of floor slabs generally increases with floor height, while peak lateral vibration acceleration shows no significant variation. Under balanced superelevation conditions, lateral acceleration spectra exceed those under straight-track conditions, with notable differences in spectral shapes across floor levels. These results offer a theoretical foundation for optimizing track geometry and structural parameters in floating slab track systems, as well as a framework for predicting and assessing driving safety and structural vibration performance.

Defined on negative as well as positive domain, we introduce a novel two-parameter survival distribution to offer enhanced flexibility and applicability in statistical modeling in the article understudy. This distribution is characterized by highly adaptable probability density with hazard rate function. Specifically, the probability density function can exhibit unimodal behavior with an inverted shape and a light left tail, or it may be monotonically decreasing with a heavy right tail. The hazard rate function demonstrates a wide range of failure pattern, including increasing, decreasing and an unconventional decreasing-increasing shapes. To evaluate the performance and suitability of the proposed model, several statistical criteria and goodness-of-fit measures are employed. Graphical tools such as Probability-Probability and Quantile-Quantile plots are also used for model validation. The theoretical development includes an in-depth examination of the quantile function and a comprehensive analysis of the moments, including mean, variance, standard deviation, covariance, skewness, and kurtosis. Further, the study explores several key statistical properties of the Two Parameter Based Exponential distribution, including order statistics, with particular focus on extreme values, reversed order statistics, upper record statistics residual lifetime function and reversed residual life function. Parameter estimation is conducted using the method of maximum likelihood. A Monte Carlo simulation study is performed to assess the efficiency and accuracy of the estimation procedure. Moreover, two real-life datasets are analyzed to compare the proposed model against existing distributions. These applications highlight the flexibility and practical relevance of the TPBE distribution in survival analysis and reliability engineering.

Wearable biosensors are reshaping personalized healthcare by offering real-time and noninvasive monitoring of important physiological biomarkers. Rapid developments in microfluidic systems, electrochemical sensing, and liquid crystal technologies have enabled flexible devices that reliably collect biofluids and support sensitive on-body measurements. This review brings together recent progress in these fields and explains how their combined use improves analytical performance, strengthens signal quality, and expands the range of measurable biomarkers. Advances in machine learning that support data interpretation and promote intelligent operation are also examined. Key challenges remain, including long-term stability, variations in biofluid composition, and the need for scalable fabrication. This review outlines key opportunities for future research and provides a cohesive perspective on the scientific and engineering directions needed to realize clinically meaningful and commercially viable wearable diagnostic systems.

Open-Graded Friction Course (OGFC) mixtures in flexible pavements are known for their efficient drainage characteristics, but concerns exist regarding their structural weaknesses and short lifespans. This study investigates methods to improve the mechanical performance of OGFC by optimizing the mix design with the introduction of two different fibers. Sisal and glass fibers were each added (0.15%, 0.3%, and 0.45%) to OGFC mixtures containing 5%–6.5% bitumen binder (0.5% increments) and 2% pond ash filler. Different fiber-based OGFC mixtures were tested for volumetric properties and mechanical characteristics such as draindown, air voids, permeability, aging, Cantabro abrasion, and indirect tensile strength. Both fiber types showed improved mechanical performance, at a 0.3% fiber dosage. Glass fibers exhibited a higher tensile strength ratio (94.27%) compared to sisal fibers (93.81%), exceeding the required criteria by a significant margin. Finally, 0.30% fibers by weight of mix was selected as the optimum dosage for OGFC mixes inclusive of glass and sisal fibers.

Y. Li and D. Qian, “Evaluation of damage characteristics of large LNG storage tanks under multiphase loading—An explosion occurs at high temperatures,” Engineering Reports 7, no. 3 (2025): e12846, https://doi.org/10.1002/eng2.12846.

There is a minor error in the second affiliation of the published article. The correct affiliation is provided below:

Xinjiang Key Laboratory of Clean Conversion and High Value Utilization of Biomass Resources, School of Resource and Environmental College, Yili Normal University, Yining, China

We apologize for this error.