Earthquakes can cause significant damage to structures, resulting in considerable financial and social losses. Enhancing the seismic capacity of existing structures through retrofitting is essential. Traditional seismic retrofitting techniques, such as reinforced concrete (RC) jacketing and steel jacketing, primarily aim to increase structural resistance. But RC jacketing is intrusive and increases mass and stiffness, steel jacketing increases cost and demands careful detailing and both approaches are often inadequate for addressing the dynamic complexities of seismic loading. As an alternative, base isolation systems provide a promising solution by concentrating deformation and energy dissipation within isolation bearings, thereby protecting the superstructure from seismic forces. This study evaluates the effectiveness of base isolation compared with conventional retrofitting methods in enhancing the seismic performance of existing structures. The experimental program included cyclic testing of four RC frame structures: one control specimen and three others retrofitted with RC jacketing, steel jacketing, and lead rubber bearings (LRB). The results indicate that the base-isolated specimen demonstrates superior energy dissipation capacity due to the favorable deformation characteristics of the LRB. Moreover, structural damage is redirected from the original columns to the newly installed transition beams, effectively preserving the integrity of the primary structure. These findings highlight the advantages of base isolation in improving seismic performance and provide valuable experimental evidence supporting its application in the retrofitting of existing structures.
Deep in situ pressure coring provides an accurate means of determining oil and gas reserve parameters. The key to achieving pressure coring at depths exceeding 5000 m lies in the ultimate bearing strength and stability of the pressure controllers. Due to the limited downhole space and the inherent technical demands of pressure coring, traditional pressure coring technology typically has an ultimate bearing pressure capacity of less than 70 MPa. The structural model of the pressure controller is designed. The stress–strain distribution of the pressure controller under external load is numerically simulated. A contact stress optimization scheme and critical sealing gap of pressure controllers are proposed. It was found that the saddle pressure controllers can ensure the fit clearance of the sealing surface and effectively control the deformation of the valve cover within 0.015 mm. The saddle pressure controllers have demonstrated an ultimate bearing strength exceeding 140 MPa, with minimal leakage. These findings have significant implications for accurate assessment of deep petroleum resources.
The proliferation of connected vehicles in the Internet of Vehicles (IoV) ecosystem has introduced new security challenges, particularly in the context of internal network attacks. Traditional public key infrastructure (PKI) technologies are no longer sufficient to ensure secure communication within a network that experiences dynamic topology changes and high vehicle density. In response, there is a growing need for a lightweight misbehavior detection framework that offers fast computation and minimal space complexity. This paper presents a novel approach using continuous-time recurrent neural networks for detecting misbehavior in the IoV and assesses their performance against the Vehicular Reference Misbehavior (VeReMi) extension dataset. We compare two recently introduced models—the liquid time-constant (LTC) network and the closed-form continuous-time (CFC) neural network—with the established convolutional neural network-long short-term memory (CNN-LSTM) model. The results indicate that continuous-time neural networks marginally outperform CNN-LSTM on evaluation metrics. Despite LTC and CFC having significantly fewer parameters, making them less complex and more space-efficient than CNN-LSTM, the latter proves to be more time-efficient. Therefore, a careful balance between runtime cost and space complexity must be considered when deploying lightweight neural networks in practical applications.
Hyperspectral image classification (HSIC) is a crucial task in remote sensing. In existing HSIC architectures, convolutional neural networks excel at capturing local information through regional feature representations, while transformers are adept at establishing long-range dependencies with the self-attention mechanism. However, these methods still encounter challenges of imbalanced global–local feature explorations and boundary feature extractions. To address these issues, this study proposes the cross-stage attention edge enhancement and Fourier-wavelet transformer integrated network (CAEEFT-Net), which effectively balances global context modeling with local detail preservation and boundary feature extraction for HSIC tasks. Specifically, for spatial feature refinement, three key modules are designed: the cross-stage attention module to enable the interaction of features across different stages, thereby strengthening the model’s feature representation ability, the global–local attention module to jointly enhance global and local features, and the pyramid-stripe attention module to capture discriminative edge features. For spectral feature extraction, this article proposes a spectral Fourier-wavelet transformer to integrate the strengths of both global frequency-domain patterns and local token-level features. Experimental results on three benchmark datasets demonstrate that CAEEFT-Net achieved superior performance compared to state-of-the-art methods, validating the effectiveness of the proposed CAEEFT-Net model for HSIC.
The integrated access and backhaul (IAB) architecture utilizes wireless backhaul to facilitate the expansion of fifth-generation (5G) New Radio (NR) networks. In an IAB network, intermediate base stations (or say IAB nodes) can be connected in a multi-hop fashion. However, optimizing resource scheduling in such a network remains a critical challenge. In this work, we present a novel method that integrates multi-user multiple-input and multiple-output (MU-MIMO) and non-orthogonal multiple access (NOMA) technologies into IAB networks. The designed two-phase algorithm has the following features: 1) support for multi-path routing and efficient resource utilization through the combined use of MU-MIMO and NOMA, 2) a novel route decision phase that selects optimal paths by considering load balancing among IAB nodes, and 3) a dynamic link scheduling phase that allocates transmission power and schedules links to maximize network capacity. Simulation results demonstrate that the proposed solution achieves significant improvements in throughput, fairness, and latency compared to existing methods.
Telecommunication, Transportation and communications
Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always remaining in the groove of rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is the expression of continuously updating emerging topics concerning relationships between urban planning, mobility, and environment, through a collection of short scientific papers written by young researchers. The Review Notes are made of five parts. Each section examines a specific aspect of the broader information storage within the main interests of TeMA Journal. In particular, the Urban planning literature review section presents recent books and journals on selected topics and issues within the global scientific panorama. For the first issue of TeMA Journal volume no. 18, this section provides a critical overview of recent reports and documents on climate change, published by different types of stakeholders. This review examines the landscape of climate change reporting through a comparative lens, focusing on key findings, strengths, weaknesses, and implications of selected publications. This contribution aims to examine reports produced by International Governmental Organizations (IGOs), analyzing their approach, findings, and potential limitations.
Transportation engineering, Urbanization. City and country
The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.
Robust methods are needed to detect how people are moving in smart public transportation systems. This paper proposes and characterizes effective means to accurately detect passengers. We analyze a public WiFi-based activity recognition (WiAR) dataset to extract human activity features from Channel State Information (CSI) data. To do so, CSI power changes caused by nearby human activity are analyzed. Our method first extracts multi-dimensional features using a Short-Time Fourier Transform (STFT) of CSI data to capture the relevant signal features. Since the environment of a transportation system changes dynamically and non-deterministically, we propose analyzing these changes with a heuristic algorithm that leverages a decision tree to automate a decision-making solution for feature selection. Principal Component Analysis (PCA) is performed before the decision tree algorithm. Reported results are compared with those obtained from the existing methods. Based on these results, we explore the effectiveness of various features such as the chirp rate, delta band power, spectral flux, and frequency of movement. This allows identifying and recommending the most effective features for the explored detection task according to observed variability, information gain, and correlation between features. The reported classification results show that using only the chirp rate estimated from CSI information as a feature, we achieve precision = 83%, True Positive <inline-formula> <tex-math notation="LaTeX">$(TP)=94\%$ </tex-math></inline-formula>, True Negative <inline-formula> <tex-math notation="LaTeX">$(TN)= 91\%$ </tex-math></inline-formula> and F1-score = 87%. Considering delta band power as an additional feature adds more information and allows getting higher performance with precision = 100%, <inline-formula> <tex-math notation="LaTeX">$TP=97\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$TN = 95\%$ </tex-math></inline-formula> and F1-score = 95%.
Transportation engineering, Transportation and communications
Abstract One critical difficulty to high‐level automated driving is the decision‐making process of automated vehicles in complicated traffic environments, especially in situations mixed of pedestrians and vehicles. This paper proposes a differentiated decision‐making algorithm to promote passing capability and efficiency in mixed traffic conditions. First, the behavioural characteristic of pedestrians, denoted as the pedestrian feature index, is estimated by a multi‐layer perception module input with quantitative analysis of pedestrian action. Based on estimation results, the decision algorithm merges pedestrian feature index into intelligent driver model and adjusts corresponding parameters, which used to be unchangeable so that the ego‐vehicle can make differential decisions according to various pedestrian features. Validation on the PIE dataset shows that the accuracy of pedestrian feature estimation is ensured. A simulation scenario is established utilizing cellular automata, and the results indicate that the proposed decision‐making algorithm can greatly improve passing efficiency under safety and manoeuvrability prerequisite.
Agriculture has a good stake in the world’s GDP. In many countries, agriculture and allied sectors have a good stake in national GDP. This paper covers details related to livestock since 1960s. The workforce has managed livestock for many decades. The workforce increases as the number of animals increases; it is an energy, time-consuming, and economically costly approach. Apart from it, there is no assurance about animal welfare in case of diseases, breeding, and feed intake issues. In the 21st century of digitalization, technology has a key role in improving overall monitoring, controlling, and processing in livestock management. This paper has gone thoroughly into the manual and automated livestock farm management, aiming welfare of animals, livestock products, consumers’ benefit, and sustainable environmental approaches.
This paper proposes a deep reinforcement learning (DRL)-based algorithm in the path-tracking controller of an unmanned vehicle to autonomously learn the path-tracking capability of the vehicle by interacting with the CARLA environment. To solve the problem of the high estimation of the Q-value of the DDPG algorithm and slow training speed, the controller adopts the deep deterministic policy gradient algorithm of the double critic network (DCN-DDPG), obtains the trained model through offline learning, and sends control commands to the unmanned vehicle to make the vehicle drive according to the determined route. This method aimed to address the problem of unmanned-vehicle path tracking. This paper proposes a Markov decision process model, including the design of state, action-and-reward value functions, and trained the control strategy in the CARLA simulator Town04 urban scene. The tracking task was completed under various working conditions, and its tracking effect was compared with the original DDPG algorithm, model predictive control (MPC), and pure pursuit. It was verified that the designed control strategy has good environmental adaptability, speed adaptability, and tracking performance.