bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self-organization and evolution of function and structure in cultured neuronal networks
Authors: Ballesteros-Esteban L.M. , Leyva I. , Almendral J.A. , Sendiña-Nadal I. 
Published: March 2023

Abstract
Cultured neuronal networks (CNNs) have recently achieved major relevance as an alternative to in vivo models. While many works investigate the evolution of functional connectivity alone, experimental evidence of the simultaneous change of the structural neuronal network substrate is scarce. In the present study, we monitored the coevolution of structural and functional connectivities of neuronal cultures grown on top of microelectrode arrays, in a setup that allows the simultaneous recording of electrophysiological signals and microphotography detailed to the single-link level. During the observed 3 weeks lifespan, initially isolated invertebrate neurons form an ex novo complex circuitry of neuronal aggregates characterized by a small-world topology, with abundant neuronal loops (high clustering) and short distances. At the same time, the observation of synchronization events among electrodes reaches a maximum, coinciding with the spatial percolation of the neuronal network. At this stage, the correlation between the structural and the corresponding functional network is the largest, with around 15% of the physical links relating electrodes that fire synchronously. As the culture matures, this correlation smoothly declines but becomes more significant. Finally, at the local level, we found that the electrodes supporting the most coherent activity, the functional hubs, are not hubs in the physical circuitry but nodes with an average degree. This study demonstrates that functional networks of self-organized neuronal systems are discreet proxies of the underlying structure. It paves the way for future investigations to elucidate the intricate relationship between structure and function in other scenarios mediated by external stimulation.
The structural model of mother-infant bonding in the first pregnancy based on the mother’s attachment style and parenting style: the mediating role of mentalization and shame
Authors: Yazdanimehr Reza , Aflakseir Abdolaziz , Sarafraz Mehdireza , Taghavi Mohammadreza 
Published: March 2023

Abstract
Introduction: This study aimed to evaluate the relationship between the mother’s attachment style and parenting style and mother-infant bonding in the first pregnancy considering the mediating role of mentalization and shame.
Methods: This was a descriptive-correlational study. The sample population included the women who had gone through their first pregnancy and were referred to the health centers in Neyshabur, Iran in 2022. In total, 330 women were selected by convenience sampling. To collect data, we used a demographic questionnaire, the Attachment Style Questionnaire, the Parenting Style Questionnaire, the Mother-Infant Bonding Scale, the Reflective Functioning Questionnaire, and the Guilt and Shame Proneness Scale. Data analysis was conducted using descriptive indices, correlation coefficients, and structural equation modeling (SEM).
Results: Overall, the findings showed that the studied model adequately fits the data. Further, the obtained results confirmed the mediating role of mentalization and shame in the relationship between the mother’s attachment style and parenting style and mother-infant bonding. A significant correlation was also observed between attachment styles, parenting styles, and mother-infant bonding (p<0.01).
Conclusion: Mother-infant bonding is correlated with the mother’s attachment style/parenting style, shame, and mentalization. Thus, we can help vulnerable mothers by improving the quality of psychological care before the first pregnancy or during pregnancy.
Application of machine vision based on genetic algorithm in image painting style method and image processing optimization
Authors: Jin Xin 
Published: March 2023

Abstract
In recent years, the research on the non photorealistic painting of watercolor painting through computer algorithms has made rapid progress, and its specific application has also been increasingly valued. This paper proposes a new machine vision method based on genetic algorithm, and applies it to the research of oil painting style and image processing optimization. First of all, it provides a stroke layout method based on machine vision important areas. It analyzes the key areas through face, visual focus analysis and other technologies, and realizes the starting point, angle calculation, accuracy optimization and image quality optimization of pen painting based on the data of this area, thus realizing the function of distinguishing the primary and secondary of oil painting. Secondly, the machine vision method based on genetic algorithm can explore the style characteristics of any oil painting graphics according to the characteristics of oil painting, and create artistic images through this method. The experimental results show that the machine vision method based on genetic algorithm not only has achieved good results in the practical application of oil painting style, but also can optimize the image processing.
Application of expression recognition based on Genetic Algorithm in English distance teaching platform
Authors: Dou Ru 
Published: March 2023

Abstract
In Distance English teaching, teachers cannot take care of every student because of their own limitations. Therefore, more advanced technology is needed to assist teachers in completing teaching. Based on this point, this paper proposes a distance English teaching assistant method based on facial expression recognition technology. First, the distance learning platform collects the image data of students through the camera, preprocesses the captured image, detects and recognizes the skin color, and finally determines the position of the face. Then, based on the theory of genetic algorithm, a model to improve the characteristics of face images is established, and the recognized facial expression images are classified by random forest algorithm, and the facial expression characteristics of students are analyzed according to the five kinds of information of the target, such as attention, suspicion, distraction, excitement and anxiety. This paper adds the design of expression recognition application to the English learning distance platform, collects and summarizes the changes of students' expressions through the mobile terminal, and reports them to the teacher as reference information in the teaching process. The results show that the facial expression recognition module carried on the distance English teaching platform has a relatively accurate recognition rate, can provide some help for English teachers, and is well received by teachers, so it is worth promoting. By studying genetic algorithm and facial expression recognition technology, this paper applies them to the design of English distance teaching platform, which has a certain auxiliary effect on English teachers' teaching behavior.
Technical framework of energy-saving construction management of intelligent building based on computer vision algorithm
Authors: Ma Weini 
Published: March 2023

Abstract
At present, China's urbanization transformation is accelerating, the construction industry is developing rapidly, and people's requirements for buildings are also increasing, which leads to the increasing energy consumption. Therefore, energy-saving construction in the construction industry is imminent. During the actual life cycle of a building project, including planning, design, construction and later operation and maintenance, energy consumption will be generated in the above processes. Based on this background, this research realized the design of intelligent building management and information system for energy conservation projects by introducing computer vision algorithm. The system is integrated with laws and regulations, economic benefits, technical steps, quality management, cost management, schedule management and other modules. The design process is relatively complex. Therefore, this work adopts modern computer vision algorithm technology and uses relevant tools to collect, store Analyze a large amount of information in each stage of energy-saving project management process, and realize information management from project approval, design, drawing review, construction to acceptance. Finally, build system interface layer, system function layer, and database support layer. The system measurement shows that the building energy consumption can be predicted easily, accurately and stably by inputting the energy consumption data collected by each part of the target building. The system realizes the combination of qualitative and quantitative research on energy conservation regulation, and improves the informatization level of China's energy conservation research and construction. This paper designs a kind of intelligent building energy saving management system by studying computer vision algorithm.
Image defogging algorithm and artificial intelligence technology innovation application in digital media image processing
Authors: Dong Liang , Gangyi Ding , Dapeng Yan , Kexiang Huang 
Published: March 2023

Abstract
Contemporary digital media has been promoted under the leadership, for video image capture and processing/using artificial intelligence technology to classify video content and other technologies have been effectively applied. Facing the current era of short video outbreak, image processing technology and digital media technology occupy an important position in the application. Image processing technology can recognize video content through video capture and processing. However, it can be found in the practical application research that many short videos have the problem of low definition, so the captured image is not clear enough, and the recognition and resolution of the image brings some adverse effects. In addition, the algorithms of image to fog of digital media can also play the role of "rescue", for some not to retrieve video and image information, through the image to fog algorithm can fix the problems, and make the image more clear, thus improve the precision of image and recognition, image information acquisition. It can be seen that the combination of image dehazing algorithm and artificial intelligence can more effectively strengthen the image processing of digital media, and facilitate the protection and classification of image data.
SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models
Authors: Wan Xiaomeng , Xiao Jiashun , Tam Sindy , CAI Mingxuan , Sugimura Rio , Wang Yang , Wan Xiang , Lin Zhixiang , Wu Angela , Yang Can 
Published: March 2023

Abstract
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and their biology. Current ST technologies based on either next generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), while providing hugely informative insights, remain unable to provide spatial characterization at transcriptome-wide single-cell resolution, limiting their usage in resolving detailed tissue structure and detecting cellular communications. To overcome these limitations, we developed SpatialScope, a unified approach to integrating scRNA-seq reference data and ST data that leverages deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate the utility of SpatialScope through comprehensive simulation studies and then apply it to real data from both seq-based and image-based ST approaches. SpatialScope provides a spatial characterization of tissue structures at transcriptome-wide single-cell resolution, greatly facilitating the downstream analysis of ST data, such as detection of cellular communication by identifying ligand-receptor interactions from seq-based ST data, localization of cellular subtypes, and detection of spatially differently expressed genes.
Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima.
Authors: Rey-Devesa Pablo , Prudencio Janire , Benítez Carmen , Bretón Mauricio , Plasencia Imelda , León Zoraida , Ortigosa Félix , Gutiérrez Ligdamis , Arámbula-Mendoza Raúl , Ibáñez Jesús M. 
Published: March 2023

Abstract
In this work we demonstrate that Shannon Entropy (SE) calculated on continuous seismic signals can be used efficiently in a volcanic monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includes two large explosions, with pyroclastic and lava flows, and intense activity of less energetic explosion, culminating with a period of quiescence. In order to confirm the success of our results, we used images of the Visual Monitoring system of Colima Volcano Observatory. Another of the objectives of this work is to show how the decrease in the SE values can be used to track minor explosive activity, helping Machine Learning algorithms to work more efficiently in the complex problem of distinguishing the explosion signals in the seismograms. We demonstrated the two big eruptions selected were forecasted successfully (6 and 2 days respectively) using the decay of the SE. We conclude that the SE could be used as a complementary tool in seismic volcano monitoring, showing its successful behaviour prior to energetic eruptions, giving time enough to alert the population and prepare for the consequences of an imminent and well predicted moment of the eruption.
The Effect of the Electrical Machine Components on Electromagnetic Wave Propagation in Rotor Temperature Monitoring System
Authors: Sonia Ben Brahim , Samia Dardouri , Amor Hammami , Ridha Bouallegue , Jacques David , Hoa Vuong Tan 
Published: March 2023

Abstract
The metallic parts of an electrical machine greatly impact the transmission of electromagnetic waves between a shaft sensor and an external receiver. A Rotor Temperature Monitoring System (RTMS) aims to enhance power transfer in open space without considering the influence of electrical machine body into account. Therefore, in this paper, an evaluation of the electrical machine bodies effect on shaft sensor transmission and propagation is realized. These effects is related to receiver antenna packaging requirements. A comparative study between two antennas is presented in this article to see which one gives a larger propagation factor using HFSS.
Vulnerability and resilience to drought and saltwater intrusion of rice farming households in the Mekong delta, Vietnam
Authors: Aalst M.A. van , Koomen Eric , de Groot H.L.F. 
Published: March 2023

Abstract
Adverse impacts of climate and environmental hazards are unevenly distributed between socioeconomic groups due to differences in exposure, vulnerability and resilience. This study examines the distribution of vulnerability and resilience to drought and salt intrusion impacts among rice farmers in the rural Mekong Deltain Vietnam. By defining both aspects independently, we can study potential differences in the socio-economic factors that steer them and analyse how these two aspects of adaptive capacity are related. Using fixed-effect regressions, we find that poorer communes are more vulnerable to direct environmental impacts (loss in rice yield). Several household characteristics that reflect a low socioeconomic status, such as low asset values, small plots, and limited education level, are also linked with higher vulnerability to direct drought impacts. High vulnerability does, however, not necessarily translate to low resilience, which we proxy by measuring indirectimpacts (loss in household income). Several household activities and characteristics help mitigate direct impacts. Our results suggest that the least resilient household group consists of smallholder, asset-low households that are unable to diversify to non-crop agriculture or off-farm employment. Supportive policies targeting this particular socioeconomic group to enable transition to non-crop or off-farm labour would substantially improve their resilience to future environmental events. Distinguishing between resilience and vulnerability enables a broader understanding of the mechanisms influencing the distribution of direct and indirect adverse impacts, which enables drafting targeted policy measures for specific socioeconomic groups.
Less-known leafy vegetable “Lapha” (Malva L.; family Malvaceae) in India and study on genetic resources
Authors: Malav Pavan kumar , Pandey Anjula , Pamarthi Ravi Kishore , Bhatt Kailash Chandra , Nivedhitha S , Pradheep K , Singh Kartar , Ahlawat Sudhir Pal 
Published: March 2023

Abstract
During survey, exploration and germplasm collection in parts of Eastern India and National Capital Region of Delhi, mainly Haryana state of India, the authors came to know that two species of Malva locally known as ‘‘lapha’’ being used as a green leafy vegetable in the surveyed areas. Published literature refers same local name and use of Malva parviflora. The critical morphological study of the taxa has delineated two taxa as Malva parviflora L and M. verticillata L, former is a weed of wastelands whereas the latter is grown as vegetable in some parts of Assam, Bihar and West Bengal. M. verticillata is preferred most by locals due to its easy-to-cultivate nature and having high nutritive/therapeutic value, which indicates it’s potential to cultivate during the lean period in those areas where few greens are grown. In the present paper, detailed study on botanical characters, range of distribution and method of utilization of both the taxa in the Indian region has been discussed. The current studies further demonstrated that allied species with similar use and taste are identified as a substitute.
Zaxinone Mimics (MiZax) Efficiently Promote Growth and Production of Potato and Strawberry Plants under Desert Climate Conditions
Authors: Wang Jian You , Jamil Muhammad , AlOtaibi Turki S. , Abdelaziz Mohamed E. , Ota Tsuyoshi , Ibrahim Omer H. , Berqdar Lamis , Asami Tadao , Mousa Magdi Ali Ahmed , Al-Babili Salim 
Published: March 2023

Abstract
Climate changes and the rapid expanding human population have become critical concerns for global food security. One of the promising solutions is the employment of plant growth regulators (PGRs) for increasing crop yield and overcoming adverse growth conditions, such as desert climate. Recently, the apocarotenoid zaxinone and its two mimics (MiZax3 and MiZax5) have shown a promising growth-promoting activity in cereals and vegetable crops under greenhouse and field conditions. Herein, we further investigated the effect of MiZax3 and MiZax5, at different concentrations, on the growth and yield of the two valuable vegetable crops, potato and strawberry, in the Kingdom of Saudi of Arabia. Application of both MiZax significantly increased plant agronomic traits, yield components and total yield, in five independent field trials from 2021 to 2022. Hence, our results indicate that MiZax are very promising PGRs that can be applied to promote the growth and yield of vegetable crops even under desert conditions and at relatively low concentrations.
3D-real-world pointing test for spatial orientation: influences of sex and cognitive impairment on the shape configuration of mental target representation
Authors: Gerb Johannes , Brandt Thomas , Dieterich Marianne 
Published: March 2023

Abstract
Background
Deficits in spatial memory are often early signs of neurological disorders. Here, we analyzed the geometrical configuration of 2D-projections of pointing performances to a memorized array of spatially distributed targets with respect to gender differences in healthy participants and patients with cognitive impairment.
Methods
56 right-handed healthy participants (28 female, mean age 48.89 ± 19.35 years) and 22 right-handed patients with cognitive impairment (12 female, mean age 71.73 ± 7.41 years) underwent a previously validated 3D-real-world pointing test (3D-RWPT). Participants were shown a 9-dot target matrix and afterwards asked to point towards each target with closed eyes in different body positions relative to the matrix. Two-dimensional projections of these pointing vectors were then quantified using morphological analyses.
Results
Shape configurations in healthy volunteers largely reflected the real-world target pattern with gender-dependent differences (ANCOVA area difference male/female = 38350.43, pbonf=3.69x10− 3**). Patients with cognitive impairment showed impaired rectangularity and made more large-scale errors, resulting in decreased overall average diameters and solidity (ANCOVA diameter difference normal cognition/cognitive impairment = 31.22, pbonf=3.19x10− 3**; solidity difference = 0.07, pbonf=6.76x10− 3**).
Conclusion
Shape configuration analysis of the 3D-RWPT target set by morphological properties appears to be a suitable holistic measure of spatial performance, differentiating sex effects and cognitive impairment when analyzed by group- and paradigm-wise averages.
Predicting object properties based on movement kinematics
Authors: Kopnarski Lena , Lippert Laura , Rudisch Julian , Voelcker-Rehage Claudia 
Published: March 2023

Abstract
In order to grasp and transport an object, grip and load forces must be scaled according to the object's properties (such as weight). To select the appropriate grip and load forces, the object weight is estimated based on experience or, in the case of robots, usually use image recognition. We propose a new approach that makes a robot's weight estimation less dependent on prior learning and, thereby, allows it to successfully grasp a wider variety of objects. This study evaluates whether it is feasible to predict an object's weight in a replacement task based on the time series of upper body angles of the active arm. Furthermore, we wanted to investigate how prediction accuracy is affected by (i) the length of the time series and (ii) different cross-validation (CV) procedures. To this end, we recorded and analyzed the movement kinematics of twelve participants during a replacement task. The participants' kinematics were recorded by an optical motion tracking system while transporting an object, 80 times in total from varying starting positions to a predefined end position on a table. The object's weight was modified (made lighter and heavier) without changing the object's visual appearance. Throughout the experiment, the object's weight was randomly changed without the participant's knowledge. To predict the object's weight, we used a discrete cosine transform to smooth and compress the upper body angles and a support vector machine for supervised learning from the achieved discrete cosine transform parameters. Results showed good prediction accuracy (78.5 - 92.7 %, depending on the CV procedure and the length of the time series). Even at the beginning of a movement (after only 300 ms), we were able to predict the object weight reliably (within a classification rate of 79.5 - 88.3 %).