bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Concepts of Religion and Confessionality for Pastoral Care and Spiritual Care in Interreligious and Intercultural Contexts: Clarifications with the Help of Religious Education and Systematic Theology
Authors: Ihben-Bahl Sabine Joy , Roser Traugott
Published: October 2023

According to current consensus definitions in healthcare, religious aspects can be part of ”spirituality” but ”spirituality” is open to non-religious traditions as well. Nevertheless, spiritual care is often provided by theologically trained pastoral caregivers belonging to religious groups and institutional bodies. How, then, do we take “religion(s)” into account when pastoral and spiritual care finds itself within a context of a diversity of religions and religious and non-religious biographies? What function does “confessionality” have—except for the fact that spiritual care professions can also be educated at a theological faculty and thus graduates are familiar with confessional premises of the respective denomination institutions? What significance do these premises have as a basis or a target for research and the fields of action and for the self-understanding of chaplains or spiritual caregivers? Our contribution draws attention to the potential for the reflection on “religion” to inform an understanding of its role in healthcare. In this regard, the term “spirituality” needs some reflection as well. When investing in such clarification, we understand that “religion” and “confessionality” need to be considered with the idea of inherent principles or even as a principle and, thus, are valuable in view of the practice of pastoral and spiritual care. These theoretical reflections are developed on the one hand by comparison with confessional religious education in pluralistic contexts, and on the other hand from systematic theology, specifically with Paul Tillich’s idea of a “Protestant principle” as an open-minded way to bring together pluralistic challenges and the religious identity in pastoral or spiritual care encounters. Preoccupation with these terms as concepts is not only necessary but valuable to encounter the other individual with respect and sincere interest and thus opens up his or her individual access to the world and life and the inherent principles in a resource-building way.
A Philosophy of Moderation: The “Center” as an Interpretive Key to the Lao–Zhuang Texts
Authors: O’Neill Rory , Peruzzi Riccardo
Published: October 2023

The “center” is a key concept in early Chinese philosophy. While readings of the Laozi 老子 and Zhuangzi 莊子 often rely on concepts of “nature” and the “natural”, this article proposes the “center” as an interpretive key that informs discussion of contemporary issues while remaining faithful to the core concerns of the texts. While both texts use the “center” to promote a philosophy of moderation, in the Laozi, “holding to the center” (shou zhong 守中) refers to a focus on one’s inner center to counteract the dangers of the external, whereas the Zhuangzi speaks of a centeredness between inner cultivation and outward socialization. In the Laozi, we examine images of one-, two- and three-dimensional centers as well as the bodily practice of focusing on the inner stomach rather than the outward-looking eyes. Our discussion of the Zhuangzi focuses on occupying the “center” (zhongyang 中央) between extreme inward and outward modes of being. The result is a philosophy of moderation that fosters a sense of humility, balance, and impartiality, cautioning against a drive for overreaching solutions for all humanity, and tempering attempts to conform to extreme “naturalness” or reject all “artificiality”.
Vegetation Trend Detection Using Time Series Satellite Data as Ecosystem Condition Indicators for Analysis in the Northwestern Highlands of Ethiopia
Authors: Alemayehu Bireda , Suarez-Minguez Juan , Rosette Jacqueline , Khan Saeed A.
Published: October 2023

Vegetation is an essential component of the terrestrial ecosystem and has changed significantly over the last two decades in the Northwestern Highlands of Ethiopia. However, previous studies have focused on the detection of bitemporal change and lacked the incorporation of entire vegetation time series changes, which are considered significant indicators of ecosystem conditions. The Normalized Difference Vegetation Index (NDVI) time series dataset from the Moderate-Resolution Imaging Spectroradiometer (MODIS) is an efficient method for analyzing the dynamics of vegetation change over a lengthy period using remote sensing techniques. This study aimed to utilize time series satellite data to detect vegetation changes from 2000 to 2020 and investigate their links with ecosystem conditions. The time-series satellite processing package (TIMESAT) was used to estimate the seasonal parameter values of NDVI and their correlation across the seasons during the study period. Break Detection for Additive Season and Trend (BFAST) was applied to identify the year of breakpoints, the direction of magnitude, and the number of breakpoints. The results were reported, analyzed, and linked to ecosystem conditions. The overall trend in the study area increased from 0.58 (2000–2004) to 0.65 (2015–2020). As a result, ecosystem condition indicators such as peak value (PV), base value (BV), amplitude (Amp), and large integral (LI) exhibited significant positive trends, particularly for Acacia decurrens plantations, Eucalyptus plantations, and grasslands, but phenology indicator parameters such as start of season (SOS), end of season (EOS), and length of season (LOS) did not show significant trends for almost any vegetation type. The most abrupt changes were recorded in 2015 (24.7%), 2012 (18.6%), and 2014 (9.8%). Approximately 30% of the vegetation changes were positive in magnitude. The results of this study imply that there was an improvement in the ecosystem’s condition following the establishment of the Acacia decurrens plantation. The findings are considered relevant inputs for policymakers and serve as an initial stage for the assessment of the other environmental and climatic implications of Acacia decurrens plantations at the local scale.
Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China
Authors: Zhang Xianglin , Xue Jie , Chen Songchao , Wang Nan , Xie Tieli , Xiao Yi , Chen Xueyao , Shi Zhou , Huang Yuanfang , Zhuo Zhiqing
Published: October 2023

Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg−1 in the plough layer (PL) and 13.92 g kg−1 in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg−1. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making.
HFCC-Net: A Dual-Branch Hybrid Framework of CNN and CapsNet for Land-Use Scene Classification
Authors: Guo Ningbo , Jiang Mingyong , Gao Lijing , Li Kaitao , Zheng Fengjie , Chen Xiangning , Wang Mingdong
Published: October 2023

Land-use scene classification (LUSC) is a key technique in the field of remote sensing imagery (RSI) interpretation. A convolutional neural network (CNN) is widely used for its ability to autonomously and efficiently extract deep semantic feature maps (DSFMs) from large-scale RSI data. However, CNNs cannot accurately extract the rich spatial structure information of RSI, and the key information of RSI is easily lost due to many pooling layers, so it is difficult to ensure the information integrity of the spatial structure feature maps (SSFMs) and DSFMs of RSI with CNNs only for LUSC, which can easily affect the classification performance. To fully utilize the SSFMs and make up for the insufficiency of CNN in capturing the relationship information between the land-use objects of RSI, while reducing the loss of important information, we propose an effective dual-branch hybrid framework, HFCC-Net, for the LUSC task. The CNN in the upper branch extracts multi-scale DSFMs of the same scene using transfer learning techniques; the graph routing-based CapsNet in the lower branch is used to obtain SSFMs from DSFMs in different scales, and element-by-element summation achieves enhanced representations of SSFMs; a newly designed function is used to fuse the top-level DSFMs with SSFMs to generate discriminant feature maps (DFMs); and, finally, the DFMs are fed into classifier. We conducted sufficient experiments using HFCC-Net on four public datasets. The results show that our method has better classification performance compared to some existing CNN-based state-of-the-art methods.
Recurrent Residual Deformable Conv Unit and Multi-Head with Channel Self-Attention Based on U-Net for Building Extraction from Remote Sensing Images
Authors: Yu Wenling , Liu Bo , Liu Hua , Gou Guohua
Published: October 2023

Considering the challenges associated with accurately identifying building shape features and distinguishing between building and non-building features during the extraction of buildings from remote sensing images using deep learning, we propose a novel method for building extraction based on U-Net, incorporating a recurrent residual deformable convolution unit (RDCU) module and augmented multi-head self-attention (AMSA). By replacing conventional convolution modules with an RDCU, which adopts a deformable convolutional neural network within a residual network structure, the proposed method enhances the module’s capacity to learn intricate details such as building shapes. Furthermore, AMSA is introduced into the skip connection function to enhance feature expression and positions through content–position enhancement operations and content–content enhancement operations. Moreover, AMSA integrates an additional fusion channel attention mechanism to aid in identifying cross-channel feature expression Intersection over Union (IoU) score differences. For the Massachusetts dataset, the proposed method achieves an Intersection over Union (IoU) score of 89.99%, PA (Pixel Accuracy) score of 93.62%, and Recall score of 89.22%. For the WHU Satellite dataset I, the proposed method achieves an IoU score of 86.47%, PA score of 92.45%, and Recall score of 91.62%, For the INRIA dataset, the proposed method achieves an IoU score of 80.47%, PA score of 90.15%, and Recall score of 85.42%.
Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
Authors: Moreno Mario , Semmling Maximilian , Stienne Georges , Hoque Mainul , Wickert Jens
Published: October 2023

Coherent observations in GNSS reflectometry are prominent in regions with smooth reflecting surfaces and at grazing elevation angles. However, within these lower elevation ranges, GNSS signals traverse a more extensive atmospheric path, and increased ionospheric effects (e.g., delay biases) are expected. These biases can be mitigated by employing dual-frequency receivers or models tailored for single-frequency receivers. In preparation for the single-frequency GNSS-R ESA “PRETTY” mission, this study aims to characterize ionospheric effects under variable parameter conditions: elevation angles in the grazing range (5° to 30°), latitude-dependent regions (north, tropic, south) and diurnal changes (day and nighttime). The investigation employs simulations using orbit data from Spire Global Inc.’s Lemur-2 CubeSat constellation at the solar minimum (F10.7 index at 75) on March, 2021. Changes towards higher solar activity are accounted for with an additional scenario (F10.7 index at 180) on March, 2023. The electron density associated with each reflection event is determined using the Neustrelitz Electron Density Model (NEDM2020) and the NeQuick 2 model. The results from periods of low solar activity reveal fluctuations of up to approximately 300 TECUs in slant total electron content, 19 m in relative ionospheric delay for the GPS L1 frequency, 2 Hz in Doppler shifts, and variations in the peak electron density height ranging from 215 to 330 km. Sea surface height uncertainty associated with ionospheric model-based corrections in group delay altimetric inversion can reach a standard deviation at the meter level.
Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion
Authors: Wang Jianping , Ma Jian , Meng Dean , Zhao Xuan , Zhang Kai
Published: October 2023

Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start–brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors.
Shadow-Imaging-Based Triangulation Approach for Tool Deflection Measurement
Authors: Terlau Marina , von Freyberg Axel , Stöbener Dirk , Fischer Andreas
Published: October 2023

As incrementally formed sheets show large geometric deviations resulting from the deflection of the forming tool, an in-process measurement of the tool tip position is required. In order to cover a measuring volume of 2.0 m × 1.0 m × 0.2 m and to achieve measuring uncertainties of less than 50 µm, a multi-sensor system based on triangulation is realized. Each shadow imaging sensor in the multi-sensor system evaluates the direction vector to an LED attached to the tool, and the three-dimensional position of the LED is then determined from the combination of two sensors. Experimental results show that the angle of view from the sensor to the LED limits both the measurement range and the measurement uncertainty. The measurement uncertainty is dominated by systematic deviations, but these can be compensated, so that the measurement uncertainty required for measuring the tool tip position in the ISF is achieved.
Automated Stratum Interface Detection Using the Optimized Drilling Specific Energy through Self-Adaptive Logistic Function
Authors: Liu Kechen , Cheng Jingyi , Sun Xin , Li Xiang , Wan Zhijun , Xing Keke , Liu Jianzhuang
Published: October 2023

The precise detection of stratum interfaces holds significant importance in geological discontinuity recognition and roadway support optimization. In this study, the model for locating rock interfaces through change point detection was proposed, and a drilling test on composite strength mortar specimens was conducted. With the logistic function and the particle swarm optimization algorithm, the drilling specific energy was modulated to detect the stratum interface. The results indicate that the drilling specific energy after the modulation of the logistic function showed a good anti-interference quality under stable drilling and sensitivity under interface drilling, and its average recognition error was 2.83 mm, which was lower than the error of 6.56 mm before modulation. The particle swarm optimization algorithm facilitated the adaptive matching of drive parameters to drilling data features, yielding a substantial 50.88% decrease in the recognition error rate. This study contributes to enhancing the perception accuracy of stratum interfaces and eliminating the potential danger of roof collapse.
Comparing NISAR (Using Sentinel-1), USDA/NASS CDL, and Ground Truth Crop/Non-Crop Areas in an Urban Agricultural Region
Authors: Kraatz Simon , Lamb Brian T. , Hively W. Dean , Jennewein Jyoti S. , Gao Feng , Cosh Michael H. , Siqueira Paul
Published: October 2023

A general limitation in assessing the accuracy of land cover mapping is the availability of ground truth data. At sites where ground truth is not available, potentially inaccurate proxy datasets are used for sub-field-scale resolution investigations at large spatial scales, i.e., in the Contiguous United States. The USDA/NASS Cropland Data Layer (CDL) is a popular agricultural land cover dataset due to its high accuracy (>80%), resolution (30 m), and inclusions of many land cover and crop types. However, because the CDL is derived from satellite imagery and has resulting uncertainties, comparisons to available in situ data are necessary for verifying classification performance. This study compares the cropland mapping accuracies (crop/non-crop) of an optical approach (CDL) and the radar-based crop area (CA) approach used for the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) L- and S-band mission but using Sentinel-1 C-band data. CDL and CA performance are compared to ground truth data that includes 54 agricultural production and research fields located at USDA’s Beltsville Agricultural Research Center (BARC) in Maryland, USA. We also evaluate non-crop mapping accuracy using twenty-six built-up and thirteen forest sites at BARC. The results show that the CDL and CA have a good pixel-wise agreement with one another (87%). However, the CA is notably more accurate compared to ground truth data than the CDL. The 2017–2021 mean accuracies for the CDL and CA, respectively, are 77% and 96% for crop, 100% and 94% for built-up, and 100% and 100% for forest, yielding an overall accuracy of 86% for the CDL and 96% for CA. This difference mainly stems from the CDL under-detecting crop cover at BARC, especially in 2017 and 2018. We also note that annual accuracy levels varied less for the CA (91–98%) than for the CDL (79–93%). This study demonstrates that a computationally inexpensive radar-based cropland mapping approach can also give accurate results over complex landscapes with accuracies similar to or better than optical approaches.
Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access
Authors: Zhang Linfeng , He Chuhong , Peng Yifeng , Liu Zhan , Zhu Xiaorong
Published: October 2023

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.
Development of a Procedure for Torsion Measurement Using a Fan-Shaped Distance Meter System
Authors: Goering Martina , Luhmann Thomas
Published: October 2023

Maximising the efficiency of wind turbines is crucial for sustainable development of renewable energy. In this context, monitoring and optimising rotor blade performance is becoming increasingly important, especially rotor blade deformation and torsion. We developed an approach for marker-free and contactless measurement of rotor blades during operation. Deformations of rotor blades can be recorded, with focus on torsion measurement. An innovative measuring system, named the fan-shaped distance meter system (FDMS), uses a combination of multiple laser scanners and photogrammetry. The focus of this work is to analyse the suitability of the FDMS for torsion measurement. We designed a torsion simulator to assess the achievable accuracy. Computer simulations and initial laboratory tests have demonstrated precise torsion measurements are possible using this method with an accuracy of 0.3°. Measurements can be carried out during operation of the wind turbine without the need to apply markers or sensors on rotor blades. By precisely recording the deformation and, in particular, torsion of rotor blades, targeted optimisation measures can be obtained in order to maximise performance of wind turbines. This innovative approach to measure the torsion of rotor blades in operation might offer great potential to increase the efficiency and life cycle of wind turbines.
The Improved Particle Swarm Optimization Method: An Efficient Parameter Tuning Method with the Tuning Parameters of a Dual-Motor Active Disturbance Rejection Controller
Authors: Deng Yi , Zhu Jiying , Liu Hai
Published: October 2023

Dual-motor control systems require high synchronization maintenance. Active disturbance rejection controllers (ADRC), renowned for their exceptional immunity to interference, rapid response time, and robustness, have gained widespread adoption as a prominent control strategy. The stability of the dual-motor system can be enhanced by employing an ADRC. However, setting ADRC parameters is challenging. This paper proposes an improved particle swarm optimization method (IPM) to alleviate the difficulty in parameter setting. We initially developed a simplified dual-motor ADRC model that includes current loop and speed loop ADRCs. Furthermore, aiming at maintaining synchronization of the dual-motor control system, the simplified dual-motor ADRC model and IPM method are combined. The experimental results demonstrate that in comparison with state-of-the-art methods, the proposed optimized dual-motor ADRC exhibits superior robustness, minimal overshoots, negligible steady-state errors, and high stability.
Unidirectional Electron-Tunnelling Flexible PDMS Strain Sensor with Aligned Carbon Nanotubes
Authors: de Rijk Tim Mike , Schewzow Sascha , Schander Andreas , Lang Walter
Published: October 2023

High-aspect-ratio carbon nanotubes can be directly mixed into polymers to create piezoresistive polymers. Reducing the cross-sensitivity and creating unidirectional sensitive sensors can be achieved by aligning the nanotubes before they are cured in the polymer layer. This research presents and characterises this alignment of carbon nanotubes inside polydimethylsiloxane and gives the corresponding strain sensor results. The influence on the alignment method, as well as the field strength, frequency and time is shown. An analytical model is created to investigate the sensor’s behaviour and determine the effect of electron-tunnelling in the sensor. A numerical model gives insight into the necessary applied field strength, frequency and time to facilitate alignment in viscous liquids. The experimental data show a two-phase piezoresistive response; first, a linear strain response, after which the more dominant electron-tunnelling piezoresistive phase starts with high gauge factors up to k ≈ 4500 in the preferential direction, depending on the carbon nanotube concentration. Gauge factors in the orthogonal direction remain low (k ≈ 22). Finally, the dynamic stability of the sensors is proven by exposing the sensors to a cyclic strain. Small initial drifts are observed but appear to stabilise after several cycles.
2S-BUSGAN: A Novel Generative Adversarial Network for Realistic Breast Ultrasound Image with Corresponding Tumor Contour Based on Small Datasets
Authors: Luo Jie , Zhang Heqing , Zhuang Yan , Han Lin , Chen Ke , Hua Zhan , Li Cheng , Lin Jiangli
Published: October 2023

Deep learning (DL) models in breast ultrasound (BUS) image analysis face challenges with data imbalance and limited atypical tumor samples. Generative Adversarial Networks (GAN) address these challenges by providing efficient data augmentation for small datasets. However, current GAN approaches fail to capture the structural features of BUS and generated images lack structural legitimacy and are unrealistic. Furthermore, generated images require manual annotation for different downstream tasks before they can be used. Therefore, we propose a two-stage GAN framework, 2s-BUSGAN, for generating annotated BUS images. It consists of the Mask Generation Stage (MGS) and the Image Generation Stage (IGS), generating benign and malignant BUS images using corresponding tumor contours. Moreover, we employ a Feature-Matching Loss (FML) to enhance the quality of generated images and utilize a Differential Augmentation Module (DAM) to improve GAN performance on small datasets. We conduct experiments on two datasets, BUSI and Collected. Moreover, results indicate that the quality of generated images is improved compared with traditional GAN methods. Additionally, our generated images underwent evaluation by ultrasound experts, demonstrating the possibility of deceiving doctors. A comparative evaluation showed that our method also outperforms traditional GAN methods when applied to training segmentation and classification models. Our method achieved a classification accuracy of 69% and 85.7% on two datasets, respectively, which is about 3% and 2% higher than that of the traditional augmentation model. The segmentation model trained using the 2s-BUSGAN augmented datasets achieved DICE scores of 75% and 73% on the two datasets, respectively, which were higher than the traditional augmentation methods. Our research tackles imbalanced and limited BUS image data challenges. Our 2s-BUSGAN augmentation method holds potential for enhancing deep learning model performance in the field.
An Archimedean Copulas-Based Approach for m-Consecutive-k-Out-of-n: F Systems with Exchangeable Components
Authors: Triantafyllou Ioannis S.
Published: October 2023

It is evident that several real-life applications, such as telecommunication systems, call for the establishment of consecutive-type networks. Moreover, some of them require more complex connectors than the ones that exist already in the literature. Thereof, in the present work we provide a signature-based study of a reliability network consisting of identical m-consecutive-k-out-of-n: F structures with exchangeable components. The dependency of the components of each system is modeled with the aid of well-known Archimedean copulas. Exact formulae for determining the expected lifetime of the underlying reliability scheme are provided under different Archimedean copulas-based assumptions. Several numerical results are carried out to shed light on the performance of the resulting consecutive-type design. Some thoughts on extending the present study to more complex consecutive-type reliability structures are also discussed.
A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU
Authors: Liu Peng , Liu Cheng , Wang Zhenpo , Wang Qiushi , Han Jinlei , Zhou Yapeng
Published: October 2023

The state-of-health (SOH) of lithium-ion batteries has a significant impact on the safety and reliability of electric vehicles. However, existing research on battery SOH estimation mainly relies on laboratory battery data and does not take into account the multi-faceted nature of battery aging, which limits the comprehensive and effective evaluation and prediction of battery health in real-world applications. To address these limitations, this study utilizes real electric vehicle operational data to propose a comprehensive battery health evaluation indicator and a deep learning predictive model. In this study, the battery capacity, ohmic resistance, and maximum output power were initially extracted as individual health indicators from actual vehicle operation data. Subsequently, a methodology that combines the improved criteria importance through inter-criteria correlation (CRITIC) weighting method with the grey relational analysis (GRA) method is employed to construct the comprehensive battery health evaluation indicator. Finally, a prediction model based on the attention mechanism and the bidirectional gated recurrent unit (Att-BiGRU) is proposed to forecast the comprehensive evaluation indicator. Experimental results using real-world vehicle data demonstrate that the proposed comprehensive health indicator can provide a thorough representation of the battery health state. Furthermore, the Att-BiGRU prediction model outperforms traditional machine learning models in terms of prediction accuracy.
Short-Term Agronomic and Economic Responses to the Adoption of Cover Crops for Corn Rotation in the Brazilian Semiarid Region
Authors: Luz João Henrique Silva da , Silva Matheus Batista da , Barbosa Luana do Nascimento Silva , Souza José Wilker Germano de , Farias Maria Raquel da Silva , Santos John Kennedy dos , Gois Maria Gleide Jane Lima De , Paulino Sivaldo Soares , Silva Ricardo Barros , Silva Dayane Mércia Ribeiro , Soares Deyvison de Asevedo , Pavinato Paulo Sergio , Santos Valdevan Rosendo dos
Published: October 2023

Cover crops (CCs) have demonstrated their significance in enhancing the agronomic and economic performance of corn in succession. In this paper, we assess the agronomic responses and short-term economic returns of adopting cover crops (CCs) for corn cultivation in the sandy soils of the semiarid region of northeast Brazil, with the aim of addressing the additional challenges of CCs adoption in semiarid regions. The field study was conducted in Arapiraca, Alagoas, under no-tillage conditions for two cropping seasons. A randomized complete block design was employed, comprising six CCs (sunn hemp, spectabilis, jack bean, pigeon pea, lab lab, and millet) treatments and one fallow, with 18 replications. The CCs were cultivated for 60 days before corn planting. Drip irrigation was applied during the grain-filling stage of corn growth. Over the two cropping seasons, the biomass and nutrient cycling of the CCs, corn yield, and economic returns were determined, as well as the total organic carbon (TOC) and Mehlich-1 extractable P levels in the soil after corn harvest. The grain yields with sunn hemp, spectabilis, and jack bean were superior (~10%) to that of the fallow (7.7 vs. 7.1 Mg ha−1), irrespective of the cropping season. Sunn hemp exhibited a higher biomass accumulation and ensured greater nutrient cycling, except for K, while lab lab and millet displayed a similar potential, although substantial variations were observed between seasons. Under sunn hemp and jack bean, the TOC increased by ~9%, particularly in the second season. Regarding the available P, spectabilis and jack bean exhibited the highest levels, with an increase of ~74% compared to the fallow (~31.1 vs. 17.9 mg dm−3). Spectabilis and lab lab demonstrated more promising results, both agronomically and economically. However, millet and sunn hemp have the potential to reduce costs over multiple cropping seasons. Therefore, the adoption of cover crops is a sustainable and economically viable agricultural practice. However, it is essential to acknowledge that our results do not represent rainfed conditions and require further investigation.
Identification of Waterlogging-Prone Areas in Nanning from the Perspective of Urban Expansion
Authors: Huang Juan , Li Jiangfeng , Huang Zhi
Published: October 2023

The objective of this study was to discern the spatial and temporal patterns of areas in Nanning that are susceptible to waterlogging, particularly during various phases of urban expansion. Furthermore, this study presents a proposal outlining strategies aimed at preventing and controlling waterlogging. These strategies are based on the integration of the concepts of sponge city and resilient city construction. This study employed remote sensing (RS) and geographic information system (GIS) techniques to provide technical support. The supervised classification method and normalized difference index method were utilized to compare and extract impervious surfaces in Nanning from 2013 to 2020. The present investigation utilized the acquired impervious surfaces to compute the fractal dimension as a weighting factor, incorporating a digital elevation model (DEM) for the purpose of conducting a hydrological analysis in ArcGIS. Based on the findings of the study, several conclusions can be derived. The following conclusions can be drawn from the study: (1) The fractal dimension of Nanning varied over the study period, with values of 1.32, 1.41, and 1.58 in 2013, 2017, and 2020, respectively. The distribution of impervious surfaces showed a decreasing trend from the city center to the periphery. Urban planning and construction activities have significantly influenced the distribution of impervious surfaces, resulting in a progressively more complex and unstable structure. (2) From 2013 to 2020, the urban expansion fractal dimension increased from 1.32 to 1.58, indicating a decrease in the stability of impervious surfaces. The areas with higher concentrations of impervious surfaces coincided with frequent waterlogging-prone areas. Furthermore, the distribution of waterlogging-prone points transformed from a concentrated pattern to a scattered one. (3) In terms of waterlogging prevention and control strategies, the old urban areas are recommended to be transformed into sponge city projects, and the new development areas are planned, designed and implemented with the concept of “resilience”.
Urban Rail Transit Station Type Identification Based on “Passenger Flow—Land Use—Job-Housing”
Authors: Feng Hongxia , Chen Yaotong , Wu Jinyi , Zhao Zhenqian , Wang Yuanqing , Wang Zhuoting
Published: October 2023

Urban rail transit stations serve as pivotal hubs that facilitate the advancement of diverse economic activities. Based on different types of metro stations, the sustainable and coordinated development of public transport and land use can be achieved through rational land use planning and the rational allocation of urban infrastructure and public service facilities. Drawing upon mobile phone signaling data and land use data, this article presents a complex classification methodology for metro stations, employing the lens of “passenger flow behavior—land use structure—job-housing density” in the context of Xi’an. The stations are categorized into six distinct types, including employment-led stations with a job–housing density balance, as well as stations characterized by job–housing mismatch with a high residential density. The results indicate a low level of coupling between the passenger flow patterns of the stations and the spatial characteristics of the station areas. In addition, the spatial distributions of the stations demonstrate a significant aggregation effect in each station type, while the degree of integration between the different station types remains limited. These findings collectively suggest that the urban rail transit stations in Xi’an have not achieved complementary development, thereby reflecting a notable trend of cross-regional commuter flow in the city.
An Assessment of the Impact of the COVID-19 Pandemic on Consumer Behavior Using the Analytic Hierarchy Process Model
Authors: Šostar Marko , Ristanović Vladimir
Published: October 2023

Consumer behavior is dynamic and can shift rapidly due to various factors. The COVID-19 pandemic introduced unprecedented market disruptions, prompting unique consumer reactions. Our foundational study dissected factors affecting consumer habits, laying the groundwork for a focused analysis of how individual consumption was impacted during the pandemic. Significantly, psychological influences emerged as a key determinant of consumer behavior in our prior findings, leading us to centralize the pandemic’s effect in our recent research. We undertook this analysis using data from 559 participants from the Republic of Croatia. Utilizing the Analytic Hierarchy Process (AHP), typically applied in analyses of decision making in businesses, we ranked the influences on consumer purchasing patterns amidst the pandemic. Our findings underscored the profound influence of these factors on consumer behavior and the resulting implications for sustainable development. This highlights the urgency for businesses to adopt flexible strategies that also support overarching sustainability objectives. The objective of the study is to analyze the impact of the pandemic on individual consumption and to understand the key psychological factors influencing consumer behavior. The main contribution of this work is the identification and ranking of pandemic influences using the AHP model, as well as understanding the implications of these behavioral changes for future planning.
Integrated Planning for Depot Location and Line Planning Problems in the Intercity Railway Network with Passenger Demand Uncertainty
Authors: Cui Zanyang , Wang Zhimei , Chen Junhua , Zhang Xingchen , Zhao Chunxiao
Published: October 2023

In this study, we present a mathematical model and solution approach for addressing the robust integrated intercity railway depot location and line planning problem (RIDLLPP), which encompasses the initial two stages of the railway planning process. Our primary objective is to identify depot locations that exhibit robustness across a range of likely future scenarios, and this is achieved by incorporating line planning decisions. The model focuses on five critical strategic determinations, namely the depot location, depot storage capacity, line operation, passenger assignment, and fleet allocation. To tackle this complex problem, we propose an iterative solution framework that combines the Differential Evolution (DE) algorithm with improved rounding heuristics (DE-IRH). To evaluate the effectiveness of our framework, we conduct a comparative analysis with the Gurobi solver using multiple medium-sized artificial instances. The results demonstrate that our proposed framework achieves an optimality gap of 4.87% while requiring less computational time. Furthermore, we validate the robustness of the model’s location choices across various input scenarios, thereby providing valuable insights for transportation planning agencies and railway companies that can inform their decision-making processes.
Application and Performance Evaluation of Industrial Internet Platform in Power Generation Equipment Industry
Authors: Jia Yan , Wang Junfeng , Han Xin , Tang Haiqi , Xiao Xiaoling
Published: October 2023

The development and application of the industrial Internet platform (IIP) has brought enterprises huge benefits, such as improving the efficiency of resource management and equipment maintenance, achieving the precipitation and reuse of industrial knowledge, and enhancing the development ability of industrial APPs, etc. Therefore, in order to accelerate the application of the IIP, promote the digital transformation of enterprises, and enhance the application effect of the IIP, it is necessary to evaluate the application level and performance of the enterprise IIP. In this paper, taking the IIP in the power generation equipment industry as the research object, the background, architecture, and implementation process of the platform is described, and an evaluation method is proposed. First, a third-level evaluation index system is proposed via reference analysis and the Delphi method from four dimensions—platform basic condition, platform technology capability, platform application service capability, and platform input–output capability—and a total of sixty-five bottom indexes are designed in particular. Then, the fuzzy analytic hierarchy process (FAHP) and particle swarm optimization (PSO) algorithm are used to determine the weights of indexes, where FAHP is used to construct a fuzzy judgment matrix and PSO is adopted to adjust the consistency of the fuzzy judgment matrix in the FAHP. Finally, through the weights of indexes and questionnaire scores, the evaluation results are obtained, and the application level and performance of the IIP in the power generation equipment industry is analyzed. Through analysis, the important indexes that have a significant influence on the evaluation are determined, and the weaknesses that need to be enhanced are also pointed out, which can guide enterprises to make targeted improvements on the IIP in the power generation equipment industry so as to ensure the sustainable development of the power generation equipment industry.
Paired Double Heavy Baryons Production in Decays of the Higgs Boson
Authors: Martynenko Alexei P. , Martynenko Fedor A.
Published: October 2023

Rare decays of the Higgs boson into a pair of diquarks cc(bb) and c¯c¯(b¯b¯) are studied within the perturbative Standard Model and relativistic quark model. The relativistic corrections determined by the relative motion of quarks are calculated using the production amplitude and the diquark wave functions. Numerical values of the decay widths of the Higgs boson into a baryon pair ccq(bbq) and c¯c¯q¯(b¯b¯q¯) are obtained.
Phenomenological Studies of Visual Mental Imagery: A Review and Synthesis of Historical Datasets
Authors: Marks David F.
Published: October 2023

This article reviews historically significant phenomenological studies of visual mental imagery (VMI), starting with Fechner in 1860 and continuing to the present. This synthesis of diverse VMI phenomenological studies in healthy adults serves as a unique resource for investigators of individual differences, cognitive development and clinical and neurological conditions. The review focuses on two kinds of VMI, “memory imagery” and “eidetic imagery”. Ten primary studies are drawn from three periods of the scholarly literature: early (1860–1929), middle (1930–1999) and recent (2000–2023). It is concluded that memory and eidetic imagery are two forms of constructive imagery, varying along a continuum of intensity or vividness. Vividness is a combination of clarity, colourfulness and liveliness, where clarity is defined by brightness and sharpness, colourfulness by image saturation and liveliness by vivacity, animation, feeling, solidity, projection and metamorphosis. The findings are integrated in a template that specifies, as a tree-like structure, the 16 properties of VMI vividness in healthy adult humans. The template takes into account the weight of evidence drawn from the accounts and reveals an extraordinary degree of consistency in reported VMI characteristics, revealed by specialized studies of healthy adult humans across time, space and culture.
Climate Change Scenarios Reduce Water Resources in the Schuylkill River Watershed during the Next Two Decades Based on Hydrologic Modeling in STELLA
Authors: Kali Suna Ekin , Amur Achira , Champlin Lena K. , Olson Mira S. , Gurian Patrick L.
Published: October 2023

The Schuylkill River Watershed in southeastern PA provides essential ecosystem services, including drinking water, power generation, recreation, transportation, irrigation, and habitats for aquatic life. The impact of changing climate and land use on these resources could negatively affect the ability of the watershed to continually provide these services. This study applies a hydrologic model to assess the impact of climate and land use change on water resources in the Schuylkill River Basin. A hydrologic model was created within the Structural Thinking Experiential Learning Laboratory with Animation (STELLA) modeling environment. Downscaled future climate change scenarios were generated using Localized Constructed Analogs (LOCA) from 2020 to 2040 for Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 emission scenarios. Three regional land use change scenarios were developed based on historical land use and land cover change trends. The calibrated model was then run under projected climate and land use scenarios to simulate daily streamflow, reservoir water levels, and investigate the availability of water resources in the basin. Historically, the streamflow objective for the Schuylkill was met 89.8% of the time. However, the model forecasts that this will drop to 67.2–76.9% of the time, depending on the climate models used. Streamflow forecasts varied little with changes in land use. The two greenhouse gas emission scenarios considered (high and medium emissions) also produced similar predictions for the frequency with which the streamflow target is met. Barring substantial changes in global greenhouse gas emissions, the region should prepare for substantially greater frequency of low flow conditions in the Schuylkill River.