Engineering reports : open access最新文献

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.

A. S. Al-Ezzi, S. M. Anas, and M. N. M. Ansari, “Characterization and Computational Modeling of Flexible Freestanding GaAs-Based Solar Cells,” Engineering Reports 7, no. 7 (2025): e70282, https://doi.org/10.1002/eng2.70282.

The Acknowledgments section in the published article is incomplete. The correct version of the Acknowledgments section is provided below:

Microfluidic droplet generation enables the rapid and efficient production of large quantities of droplets to be used in various fields such as medical science and biology. While polydisperse droplets are inherent in bulk emulsion production, which can be potentially used for combinatorial experimentation in addition to monodisperse droplets, microfluidic chip platforms offer superior control for post-processing applications and are better suited for integration within miniaturized systems. In this study, we present a simple yet robust method for generating droplet aggregation, which could be used for applications where the polydisperse droplets are advantageous in the context of microfluidics. This approach offers a significantly shorter timescale (in a fraction of a second) compared to existing methods in the literature. The generated droplets rely on the hydrodynamic instability of an aqueous interface within the framework of the pressure barrier principle. This approach requires adjustments to geometry and surface wettability properties, resulting in a distinct mode of droplet generation. This approach not only leads to a platform for the water-in-air microfluidics systems but also facilitates the integration of water-in-oil emulsion into microfluidic devices as a subsequent step. It was also observed that the polydisperse droplets are only generated in Glass-PDMS chips, not PDMS-PDMS chips, and the main channel height should be critically narrow (below 10 μm in our method) to allow the system to generate droplets. The generated droplets have diameters between 1 and 7 μm, with the majority concentrated in the 2–3 μm size band.

A comprehensive review of the current trends, applications, and innovations within the realm of smart fisheries was performed. Particular focus was placed on the integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). Recognizing the critical role fisheries play in global economies—especially in developing countries like Bangladesh—this study examines how these technological advancements can tackle urgent issues such as overfishing, disease management, and environmental monitoring. Through an in-depth exploration of recent literature, we highlight successful implementations, pinpoint key knowledge gaps, and outline future research directions. The ultimate aim of this review is to shed light on how smart fishing can enhance sustainability, improve productivity, and strengthen the resilience of the fishing industry.

Urban-fringe zones represent critical regions for forest fire prevention, yet culturally driven fire risks—particularly those induced by ritual activities—remain underexplored. This study proposes a Ritual Ignition Probability Model (RIPM) to decipher the spatiotemporal coupling mechanisms of wildfire risk in the urban–rural transitional areas of Kaifu District, Changsha, China. By integrating multi-source data (2018–2022)—including ritual activity intensity, ecological factors, and resource allocation metrics—the model quantifies the synergistic effects of lunar festival cycles, fuel accumulation dynamics, and delayed response mechanisms. RIPM employs a Bayesian hierarchical framework to address data heterogeneity and incorporates cultural drivers such as ritual activity risk (R) to optimize risk prediction. Empirical validation demonstrates that RIPM improves prediction accuracy by approximately 30% and reduces emergency response time by 69%. Key findings reveal that 68% of historical wildfires originated from ritual activities, with 87% concentrated within a 1-km buffer of urban boundaries. Policy recommendations include dynamic resource allocation (e.g., increasing fire suppression equipment reserves by 1.5× during peak ritual periods) and culturally adaptive governance innovations (e.g., designated e-incineration zones). By bridging cultural practices and ecological vulnerability, this study advances wildfire risk management theory and provides a replicable analytical framework for global urbanizing regions.

In dynamically evolving supply chain networks, identifying high-risk nodes and abnormal behavior patterns is crucial for risk early warning and system stability. Existing methods mainly rely on static graph modeling or discriminative learning, which struggle to capture temporal evolution and often fail to detect “camouflaged normal” risk nodes under feature obfuscation. To address this, we propose the Temporal-Generative Relation-aware Risk Network (TG-RRNet) to systematically tackle key challenges in dynamic high-risk node identification. TG-RRNet first constructs a time-driven heterogeneous dynamic graph sequence, integrating three types of multimodal information including attribute similarity, historical interaction intensity, and historical risk factor to model the structural evolution process. A temporal-aware graph neural network with temporal decay and graph attention extracts dynamic node representations and captures risk propagation paths. To model latent abnormal patterns, a generative anomaly detection module uses a variational autoencoder to learn latent representations and jointly measures potential risks through reconstruction errors and KL divergence. Finally, a multimodal cross-attention mechanism dynamically fuses structured features, graph representations, and unstructured logs to generate unified risk representations for prediction. Experiments on real-world supply chain datasets show that TG-RRNet significantly outperforms state-of-the-art methods in high-risk node identification and anomaly detection, demonstrating strong practical value and generalization. Code is available at: https://github.com/PinmengLi/TG-RRNet.git.