Search Results (140)

Selective voter mobilization dominates U.S. elections, with campaigns prioritizing swing voters to win critical states. While effective for a short-term period, this strategy deepens policy polarization, marginalizes minorities, and undermines representative democracy. This paper investigates voter turnout disparities and policy manipulation using advanced time series forecasting models (ARIMA, LSTM, and seasonal decomposition). Analyzing demographic and geographic data, we uncover significant turnout inequities, particularly for marginalized groups, and propose actionable reforms to enhance equitable voter participation. By integrating data-driven insights with theoretical perspectives, this study offers practical recommendations for campaigns and policymakers to counter polarization and foster inclusive democratic representation. Full article

To investigate how severe wear at No. 12 turnout frogs in an urban rail transit line operating at speeds over 120 km/h on the dynamic performance of the vehicle, a vehicle–frog coupled dynamic model was established by employing the 2021 version of SIMPACK software. Profiles of No. 12 alloy steel frogs and metro wheel rims were measured to simulate wheel–rail interactions as the vehicle traverses the turnout, using both brand-new and worn frog conditions. The experimental results indicate that increased service life deepens frog wear, raises equivalent conicity, and intensifies wheel–rail forces. When a vehicle passes through the frog serviced for over 17 months at the speed of 120 km/h, the maximum derailment coefficient, lateral acceleration of the car body, and lateral and vertical wheel–rail forces increased by 0.14, 0.17 m/s², 9.52 kN, and 105.76 kN, respectively. The maximum contact patch area grew by 35.73%, while peak contact pressure rose by 236 MPa. To prevent dynamic indicators from exceeding safety thresholds and ensure train operational safety, it is recommended that the frog maintenance cycle be limited to 12 to 16 months. Full article

With China’s high-speed rail network undergoing rapid expansion, turnouts constitute critical elements whose safety and stability are essential to railway operation. At present, the efficiency of wheel–rail force safety monitoring conducted in the small hours reserved for the construction and maintenance of operating lines without marking train operation lines is relatively low. To enhance the efficiency of turnout safety monitoring, in this study, a three-dimensional BIM model of the No. 42 turnout was established and a corresponding wheel–rail force monitoring scheme was devised. Collision detection for monitoring equipment placement and construction process simulation was conducted using Navisworks, such that the rationality of cable routing and the precision of construction sequence alignment were improved. A train wheel–rail force analysis program was developed in MATLAB R2022b to perform signal filtering, and static calibration was applied to calculate key safety evaluation indices—namely, the coefficient of derailment and the rate of wheel load reduction—which were subsequently analyzed. The safety of the No. 42 turnout and the effectiveness of the proposed monitoring scheme were validated, theoretical support was provided for train operational safety and turnout maintenance, and technical guidance was offered for whole-life-cycle management and green, sustainable development of railway infrastructure. Full article

Considering the greater contact stress of turnout rails during wear and the development of heavy-haul railways, twin-disc sliding–rolling wear tests were performed on U75VH heat-treated rail steels applied for turnouts under high contact stress ranging from 1980 MPa to 2270 MPa. The microstructure of the worn surfaces was analyzed using optical microscope (OM), scanning electron microscope (SEM), 3D microscope, electron backscatter diffraction (EBSD), and hardness tests. The results indicated that after 10 h of wear, the weight loss was 63 mg at a contact stress of 1980 MPa, while it reached 95 mg at a contact stress of 2270 MPa. At a given contact stress, the wear rate increased with increasing wear time, while a nearly linear increase in wear rate was observed with increasing contact stress. As wear time and contact stress increased, the worn surface showed more pronounced wear morphology, leading to greater surface roughness. Crack length significantly increased with wear time, and higher contact stress facilitated crack propagation, resulting in longer, deeper cracks. After 10 h of wear under a contact stress of 2270 MPa, large-scale cracks with a maximum length of 128.29 μm and a maximum depth of 31.10 μm were formed, indicating severe fatigue wear. Additionally, the thickness of the plastic deformation layer increased with the wear time and contact stress. The surface hardness was dependent on the thickness of this layer. After 10 h of wear under the minimum and maximum contact stresses, hardening rates of 0.39 and 0.48 were achieved, respectively. Full article

Cultural tourism can act as a driver for inner area development, bringing about a range of socio-economic benefits through economic stimulation, quality of life improvement, and cultural heritage preservation. Inner territories, set apart by geographic marginality and low population density, hold a rich cultural and environmental heritage that, however, remains off the radar and left behind. Guided by the principles of endogenous local development, this article seeks to contribute to the existing body of research by proposing potential strategies for local growth rooted in cultural tourism. From this perspective, we identified the Basilicata region (Southern Italy) as a proper test area. The region is rich in archaeological, monumental and museum evidence, but is characterized, except in a few areas, by a low rate of tourist turnout. Through a replicable, comprehensive, and flexible methodology—drawing on bibliographic research, analysis of archaeological, archival, erudite and antiquarian sources, and carrying out field surveys—the different points of interest in the region have been brought together under specific cultural themes. Results include the design of three detailed routes (Via Herculia, Frederick II’s, and St Michael’s cultural routes) useful for three different types of tourism (sustainable, emotional, and accessible). Possible scenarios for valorization and fruition are also proposed, paying particular attention to digital technologies. Thus, this research aligns with Sustainable Development Goals (SDGs) 8 and 11 promoting cultural heritage valorization and preservation, shoring up economic revitalization, stepping up community engagement, and pushing forward environmentally friendly tourism practices. Research findings can attract the interest of a wide range of stakeholders such as tourism professionals, local authorities, cultural and creative industries, local communities and entrepreneurs, as well as academics and researchers. The methodological approach can be considered for the valorization and tourist enjoyment of inner areas in other countries, with particular focus on those falling within the Mediterranean region which is rich in cultural heritage, environmental value, and socio-economic potential. Full article

Cotton micronaire (MIC) is an essential fiber quality index that characterizes both fiber maturity and fineness components. This study compared how MIC affects the fiber high volume instrument (HVI) and advanced fiber information system (AFIS) qualities between Deltapine^® and PhytoGen upland varieties. There were noticeable differences among HVI and AFIS qualities from Deltapine^® fiber samples and PhytoGen samples, with significant differences om HVI strength and elongation. MIC development benefited fiber HVI strength enhancement and also HVI short fiber index (SFI), AFIS neps, AFIS short fiber contents, and AFIS immature fiber content (IFC) reduction, all of which were desired. Adversely, MIC evolution could cause undesired HVI Rd lowering, HVI +b boosting, and AFIS UQL(w), and a decrease in L5%(n) in fiber. Further, MIC values were not related with lint turnout, but they were positively and greatly correlated with algorithmic M_IR values of the attenuated total reflection in Fourier transform infrared (ATR FT-IR) spectra. The results demonstrated the applicability of the ATR FT-IR technique combined with the M_IR approach for rapid laboratory MIC assessment at early MIC testing in remote/breeding locations. Full article

This study evaluates the link between economic voting and electoral behavior in the 2024 European Parliament (EP) elections. This study is grounded in both selective perception and economic voting theories, examining how different independent factors could interact with electoral behavior. In this regard, the research aims to achieve several research directions: (i) the evaluation of the statistical differences in voters’ turnout in 2024 EP elections by geographical regions; (ii) the analysis of the interaction between voters’ perceptions of the current or future economic situations and voter turnout; (iii) the analysis of the interaction between objective economic conditions and electoral behavior. Using both multiple linear regression and logistic models, the study highlights that voter turnout and incumbent party reelection are significantly related to voters’ perceptions of the current or future state of the national economy. The results reveal that regional differences in voter turnout are largely explained by significant differences in voters’ economic perceptions, while the decision to vote for the incumbent party is driven by future economic expectations. The empirical findings underscore the pivotal role played by subjective perceptions in shaping electoral behavior, illustrating that political attitudes and behaviors are derived from personal interpretation of the national economic situations. Beyond theoretical perspectives that highlight the link between psychological processes and voting, the paper might have several practical implications for academics or decision makers interested in the field of electoral behavior. Full article

A model transformation method based on state refinement and semantic mapping is proposed to address the challenges of high modeling complexity and resource consumption in symbolic validation of industrial software requirements. First, a rule-based semantic mapping system is constructed through the explicit definition of element correspondence between statechart components and verification models, coupled with a composite state-level refinement strategy to structurally optimize model hierarchy. Second, an automated transformation algorithm is developed to bridge graphical modeling tools with formal verification environments, supported by quantitative evaluation metrics for mapping validity. To demonstrate its practical applicability, the methodology is systematically applied to railway infrastructure safety—specifically the railroad turnout control system—as a critical case study. The experimental implementation converts operational statecharts of turnout control logic into optimized NuSMV models. Not only did the models remain intact, but the state space was also effectively reduced through the optimization of the hierarchical structure. In the validation phase, the converted model is tested for robustness using the fault injection method, and boundary condition anomalies that are not explicitly stated in the requirement specification are successfully detected. The experimental results show that the validation model generated by this method has improved validation efficiency in the NuSMV tool, which is significantly better than the traditional conversion method. Full article

Occupational exposure as a firefighter was recently classified as carcinogenic to humans by the IARC. Fire instructors’ exposure to carcinogenic PAHs is a major concern, and studies that have tried to assess the determinants of their exposure are scarce. An air and biomonitoring study was conducted in fire instructors performing simulated training exercises in enclosed containers. Air samples were collected, as well as urine samples from 22 firefighting instructors, and skin wipes were collected from FFs’ skin at the end of the exercises. PAH metabolites (1-hydroxypyrene, 3-hydroxybenzo(a)pyrene, 2/3-hydroxyfluorene, and 2/3-hydroxyphenanthrene) were measured in urine samples at three sampling times (beginning of shift, end of shift, and next morning). Airborne PAHs were dominated by low molecular weight compounds (naphthalene), and levels were as high as 67 µg·m⁻³ close to the containers, decreasing at higher distances. Skin contamination was observed both on the neck/face and hands/wrists of fire instructors and pilots. Ten times lower skin contamination was observed when nitrile undergloves were worn. High internal exposure was measured, with 1-hydroxypyrene and 3-hydroxybenzo(a)pyrene levels frequently exceeding maximum recommended values in occupational settings (up to 2.8 µmol/mol creatinine for 1-OHP, 14 µmol/mol creatinine for ΣOH-PAH, and 1.0 nmol/mol creatinine for 3-OHBaP), whereas benzene exposure was revealed to be very low. These types of exposure were found to derive both from dermal absorption (combustion products deposited on the skin) and inhalation (when removing SCBA outside the containers). Several recommendations are proposed in order to reduce both exposure routes (nitrile undergloves and half-masks in the vicinity of containers), harmonise decontamination (PPEs) and cleaning procedures, and prevent the dermal absorption of PAH from turnout gear. This study emphasises the complex PAH exposure profiles of fire instructors and characterises the main drivers of exposure, highlighting the need for better mitigation strategies. Full article

Severe changes in the profiles of the crossing nose are caused by large dynamic contact forces. To predict these forces as well as the profile evolution, the Whole System Model (WSM) was developed. However, it uses computationally expensive FE simulations. As a replacement, the semi-physical plasticity and wear model (SPPW) has been developed, thus majorly enhancing the overall performance of the WSM. The SPPW considers the influence of wear, plasticity, and wheel-profile-related effects. Its results have shown an overall good correlation with FE results, laboratory data for different materials, and field data from a real crossing. Due to the semi-physical nature of the model, the required computational time for the predictions was significantly reduced compared to FE simulations: minutes instead of weeks. The SPPW will be useful for time-efficient rail damage prediction, like wear and plastic deformation, and, as part of the WSM, contribute to a fast holistic track damage prognosis. Full article

The self-contained breathing apparatus (SCBA) is an integral part of the structural firefighting personal protective equipment (PPE) ensemble. However, when donned, it adds significant weight and restriction, interfering with the fit of the turnout suit and the ventilation within the clothing system. This may result in a reduction of air gaps within the clothing microclimate, quickening the onset of heat strain. Therefore, the purpose of this study was to assess the impact of the SCBA on air gaps in structural firefighting turnout suits. Nine active-duty male firefighter participants were scanned in a three-dimensional body scanner in four garment configurations (compression, base layers, turnout suit, and turnout with SCBA). Torso volume, surface area, and air gaps were calculated alongside ease measurements. Findings demonstrated a 59% increase in torso volume when donning the turnout suit over base layers compared to a 1.2% reduction in torso volume when donning the SCBA. The change in torso air gap volume and distance were also found to be negligible when donning the SCBA. This study lays the foundation for full systems ensemble research needed to better understand how the design, weight, and fit of the SCBA impacts the thermal comfort, mobility, and protection of structural firefighters. Full article

Railway turnout is a critical railway infrastructure that guides trains in switching tracks. Over time, uneven rail wear can lead to switch rail reduction value (SRRV) deviation, a typical structural defect that compromises turnout functionality and jeopardizes train operation safety. Current SRRV deviation detection methods rely primarily on inefficient manual inspections, making it difficult to ensure operational safety. To address this issue, the study carried out a comprehensive investigation combining numerical and experimental analyses. First, a rigid–flexible coupled dynamics model of a vehicle-turnout system was developed to analyze the wheel–rail dynamic interaction forces and contact relationships under various SRRV deviation conditions. The results revealed that SRRV deviation significantly affects both wheel–rail interaction forces and the turnout structural irregularity wavelength. Thus, based on discrete wavelet transform (DWT), a wheel–rail force trend component was derived that can effectively analyze the turnout structural irregular wavelength, and the mapping relationship between SRRV deviation and the wheel–rail force trend component was then established. Finally, an efficient and accurate method for identifying SRRV deviation based on wheel–rail force trend component was proposed and validated using field-measured data from trains passing through turnouts. This study contributes to the timely detection of track defects, helping to prevent safety incidents during train operations. Full article

The turnout switch machine, a vital outdoor component of railway signaling, controls train steering amidst complex operations and high frequencies. Its malfunction significantly disrupts train operations, potentially causing derailments. This paper proposes a sound-based fault diagnosis method, termed ERS (a method combining EMD, ReliefF, and SVM), for effective monitoring and detection of turnout switch machines. The method employs Eigenmode Decomposition (EMD) to smooth the sound signal, reduce noise, and extract key statistical parameters of both the time and frequency domains. To address redundant information in high-dimensional features, the ReliefF algorithm is utilized for feature selection, dimension reduction, and fault classification based on weighted parameters. Subsequently, the selected feature parameters are used to train the Support Vector Machine (SVM). A comparison with results obtained without ReliefF feature selection demonstrates the necessity of this step. The results show that the fault diagnosis accuracy reaches 98% in the positioning work mode and 95.67% in the reversing work mode, verifying the method’s effectiveness and feasibility. Full article

To lighten the workload of train drivers and enhance railway transportation safety, a novel and intelligent method for railway turnout identification is investigated based on semantic segmentation. More specifically, a railway turnout scene perception (RTSP) dataset is constructed and annotated manually in this paper, wherein the innovative concept of side rails is introduced as part of the labeling process. After that, based on the work of Deeplabv3+, combined with a lightweight design and an attention mechanism, a railway turnout identification network (RTINet) is proposed. Firstly, in consideration of the need for rapid response in the deployment of the identification model on high-speed trains, this paper selects the MobileNetV2 network, renowned for its suitability for lightweight deployment, as the backbone of the RTINet model. Secondly, to reduce the computational load of the model while ensuring accuracy, depth-separable convolutions are employed to replace the standard convolutions within the network architecture. Thirdly, the bottleneck attention module (BAM) is integrated into the model to enhance position and feature information perception, bolster the robustness and quality of the segmentation masks generated, and ensure that the outcomes are characterized by precision and reliability. Finally, to address the issue of foreground and background imbalance in turnout recognition, the Dice loss function is incorporated into the network training procedure. Both the quantitative and qualitative experimental results demonstrate that the proposed method is feasible for railway turnout identification, and it outperformed the compared baseline models. In particular, the RTINet was able to achieve a remarkable mIoU of 85.94%, coupled with an inference speed of 78 fps on the customized dataset. Furthermore, the effectiveness of each optimized component of the proposed RTINet is verified by an additional ablation study. Full article

The incompleteness of the existing knowledge graphs in the railway domain creates information gaps, impacting their quality and effectiveness in the operation and maintenance of high-speed railway turnout switch machines. To address this, we propose a multi-layer model (KBGC) that combines KG-BERT, graph attention network (GAT), and Convolutional Embedding Network (ConvE) for knowledge graph completion in railway maintenance. KG-BERT fine-tunes a pre-trained BERT model to extract deep semantic features from entities and relationships, converting them into graph structures. GAT captures key structural relationships between nodes using an attention mechanism, producing enriched semantic and structural embeddings. Finally, ConvE reshapes and convolves these embeddings to learn complex entity interactions, enabling accurate link prediction. Extensive experiments on the HRTOM dataset, containing triplet data from high-speed railway turnout switch machines, demonstrate the model’s effectiveness, achieving an MRR of 50.8% and a Hits@10 of 60.7%. These findings show that the KBGC model significantly improves knowledge graph completion, aiding railway maintenance personnel in decision making and preventive maintenance, and providing new tools for railway maintenance applications. Full article