bluuebunny/crossref_metadata_2025 huggingface dataset:
DVST: Deformable Voxel Set Transformer for 3D Object Detection from Point Clouds
Authors: Ning Yaqian , Cao Jie , Bao Chun , Hao Qun
Published: December 2023

The use of a transformer backbone in LiDAR point-cloud-based models for 3D object detection has recently gained significant interest. The larger receptive field of the transformer backbone improves its representation capability but also results in excessive attention being given to background regions. To solve this problem, we propose a novel approach called deformable voxel set attention, which we utilized to create a deformable voxel set transformer (DVST) backbone for 3D object detection from point clouds. The DVST aims to efficaciously integrate the flexible receptive field of the deformable mechanism and the powerful context modeling capability of the transformer. Specifically, we introduce the deformable mechanism into voxel-based set attention to selectively transfer candidate keys and values of foreground queries to important regions. An offset generation module was designed to learn the offsets of the foreground queries. Furthermore, a globally responsive convolutional feed-forward network with residual connection is presented to capture global feature interactions in hidden space. We verified the validity of the DVST on the KITTI and Waymo open datasets by constructing single-stage and two-stage models. The findings indicated that the DVST enhanced the average precision of the baseline model while preserving computational efficiency, achieving a performance comparable to state-of-the-art methods.
Research on the Extraction Method Comparison and Spatial-Temporal Pattern Evolution for the Built-Up Area of Hefei Based on Multi-Source Data Fusion
Authors: Huang Jianwei , Chu Chaoqun , Wang Lu , Wu Zhaofu , Zhang Chunju , Geng Jun , Zhu Yongchao , Yu Min
Published: December 2023

With the development of urban built-up areas, accurately extracting the urban built-up area and spatiotemporal pattern evolution trends could be valuable for understanding urban sprawl and human activities. Considering the coarse spatial resolution of nighttime light (NTL) data and the inaccurate regional boundary reflection on point of interest (POI) data, land surface temperature (LST) data were introduced. A composite index method (LJ–POI–LST) was proposed based on the positive relationship for extracting the boundary and reflecting the spatial-temporal evolution of urban built-up areas involving the NTL, POIs, and LST data from 1993 to 2018 in this paper. This paper yielded the following results: (1) There was a spatial-temporal pattern evolution from north-east to south-west with a primary quadrant orientation of IV, V, and VI in the Hefei urban area from 1993–2018. The medium-speed expansion rate, with an average value of 14.3 km2/a, was much faster than the population growth rate. The elasticity expansion coefficient of urbanization of 1.93 indicated the incongruous growth rate between the urban area and population, leading to an incoordinate and unreasonable development trend in Hefei City. (2) The detailed extraction accuracy for urban and rural junctions, urban forest parks, and other error-prone areas was improved, and the landscape connectivity and fragmentation were optimized according to the LJ–POI–LST composite index based on a high-resolution remote sensing validation image in the internal spatial structure. (3) Compared to the conventional NTL data and the LJ–POI index, the LJ–POI–LST composite index method displayed an extraction accuracy greater than 85%, with a similar statistical and landscape pattern index result. This paper provides a suitable method for the positive relationship among these LST, NTL, and POI data for accurately extracting the boundary and reflecting the spatial-temporal evolution of urban built-up areas by the fusion data.
Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition
Authors: Pan Bin , Shi Xianjian
Published: December 2023

Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities.
Intelligent Head-Mounted Obstacle Avoidance Wearable for the Blind and Visually Impaired
Authors: Xu Peijie , Song Andy , Wang Ke
Published: December 2023

Individuals who are Blind and Visually Impaired (BVI) take significant risks and dangers on obstacles, particularly when they are unaccompanied. We propose an intelligent head-mount device to assist BVI people with this challenge. The objective of this study is to develop a computationally efficient mechanism that can effectively detect obstacles in real time and provide warnings. The learned model aims to be both reliable and compact so that it can be integrated into a wearable device with a small size. Additionally, it should be capable of handling natural head turns, which can generally impact the accuracy of readings from the device’s sensors. Over thirty models with different hyper-parameters were explored and their key metrics were compared to identify the most suitable model that strikes a balance between accuracy and real-time performance. Our study demonstrates the feasibility of a highly efficient wearable device that can assist BVI individuals in avoiding obstacles with a high level of accuracy.
A Missing Traffic Data Imputation Method Based on a Diffusion Convolutional Neural Network–Generative Adversarial Network
Authors: Zhang Chenchen , Zhou Lei , Xiao Xuemei , Xu Dongwei
Published: December 2023

Traffic state data are key to the proper operation of intelligent transportation systems (ITS). However, traffic detectors often receive environmental factors that cause missing values in the collected traffic state data. Therefore, aiming at the above problem, a method for imputing missing traffic state data based on a Diffusion Convolutional Neural Network–Generative Adversarial Network (DCNN-GAN) is proposed in this paper. The proposed method uses a graph embedding algorithm to construct a road network structure based on spatial correlation instead of the original road network structure; through the use of a GAN for confrontation training, it is possible to generate missing traffic state data based on the known data of the road network. In the generator, the spatiotemporal features of the reconstructed road network are extracted by the DCNN to realize the imputation. Two real traffic datasets were used to verify the effectiveness of this method, with the results of the proposed model proving better than those of the other models used for comparison.
Zero-Shot Traffic Sign Recognition Based on Midlevel Feature Matching
Authors: Gan Yaozong , Li Guang , Togo Ren , Maeda Keisuke , Ogawa Takahiro , Haseyama Miki
Published: December 2023

Traffic sign recognition is a complex and challenging yet popular problem that can assist drivers on the road and reduce traffic accidents. Most existing methods for traffic sign recognition use convolutional neural networks (CNNs) and can achieve high recognition accuracy. However, these methods first require a large number of carefully crafted traffic sign datasets for the training process. Moreover, since traffic signs differ in each country and there is a variety of traffic signs, these methods need to be fine-tuned when recognizing new traffic sign categories. To address these issues, we propose a traffic sign matching method for zero-shot recognition. Our proposed method can perform traffic sign recognition without training data by directly matching the similarity of target and template traffic sign images. Our method uses the midlevel features of CNNs to obtain robust feature representations of traffic signs without additional training or fine-tuning. We discovered that midlevel features improve the accuracy of zero-shot traffic sign recognition. The proposed method achieves promising recognition results on the German Traffic Sign Recognition Benchmark open dataset and a real-world dataset taken from Sapporo City, Japan.
Real Aperture Radar Super-Resolution Imaging for Sea Surface Monitoring Based on a Hybrid Model
Authors: Tan Ke , Zhou Shengqi , Lu Xingyu , Yang Jianchao , Su Weimin , Gu Hong
Published: December 2023

In recent years, super-resolution imaging techniques have been intensely introduced to enhance the azimuth resolution of real aperture scanning radar (RASR). However, there is a paucity of research on the subject of sea surface imaging with small incident angles for complex scenarios. This research endeavors to explore super-resolution imaging for sea surface monitoring, with a specific emphasis on grounded or shipborne platforms. To tackle the inescapable interference of sea clutter, it was segregated from the imaging objects and was modeled alongside I/Q channel noise within the maximum likelihood framework, thus mitigating clutter’s impact. Simultaneously, for characterizing the non-stationary regions of the monitoring scene, we harnessed the Markov random field (MRF) model for its two-dimensional (2D) spatial representational capacity, augmented by a quadratic term to bolster outlier resilience. Subsequently, the maximum a posteriori (MAP) criterion was employed to unite the ML function with the statistical model regarding imaging scene. This hybrid model forms the core of our super-resolution methodology. Finally, a fast iterative threshold shrinkage method was applied to solve this objective function, yielding stable estimates of the monitored scene. Through the validation of simulation and real data experiments, the superiority of the proposed approach in recovering the monitoring scenes and clutter suppression has been verified.
A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity
Authors: Tan Qiancheng , Qin Yonghui , Tang Rui , Wu Sixuan , Cao Jing
Published: December 2023

Sensor-based human activity recognition is now well developed, but there are still many challenges, such as insufficient accuracy in the identification of similar activities. To overcome this issue, we collect data during similar human activities using three-axis acceleration and gyroscope sensors. We developed a model capable of classifying similar activities of human behavior, and the effectiveness and generalization capabilities of this model are evaluated. Based on the standardization and normalization of data, we consider the inherent similarities of human activity behaviors by introducing the multi-layer classifier model. The first layer of the proposed model is a random forest model based on the XGBoost feature selection algorithm. In the second layer of this model, similar human activities are extracted by applying the kernel Fisher discriminant analysis (KFDA) with feature mapping. Then, the support vector machine (SVM) model is applied to classify similar human activities. Our model is experimentally evaluated, and it is also applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that the proposed approach achieves recognition accuracies of 97.69%, 97.92%, 98.12%, and 90.6%, indicating excellent recognition performance. Additionally, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier to assess the model’s generalization ability. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.
Clustering and Beamwidth Optimization for UAV-Assisted Wireless Communication
Authors: Zhao Weidong , Zhang Jun , Li Dongxing
Published: December 2023

With the development of wireless communication technology, unmanned aerial vehicles (UAV) are now widely used in many complex communication scenarios. When a UAV serves as an aerial base station for urban and rural ground users or marine users, it is necessary to consider the clustering of ground users and the energy efficiency of the UAV since the users are usually randomly distributed. For the scenario with randomly distributed ground users and different densities of ground users in urban and rural areas, a clustering and beamwidth optimization method for UAV-assisted wireless communication is proposed. Firstly, the energy efficiency expression of a UAV serving ground users was derived in a downlink wireless communication system assisted by a UAV. Secondly, based on the geographical location information of non-uniformly distributed users, an improved k-means method is proposed to cluster ground users, ensuring that the number of users in each cluster is within an appropriate range. Then, based on the clustering results, a fixed-point iteration (FPI) algorithm was proposed to design the optimal beamwidth of UAVs and improve their energy efficiency. Finally, the superiority of the proposed algorithm in improving energy efficiency was verified through simulation analysis, and the impact of parameters such as the cluster number and transmission power on system energy efficiency was also analyzed.
The Long Arm of the State: Transnational Repression against Exiled Activists from the Arab Gulf States
Authors: Abushammalah Noor J. E.
Published: December 2023

The Arab Spring was a period of intense activism demanding democracy and freedom that swept across the Middle East and North Africa (MENA) region. While previous research has focused on the role of diaspora communities in the uprisings and the strategies employed by regimes to suppress them, it has primarily centered on countries that experienced large-scale revolutions and endured severe consequences. Consequently, the current literature has failed to explore the situation of Arab Gulf dissidents living in exile, instead focusing on a few isolated incidents. This paper examines the transnational repression (TR) campaign of the Arab Gulf states (AGSs). Drawing on the literature about the long arm of authoritarianism and TR, this paper explores the various TR methods employed by the AGSs to silence activists living abroad. The paper finds that the nature of TR in the Arab Gulf region is unique when compared with other MENA countries. The TR campaign of AGSs is alarmingly expanding, using various mechanisms and resources, making the region one of the world’s leading perpetrators. The methods employed by the AGSs include travel bans as part of their coercion by proxy, digital transnational repression, and the use of multilateral organizations as tools of repression. Additionally, this paper highlights the AGSs’ support of other countries’ TR.
Multi-Objective Design of UAS Air Route Network Based on a Hierarchical Location–Allocation Model
Authors: Liu Zhaoxuan , Nie Lei , Xu Guoqiang , Li Yanhua , Guan Xiangmin
Published: December 2023

This research concentrates on the Unmanned Aircraft System (UAS) demand sites’ hierarchical location–allocation problem in air route network design. With demand sites (locations where UAS operations are requested) organized and allocated according to the spatial hierarchy of UAS traffic flows, the hierarchical structure guarantees resource conservation and economies of scale through traffic consolidation. Therefore, in this paper, the UAS route network with a three-level hierarchy is developed under a multi-objective decision-making framework, where concerns about UAS transportation efficiency from the user side and construction efficiency from the supplier side are both simultaneously considered. Specifically, a bi-level Hybrid Simulated Annealing Genetic Algorithm (HSAGA) with global and local search combined is proposed to determine the optimal number, location, and allocation of hierarchical sites. Moreover, using the information of site closeness and UAS demand distribution, two problem-specific local search operators are designed to explore elite neighborhood regions instead of all the search space. A case study based on the simulated UAS travel demand data of the Beijing area in China was conducted to demonstrate the effectiveness of the proposed method, and the impact of critical parameter settings on the network layout was explored as well. Findings from this study will offer new insights for UAS traffic management in the future.
Applications of Information Technology in Building Carbon Flow
Authors: Li Clyde Zhengdao , Deng Yiqian , Ya Yingyi , Tam Vivian W. Y. , Lu Chen
Published: December 2023

The construction industry, as one of the three major carbon emission (CE) industries, accounts for about 39% of the global CE. Thus, approaches for energy saving and emission reduction (ES/ER) cannot be delayed. With the advent of the Industry 4.0 era, information technology (IT) is used to investigate CE in the construction industry, which provides great convenience for measuring and calculating building carbon emissions (BCE) and proposing effective ES/ER measures. However, limited studies have provided a holistic overview of the application of IT in BCE. To fill this gap, this study searched related articles and screened 170 relevant papers. Based on the characteristics of the literature, building carbon flow (BCF) was defined. Based on scientometric analysis and network mapping analysis, combined with quantitative and qualitative analysis methods, the functions, advantages, and limitations of IT in each stage of BCF research were reviewed. Finally, the research trends and future research directions of IT in the BCF were discussed. Specifically, the building information model technology penetrates the whole process of BCF research, deep learning and artificial intelligence have great potential in BCF research, and multi-information technology integration will become the focus of subsequent research in the construction industry.
Influence of Consumption Decisions of Rural Residents in the Context of Rapid Urbanization: Evidence from Sichuan, China
Authors: Lin Xu , Qi Yanbin
Published: December 2023

Background: Promoting the transformation and upgrading of China’s rural consumer market is of strategic significance for maintaining sustainable economic development in the process of urbanization. Research objects and methods: Our objectives were to explore the influencing factors of rural residents’ consumption decision making and to provide reference for formulating relevant policies. This study adopted the questionnaire survey method and collected 300 valid questionnaires. Through statistical analysis of questionnaire data and multiple linear regression analysis, the key factors affecting rural residents’ consumption decision making were determined. Results: Education level, family income, consumption view and sociocultural factors are the main factors affecting the consumption decision of rural residents. In addition, personal cognition and attitude are also important factors affecting rural residents’ consumption decisions. In addition, social influence also has a certain positive impact on consumer decisions. Conclusion: The key factors affecting rural residents’ consumption decision making are external factors, including education level and sociocultural factors, and internal factors, namely family income and family consumption concept. At the same time, personal cognition and attitude also play an important role in consumption decisions. The purpose of this study is to provide objective suggestions for improving and expanding the rural consumption market at the policy level, so as to promote the upgrading of rural consumption in China. The influence of rural residents’ consumption decisions on local economic stability was investigated. The economic stability of farmers’ consumption can be understood by analyzing their consumption tendency and consumption pattern.
Research on the Application of NbS in Watershed Ecological Restoration: A Case Study of Jiulong River Watershed Shan-Shui Initiative
Authors: Li Wei , Sun Rui , Tian Ye
Published: December 2023

Nature-based solutions (NbS) rapidly develop globally to address societal challenges and provide human well-being and biodiversity. Watershed restoration plays an essential role in enhancing the ecological and socio-economic benefits of the region. The design and implementation of watershed restoration projects are crucial to their effectiveness, and NbS has been applied as a concept in ecosystem-related projects. This paper proposes an evaluation method to assess the implementation of the Shan-Shui Initiative in the Jiulong River Watershed restoration projects based on the eight criteria proposed by the International Union for the Conservation of Nature (IUCN) Global Standard for Nature-Based Solutions. The aim is to standardize the implementation process of watershed restoration projects to achieve more significant benefits and practically apply the concept of NbS in watershed restoration. The implementation scheme of the Shan-Shui Initiative in the Jiulong River Watershed is designed to evaluate and improve the design and implementation scheme of ecological restoration projects in the actual basin. According to the assessment results, the degree of matching based on NbS for the implementation scheme of the Jiulong River Watershed Shan-Shui Initiative is 73%, which meets the criteria of NbS but needs to be improved in terms of monitoring and assessment, synergistic management, and benefit trade-offs.
Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin: A Step towards Sustainable Manufacturing
Authors: Zhang Zhongfei , Qu Ting , Zhao Kuo , Zhang Kai , Zhang Yongheng , Liu Lei , Wang Jun , Huang George Q.
Published: December 2023

In the quest for sustainable production, manufacturers are increasingly adopting mixed-flow production modes to meet diverse product demands, enabling small-batch production and ensuring swift delivery. A key aspect in this shift is optimizing material distribution scheduling to maintain smooth operations. However, traditional methods frequently encounter challenges due to outdated information tools, irrational task allocation, and suboptimal route planning. Such limitations often result in distribution disarray, unnecessary resource wastage, and general inefficiency, thereby hindering the economic and environmental sustainability of the manufacturing sector. Addressing these challenges, this study introduces a novel dynamic material distribution scheduling optimization model and strategy, leveraging digital twin (DT) technology. This proposed strategy aims to bolster cost-effectiveness while simultaneously supporting environmental sustainability. Our methodology includes developing a route optimization model that minimizes distribution costs, maximizes workstation satisfaction, and reduces carbon emissions. Additionally, we present a cloud–edge computing-based decision framework and explain the DT-based material distribution system’s components and operation. Furthermore, we designed a DT-based dynamic scheduling optimization mechanism, incorporating an improved ant colony optimization algorithm. Numerical experiments based on real data from a partner company revealed that the proposed material distribution scheduling model, strategy, and algorithm can reduce the manufacturer’s distribution operation costs, improve resource utilization, and reduce carbon emissions, thereby enhancing the manufacturer’s economic and environmental sustainability. This research offers innovative insights and perspectives that are crucial for advancing sustainable logistics management and intelligent algorithm design in analogous manufacturing scenarios.
The Relationship between High-Tech Industrial Agglomeration and Regional Innovation: A Meta-Analysis Investigation in China
Authors: Ge Lanqing , Li Chunyan , Sun Lei , Hu Weina , Ban Qi
Published: December 2023

High-tech industrial agglomeration enhances the technological level and value of regional industries. It is considered to be a new and effective way to drive China’s regional innovation and development at present. Numerous studies indicate that high-tech industrial agglomeration contributes positively to regional innovation, but the current academic discussion on this issue has not yet reached a unified opinion. In various research contexts, it has also been pointed out that there may be a negative correlation or non-linear relationship between the two. This contradictory relationship makes it difficult to generalize the current research findings to realistic application scenarios. Therefore, to clarify the relationship between the two scientifically, this study employs meta-analysis, reanalyzing 833 effect values derived from 69 independent research samples based on Chinese data. The findings reveal a moderately positive correlation (r = 0.204) between the agglomeration of high-tech industries and regional innovation in China. In particular, high-tech industrial agglomeration significantly contributes to regional innovation under the paths of diversified agglomeration and competitive agglomeration. We further found that sampling region, measurement approach, measurement perspective, research methodology, and year of publication all exhibit significant moderating effects on the relationship between the two variables. Based on meta-analysis, this study not only scientifically responds to the controversy of the relationship between high-tech industrial agglomeration and regional innovation but also further reveals the inner conduction mechanism between the two. It is of great significance in exploring future studies in related fields.
Two Improved Constraint-Solving Algorithms Based on lmaxRPC3rm
Authors: Pan Xirui , Cheng Zhuyuan , Zhang Yonggang
Published: December 2023

The Constraint Satisfaction Problem (CSP) is a significant research area in artificial intelligence, and includes a large number of symmetric or asymmetric structures. A backtracking search combined with constraint propagation is considered to be the best CSP-solving algorithm, and the consistency algorithm is the main algorithm used in the process of constraint propagation, which is the key factor in constraint-solving efficiency. Max-restricted path consistency (maxRPC) is a well-known and efficient consistency algorithm, whereas the lmaxRPC3rm algorithm is a classic lightweight algorithm for maxRPC. In this paper, we leverage the properties of symmetry to devise an improved pruning strategy aimed at efficiently diminishing the problem’s search space, thus enhancing the overall solving efficiency. Firstly, we propose the maxRPC3sim algorithm, which abandons the two complex data structures used by lmaxRPC3rm. We can render the algorithm to be more concise and competitive compared to the original algorithm while ensuring that it maintains the same average performance. Secondly, inspired by the RCP3 algorithm, we propose the maxRPC3simR algorithm, which uses the idea of residual support to cut down the redundant operation of the lmaxRPC3rm algorithm. Finally, combining the domain/weighted degree (dom/wdeg) heuristic with the activity-based search (ABS) heuristic, a new variable ordering heuristic, ADW, is proposed. Our heuristic prioritizes the selection of variables with symmetry for pruning, further enhancing the algorithm’s pruning capabilities. Experiments were conducted on both random and structural problems separately. The results indicate that our two algorithms generally outperform other algorithms in terms of performance on both problem classes. Moreover, the new heuristic algorithm demonstrates enhanced robustness across different problem types when compared to various existing algorithms.
An Efficient Method for Variable Selection Based on Diagnostic-Lasso Regression
Authors: Alshqaq Shokrya Saleh , Abuzaid Ali H.
Published: December 2023

In contemporary statistical methods, robust regression shrinkage and variable selection have gained paramount significance due to the prevalence of datasets characterized by contamination and an abundance of variables, often categorized as ‘high-dimensional data’. The Least Absolute Shrinkage and Selection Operator (Lasso) is frequently employed in this context for both the model and selecting variables. However, no one has attempted to apply regression diagnostic measures to Lasso regression, despite its power and widespread practical use. This work introduces a combined Lasso and diagnostic technique to enhance Lasso regression modeling for high-dimensional datasets with multicollinearity and outliers. We utilize a diagnostic Lasso estimator (D-Lasso). The breakdown point of the proposed method is also discussed. Finally, simulation examples and analyses of real data are provided to support the conclusions. The results of the numerical examples demonstrate that the D-Lasso approach performs as well as, if not better than, the robust Lasso method based on the MM-estimator.
The Exploration of Integrating the Midjourney Artificial Intelligence Generated Content Tool into Design Systems to Direct Designers towards Future-Oriented Innovation
Authors: Yin Hu , Zhang Zipeng , Liu Yuanyuan
Published: December 2023

In an age where computing capabilities are expanding at a breathtaking pace, the advent of Artificial Intelligence-Generated Content (AIGC) technology presents unprecedented opportunities and challenges to the future of design. It is crucial for designers to investigate how to utilize this powerful tool to facilitate innovation effectively. As AIGC technology evolves, it will inevitably shift the expectations of designers, compelling them to delve deeper into the essence of design creativity, transcending traditional sketching or modeling skills. This study provides valuable insights for designers on leveraging AIGC for forward-thinking design innovation. We focus on the representative AIGC tool, “Midjourney”, to explore its integration into design systems for collaborative innovation among content creators. We introduce an AIGC-based Midjourney path for product design and present a supporting tool card set: AMP-Cards. To confirm their utility, we undertook extensive validation through advanced prototype design research, task-specific project practices, and interdisciplinary collaborative seminars. Our findings indicate that AIGC can considerably enhance designers’ efficiency during product development, especially in the “explorative product shape” phase. The technology excels in identifying design styles and quickly producing varied design solutions. Moreover, AIGC’s capacity to swiftly translate creators’ concepts into visual forms greatly aids in multidisciplinary team communication and innovation.
Potential Toxicity and Mechanisms of T-2 and HT-2 Individually or in Combination on the Intestinal Barrier Function of Porcine Small Intestinal Epithelial Cells
Authors: He Weihua , Wang Jianhua , Han Mengyi , Wang Lihua , Li Ling , Zhang Jiahui , Chen Siqi , Guo Jiayi , Zhai Xiaohu , Yang Junhua
Published: December 2023

Under natural conditions, T-2 toxin can be easily metabolized to HT-2 toxin by deacetylation, and T-2 and HT-2 are usually co-contaminated in grain and feed at a high detected rate. Our previous information indicated that T-2 toxin could injure the function of the intestinal barrier, but the combined toxicity and mechanism of T-2 and HT-2 on the intestinal cells of porcines are still unknown. Therefore, we aimed to explore T-2 and HT-2 individually and combined on cellular viability, cell membrane integrity, the expression of tight junction-related proteins, and the generation of inflammatory factors in porcine intestinal epithelial cells (IPEC-J2). The results showed that T-2 and HT-2, individually or in combination, could induce a decrease in cell viability, an increase in LDH release and IL-1, IL-6, and TNF-α generation, and a decrease in the anti-inflammatory factor IL-10. Based on the analysis of immunofluorescence staining, real-time PCR, and western blotting, the tight junction protein expressions of Claudin-1, Occludin, and ZO-1 were significantly decreased in the T-2 and HT-2 individual or combination treated groups compared with the control. Furthermore, all the parameter changes in the T-2 + HT-2 combination group were much more serious than those in the individual dose groups. These results suggest that T-2 and HT-2, individually and in combination, could induce an intestinal function injury related to an inflammatory response and damage to the intestinal barrier function in porcine intestinal epithelial cells. Additionally, T-2 and HT-2 in combination showed a synergistic toxic effect, which will provide a theoretical basis to assess the risk of T-2 + HT-2 co-contamination in porcine feed.
Mitochondrial Oxidative Phosphorylation in Viral Infections
Authors: Purandare Neeraja , Ghosalkar Esha , Grossman Lawrence I. , Aras Siddhesh
Published: December 2023

Mitochondria have been identified as the “powerhouse” of the cell, generating the cellular energy, ATP, for almost seven decades. Research over time has uncovered a multifaceted role of the mitochondrion in processes such as cellular stress signaling, generating precursor molecules, immune response, and apoptosis to name a few. Dysfunctional mitochondria resulting from a departure in homeostasis results in cellular degeneration. Viruses hijack host cell machinery to facilitate their own replication in the absence of a bonafide replication machinery. Replication being an energy intensive process necessitates regulation of the host cell oxidative phosphorylation occurring at the electron transport chain in the mitochondria to generate energy. Mitochondria, therefore, can be an attractive therapeutic target by limiting energy for viral replication. In this review we focus on the physiology of oxidative phosphorylation and on the limited studies highlighting the regulatory effects viruses induce on the electron transport chain.
Molecular Epidemiology of Respiratory Syncytial Virus during 2019–2022 and Surviving Genotypes after the COVID-19 Pandemic in Japan
Authors: Yoshioka Sayaka , Phyu Wint Wint , Wagatsuma Keita , Nagai Takao , Sano Yasuko , Taniguchi Kiyosu , Nagata Nobuo , Tomimoto Kazuhiko , Sato Isamu , Kaji Harumi , Sugata Ken , Sugiura Katsumi , Saito Naruo , Aoki Satoshi , Suzuki Eitaro , Shimada Yasushi , Hamabata Hirotsune , Chon Irina , Otoguro Teruhime , Watanabe Hisami , Saito Reiko
Published: December 2023

To evaluate the changes in respiratory syncytial virus (RSV) collected between 2019 and 2022, we analyzed RSV-A and RSV-B strains from various prefectures in Japan before and after the COVID-19 pandemic. RT-PCR-positive samples collected from children with rapid test positivity at outpatient clinics in 11 prefectures in Japan were sequenced for the ectodomain of the G gene to determine the genotype. Time-aware phylogeographic analyses were performed using the second hypervariable region (HVR) of the G gene from 2012 to 2022. Of 967 samples, 739 (76.4%) were found to be RSV-positive using RT-PCR. RSV peaked in September 2019 but was not detected in 2020, except in Okinawa. Nationwide epidemics occurred with peaks in July 2021 and 2022. The genotype remained the same, ON1 for RSV-A and BA9 for RSV-B during 2019–2022. Phylogeographic analysis of HVR revealed that at least seven clusters of RSV-A had circulated previously but decreased to two clusters after the pandemic, whereas RSV-B had a single monophyletic cluster over the 10 years. Both RSV-A and RSV-B were transferred from Okinawa into other prefectures after the pandemic. The RSV epidemic was suppressed due to pandemic restrictions; however, pre-pandemic genotypes spread nationwide after the pandemic.
COVID-19 Vaccination and Immunosuppressive Therapy in Immune-Mediated Inflammatory Diseases
Authors: Serra López-Matencio José M. , Vicente-Rabaneda Esther F. , Alañón Estefanía , Aranguren Oyarzabal Ainhoa , Martínez Fleta Pedro , Castañeda Santos
Published: December 2023

The COVID-19 vaccination program has probably been the most complex and extensive project in history until now, which has been a challenge for all the people involved in the planning and management of this program. Patients with immune-mediated inflammatory diseases (IMIDs) on immunosuppressive therapy have required special attention, not only because of the particular haste in carrying out the process but also because of the uncertainty regarding their response to the vaccines. We now have strong scientific evidence that supports the hypothesis that immunosuppressive therapy inhibits the humoral response to vaccines against other infectious agents, such as influenza, pneumococcus and hepatitis B. This has led to the hypothesis that the same could happen with the COVID-19 vaccine. Several studies have therefore already been carried out in this area, suggesting that temporarily discontinuing the administration of methotrexate for 2 weeks post-vaccination could improve the vaccine response, and other studies with various immunosuppressive drugs are in the same line. However, the fact of withholding or interrupting immunosuppressive therapy when dealing with COVID-19 vaccination remains unclear. On this basis, our article tries to compile the information available on the effect of immunosuppressant agents on COVID-19 vaccine responses in patients with IMIDs and proposes an algorithm for the management of these patients.
The Effect of Soluble Sugar Degradation on the Evaporation of Compacted Clay
Authors: Guo Yuliang , Cao Liwen , Sang Zihaohan , Zhang Rui
Published: December 2023

In arid climates, evaporation and water loss in surface soil can lead to the development of shrinkage cracks in the soil. The crack network in contaminated soil sites can become a rapid pathway for the infiltration and transport of contaminations, thereby increasing the range of soil contamination. Dense contaminated clay samples were prepared by using glucose as a representative soluble sugar of domestic source contaminations. Through indoor evaporation simulation tests, the effect of soluble sugar anaerobic degradation on the water loss, deformation, and crack growth of compacted clay was analyzed, and the mechanism of this effect was revealed. The results showed that glucose increased the water-holding capacity of clay, while the anaerobic degradation of glucose decreased the water-holding capacity of clay. Although glucose anaerobic degradation reduced the overall deformation of dense clay, it promoted the development of evaporative cracks on the surface of dense clay. Soluble sugar anaerobic degradation mainly affected the evaporative cracking of clay by “forming hydrogen bonds to reduce the rate of evaporative water loss in clay” and “generating CO2 to alter the structure of the clay”.
Influence of Climatic Factors on the Water Footprint of Dairy Cattle Production in Hungary—A Case Study
Authors: Waltner István , Ribács Attila , Gémes Borbála , Székács András
Published: December 2023

Our study aims to provide a look at how the production of dairy cattle is affecting water resources in Hungary. Utilizing the AquaCrop model and field data from a selected field in Hungary, we focused on the evapotranspiration (ET) and water footprint (WF) of maize (the dominant component of silage mixes), while for other feed crops, we obtained data from scientific literature sources. We also considered drinking and servicing water consumption of dairy cattle, utilizing observations from a specific farm, as well as estimating potential heat stress at the country level. Our findings indicated increasing trends of crop ET as well as biomass production for maize, without significant correlations between the two parameters. Spatiotemporal analysis revealed a significant rise in the number of days with potential heat stress based on temperature-humidity indices, manifesting in practically the entire area of Hungary. Thus, while crop ET rates and corresponding crop water use values (4989–5342 m3/ha) did not show substantial changes, maize WF in silage cultivation rose from 261.9 m3/t dry biomass in 2002 to 378.0 m3/t dry biomass in 2020. Feed and water intake was subsequently recorded on a cattle farm and assessed as green and blue water use. Drinking (blue) water uptake, ranging between 74.7 and 101.9 L/dairy cow/day, moderately correlated with temperature-humidity indices as heat stress indicators (r2 = 0.700–0.767, p < 0.05). Servicing water was not recorded daily, but was calculated as a daily average (18 L/dairy cow/day), and was also considered in blue water usage. In contrast, feed consumption at the cattle farm corresponded to 13,352 ± 4724 L green water/dairy cow/day. Our results indicate that while the WF of animal feed remains a dominant factor in the total water use of dairy cattle farms, drinking water consumption and related costs of adaptive measures (such as adaptive breeding, modified housing, and technological measures) are expected to increase due to potential heat stress, particularly in selected regions where farmers should focus more on housing and technological solutions, as well as selecting for thermotolerance.
Acute and Subchronic Exposure of the Common Carp (Cyprinus carpio) to Herbicide S-Metolachlor
Authors: Rašković Božidar , Poleksić Vesna , Vuković Gorica , Špirović Trifunović Bojana , Božić Gavrilo , Ćupić Miladinović Dejana , Marković Zoran , Brkić Dragica
Published: December 2023

S-metolachlor is one of the most frequently used herbicides worldwide. However, toxicity studies of this herbicide to aquatic organisms are scarce. In the present study, two experiments were conducted to test the effects of S-metolachlor on common carp, one of the most economically important fish species, with a distribution throughout the world: (1) 96 h acute semi-static toxicity test, aiming to determine LC50; (2) a subchronic semi-static test that lasted 28 days, in which juvenile carp were exposed to 3%, 8%, and 25% of previously determined LC50—0.5 mg·L−1, 1.4 mg·L−1, and 4.1 mg·L−1, respectively. Several biomarkers were employed to assess fish responses to toxicants. Blood biochemistry analysis and nuclear alterations of erythrocytes did not show any difference among experimental groups. Semi-quantitative histopathological analysis revealed mild alterations in the gills and liver, where oedema of secondary epithelium of gills and leukocyte infiltration in liver were significantly higher in fish exposed to 1.4 mg·L−1 and 4.1 mg·L−1. Histopathological indices in liver, as well as the total histopathological index, also showed significantly higher scores in the same groups. Bioconcentration factors of S-metolachlor ranged from 3.2 to 9.4, depending on the experimental group.
Effect of Deficit Irrigation on Growth Parameters of the Salvia splendens L. Plant
Authors: Sezen Işık , Yağanoğlu Sevda , Akpınar Külekçi Elif , Karahan Ayşe
Published: December 2023

This study aims to investigate alterations in the developmental parameters of Salvia splendens L., a commonly utilized seasonal flower associated with excessive water consumption in urban green spaces, through the implementation of deficit irrigation practices. Four distinct irrigation treatments, which entailed maintaining the evaporation pot’s water level at 100% (control), 75%, 50%, and 25% of the pot’s water-holding capacity, were established. This study scrutinized 18 growth parameters to assess the impact of varying water application levels. The findings of this research revealed that Salvia splendens L. plants exhibited more substantial improvements in 17 out of 18 assessed parameters when subjected to 75% water application (representing a 25% reduction in water supply) in comparison to 100% water application (with no reduction). Notably, the only parameter negatively affected by reduced water availability in Salvia splendens L. was the diameter of the flowers. Thus, it is recommended to reduce water application by 25% when cultivating Salvia splendens L. in urban areas. Such a measure is expected to yield substantial water conservation benefits in urban landscaping. Consequently, it is advisable to promote the frequent utilization of Salvia splendens L. plants in urban green spaces, given their robust development even under conditions of water scarcity.
Effect of Flow on the Energy Conversion Characteristics of Multiphase Pumps Based on Energy Transport Theory
Authors: Tang Manqi , Shi Guangtai , Lv Wenjuan , Peng Xiaodong , Huang Zongliu
Published: December 2023

Multiphase pumps operate under different flow conditions with different work performance. In order to reveal the energy conversion regulations in multiphase pumps under different flows, this paper presents an analysis of the effects of different flows on the pressure propulsion power, Lamb vector dispersion, and vortex enstrophy dissipation in the pressurization unit of a multiphase pump based on energy transport theory. It is found that at different flows, the pressure propulsion power near the impeller inlet decreases sharply, the pressure propulsion power is mainly located in the first half of the impeller near the suction side of the blade, and with the increase in the flow, the pressure propulsion power in the pressurization unit increases gradually, as well as its energy loss, while the Lamb vector dispersion gradually increases and the area of scattering region tends to be narrow under the small impeller tip clearance, while the Lamb vector dispersion region area slowly decreases with the flow rate when the impeller tip clearance is larger. The effect of flow on the vortex enstrophy dissipation in the multiphase pump is mainly located in the middle of the impeller near the blade pressure surface, and as the flow increases, the value of the vortex enstrophy dissipation in the impeller pressurization unit increases accordingly, and the vortex enstrophy dissipation in the first half of the impeller is even more chaotic. The investigation results have significant theoretical meaning for the deep mastery of the energy conversion characteristics in multiphase pumps.