bluuebunny/crossref_metadata_2025 huggingface dataset:
Faith in frames: unveiling therapeutic narratives in religion-related cinema through computational analysis
Authors: Xue Bai , Wang Zhongrui , Liu Yuqing , Song Yao
Published: May 2024

Introduction

This study explores the emotional impact of religion-related films through a “cinematherapy” lens. It aims to analyze the emotional patterns in a curated selection of religion-related films compared to a broader sample of acclaimed movies using facial recognition with YOLOv5 object detection. The study aims to uncover the potential therapeutic application of religion-related films.

Methods

Facial recognition with YOLOv5 object detection was utilized in this study to analyze the emotional patterns in religion-related films. A curated selection of these films was compared to a broader sample of acclaimed movies to identify any distinct emotional trajectories.

Results

The analysis of the emotional patterns revealed that religion-related films exhibited a subtler range of emotions compared to the broader film spectrum. This finding suggests that these films potentially create a safe space for contemplation, aligning with the profound themes often explored in religion-related films. Interestingly, the emotional arc observed in the films mirrored the spiritual journeys depicted in them. The films started with a low point of separation, transitioned through challenges, and culminated in a peak representing spiritual transformation.

Discussion

These findings suggest promise for the therapeutic application of religion-related films. The muted emotional expression in these films creates a safe space for self-reflection, enabling viewers to connect with the struggles of the characters and explore their own values when faced with complex religious ideas. This emotional engagement may contribute to therapeutic goals such as introspection and personal growth. The study unveils the unique emotional power of religion-related films and paves the way for further research on their potential as therapeutic tools. It emphasizes the need for continued exploration of the emotional impact of these films and their capacity to aid in therapeutic goals.
The impact of basic pension for urban and rural residents on the subjective well-being of the older adult in Chinese rural areas
Authors: Yang Jianhai , Li Ziying , Zhang Jiexin , Zang Zheng
Published: May 2024

Introduction

As an important component of the social security system, basic pension insurance for urban and rural residents is expected to improve the quality of life of rural older adult people and make their lives better and happier. This article mainly studies the relationship between the basic pension for urban and rural residents and the subjective well-being of older adult people in rural China.

Methods

This paper uses data from the China Health and Retirement Longitudinal Study (CHARLS) for the years 2018 and 2020. It selected samples of rural older adult people aged 60 and above, ultimately obtaining 9,310 samples. The impact of the basic pension for urban and rural residents on the subjective well-being of rural older adult people was estimated by constructing Ordinary Least Squares (OLS) estimation methods and ordered logistic regression models. The robustness of the results was verified by changing the regression methods, and the samples were divided into different groups for heterogeneity analysis according to three different standards.

Results

The results show that the basic pension for urban and rural residents significantly improves the life satisfaction of rural older adult, reduces their degrees of depression, and thereby enhances their subjective well-being. The impact of the basic pension for urban and rural residents is more significant for older adult individuals in areas with a higher gender ratio, those suffering from chronic diseases, and those in the eastern regions of the country. Further verification indicates that the basic pension for urban and rural residents enhances the subjective well-being of the rural older adult by improving their health status and reducing their labor supply.

Discussion

Most of the existing research on basic pension insurance for urban and rural residents and subjective well-being has been conducted from the perspective of whether individuals are enrolled in the pension scheme or whether they received a pension. However, there are few studies analyzing from the perspective of the amount of pension benefits received by residents. The results of this study help to enrich the research perspective on the basic pension insurance system for urban and rural residents in China and expand the understanding of the impact and value of the basic pension for urban and rural residents.
Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt
Authors: Liu Xu-dong , Hou Bo-han , Xie Zhong-jun , Feng Ning , Dong Xiao-ping
Published: May 2024

Objective

This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods.

Method

The GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE).

Result

For African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model’s robustness.

Conclusion

GRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.
Exploring the current state of technology transfer in the United States: perspectives and improvement strategies from the experts
Authors: Eidlisz Jordan , von Simson Isabelle , Gold-von Simson Gabrielle
Published: May 2024

Technology transfer (TT) is a necessary, yet complex process to convey and disseminate scientific knowledge to the commercial sector. However, multiple barriers in TT can impede commercialization and innovative progress. To cultivate a deeper understanding, we conducted five interviews with strategic, elite leaders in different areas of TT in the United States. Experts shared their perspectives on the current state of TT, what needs improvement, and potential solutions to enhance the TT landscape, with a focus on biotechnology and medical devices. The formation of strong management teams, a comprehension of the regulatory, reimbursement, and funding pathways and policies, and thorough market assessments were noted as key aspects for venture success. Collaboration with Technology Transfer Offices (TTOs), industry experts, and strategic partners are also essential to support academic innovators and guide them throughout the complex commercialization process. There is agreement that a venture should have a defined vision and clear goals with a robust business case for the innovation; early involvement of TTOs is essential. Comprehension of the complexities and key facets of TT, while also streamlining the process, will better position biomedical innovators for success.
The role of data-driven methods in power system security assessment from aggregated grid data
Authors: De Caro Fabrizio , Giannuzzi Giorgio Maria , Pisani Cosimo , Iuliano Silvia , Vaccaro Alfredo
Published: May 2024

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.
The high energy X-ray probe (HEX-P): the future of hard X-ray dual AGN science
Authors: Pfeifle Ryan W. , Boorman Peter G. , Weaver Kimberly A. , Buchner Johannes , Civano Francesca , Madsen Kristin , Stern Daniel , Torres-Albà Núria , Nardini Emanuele , Ricci Claudio , Marchesi Stefano , Ballantyne D. R. , Sicilian Dominic , Chen Chien-Ting , Kammoun Elias , Hickox Ryan C. , García Javier A. , Mallick Labani
Published: May 2024

A fundamental goal of modern-day astrophysics is to understand the connection between supermassive black hole (SMBH) growth and galaxy evolution. Merging galaxies offer one of the most dramatic channels for galaxy evolution known, capable of driving inflows of gas into galactic nuclei, potentially fueling both star formation and central SMBH activity. Dual active galactic nuclei (dual AGNs) in late-stage mergers with nuclear pair separations <10 kpc are thus ideal candidates to study SMBH growth along the merger sequence since they coincide with the most transformative period for galaxies. However, dual AGNs can be extremely difficult to confirm and study. Hard X-ray (>10 keV) studies offer a relatively contamination-free tool for probing the dense obscuring environments predicted to surround the majority of dual AGN in late-stage mergers. To date, only a handful of the brightest and closest systems have been studied at these energies due to the demanding instrumental requirements involved. We demonstrate the unique capabilities of HEX-P to spatially resolve the soft and - for the first time - hard X-ray counterparts of closely-separated (∼2″−5″) dual AGNs in the local Universe. By incorporating physically-motivated obscuration models, we reproduce realistic broadband X-ray spectra expected for deeply embedded accreting SMBHs. Hard X-ray spatially resolved observations of dual AGNs—accessible only to HEX-P—will hence transform our understanding of dual AGN in the nearby Universe.
Limited usefulness of the IS6110 touchdown-PCR in blood for tuberculin skin test false-negative cattle with serological response to Mycobacterium bovis
Authors: Encinas Micaela , Ferrara Muñiz Ximena , Sammarruco Romina Ayelén , Ruiz Menna Victoria , Garro Carlos Javier , Delgado Fernando , Macías Analía , Magnano Gabriel , Zumárraga Martín José , Garbaccio Sergio Gabriel , Eirin María Emilia
Published: May 2024

Ante-mortem diagnosis of bovine tuberculosis (bTB) is based mainly on the tuberculin skin test (TST) and the ɣ-IFN release assay (IGRA). Some infected animals escape screening tests, thus, limit herd sanitation. Previous reports have suggested a predominant pattern of multi-organ lesions attributable to Mycobacterium bovis (the causative agent of bTB) bacteraemia. A case–control study was conducted to investigate blood PCR as an alternative tool for improving ante-mortem detection of TST false-negative bovines. Cases comprised 70 TST false-negative bovines (cases), which were serology positive, and controls included 81 TST positive bovines; all of them confirmed as infected with M. bovis. Detection of the IS6110 target through touchdown blood-PCR (IS6110 TD-PCR) was performed. The positivity of the blood-PCR was 27.2% in the control group. This performance was similar to the 15% obtained among cases (p = 0.134). Most cases identified by the IS6110 TD-PCR exhibited focalized lesions (p = 0.002). Results demonstrated that blood-PCR could detect TST false-negative cattle, even if they are negative for IGRA. Considering that cases exhibited humoral response to M. bovis, further studies conducted in a pre-serological stage could provide evidence about the real contribution of the technique in herds.
Spatial Analysis on Resource Utilization, Environmental Consequences and Sustainability of Rice–Crayfish Rotation System in Jianghan Plain, China
Authors: Shi Hang , Han Guang , Hu Naijuan , Qu Shuyang , Zhu Liqun
Published: May 2024

The rice–crayfish rotation system (RCR), originating in the Jianghan Plain, is developing rapidly in various regions of China and has been characterized by unbalanced regional development, which has also led to widespread concerns and discussion on its environmental impacts and sustainability. This study selects representative RCR production areas in the Jianghan Plain, including Jianli, Qianjiang, Shishou, Shayang, Gong’an and Honghu, to analyze resource inputs, resource utilization efficiency, environmental impacts and sustainability by employing the emergy analysis method. Our analysis of Jianli, Honghu, Qianjiang, Gong’an, Shishou and Shayang reports total emergy inputs ranging from 6.46 × 1016 to 8.25 × 1016, with renewable rates between 78.38% and 84.34%. Shishou leads in the unit emergy value (5.58 × 10−1) and the emergy yield ratio (5.30). The sustainability evaluation finds that the environmental loading ratio is from 0.19 to 0.28 and the emergy index for sustainable development varies between 1.27 and 3.00. This analysis indicates that the southern regions have higher inputs and efficiency, with southeastern areas showing lower environmental impact and higher sustainability. We also underscore the impact of non-renewable resources on environmental outcomes and sustainability, suggesting tailored development strategies for the rice–crayfish rotation system’s optimization and sustainable growth.
The Recognition and Utilization of Natural Genetic Resources for Advances in Plant Biology through Genomics and Biotechnology
Authors: Tan Guofei , Que Feng , Zhang Jian
Published: May 2024

Biological diversity is the most essential gift from nature, and many elite or superior genes that confer favorable traits remain to be discovered and utilized [...]
Cross-Regional Crop Classification Based on Sentinel-2
Authors: He Jie , Zeng Wenzhi , Ao Chang , Xing Weimin , Gaiser Thomas , Srivastava Amit Kumar
Published: May 2024

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.
Aerobic Vaginal Microflora in Gestational and Non-Gestational Bitches (Canis lupus familiaris)
Authors: Pop Raul Alexandru , Vasiu Iosif , Meroni Gabriele , Martino Piera Anna , Dąbrowski Roman , Tvarijonaviciute Asta , Fiţ Iosif Nicodim
Published: May 2024

The vaginal tract comprises commensal microorganisms, which play an essential role in the health of the reproductive tract. Any dysbiosis in the vaginal microenvironment may lead to severe urinary tract infections or even infertility. This study aimed to evaluate the aerobic bacterial flora isolated from vaginal samples from 100 lactating bitches in the antepartum period (n = 3), postpartum period (n = 80), and with Lactatio sine graviditate (n = 17). Before vaginal swabs, all the bitches went through a gynecology consult, along with milk and blood sampling. Standard microbiological techniques were used for bacterial isolation. Among the 100 vaginal samples analyzed, 82% had a positive microbiological outcome, while 18% were negative. The microbiologic profile listed 17 different genera. The main isolated bacterial families were Micrococcaceae, Staphylococcaceae, Morganellaceae, Bacillaceae, and Rhizobiaceae. At the same time, strains like Agrobacterium radiobacter, Ochrobactrum anthropi, Chromobacterium violaceum, Burkholderia mallei, Bacillus pumllus, or Staphylococcus xylosus were isolated for the first time from the vaginal secretion of lactating bitches. The microbiological data demonstrates that lactating bitches’ vaginal discharge is heterogeneous and may be affected by coitus, sampling season, age, and reproductive status.
A Long-Term Video Tracking Method for Group-Housed Pigs
Authors: Yang Qiumei , Hui Xiangyang , Huang Yigui , Chen Miaobin , Huang Senpeng , Xiao Deqin
Published: May 2024

Pig tracking provides strong support for refined management in pig farms. However, long and continuous multi-pig tracking is still extremely challenging due to occlusion, distortion, and motion blurring in real farming scenarios. This study proposes a long-term video tracking method for group-housed pigs based on improved StrongSORT, which can significantly improve the performance of pig tracking in production scenarios. In addition, this research constructs a 24 h pig tracking video dataset, providing a basis for exploring the effectiveness of long-term tracking algorithms. For object detection, a lightweight pig detection network, YOLO v7-tiny_Pig, improved based on YOLO v7-tiny, is proposed to reduce model parameters and improve detection speed. To address the target association problem, the trajectory management method of StrongSORT is optimized according to the characteristics of the pig tracking task to reduce the tracking identity (ID) switching and improve the stability of the algorithm. The experimental results show that YOLO v7-tiny_Pig ensures detection applicability while reducing parameters by 36.7% compared to YOLO v7-tiny and achieving an average video detection speed of 435 frames per second. In terms of pig tracking, Higher-Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTP), and Identification F1 (IDF1) scores reach 83.16%, 97.6%, and 91.42%, respectively. Compared with the original StrongSORT algorithm, HOTA and IDF1 are improved by 6.19% and 10.89%, respectively, and Identity Switch (IDSW) is reduced by 69%. Our algorithm can achieve the continuous tracking of pigs in real scenarios for up to 24 h. This method provides technical support for non-contact pig automatic monitoring.
Mycobacterium kansasii Infection in a Farmed White-Tailed Deer (Odocoileus virginianus) in Florida, USA
Authors: Cottingham Sydney L. , Cheng An-Chi , de Oliveira Viadanna Pedro H. , Subramaniam Kuttichantran , Craft William F. , Iredale Marley E. , Wisely Samantha M. , Campos Krauer Juan M.
Published: May 2024

A 7-year-old farmed white-tailed deer doe was transported to a Levy County, Florida property and began to decline in health, exhibiting weight loss and pelvic limb weakness. The doe prematurely delivered live twin fawns, both of which later died. The doe was treated with corticosteroids, antibiotics, gastric cytoprotectants, and B vitamins but showed no improvement. The doe was euthanized, and a post mortem examination was performed under the University of Florida’s Cervidae Health Research Initiative. We collected lung tissue after the animal was euthanized and performed histological evaluation, using H&E and Ziehl–Neelsen (ZN) staining, and molecular evaluation, using conventional PCR, followed by Sanger sequencing. The microscopic observations of the H&E-stained lung showed multifocal granuloma, while the ZN-stained tissue revealed low numbers of beaded, magenta-staining rod bacteria inside the granuloma formation. Molecular analysis identified the presence of Mycobacterium kansasii. This isolation of a non-tuberculous Mycobacterium in a white-tailed deer emphasizes the importance of specific pathogen identification in cases of tuberculosis-like disease in farmed and free-ranging cervids. We report the first case of M. kansasii infection in a farmed white-tailed deer (Odocoileus virginianus) in Florida. Although M. kansasii cases are sporadic in white-tailed deer, it is important to maintain farm biosecurity and prevent farmed cervids from contacting wildlife to prevent disease transmission.
Cage-Free Pullets Minimally Affected by Stocking Density Stressors
Authors: Abraham Meagan E. , Robison Cara I. , Serpa Priscila B. S. , Strandberg Natalia J. , Erasmus Marisa A. , Fraley Gregory S. , Erf Gisela F. , Karcher Darrin M.
Published: May 2024

Management choices during the pullet phase can affect behavior, welfare, and health later in life, but few studies have evaluated the pullet phase, particularly in extensive housing systems. This study was a 2 × 2 factorial randomized complete block design (RCBD) with two strains and two stocking densities. The Lohmann LB-Lite and Lohmann LSL-Lite were housed on the floor at high-stocking density (619–670 cm2/bird) and low-stocking density (1249–1352 cm2/bird), which changed with age from 2 to 16 weeks of age (WOA). Bird-based measures of appearance, blood parameters, organ measurements, and production values were evaluated. Stocking density alone affected (p < 0.05) only relative bursal weight (% of body weight)—3.32% in the low-density versus 3.08% in the high-density group. High-stocking density was correlated with decreased uniformity (high—89.33 ± 0.24%; low—90.41 ± 0.24; p < 0.02) and worse feather coverage in the brown strain. High-stocking density was correlated with greater uniformity (High—90.39 ± 0.24%; Low—88.47 ± 0.24%; p < 0.001) and better feather coverage in the white strain. This study’s feed conversion ratio (FCR) was improved by 0.07 in the low-stocking density for both strains. The remaining parameters were affected by strain and age only. Thus, while stocking density effects vary slightly depending on the strain used, cage-free pullets had limited negative effects at both the high and low-stocking densities tested in this study; there were few to no changes in the numerous bird-based welfare parameters tested.
Alginate Oligosaccharides Protect Gastric Epithelial Cells against Oxidative Stress Damage through Induction of the Nrf2 Pathway
Authors: Acevedo Samantha , Covarrubias Alejandra A. , Haeger Paola , Pancetti Floria , Tala Fadia , de la Fuente-Ortega Erwin
Published: May 2024

Gastric diseases represent a significant global public health challenge, characterized by molecular dysregulation in redox homeostasis and heightened oxidative stress. Although prior preclinical studies have demonstrated the cytoprotective antioxidant effects of alginate oligosaccharides (AOSs) through the Nrf2 pathway, whether such mechanisms apply to gastric diseases remains unclear. In this study, we used the GES-1 gastric cell line exposed to hydrogen peroxide (H2O2) as a damage model to investigate the impact of AOS on cell viability and its associated mechanisms. Our results revealed that pre-incubation with AOS for either 4 h or 24 h significantly improved the viability of GES-1 cells exposed to H2O2. In addition, AOS reduced the intracellular ROS levels, activating the Nrf2 signaling pathway, with increased Nrf2 protein and mRNA expression and a significant upregulation of the target genes HO-1 and NQO1. The activation of Nrf2 was correlated with decreased Keap1 protein expression and an increased level of the autophagy protein p62/SQSTM1, suggesting the activation of Nrf2 through a noncanonical pathway. This study suggests that AOS is a potential treatment for protecting gastric epithelial cells from oxidative stress by activating the p62/SQSTM1-Keap1-Nrf2 axis and laying the foundation for future investigations about its specific therapeutic mechanisms.
Power System Signal-Detection Method Based on the Accelerated Unsaturated Stochastic Resonance Principle
Authors: Sun Shuqin , Qi Xin , Yuan Zhenghai , Tang Xiaojun , Li Zaihua
Published: May 2024

The classical bistable stochastic resonance algorithm has an inherent output saturation defect that restricts the amplitude of the output signal. This paper examines the causes of this phenomenon and its negative impact on the detection of weak signals. Proposing the Unsaturated Bistable Stochastic Resonance (UBSR) detection algorithm involves constructing a segmented potential function using a linear function to eliminate the effect of higher-order terms in the classical stochastic resonance algorithm. A new type of segmented potential function has been created by combining exponential and linear functions. This new function helps to eliminate the impact of higher-order terms in classical algorithms while also improving the noise immunity of the stochastic resonance system. This results in the development of the accelerated stochastic resonance (ASR) detection algorithm. In this paper, the Kramers escape rate and output signal-to-noise ratio of two improved stochastic resonance algorithms are theoretically derived and compared with the classical bistable stochastic resonance algorithms, and the proposed algorithms are able to effectively avoid the output saturation phenomenon and have more excellent detection performance under strong background noise.
Tribological Properties of Aluminium Matrix Syntactic Foams Manufactured with Aluminium Smelter Waste
Authors: Mehta Bhavik , Zhao Yuyuan
Published: May 2024

Aluminium smelter waste (ASW) is a big contributor to landfills, and its recycling has been of great interest. This study investigates the tribological properties of aluminium matrix syntactic foams manufactured using an Al 6082 alloy and ASW. Ball-on-disc tests were conducted under both dry and lubricated conditions. Under dry sliding conditions, the coefficient of friction (COF) had an initial sharp increase, followed by a gradual decrease and finally a steady state as the sliding distance increased. The wear surfaces showed the presence of adhesive, abrasive and oxidative wear, with some presence of delamination. Syntactic foams containing small ASW particles led to a decrease in surface roughness, decrease in the average COF and decrease in specific wear. Heating large ASW particles before manufacturing the syntactic foams enhanced overall wear properties because the particles are hardened due to a compositional change. The T6 treatment of the syntactic foams enhanced the wear properties due to the hardening of the Al matrix. The average COF of the ASW syntactic foams was higher than that of the E-sphere syntactic foam, which was predominantly abrasive wear. The specific wear of the ASW syntactic foams can be higher or lower than the E-sphere syntactic foam, depending on the ASW particle size. Under lubricated sliding test conditions, the wear was reduced significantly, and the type changed from predominantly adhesive to predominantly abrasive. The porous ASW particles acted as lubricant reservoirs and provided a constant supply of lubricant, further improving the lubrication effect.
Revealing Spatial–Temporal Patterns of Sea Surface Temperature in the South China Sea Based on Spatial–Temporal Co-Clustering
Authors: He Qi , Xu Zhuangzhuang , Song Wei , Geng Lijia , Huang Dongmei , Du Yanling
Published: May 2024

To discover the spatial–temporal patterns of sea surface temperature (SST) in the South China Sea (SCS), this paper proposes a spatial–temporal co-clustering algorithm optimized by information divergence. This method allows for the clustering of SST data simultaneously across temporal and spatial dimensions and is adaptable to large volumes of data and anomalous data situations. First, the SST data are initially clustered using the co-clustering algorithm. Second, we use information divergence as the loss function to refine the clustering results iteratively. During the iterative optimization of spatial clustering results, we treat the temporal dimension as a constraint; similarly, during the iterative optimization of temporal clustering, we treat the spatial dimension as a constraint. This is to ensure better robustness of the algorithm. Finally, this paper conducts experiments in the SCS to verify our algorithm. According to the analysis of the experimental results, we have drawn the following conclusions. First, the use of the spatial–temporal co-clustering algorithm reveals that the SST in the SCS exhibits strong seasonal patterns in the temporal clustering results. The spatial distribution of SST varies significantly in different seasons. There is a slight difference in SST between the northern and southern regions of the SCS in winter, but the largest difference is in summer. Second, during ocean anomalies, our proposed algorithm can identify the corresponding abnormal patterns. When ENSO occurs, the seasonal distribution pattern of SST in the SCS is destroyed and replaced by an abnormal temporal pattern. The results indicate that during ENSO events, the SST in specific months in the SCS exhibits a correlation with the SST observed 4–5 months afterward.
Computational Fluid Dynamics Investigation of Hydrodynamic Forces and Moments Acting on Stern Rudder Plane Configurations of a Submarine
Authors: Phan Thanh Long , Mai Thi Loan , Nguyen Tien Thua
Published: May 2024

This study presents the predicted hydrodynamic characteristics of different rudder plane configurations on the stern of a full-scale submarine in deep water, which are obtained using the Reynolds-Averaged Navier–Stokes method in Ansys Fluent Solver. First, the results obtained for the X-rudder plane configuration are verified according to previous numerical and experimental results in order to assess the accuracy of the simulation procedure. The X-rudder plane, Y-rudder plane, and Cross-rudder plane configurations in deep water with deflection angles ranging from −21 degrees to +21 degrees are then simulated. Next, the hydrodynamic forces and moments of the Cross-plane, X-plane, and Y-plane rudder configurations obtained through simulation are analyzed using Taylor’s expansion to estimate the hydrodynamic coefficients. The obtained results demonstrate that the X-force of the X-plane rudder configuration is larger than the corresponding forces acting on the Cross-plane rudder and Y-plane rudder configurations. Meanwhile, the Y-force and Z-force of the X-plane rudder configuration are significantly greater than the corresponding forces of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the y- and z-axes. However, the roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than that under the X-plane rudder configuration.
Energy Bus-Based Matrix Modeling and Optimal Scheduling for Integrated Energy Systems
Authors: Zhang Lizhi , Li Fan
Published: May 2024

Integrated energy systems (IESs) can easily accommodate renewable energy resources (RESs) and improve the utilization efficiency of fossil energy by integrating various energy production, conversion, and storage technologies. However, the coupled multi-energy flows and the uncertainty of RESs bring challenges regarding optimal scheduling. Therefore, this study proposes an energy bus-based matrix-modeling method and a coordinated scheduling strategy for the IES. The matrix-modeling method can be used to formulate the steady- and transient-state balances of the multi-energy flows, and the transient model can clearly express the multi-time-scale characteristics of the different energy flows. The model parameters are fitted with data from experiments and the literature. To address the inherent randomness of the RESs and loads, a coordinated scheduling strategy is designed that contains two components: day-ahead optimization and rolling optimization. Day-ahead optimization uses the system steady-state model and multiple scenarios from the RES and load forecast data to minimize the operation cost while rolling optimization is based on the system’s transient-state model and aims to achieve the optimal real-time scheduling of the energy flows. Finally, a case study is conducted to verify the advantages and effectiveness of the proposed model and optimization method. The results show that stochastic optimization reduces the total daily cost by 1.48% compared to deterministic optimization when considering the prediction errors associated with the RESs and loads, highlighting the stronger adaptability of stochastic optimization to prediction errors. Moreover, rolling optimization based on the system’s transient-state model can reduce the errors between day-ahead scheduling and rolling correction.
Evaluation of Selected Heavy Metal Contaminants as Well as Nitrates and Nitrites in the Microgreens of Nigella (Nigella sativa L.), Safflower (Carthamus tinctorius L.), and Camelina (Camelina sativa L.) at Different Stages of Vegetation
Authors: Kapusta-Duch Joanna , Smoleń Sylwester , Jędrszczyk Elżbieta , Leszczyńska Teresa , Borczak Barbara , Kusznierewicz Barbara
Published: May 2024

Microgreens are a new, rapidly growing group of foodstuffs. The decorative function of these is often accompanied by their use in traditional dishes. As microgreens are eaten at very early stages, when the development of the epidermis is at its minimum, the bioavailability of minerals will be found to be higher in microgreens then in mature vegetables. So, microgreens can be an excellent functional food, especially for mineral-deficient populations, although they can also be a source of contaminants such as heavy metals or nitrates and nitrites. The purpose of this study was to measure the levels of selected heavy metals (i.e., cadmium, arsenic, lead, chromium, aluminium, zinc, copper, cobalt, molybdenum, manganese, vanadium, boron, antimony, thallium, titanium and strontium), as well as nitrates and nitrites, in microgreens at various stage of vegetation, using uncommon oilseed plants like nigella—Nigella sativa L., safflower—Carthamus tinctorius L., and camelina—Camelina sativa L. The examined microgreens of rare oilseed plants may be a source of contaminants and nitrates. The mineral profile of these plants is mainly determined by their genotype. Microgreens’ cultivation involves compliance with safety standards and replicable conditions to guarantee that the highest nutritional value is reached at the lowest possible contaminant level.
Source Rock Prediction Using Well Log and Seismic Data: A Study of the Albian Stage in the Côte d’Ivoire Basin
Authors: Wang Boyu , Li Yuezhe , Yin Jie , Ye Lin , Wang Zhenqi
Published: May 2024

The Côte d’Ivoire Basin is one of the most important oil production areas in Africa; and it is also the focus of global oil and gas exploration, but the degree of oil exploration and development in this basin are still in the early stages. Based on a comprehensive analysis of drilling, logging, and geochemical data in the Côte d’Ivoire Basin, this study comprehensively evaluates the Cretaceous lacustrine source rocks in the study area, establishes a total organic carbon (TOC) quantitative prediction model by using the total organic carbon method, determines the relationship between seismic reflections and the geochemical characteristics of the source rocks, and tracks the main source rocks horizontally. The results show that the TOC of the Lower Cretaceous Albian stage source rocks is 2.63 wt.%, the kerogen type II is dominant, and the vitrinite reflectance (RO) ranges from 0.3% to 0.9%. The source rocks are mainly in the low-mature to mature stage, and they are generally of good and premium quality. The distribution range of high-quality source rocks with TOC values of 4.00~5.00% is predicted using the total organic carbon method, and useing the seismic method, we determine that high-quality source rocks with larger thickness values and better continuity are mainly developed in the eastern depression center of the basin.
Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
Authors: Oglat Ammar A. , Abukhalil Tala
Published: May 2024

Ultrasound is a highly adaptable medical imaging modality that offers several applications and a wide range of uses, both for diagnostic and therapeutic purposes. The principles of sound wave propagation and reflection enable ultrasound imaging to function as a highly secure modality. This technique facilitates the production of real-time visual representations, thereby assisting in the evaluation of various medical conditions such as cardiac, gynecologic, and abdominal diseases, among others. The ultrasound modality encompasses a diverse range of modes and mechanisms that serve to enhance the methodology of pathology and physiology assessment. Doppler imaging and US elastography, in particular, are two such techniques that contribute to this expansion. Elastography-based imaging methods have attracted significant interest in recent years for the non-invasive evaluation of tissue mechanical characteristics. These techniques utilize the changes in soft tissue elasticity in various diseases to generate both qualitative and quantitative data for diagnostic purposes. Specialized imaging techniques collect data by identifying tissue stiffness under mechanical forces such as compression or shear waves. However, in this review paper, we provide a comprehensive examination of the fundamental concepts, underlying physics, and limitations associated with ultrasound elastography. Additionally, we present a concise overview of its present-day clinical utilization and ongoing advancements across many clinical domains.
Numerical Calculation and Experimental Study of the Axial Force of Aero Fuel Centrifugal Pumps
Authors: Yan Shebin , Kan Yinhui , Li Xin , Xiao Lingfei , Ye Zhifeng
Published: May 2024

Axial force is one of the important factors affecting the life and reliability of centrifugal pumps. Based on the SST turbulence model, the unsteady internal flow field of an aero fuel centrifugal pump under various working conditions was analyzed by using the finite volume method and the axial force of the impeller component was predicted. The position servo force measuring system was used to measure the axial force of the fuel centrifugal pump and the theoretical formula of axial force was modified according to the numerical results and experimental values. The study shows that the pressure distribution of the front and rear pump chambers presented uneven circumferential distribution under the influence of dynamic and static interference through numerical simulation. The simulated head number is basically consistent with the test result and the maximum error of the axial force value obtained by the numerical calculation and the experimental value was 9.7% under different speeds, which verified the accuracy of the numerical simulation. Furthermore, the modified formula can accurately calculate the axial force of the fuel centrifugal pump with an error of less than 9.88%. The results of the study provide a theoretical basis for the calculation and balance of axial force in an aero aero fuel centrifugal pump.
A Historical Survey of Advances in Transformer Architectures
Authors: Sajun Ali Reza , Zualkernan Imran , Sankalpa Donthi
Published: May 2024

In recent times, transformer-based deep learning models have risen in prominence in the field of machine learning for a variety of tasks such as computer vision and text generation. Given this increased interest, a historical outlook at the development and rapid progression of transformer-based models becomes imperative in order to gain an understanding of the rise of this key architecture. This paper presents a survey of key works related to the early development and implementation of transformer models in various domains such as generative deep learning and as backbones of large language models. Previous works are classified based on their historical approaches, followed by key works in the domain of text-based applications, image-based applications, and miscellaneous applications. A quantitative and qualitative analysis of the various approaches is presented. Additionally, recent directions of transformer-related research such as those in the biomedical and timeseries domains are discussed. Finally, future research opportunities, especially regarding the multi-modality and optimization of the transformer training process, are identified.
Outlier Detection for Permanent Magnet Synchronous Motor (PMSM) Fault Detection and Severity Estimation
Authors: Koutrakos Konstantinos , Mitronikas Epameinondas
Published: May 2024

Today, Permanent Magnet Synchronous Motors (PMSMs) are a dominant choice in industry applications. During operation, different possible faults in the system can occur, so early and automated fault detection and severity estimation are required to ensure smooth operation and optimal maintenance planning. In this direction, outlier detection methods are employed in this paper. The motor’s current signals are used to extract useful indicators of the fault, along with d-q transform. Statistical indicators in both time and frequency domains are selected to describe fault-related patterns. Based on the extracted features, three outlier detection methods are investigated: the Isolation Forest, the One Class Support Vector Machine, and the Robust Covariance Ellipse. Each method is investigated through different model parameters to evaluate fault detection and severity estimation capabilities. Finally, an ensemble approach is proposed based on decisions and outlier score ensemble. The proposed methodology is verified through different operating conditions in a PMSM test bench.
A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
Authors: Lee Hyunseok , Park Young-Sang , Yang Songho , Lee Hoyul , Park Tae-Jin , Yeo Doyeob
Published: May 2024

With the widespread adoption of smart farms and continuous advancements in IoT (Internet of Things) technology, acquiring diverse additional data has become increasingly convenient. Consequently, studies relevant to deep learning models that leverage multimodal data for crop disease diagnosis and associated data augmentation methods are significantly growing. We propose a comprehensive deep learning model that predicts crop type, detects disease presence, and assesses disease severity at the same time. We utilize multimodal data comprising crop images and environmental variables such as temperature, humidity, and dew points. We confirmed that the results of diagnosing crop diseases using multimodal data improved 2.58%p performance compared to using crop images only. We also propose a multimodal-based mixup augmentation method capable of utilizing both image and environmental data. In this study, multimodal data refer to data from multiple sources, and multimodal mixup is a data augmentation technique that combines multimodal data for training. This expands the conventional mixup technique that was originally applied solely to image data. Our multimodal mixup augmentation method showcases a performance improvement of 1.33%p compared to the original mixup method.
STFEformer: Spatial–Temporal Fusion Embedding Transformer for Traffic Flow Prediction
Authors: Yang Hanqing , Wei Sen , Wang Yuanqing
Published: May 2024

In the realm of Intelligent Transportation Systems (ITSs), traffic flow prediction is crucial for multiple applications. The primary challenge in traffic flow prediction lies in the handling and modeling of the intricate spatial–temporal correlations inherent in transport data. In recent years, many studies have focused on developing various Spatial–Temporal Graph Neural Networks (STGNNs), and researchers have also begun to explore the application of transformers to capture spatial–temporal correlations in traffic data. However, GNN-based methods mainly focus on modeling spatial correlations statically, which significantly limits their capacity to discover dynamic and long-range spatial patterns. Transformer-based methods have not sufficiently extracted the comprehensive representation of traffic data features. To explore dynamic spatial dependencies and comprehensively characterize traffic data, the Spatial–Temporal Fusion Embedding Transformer (STFEformer) is proposed for traffic flow prediction. Specifically, we propose a fusion embedding layer to capture and fuse both native information and spatial–temporal features, aiming to achieve a comprehensive representation of traffic data characteristics. Then, we introduce a spatial self-attention module designed to enhance detection of dynamic and long-range spatial correlations by focusing on interactions between similar nodes. Extensive experiments conducted on three real-world datasets demonstrate that STFEformer significantly outperforms various baseline models, notably achieving up to a 5.6% reduction in Mean Absolute Error (MAE) on the PeMS08 dataset compared to the next-best model. Furthermore, the results of ablation experiments and visualizations are employed to clarify and highlight our model’s performance. STFEformer represents a meaningful advancement in traffic flow prediction, potentially influencing future research and applications in ITSs by providing a more robust framework for managing and analyzing traffic data.