Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth and Growth Factors

The majority of articles selected for this chapter refer to novel pathophysiological mechanisms that have recently been elucidated. Levels of expression and methylation as well as specific polymorphisms in the IGF2 gene were shown to play a key role not only in fetal growth but also in early postnatal weight gain. IGFBP-4 was reported to be a key mediator between nutrition and adipose tissue expansion. FGF21 was shown to exert anti-inflammatory and antifibrotic actions in adipose tissue of db/db mice and to improve functional and morphological features of kidneys in diabetic nephropathy. IGF-I was revealed to stabilize the atherosclerotic plaque by enhancing the synthesis of collagen type 1. GH, through the activation of EGF receptor, was described to restore the liver regeneration capacity impaired by steatosis. Interplay between SIRT1 and the GH/IGF-I axis was reported to represent a crucial adaptive response to fasting in the liver. GH was even demonstrated to block the progression to type 1 diabetes in NOD mice by favorably modulating the immune response. Finally, the mechanism of the year is undoubtedly the elucidation of JAK2 activation by the GH receptor, which represents a molecular model common to all class I cytokine receptors. All of these novel mechanisms and actions were identified in animal and/or cellular models, thus making their translation into human physiology as well as clinical utility a working hypothesis to be tested in the near future. Nevertheless, this body of experimental knowledge about mechanisms directly or indirectly involved in many human pathologies may be considered a significant leap forward for the development of new therapeutic strategies based on the modulation of growth factors.
Galaxy morphology classification with densenet

The classification of galaxies has always been an essential topic in astronomy, which can help to understand how galaxies form and evolve. This paper uses an effective deep-learning architecture, DenseNet-201, to classify galaxy morphology. Because galaxies are only concentrated in the center of the images, we preprocess the data in the way of reframing the images from the size of 256 × 256 × 3 to 224 × 224 × 3 which can eliminate all random noises like any other sub-object. The proposed method, DenseNet, connects all layers to each other. By using the DenseBlock+Transition structure, it realizes feature reuse and reduces the number of features, which could improve computational efficiency. We compare DenseNet-201 with VGG16 and MobileNetV2. VGG16 is very neat and contains multiple Conv->Conv->Max_pool structures, and the essential part of MobileNet is depthwise separable convolution. The whole network appears to be stacks of deeply detachable convolution modules. Applying DenseNet-201 to the dataset, Galaxy10 DECals, we achieved 84.1% accuracy of classification, higher than VGG16 of 79% and MobileNetV2 of 78%.
Pseudolabel Decision-Theoretic Rough Set

In decision-theoretic rough set (DTRS), the decision costs are used to generate the thresholds for characterizing the probabilistic approximations. Similar to other rough sets, many generalized DTRS can also be formed by using different binary relations. Nevertheless, it should be noticed that most of the processes for calculating binary relations do not take the labels of samples into account, which may lead to the lower discrimination; for example, samples with different labels are regarded as indistinguishable. To fill such gap, the main contribution of this paper is to propose a pseudolabel strategy for constructing new DTRS. Firstly, a pseudolabel neighborhood relation is presented, which can differentiate samples by not only the neighborhood technique but also the pseudolabels of samples. Immediately, the pseudolabel neighborhood decision-theoretic rough set (PLNDTRS) can be constructed. Secondly, the problem of attribute reduction is explored, which aims to further reduce the PLNDTRS related decision costs. A heuristic algorithm is also designed to find such reduct. Finally, the clustering technique is employed to generate the pseudolabels of samples; the experimental results over 15 UCI data sets tell us that PLNDTRS is superior to DTRS without using pseudolabels because the former can generate lower decision costs. Moreover, the proposed heuristic algorithm is also effective in providing satisfied reducts. This study suggests new trends concerning cost sensitivity problem in rough data analysis.
Genetic differentiation between African and European Midwife Toads (Alytes, Discoglossidae)

Toads of the genus Alytes from the Rif mountains in Morocco are electrophoretically very similar to Iberian Alytes obstetricans (DieN = 0.07). Genetic distance estimates across the Straits of Gibraltar do not exceed the values found among European samples. The data point to relatively recent colonization or anthropogenic introduction of the Midwife Toad in Africa. The notion of Pasteur & Bons (1962) that Alytes from the Rif mountains might represent a separate species is not corroborated.
Dynamic Sliding Mode Control With PID Surface for Trajectory Tracking of a Multirotor Aircraft

This paper deals with the design of a robust controller to attenuate matched and unmatched uncertain dynamics as well as external disturbances effects by considering the actuator bandwidth to stabilize the mechanical dynamics of an Unmanned Aerial Vehicle. To this end, the performance of a sliding mode controller is improved with the combination of the attractive ellipsoid method. Likewise, it is guaranteed that the system trajectory arrives into a minimal size invariant set in finite time by using a workable control input. Finally, in order to evaluate the effectiveness of the proposed control approach, a comparative study with a robust Proportional Derivative controller, an integral sliding mode controller and a dynamic sliding mode controller with Proportional Integral Derivative sliding surface was conducted. In order to validate the effectiveness of the proposed controllers, the dynamics of a multirotor aircraft was used to conduct numerical simulations.
How to Disturb Network Reconnaissance: A Moving Target Defense Approach Based on Deep Reinforcement Learning

With the explosive growth of Internet traffic, large sensitive and valuable information is at risk of cyber attacks, which are mostly preceded by network reconnaissance. A moving target defense technique called host address mutation (HAM) helps facing network reconnaissance. However, there still exist several fundamental problems in HAM: 1) current approaches cannot be self-adaptive to adversarial strategies; 2) network state is time-varying because each host decides whether to mutate IP address; and 3) most methods mainly focus on enhancing security, but ignore the survivability of existing connections. In this paper, an Intelligence-Driven Host Address Mutation (ID-HAM) scheme is proposed to address aforementioned challenges. We firstly model a Markov decision process (MDP) to describe the mutation process, and design a seamless mutation mechanism. Secondly, to remove infeasible actions from the action space of MDP, we formulate address-to-host assignments as a constrained satisfaction problem. Thirdly, we design an advantage actor-critic algorithm for HAM, which aims to learn from scanning behaviors. Finally, security analysis and extensive simulations highlight the effectiveness of ID-HAM. Compared with state-of-the-art solutions, ID-HAM can decrease maximum 25% times of scanning hits while only influencing communication slightly. We also implemented a proof-of-concept prototype system to conduct experiments with multiple scanning tools.
Comparison of Serum and Urine as Sources of miRNA Markers for the Detection of Ovarian Cancer

Ovarian cancer is the second most fatal gynecological cancer. Early detection, which could be achieved through widespread screening, has not yet had an impact on mortality. The aim of our pilot study was to investigate the expression of miRNAs analyzed by a human miRNA microarray chip in urine and serum of patients with ovarian cancer. We analyzed three serum and three urine samples from healthy donors and five serum and five urine samples from patients with ovarian cancer taken at first diagnosis, before any treatment. We selected the seven miRNAs with the highest expression fold change in the microarray chip (cancer vs. control) in urine and serum, for validation by qPCR. We were able to validate two of the seven miRNAs in serum. In contrast to these findings, we were able to validate all of the top seven miRNAs identified in urine using qPCR. The top seven miRNAs in urine identified by microarray chip showed significantly greater differences in expression between patients with ovarian cancer and healthy donors compared to serum. Based on our finding, we can suggest that urine as a biomaterial is more suitable than serum for miRNA profiling by microarray chip in the search for new biomarkers in ovarian cancer.
The Mesoscale Numerical Simulation of the Effectiveness of an Artificial Precipitation Enhancement Operation

According to the mechanism and functions of the AgI seeding, the AgI seeding course was coupled into the operational mesoscale Weather Research and Forecasting(WRF) mode with reasonable boundary and initial conditions. Then the model is applied to analyse convective artificial precipitation enhancement over the Tian Mu Lake at Li Yang for the improvement of the ecological environment. The results from analysis of numerical simulation show that after seeding the strong echo area of cloud enlarges, the average echo intention increases significantly and the rainfall on the ground increases. The microphysical analysis show that after seeding the content of the ice, snow and grauple increases, correspondingly the content of the rain water in the low-level increases as well, which results in the enhancement of the precipitation. Keywords-AgI seeding; the effectiveness of the precipitation enhancement; mesoscale numerical simulation; the ecological environment
A Multi-Stream Network for Mesh Denoising Via Graph Neural Networks with Gaussian Curvature

3D meshes are getting popular in both research and industry. However, the meshes obtained via the 3D scanning equipment frequently contain a high level of noise. In this paper, we present a Gaussian Curvature Driven Multi-stream Network (GCM-Net) based on graph convolutional networks. This network can remove the noise while preserving the essential features during the 3D mesh denoising process. Our method is the first attempt to apply the high-order feature (i.e., Gaussian curvature) in the denoising task, which is more descriptive for the shape of the mesh. GCM-Net consists of curvature stream, vertex stream, and face normal stream, where the curvature stream focuses on the high-order Gaussian curvature feature of 3D mesh. Our method achieves state-of-the-art results on a publicly available dataset, demonstrating its effectiveness. The proposed method can be applied in various applications, such as 3D human body modeling, metaverse, object tracking and biomedical visualization.
EARTHWORK FAILURES IN RESPONSE TO EXTREME WEATHER

The paper provides insight into the impact of the extreme weather of the winter of 2000/2001 on the earthwork infrastructure of the Southern Region. Commencing with an overview of the preceding weather patterns, and consequences for soil moisture, the paper describes the preparatory and triggering factors leading to the failures and the distribution of events. The range of emergency and longer-emergency term remediation solutions is illustrated from a selection of the 160 sites on the emergency action plan and a simple risk analysis routine, devised for site prioritisation, is described. The paper concludes with an overview of the structure and methodology of the earthwork asset database required for the pro-active evaluation of earthwork condition. For the covering abstract see ITRD E122683.
Understanding innovation adoption : effects of orientation , pressure and control on adoption

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Researches advanced in fine-grained image classification based on deep learning

In the recent past, the task of fine-grained image recognition has become a hot research topic for scholars. Due to its substantial intra-class and little inter-class variance, fine-grained image classification task is more difficult than traditional image classification task. From a deep learning standpoint, this paper first introduces representative algorithms for strongly supervised fine-grained image classification, consisting of the Part-based R-CNN algorithm, the Pose Normalized CNN algorithm, and the Mask-CNN model. Second, weakly supervised process in image classification is expounded from the representative methods of attention mechanism, end-to-end visual coding, zero-shot learning with adaptive domain decomposition. Third, in the experimental part, the most popular databases for precise picture categorization, the evaluation metrics and conclusion of the algorithms are listed. Finally, it summarizes and looks forward, points out the problems and deficiencies of current research, and gives guidance for possible solutions in the future.
Holding space for wellbeing: Care and ethics of exclusion

In this exploratory paper, we consider how an ethics of care can be applied to designing for health and wellbeing. We start from defining design as a careful assembling around absences and attending to things that are excluded, with the explicit intent of finding alternatives to how bodies are made through design. We discuss the potentials and challenges of holding space for idiosyncratic practices of wellbeing. We show how an ethics of exclusion can be deployed to analyse how we hold space in our own design processes and propose future research pathways.
Video-Based Learning Recommender Systems: A Systematic Literature Review

Recently, using videos as a learning resource has received a lot of attention and turned widely exploited as an effective learning tool. With the rapid spread of instructional videos, the number of these tools in all disciplines is becoming very high. It is fractious for learners to find video courses adapted to their needs. Recommender system is an important tool; it can help learners for better navigation and better guidance to access and reach information and interesting videos content and restrict the number of choices provided. This article presents results of a systematic literature review on video-based learning recommender systems. This systematic literature review included the papers indexed in five scientific digital libraries and published between 2009 and 2023. Our review focuses on examining both the technical and pedagogical aspects of video-based learning recommender systems. In terms of technical aspects, we analyze the most commonly used recommendation approaches in video-based learning, as well as additional methods and techniques employed to enhance the effectiveness of video-based learning recommendations. We also explore the variables and data sources that have been investigated in the process of recommending videos for learning purposes, along with the evaluation metrics used to assess the performance of these systems. Regarding the pedagogical aspects, we investigate the learning environments where these systems have been implemented, the subjects and educational programs that hold primary importance in video-based learning recommendations, and the overall learning process.
Soybean Futures Price Prediction Model Based on EEMD-NAGU

The soybean futures market in China occupies an important position in the agricultural product futures market. The research on the fluctuation of soybean futures price and the prediction of the future price trend has always been the focus of extensive attention in the field of agricultural economics. This paper proposes an EEMD-NAGU hybrid prediction model for soybean futures price based on Ensemble Empirical Mode Decomposition (EEMD) and New Attention Gate Unit (NAGU). The model first uses EEMD to process soybean futures price data into multiple Intrinsic Mode Functions (IMFs) and residual sequence. Then calculates the sample entropy of each IMF and reconstructed the IMF into three components of low-frequency, medium-frequency, and high-frequency, according to the size of the sample entropy. NAGU is formed by embedding the Attention mechanism (Attention) into the Gate Recurrent Unit (GRU) structure, further improving the learning capability of the model. NAGU splits the original reset gate and update gate, sets up two-stage respectively, and uses different activation functions to capture the information in historical data better. Soybean futures price data is complex nonlinearities and contain more noise. In this model, EEMD plays denoises the time series data and fixes the model input. NAGU can perform differential learning on data and finally produce prediction results. EEMD-NAGU is compared with thirteen other prediction models (Support Vector Regression (SVR), LSTM, GRU, NAGU, EEMD-LSTM, EEMD-GRU, EEMD-NGU, Attention-LSTM, Attention-GRU, Attention-NGU, EEMD-Attention-LSTM, EEMD-Attention-GRU, and EEMD-Attention-NGU). The evaluation indexes of the experiment are Mean Absolute Error (MAE), Mean Square Error (MSE), and R Squared ( $R^{2}$ ). The experimental results show that the EEMD-NAGU model outperforms other models with better prediction performance. The model can be widely used to predict the price of wheat, corn, gold, oil, and other time series data.
Experimental study of a diesel engine heat pump in heating 1 mode for domestic retrofit application 2

s 6 An engine driven heat pump (ENHP) can provide better efficiency compared to electric heat 7 pump (EHP) considering primary energy consumption. The present work aimed to find 8 suitability of diesel engine heat pump as a domestic retrofit application for off or weak 9 gas/electricity network area. For this project work, water-to-water heat pump test facility was 10 developed which consisted heat pump, diesel engine and heat recovery arrangements. The 11 system performance was evaluated for 65°C flow temperature from condenser at three 12 different engine speeds (1600, 2000 & 24000 rpm) and four evaporator water inlet 13 temperature (0, 5, 10 & 15°C). The system performance was evaluated by heating capacity, 14 isentropic efficiency, coolant heat recovery, exhaust gas heat recovery and PER. 15 Performance analyses showed that heat recovery contributed 33% in total heat output where 16 heat recovery was in a range of 1.7 to 3.7 kW. PER varied in the range of 0.9 to 1.4 showing 17 good potential in terms of 35-65% primary energy saving and 23-42% CO2 emissions 18 reduction compared to conventional system. DEHP optimisation showed ability to meet 19 water flow temperature requirement of 65-73°C by speed variations and heat recovery 20 providing good potential to meet heating demand during winter and summer periods in 21 retrofit settings. 22 23
A Multi-Objective Resource Pre-Allocation Scheme Using SDN for Intelligent Transportation System

As 5-th Generation (5G) mobile communication and edge computing technologies mature, Intelligent Transportation System (ITS) are gradually becoming a reality. In the 5G heterogeneous network, resources such as computing, storage, and communication are allocated to each Road Side Unit (RSU) to provide intelligent services for vehicles. However, the existing average allocation method based on historical experience can easily lead to over-concentration or insufficient resources, which causes waste and reduces the Quality of Service (QoS). To solve this problem, this paper proposes a Multi-Objective Neural Time-series Prediction (M-ONTP) scheme for resource pre-allocation scenario in ITS. The scheme takes into account the complexity and diversity of service resource, innovatively treats the number of vehicles and communication power as joint optimization metrics, and proposes a multi-objective learning model. Benefiting from the vehicle data collected by RSUs in real time, we utilize historical traffic information to predict future road load and rely on Software Defined Network (SDN) to design a flexible resource pre-allocated architecture for ITS. To enhance the effectiveness of feature capture, M-ONTP also organically integrates various neural networks, which can appropriately handle large-scale time-series traffic flow. And we choose two layers of road data for fitting, which ensures that the model has a wide horizon to receive sufficient information. SUMO-based simulation experiments show that our scheme accurately realizes the prediction of joint objective and has significant performance advantage over other models. Meanwhile, our pre-allocation strategy reduces the total resource consumption by about 7%, increases the sufficiency rate by about 7%, and decreases the redundancy by about 12% while ensuring enough service resource to maintain normal QoS, which validates the effectiveness of M-ONTP.
Accounting for long-term serial correlation in a linear regression problem

The method is proposed for accounting for the long-term serial correlation (autocorrelation) of data in a linear regression problem. A serially correlated residual series is presented as an autoregressive (AR) process of an order, k, that can be much larger than 1, and the autocorrelation (AK) function of the processes is calculated by solving the system of the Yule-Walker equations. Given the AK function, the corresponding AK matrix is constructed which enters the formulas for estimates of regression coefficients and their errors. The efficiency of the method is demonstrated on the base of the multiple regression analysis of data of ground-based measurements of the column NO2 content at the Zvenigorod Research Station, Russia, and overpass satellite total ozone measurements over the station. Estimates of regression coefficients and their errors depend on the AR order, k. At first the error increases with increasing k. Then it can approach its maximum and thereafter begin to decrease. In the case of NO2 and O3 at Zvenigorod the errors more than double in their maxima compared to the beginning values. The decrease in the error stops at larger k if k approaches the value such that the AR process of this order is able to account for important features of the AK function of the residual series. The multiple linear regression model for NO2 and O3 observations includes the seasonally dependent linear trends and the effects of the solar cycle, the quasi-biennial oscillation, the North Atlantic and Southern Oscillations, and the Pinatubo volcanic eruption. Annual and seasonally dependent estimates of these effects in NO2 and O3 have been obtained taking into account the serial correlation as long as 50 months.