Search Results (123)

Firefighters are exposed to hazardous chemicals at fire scenes, including polycyclic aromatic hydrocarbons (PAHs) among many others, which pose significant health risks. Current laundering practices are ineffective at removing persistent contaminants from turnout gear, necessitating further research to optimize cleaning methods. This study explores the impact of presoaking prior to the laundering process and the factors that can affect its effectiveness, including the presoaking duration and detergent concentration, in PAH removal when laundering. For this, contaminated fabric swatches were subjected to various presoaking durations (1, 3, and 12 h) and detergent concentrations (99:1 and 90:10 water-to-detergent ratios) before undergoing bench-scale washing. The cleaning efficacy was assessed for 16 PAH compounds, including both low-molecular-weight (LMW) PAHs and high-molecular-weight (HMW) PAHs. Moreover, the removal mechanisms of PAHs from turnout gear were fundamentally explained using partition coefficients and standard affinities with different parameters during washing. The results demonstrate that 3 h and 12 h of presoaking lead to 2.8 and 4.3 times greater HMW PAH removal, respectively. After 12 h of presoaking in a 90:10 water-to-detergent ratio, 97% of the LMW PAHs and 78% of the HMW PAHs were removed, compared to only an 11% removal of the HMW PAHs with a 99:1 ratio. Additionally, direct washing with a 90:10 ratio achieved comparable efficacy to that of presoaking with the same water-to-detergent ratio, indicating the crucial role of detergent concentration during laundering. These findings offer valuable insights for optimizing firefighter safety practices, emphasizing the role of presoaking and the appropriate methods to perform presoaking to mitigate firefighters’ occupational exposure risks to toxic substances and ensure gear reliability. Full article

Confronted with China’s burgeoning population and finite arable land resources, the enhancement of sustainable arable land efficiency is of paramount importance. This study, grounded in the United Nations Sustainable Development Goals (SDGs), introduces a robust framework for assessing sustainable arable land use. Utilizing the Sustainable Utilization of Arable Land (SUA) indicator system, the DGA–Super-SBM model, the Malmquist–Luenberger production index, and the TO–Fisher–OSM algorithm, we evaluated the efficiency of sustainable utilization of arable land (ESUA) in 52 prefecture-level cities within China’s major grain-producing regions of the Yellow and Huaihai Seas. We analyzed the cropland utilization patterns from 2010 to 2020, examining the influence of these patterns on sustainable utilization efficiency. Our findings indicate that between 2010 and 2020, the arable land usage in these regions exhibited minimal transformation, primarily shifting towards construction land and conversely from grassland and water systems. Notably, the ESUA of arable land demonstrated an upward trend, characterized by pronounced spatial clustering, enduring high efficiency in the northern regions, and a significant surge in the southern sectors. The declining ESUA (D-ESUA) trend was general but increased in half of the cities. The change in the center of gravity of ESUA correlated with the north–south movement of the proportion of cultivated land area, the turn-in rate, and the turn-out rate, yet moved in the opposite direction to that of cultivated land density and yield per unit area. Variables such as the replanting index, cropland density, yield per unit area, and cropland turn-in rate significantly affected ESUA. These findings offer a scientific basis and decision-making support for optimizing the utilization pattern of arable land and achieving a rational allocation of arable land resources. Full article

Mechanical condition monitoring data in real engineering are often severely unbalanced, which can lead to a decrease in the stability and accuracy of intelligent diagnosis methods. In this paper, a fault diagnosis method based on the SMOTE + Tomek Link and dual-channel feature fusion is proposed to improve the performance of the sample imbalance fault diagnosis method, taking the piston pump of a turnout rutting machine as the research object. Combining the data undersampling method and the oversampling method to redistribute the collected normal data and fault data makes the diagnostic model have better diagnostic performance in the case of insufficient fault samples. And, in order to fully utilize the global features and local features, a global–local feature complementary module (GLFC) is proposed. Firstly, the generated data similar to the original data are constructed using the SMOTE + Tomek Link method; secondly, the generated data are input into a GLFC module and BiGRU at the same time, the GLFC module extracts the spatial global features and local features of the original vibration data, and BiGRU extracts the temporal information features of the original vibration data, and fuses the extracted feature information, and inputs the fused features into the attention layer; finally, a GLFC module is proposed by the SMOTE + Tomek Link method to make full use of the global features and local features. The extracted feature information is fused, and the fused features are input to the attention layer; finally, the fault classification is completed by the softmax classifier. In this paper, the accuracy and robustness of the proposed model are demonstrated through experiments. Full article

The consistently low yield turnout of maize on farmers’ fields owing to drought and the nutritional challenges attributable to the consumption of white endosperm maize pose a major threat to food and nutritional security in Sub-Saharan Africa (SSA). The objectives of this study were to assess the performance of newly developed extra-early maturing orange hybrids under managed drought and well-watered conditions, compare the outcomes of multiple-trait base index and multi-trait genotype–ideotype distance index selection procedures, and identify drought-tolerant hybrids with stable performance across contrasting environments for commercialization in SSA. One hundred and ninety orange hybrids and six checks were evaluated under managed drought and well-watered conditions at Ikenne for two seasons between 2021 and 2023. A 14 × 14-lattice design was used for the field evaluations under both research conditions. Drought stress was achieved by the complete withdrawal of irrigation water 25 days after planting. Results revealed significant differences among the hybrids under drought and well-watered conditions. Grain yield, ears per plant, and plant aspect under managed drought were correlated to the same traits under well-watered conditions, suggesting that the expression of these traits is governed by common genetic factors. Twenty-nine hybrids were identified as top-performing drought-tolerant hybrids by the multiple-trait base index and the multi-trait genotype–ideotype distance index. Of the selected outstanding 29 hybrids, 34% were derived from crosses involving the tester TZEEIOR 197, demonstrating the outstanding genetic potential of this inbred line. Further analysis of the 29 selected hybrids revealed TZEEIOR 509 × TZEEIOR 197 as the hybrid that combined the most drought-tolerant adaptive traits. However, the hybrids TZEEIOR 526 × TZEEIOR 97, TZEEIOR 384 × TZEEIOR 30, TZEEIOR 515 × TZEEIOR 249, TZEEIOR 510 × TZEEIOR 197, TZEEIOR 479 × TZEEIOR 197, and TZEEIOR 458 × TZEEIOR 197 were identified as the most stable hybrids across drought and well-watered conditions. These hybrids should be extensively tested in multi-location trials for deployment and commercialization in SSA. Full article

Although electoral systems are a traditional focus in political science, limited research exists on the characteristics of overseas constituency representation. This study aims to quantitatively elucidate these characteristics through a comparative analysis of the election systems in eight countries. This study analyzes overseas constituency representative systems while focusing on key factors such as the number of eligible voters, seats, voter turnout, and representativeness (value of a single vote). Voter turnout in overseas districts varies significantly among these countries. Notably, Croatia and Romania exhibit exceptionally high voter turnouts in overseas districts. Common characteristics in high-turnout countries include a higher representativeness in overseas districts than the home country and a small proportion of overseas voters in the total electorate. This dynamic incentivizes overseas voters to participate in elections to reflect their minority opinions in national politics. Furthermore, it potentially leads to a higher voter turnout in overseas districts than in the home country. Full article

Subjected to complex loadings from the wheel–rail interaction, turnout rail is prone to crack damage. This paper aims to develop a condition evaluation method for crack-alike damage detection of in-service turnout rail. A covariance-based structural condition index (CI) is firstly constructed by fusing the time-frequency components of responses, generating a series of patterns governed by the interrelationships between column members in the CI matrix. The damage-sensitive interrelationships latent in CI are then modeled using Bayesian regression and historical data, and baseline patterns are built with predictions of the models and new inputs. The deviations between the baseline patterns and the actual patterns of the newly observed CI members are quantitatively assessed. To synthetically consider the individual assessment results, a technique is developed to combine the individual assessment results into one synthetic result by designing a group of suitable weights taking into consideration both probabilistic confidence and reference model error. If the deviations are within a tolerable range, no damage is flagged; otherwise, damage existence and severity are reported. A case study is conducted, in which monitoring data from the database of a railway turnout are applied to build the CI matrix and examine the damage identification performance of this method. Good agreement between actual conditions and assessment results is found in different testing scenarios in the case study, demonstrating the effectiveness of the proposed method. Full article

In the interaction of the rolling stock and the upper structure of the railway track, intense dynamic loads occur. They have a destructive effect both on the parts of the rolling stock and on the elements of the superstructure of the track. In order to develop a durable, rational and reliably functioning design of cars and locomotives with good dynamic properties and good indicators of the impact of rolling stock on the railway track, along with theoretical computational studies, experimental studies are also required, which are usually the final stage in the design and implementation of rolling stock or the modernization of existing ones, such as locomotives and wagons, in order to improve their strength and dynamic performance. This article presents the results of field tests to determine the dynamic performance of the type CKD6e diesel locomotive. The description of the preparation of the CKD6e shunting locomotive for testing is given. An analysis of the dynamic performance of a diesel locomotive during the passage of turnouts, on a straight section of the track and in a curve with a radius of 400 m, was carried out. The studies performed showed that the minimum value of the stability factor against wheel derailment on a straight section of the track is significantly higher than the standard value. The experimentally obtained ratio of frame forces to the static load from the wheelset on the rails, the coefficients of vertical dynamics of the first and the second stages of suspension and the coefficient of stability against derailment of the wheel from the rail were registered on the track section in a curve with a radius of 400 m meet the current requirements. A calculation scheme and equations of vertical oscillations are proposed, an analysis is carried out according to the graphs of movements of bogies and a locomotive body when moving along irregularities of different lengths at different speeds. Full article

The fault diagnosis of a switch machine is vital for high-speed railway operations because switch machines play an important role in the safe operation of high-speed railways, which often have faults because of their complicated working conditions. To improve the accuracy of turnout fault diagnosis for high-speed railways and prevent accidents from occurring, a combination of bi-directional long short-term memory (BiLSTM) with the multiple learning classification based on associations (MLCBA) model using the operation and maintenance text data of switch machines is proposed in this research. Due to the small probability of faults for a switch machine, it is difficult to form a diagnosis with the small amount of sample data, and more fault text features can be extracted with feedforward in a BiLSTM model. Then, the high-quality rules of the text data can be acquired by replacing the SoftMax classification with MLCBA in the output of the BiLSTM model. In this way, the identification of switch machine faults in a high-speed railway can be realized, and the experimental results show that the Accuracy and Recall of the fault diagnosis can reach 95.66% and 96.29%, respectively, as shown in the analysis of the ZYJ7 turnout fault text data of a Chinese railway bureau from five recent years. Therefore, the combined BiLSTM and MLCBA model can not only realize the accurate diagnosis of small-probability turnout faults but can also prevent high-speed railway accidents from occurring and ensure the safe operation of high-speed railways. Full article

The intelligent maintenance of railway equipment plays a pivotal role in advancing the sustainability of transportation and manufacturing. Railway turnouts, being an essential component of railway infrastructure, often encounter various faults, which present operational challenges. Existing fault diagnosis methods for railway turnouts primarily utilize vectorized monitoring data, interpreted either through vector-based models or distance-based measurements. However, these methods exhibit limited interpretability or are heavily reliant on standard curves, which impairs their performance or restricts their generalizability. To address these limitations, a railway turnouts fault diagnosis method with monitoring signal images and support matrix machine is proposed herein. In addition, a pinball loss-based multiclass support matrix machine (PL-MSMM) is designed to address the noise sensitivity limitations of the multiclass support matrix machine (MSMM). First, the time-series monitoring signals in one dimension are transformed into images in two dimensions. Subsequently, the image-based feature matrix is constructed. Then, the PL-MSMM model is trained using the feature matrix to facilitate the fault diagnosis. The proposed method is evaluated using a real-world operational current dataset, achieving a fault identification accuracy rate of 98.67%. This method outperforms the existing method in terms of accuracy, precision, and F1-score, demonstrating its superiority. Full article

A high prevalence of both squamous (ESGD) and glandular (EGGD) ulcers was previously found in, mainly young, Icelandic horses coming into training for the first time. This study evaluated risk factors for gastric ulcers in Icelandic riding horses at various ages and stages of training. The horses (n = 211) were gastroscoped from 21 equine establishments across Iceland. A variety of morphometric, clinical, behavioural and management factors were evaluated as potential risk factors for gastroscopically significant (grade ≥ 2/4: found in 27% of horses) or gastroscopically severe (grade 3 or 4/4: found in ~10% of horses) ESGD or gastroscopically significant EGGD (grade ≥ 1/2: found in 46.4%). Body condition score (BCS), cresty neck score (CNS), stable/turnout behaviour, exercise intensity/frequency and age were not significantly associated with ESGD or EGGD ulcer score. However, having come off the pasture into training for 4 weeks or less was a significant risk factor for gastroscopically significant and severe ESGD compared to 5 weeks or more. For both EGGD and ESGD, “region” was important. Gastroscopically significant EGGD and gastroscopically severe ESGD were more prevalent in those showing clinical signs often associated with ulcers. Geldings were more likely to have gastroscopically significant ESGD than both mares and stallions and more EGGD than stallions. Being stabled, but spending >2 h/day out in the paddock, compared with <2 h paddock time or full-time turnout, was protective for gastroscopically significant ESGD as was being fed complementary feed (all fed <1 g non-structural carbohydrate (NSC)/kg/BW/meal). Being at a training establishment for >4 weeks was protective for gastroscopically significant and gastroscopically severe ESGD but not EGGD. This study confirms the relatively low prevalence of ESGD in Icelandic horses being kept in training establishments and fed low NSC diets but highlights the high prevalence of EGGD. Full article

Train distance estimation in a turnout area is an important task for the autonomous driving of urban railway transit, since this function can assist trains in sensing the positions of other trains within the turnout area and prevent potential collision accidents. However, because of large incident angles on object surfaces and far distances, Lidar or stereo vision cannot provide satisfactory precision for such scenarios. In this paper, we propose a method for train distance estimation in a turnout area based on monocular vision: firstly, the side windows of trains in turnout areas are detected by instance segmentation based on YOLOv8; secondly, the vertical directions, the upper edges and lower edges of side windows of the train are extracted by feature extraction; finally, the distance to the target train is calculated with an appropriated pinhole camera model. The proposed method is validated by practical data captured from Hong Kong Metro Tsuen Wan Line. A dataset of 2477 images is built to train the instance segmentation neural network, and the network is able to attain an MIoU of 92.43% and a MPA of 97.47% for segmentation. The accuracy of train distance estimation is then evaluated in four typical turnout area scenarios with ground truth data from on-board Lidar. The experiment results indicate that the proposed method achieves a mean RMSE of 0.9523 m for train distance estimation in four typical turnout area scenarios, which is sufficient for determining the occupancy of crossover in turnout areas. Full article

Maglev systems represent a cutting-edge high-speed transport technology, and turnout switches play a critical role in the creation of a highly branched network. There are two common types of turnouts for high-temperature superconducting (HTS) Maglev systems: mechanical and electromagnetic. Due to the many advantages, an electromagnetic turnout is a better choice for a Maglev system than a mechanical one. However, there is a difference in the distribution of the magnetic field over the turnout area and the permanent magnetic track, which cannot meet the safety requirements of the Maglev system. This article proposes a modernized design of an electromagnetic switch based on previously proposed optimization solutions by placing a diamagnetic screen between two electromagnetic poles of an electromagnet, thereby reducing the scattering fluxes between them. The method of diamagnetic screening and experimental methodology are described in this article. The experiment was carried out using a three-dimensional magnetic field scanner to provide results on the distribution of the magnetic field and the increase in the magnetic induction value over the electromagnet poles. Thus, this article provides valuable suggestions for improving the design of the electromagnetic turnout of HTS Maglev systems. Moreover, the proposed method can be applied to any magnetic device or electric machine with an open magnetic circuit. Full article

Managing the logistics and safety of an election system, from delivering voting machines to the right locations at the right time to ensuring that voting lines remain reasonable in length is a complex problem due to the scarcity of resources, especially human poll workers, and the impact of human behavior and disrupting events on the performance of this critical system. These complexities grew with the need for physical distancing during the COVID-19 pandemic coinciding with multiple key national elections, including the 2020 general presidential election in the USA. In this paper, we propose a digital clone platform leveraging agent-based simulation to model and experiment with resource allocation decisions and voter turnout fluctuations and facilitate “what-if” scenario testing of any election. As a use case, we consider three different concurrent polling location problems, namely, resource allocation, polling layout, and management. The main aim is to reduce voter waiting time and provide visibility of different scenarios for polling and state-level managers. We explain the proposed simulation platform based on Fulton County for the 2020 presidential US election. Fulton County had 238 polling locations in 2020, which provided publicly available voter turnout data. The developed platform realistically models at the county level and at specific locations, suggesting the possible allocation of finite resources among locations in the county and the configuration of each location, accounting for physical, legal, and technical constraints. Multiple realistic scenarios were developed and embedded into the simulation platform to evaluate and verify the different systems. The system performance and key attributes of the election system, such as waiting time, resource utilization, and layout safety, were tested and validated. Full article

For future requirements, asset management of turnouts needs to rely on data-based assessment tools. These tools must enable the quantification of quality behaviour of turnouts and identify causes of poor behaviour. In this paper, we provide a toolbox addressing these requirements. We use track geometry as the main criterion for quality behaviour in combination with additional indicators, each associated with a different component, to understand turnout performance. The toolbox is applied to five similar turnouts to compare their performance. It is revealed that one of the turnouts performs significantly worse than the others. A deeper analysis can identify worn ballast in several areas of the turnout as the cause of poor performance. Problems in the ballast bed can be attributed to worn insulated rail joints as well as to stiffness changes in the transition areas of the turnout. Full article

Globally, natural wetlands have suffered severe ecological degradation (vegetation, soil, and biotic community) due to multiple factors. Understanding the spatiotemporal dynamics and driving forces of natural wetlands is the key to natural wetlands’ protection and regional restoration. In this study, we first investigated the spatiotemporal evolutionary trends and shifting characteristics of natural wetlands in the Northeast Plain of China from 1990 to 2020. A dataset of driving-force evaluation indicators was constructed with nine indirect (elevation, temperature, road network, etc.) and four direct influencing factors (dryland, paddy field, woodland, grassland). Finally, we built the driving force analysis model of natural wetlands changes to quantitatively refine the contribution of different driving factors for natural wetlands’ dynamic change by introducing the sparrow search algorithm (SSA) and extreme gradient boosting algorithm (XGBoost). The results showed that the total area of natural wetlands in the Northeast Plain of China increased by 32% from 1990 to 2020, mainly showing a first decline and then an increasing trend. Combined with the results of transfer intensity, we found that the substantial turn-out phenomenon of natural wetlands occurred in 2000–2005 and was mainly concentrated in the central and eastern parts of the Northeast Plain, while the substantial turn-in phenomenon of 2005–2010 was mainly located in the northeast of the study area. Compared with a traditional regression model, the SSA-XGBoost model not only weakened the multicollinearity of each driver but also significantly improved the generalization ability and interpretability of the model. The coefficient of determination (R²) of the SSA-XGBoost model exceeded 0.6 in both the natural wetland decline and rise cycles, which could effectively quantify the contribution of each driving factor. From the results of the model calculations, agricultural activities consisting of dryland and paddy fields during the entire cycle of natural wetland change were the main driving factors, with relative contributions of 18.59% and 15.40%, respectively. Both meteorological (temperature, precipitation) and topographic factors (elevation, slope) had a driving role in the spatiotemporal variation of natural wetlands. The gross domestic product (GDP) had the lowest contribution to natural wetlands’ variation. This study provides a new method of quantitative analysis based on machine learning theory for determining the causes of natural wetland changes; it can be applied to large spatial scale areas, which is essential for a rapid monitoring of natural wetlands’ resources and an accurate decision-making on the ecological environment’s security. Full article