Miracle-dz/UniFAR-FTUs huggingface dataset:
Revised morphology and barcoding of Strobilanthes andersonii Bedd. a critically endangered plant

Strobilanthes is a genus of perennial flowering shrubs with about 400 species. Due to the infrequent flowering period and monocarpic nature the availability of reproductive parts – the primary requisites for species identification – is comparatively a rare chance. Hence, other methods, such as molecular techniques, are vital to differentiate Strobilanthes species in the vegetative stages. The establishment of DNA barcode database is a suitable method. The DNA barcode region ITS nuclear ribosomal DNA of Strobilanthes andersonii Bedd was sequenced. A revised morphology of S. andersonii Bedd is also prepared. A ML phylogram was constructed to evaluate the phylogenetic perspective of S. andersonii Bedd.
Goitered Gazelle Gazella subgutturosa Responded to Human Disturbance by Increasing Vigilance Rather than Changing the Group Size

Simple Summary Wild animals perceive nonlethal human activities as a potential threat, and they respond to human disturbances in a variety of ways: changes in group size and time-investment on vigilance are common behaviors. In China, nature reserves are generally divided into three functional zones: the core zone, the buffer zone, and the experimental zone, which represent low, medium, and high human disturbance levels, respectively. However, mining developments in the three zones pose threats to goitered gazelle living in the Kalamaili Nature Reserve, China. In this study, we found that human disturbances have larger effects on smaller-sized, single-sex groups rather than larger-sized, mixed-sex groups. Goitered gazelle spend more time being vigilant in the experimental zone than in the other two zones. In the experimental zone, a greater linear decrease with respect to group size was observed. Our results indicate that intensive human disturbances in the core and buffer zones brought goitered gazelle and humans closer together, which induced changes in vigilance and grouping patterns of this social species. Ultimately, this may have negative effects on the fitness and survival of the species. Abstract Nonlethal human disturbances have been confirmed to have a negative effect on wildlife in a variety of ways, including on behaviors. In many studies, the impact of human disturbances on gregarious species of wildlife is assessed through changes in their social organization and vigilance. In our study in the Kalamaili Nature Reserve, China, we wanted to estimate the impacts of differing levels of human disturbances on two different group types (mixed-sex and all-male) of goitered gazelle, Gazella subgutturosa, living in three functional zones (experimental, buffer, and core zones), which represent high, medium, and low human disturbance levels, respectively. In addition, we studied the time spent vigilant as a function of group size with different levels of human disturbances in the three zones. In general, mixed-sex gazelle groups were of similar sizes in the three different zones, while all-male groups slightly differed in their sizes between the experimental and buffer zones. This may indicate that human disturbances have varying effects on the different group types, with smaller-sized, single-sex groups being more significantly affected by human disturbances than larger-sized, mixed-sex groups. Goitered gazelle showed higher vigilance levels in the experimental zone than in the two other zones. A trend of decreasing vigilance varying linearly with group size was also found in the three zones, and the rate of decrease was higher in the experimental zone. Increased habitat fragmentation and human activities brought goitered gazelle and humans closer together in the core zone. Therefore, stopping mining activities and returning the reserve to a continuous habitat with fewer environmental disturbances is the best way to establish and protect a stable population of this endangered species of gazelle.
BERTBooster: A knowledge enhancement method jointing incremental training and gradient optimization

The knowledge‐enhanced BERT model solves the problem of lacking knowledge in downstream tasks by injecting external expertize, and achieves higher accuracy compared with BERT model. However, owning to large‐scale external knowledge is utilized into knowledge‐enhanced BERT, some shortcomings comes such as information noise, lower accuracy and weak generalization ability, and so on. To solve this problem, a knowledge enhancement method BERTBooster which combines incremental learning and gradient optimization is proposed. BERTBooster disassembles the input text corpus into entity noun sets through entity noun recognition, and uses the incremental learning task denoising entity auto‐encoder to create an incremental task set of entity nouns and external knowledge triples. Furthermore, BERTBooster introduces a new gradient optimization algorithm ChildTuningF into BERT model to improve the generalization ability. BERTBooster can effectively improve the factual knowledge cognition ability of CAGBERT model and improve the accuracy of the model in downstream tasks. Experiments are carried out on six public data sets such as Book_Review, LCQMC, XNLI, Law_QA, Insureace_QA, and NLPCC‐DBQA. The experimental results show that the accuracy rate in downstream tasks is increased by 0.65% on average after using BERTBooster on CAGBERT.
User Perceptions and Needs Analysis of a Virtual Coach for Active and Healthy Ageing—An International Qualitative Study

Virtual coaching systems show great potential for meeting the challenges of demographic change. However, the proportion of older users in the field of digital technologies is far behind that of younger people. As part of the e-VITA project, semi-structured interviews were conducted in Japan, France, Italy and Germany with 58 people aged 65 and over, and the content was analyzed with the aim of obtaining information about how older adults organize their everyday lives, also with regard to the COVID-19 pandemic, how they deal with their health, what role digital technologies play in the lives of the interviewees and why they oppose progressive digitization. Second, the survey asked why the older adults oppose a virtual coach, which is to be developed in the e-VITA project to support older adults in healthy and active aging, and what barriers they see in a possible implementation. It was found that older respondents lead active, varied lives and that the COVID-19 pandemic contributed to the increased use of digital solutions. In addition, respondents were consciously addressing their own health. With regard to a virtual coach, barriers were seen primarily in the area of data security and sharing. It can be concluded from this that heterogeneity among older user groups should be taken into account when developing virtual coaches. In addition, aspects of data security and data protection should be presented in a clearly understandable and transparent manner.
Bacterial Cellulose-Based Biofilm Forming Agent Extracted from Vietnamese Nata-de-Coco Tree by Ultrasonic Vibration Method: Structure and Properties

Bacterial cellulose has recently received more attention in several fields including biology and biomedical applications due to its outstanding physicochemical properties such as thermal stability, biodegradability, good water holding capacity, and high tensile. Cellulose, the most abundant biomolecule on Earth, is available in large amounts in plants. However, cellulose in plants is accompanied by other polymers such as hemicellulose, lignin, and pectin. On the other hand, highly purified bacterial cellulose without impurities is produced by several microorganisms. In which, the most active producer is Acetobacter xylinum. A. This study developed a new process using sonication to isolate bacterial cellulose from nata-de-coco Vietnam. Sonicating time and temperature, two important engineering factors, were considered and discussed (Temperature: 55, 60, 65, 70°C; Time: 15, 30, 60, 90 min). Research results have established that the ultrasonic vibration time of 60 minutes at 65 degrees Celsius gives the best structural properties of BC. The morphology, structural, and thermal properties of the obtained films were investigated by SEM, FTIR, and TGA. Besides, tensile strength was also evaluated. The results show that sonication is not only a favorable technique to isolate cellulose nanofibers but it also enhances their crystallinity.
Dietary acid load and esophageal cancer risk: A case‐control study

A high dietary acid load (DAL) can produce metabolic acidosis, which is linked to cancer development through mechanisms of inflammation and cell transformation. There is limited epidemiological evidence linking DAL and cancer risk; however, none of the published studies focused on DAL and esophageal cancer (EC) risk in particular. Therefore, we sought to explore this association in the present study.
Pipe failure modelling for water distribution networks using boosted decision trees

Abstract Pipe failure modelling is an important tool for strategic rehabilitation planning of urban water distribution infrastructure. Rehabilitation predictions are mostly based on existing network data and historical failure records, both of varying quality. This paper presents a framework for the extraction and processing of such data to use it for training of decision tree-based machine learning methods. The performance of trained models for predicting pipe failures is evaluated for simple as well as more advanced, ensemble-based, decision tree methods. Bootstrap aggregation and boosting techniques are used to improve the accuracy of the models. The models are trained on 50% of the available data and their performance is evaluated using confusion matrices and receiver operating characteristic curves. While all models show very good performance, the boosted decision tree approach using random undersampling turns out to have the best performance and thus is applied to a real world case study. The applicability of decision tree methods for practical rehabilitation planning is demonstrated for the pipe network of a medium sized city.
Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing

: Population change and economic development are closely related and interact. The coordinated development of population and economy has increasingly become a hot topic in modern society. Based on the data of resident population and GDP of 38 districts and counties in Chongqing from 2010 to 2019, this paper uses ArcGIS and GeoDa, adopts gravity center method, geographic concentration, inconsistency index, spatial autocorrelation and other research methods to systematically analyze the change of gravity center of population and economy in Chongqing, the spatial distribution and agglomeration degree of population and economy, the coordinated development of population and economy, and the spatial correlation of population and economy. The results show that: (1) Chongqing's population and economic center of gravity show signs of migration to the main urban area of Chongqing; (2)The geographical concentration of population and economy shows that the population and economy of Chongqing are mainly concentrated in the main urban area and its surrounding counties; (3) The inconsistency index between population and economy shows that population agglomeration and economic agglomeration are coordinated in half districts and counties of Chongqing; (4)The spatial autocorrelation analysis shows that the spatial distribution of Chongqing's population and economy has a relatively obvious positive spatial correlation, and areas with similar population and economic coordination tend to congregate in space.
Efficient Massive Computing for Deformable Volume Data Using Revised Parallel Resampling

In this paper, we propose an improved parallel resampling technique. Parallel resampling is a deformable object generation method based on volume data applied to medical simulations. Existing parallel resampling is not suitable for massive computing, because the number of samplings is high and floating-point precision problems may occur. This study addresses these problems to obtain improved user latency when performing medical simulations. Specifically, instead of interpolating values after volume sampling, the efficiency is improved by performing volume sampling after coordinate interpolation. Next, the floating-point error in the calculation of the sampling position is described, and the advantage of barycentric interpolation using a reference point is discussed. The experimental results showed a significant improvement over the existing method. Volume data comprising more than 600 images used in clinical practice were deformed and rendered at interactive speed. In an Internet of Everything environment, medical imaging systems are an important application, and simulation image generation is also valuable in the overall system. Through the proposed method, the performance of the whole system can be improved.
Bisphenols A and S Alter the Bioenergetics and Behaviours of Normal Urothelial and Bladder Cancer Cells

Simple Summary This research brings new knowledge on the potential roles of bisphenol A and bisphenol S on bladder cancer progression. By assessing the impact of bisphenols A and S on normal urothelial cells and non-invasive and invasive bladder cancer cells, this study aimed to demonstrate that these endocrine-disrupting chemicals could promote bladder cancer progression through the alteration of the bioenergetics and behaviours of healthy and cancerous bladder cells. These results could provide a better understanding of the pathophysiology of bladder cancer and its hormone-sensitive characteristics. Furthermore, this study suggests that bisphenols A and S could affect bladder cancer recurrence, progression and patient prognosis. Abstract Bisphenol A (BPA) and bisphenol S (BPS) are used in the production of plastics. These endocrine disruptors can be released into the environment and food, resulting in the continuous exposure of humans to bisphenols (BPs). The bladder urothelium is chronically exposed to BPA and BPS due to their presence in human urine samples. BPA and BPS exposure has been linked to cancer progression, especially for hormone-dependent cancers. However, the bladder is not recognized as a hormone-dependent tissue. Still, the presence of hormone receptors on the urothelium and their role in bladder cancer initiation and progression suggest that BPs could impact bladder cancer development. The effects of chronic exposure to BPA and BPS for 72 h on the bioenergetics (glycolysis and mitochondrial respiration), proliferation and migration of normal urothelial cells and non-invasive and invasive bladder cancer cells were evaluated. The results demonstrate that chronic exposure to BPs decreased urothelial cells’ energy metabolism and properties while increasing them for bladder cancer cells. These findings suggest that exposure to BPA and BPS could promote bladder cancer development with a potential clinical impact on bladder cancer progression. Further studies using 3D models would help to understand the clinical consequences of this exposure.
Edge Computing for Internet of Everything: A Survey

In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy preservation, and deployment issues of edge node. These issues can not be well addressed by conventional approaches. Thanks to the rapid development of upcoming technologies, such as artificial intelligence (AI), blockchain, and microservices, novel and more effective solutions have emerged and been applied to solve existing challenges. In addition, edge computing can be deeply integrated with technologies in other domains (e.g., AI, blockchain, 6G, and digital twin) through interdisciplinary intersection and practice, releasing the potential for mutual benefit. These promising integrations need to be further explored and researched. In addition, edge computing provides strong support in applications scenarios, such as remote working, new physical retail industries, and digital advertising, which has greatly changed the way we live, work, and study. In this article, we present an up-to-date survey of the edge computing research. In addition to introducing the definition, model, and characteristics of edge computing, we discuss a set of key issues in edge computing and novel solutions supported by emerging technologies in IoE era. Furthermore, we explore the potential and promising trends from the perspective of technology integration. Finally, new application scenarios and the final form of edge computing are discussed.
Growing rice (Oryza sativa L.) in the northern Gangetic plains of India. How suitable are they? A GIS-based multi-criteria analysis

This study focuses on land suitability analysis, wherein the land best suitable for rice crop cultivation was identified in the Gangetic plains of India. The project was done with the help of a geographical information system (GIS)-based multi-criteria analysis approach using the Q-GIS long-term version (free software). Annual precipitation, annual mean temperature, slope, and the distance from rivers were the criteria selected for creating the suitability map. The suitability factors were ranked with the help of an analytical hierarchical process. The resultants were then used to construct the suitability map using QGIS long-term release version. One factor which can alter our results obtained here is the use of river distance as a criterion. In this paper, we wanted to see which areas will be highly suitable for rice cultivation if only the river water is used for irrigation. This is done to avoid the overexploitation of the groundwater resources for irrigation which nevertheless is depleting at an alarming rate in India. The maximum distance for river water irrigation was taken as 20 km. Although, after using distance from the river as a criterion, our results show that the Gangetic plain is moderately suitable for rice cultivation.
Predefined-time Sliding Mode Control for Attitude Tracking Control of Space Free-flying Robots

Aiming at the attitude tracking control of space free-flying robots, a predefined-time sliding mode control (PTSMC) scheme is proposed. First, the kinematic and dynamical models of the space free-flying robot are constructed, and the uncertainties and external perturbations of the system are considered. Then, a predefined-time sliding surface is constructed. A nonconservative PTSMC scheme is designed based on this surface, and the discontinuous chattering is alleviated by the boundary layer technique. With the Lyapunov theory, the predefined-time stability of the proposed controller is demonstrated. Finally, the numerical simulation shows that compared with the existing predefined-time controllers, the upper bound on the settling time of the proposed controller is more nonconservative and has higher tracking accuracy than the traditional PD control and the nonsingular terminal sliding mode control. Meanwhile, the effectiveness of the controller is illustrated by the attitude tracking experiment of a 3-DOF air-floating robot.
Swin-MFA: A Multi-Modal Fusion Attention Network Based on Swin-Transformer for Low-Light Image Human Segmentation

In recent years, image segmentation based on deep learning has been widely used in medical imaging, automatic driving, monitoring and security. In the fields of monitoring and security, the specific location of a person is detected by image segmentation, and it is segmented from the background to analyze the specific actions of the person. However, in low-illumination conditions, it is a great challenge to the traditional image-segmentation algorithms. Unfortunately, a scene with low light or even no light at night is often encountered in monitoring and security. Given this background, this paper proposes a multi-modal fusion network based on the encoder and decoder structure. The encoder, which contains a two-branch swin-transformer backbone instead of the traditional convolutional neural network, fuses the RGB and depth features with a multiscale fusion attention block. The decoder is also made up of the swin-transformer backbone and is finally connected via the encoder with several residual connections, which are proven to be beneficial in improving the accuracy of the network. Furthermore, this paper first proposes the low light–human segmentation (LLHS) dataset of portrait segmentation, with aligned depth and RGB images with fine annotation under low illuminance, by combining the traditional monocular camera and a depth camera with active structured light. The network is also tested in different levels of illumination. Experimental results show that the proposed network has good robustness in the scene of human segmentation in a low-light environment with varying illumination. The mean Intersection over Union (mIoU), which is often used to evaluate the performance of image segmentation model, of the Swin-MFA in the LLHS dataset is 81.0, is better than those of ACNet, 3DGNN, ESANet, RedNet and RFNet at the same level of depth in a mixed multi-modal network and is far ahead of the segmentation algorithm that only uses RGB features, so it has important practical significance.
StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic

In this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic. StEduCov consists of 16,572 tweets gathered over 15 months, from March 2020 to May 2021, using the Twitter API. The tweets were manually annotated into the classes agree, disagreeor neutral. We performed benchmarking on the dataset using state-of-the-art and traditional machine learning models. Specifically, we trained deep learning models—bidirectional encoder representations from transformers, long short-term memory, convolutional neural networks, attention-based biLSTM and Naive Bayes SVM—in addition to naive Bayes, logistic regression, support vector machines, decision trees, K-nearest neighbor and random forest. The average accuracy in the 10-fold cross-validation of these models ranged from 75% to 84.8% and from 52.6% to 68% for binary and multi-class stance classifications, respectively. Performances were affected by high vocabulary overlaps between classes and unreliable transfer learning using deep models pre-trained on general texts in relation to specific domains such as COVID-19 and distance education.