Miracle-dz/UniFAR-FTUs huggingface dataset:
Amination of ether-linked polymers via the application of Ullmann-coupling reaction: synthesis, characterization, porosity, and thermal stability evaluation

Abstract In this report, an open-chain ether-linked polymer (PE-AF) has been prepared via one-step co-condensation polymerization of 1,4-dibromo-2,5-difluorobenzene with bisphenol AF. The new polymer has shown a good solubility in non-polar solvents, good thermal stability (up to 300 °C), high char residue with a limiting oxygen index (LOI) of 33.5, surface morphology with random fluffy-like texture, and a non-porosity nature with a surface area (SA) of 35 m2g−1. Tailoring these properties has been achieved by considering post-modification on the Bromo-sites of the polymer backbone. The post-modification was attained by applying Ullmann-coupling reaction of the Bromo-sites of PE-AF and aromatic diamines in presence of a catalytic amount of copper iodide. Upon inclusion of the aromatic diamines within the polymer’s backbone, the new polymers have depicted aggregated surface morphology (e.g. spherical particles ca. 0.3–0.1 µm), pronounced porosity nature (SA up to 354 m2g−1), varied range of pore size distribution, and higher thermal stability with lower char residue contents when compared with PE-AF. The successful production of the polymers was confirmed by (C, H, O, and N) elemental analysis, solid-state 13C-NMR, and infrared (IR) spectroscopy. The application of Ullmann-coupling raises the opportunity to create cavities and pores within the polymer’s framework. Though this modification lowered the LOI of the polymers and their flame-retardant tendency, it enhanced their opportunity for gas capture and separation applications.
The impact of HPV vaccination beyond cancer prevention: effect on pregnancy outcomes

ABSTRACT While the benefits of human papillomavirus (HPV) vaccination relating to cervical cancer prevention have been widely documented, recent published evidence is suggestive of an impact on adverse pregnancy outcomes (APOs) in vaccinated mothers and their infants, including a reduction in rates of preterm births and small for gestational age infants. In this review, we examine this evidence and the possible mechanisms by which HPV vaccination may prevent these APOs. Large-scale studies linking HPV vaccination status with birth registries are needed to confirm these results. Potential confounding factors to consider in future analyses include other risk factors for APOs, and historical changes in both the management of cervical precancerous lesions and prevention of APOs. If confirmed, these additional benefits of HPV vaccination in reducing APO rates will be of global significance, due to the substantial health, social and economic costs associated with APOs, strengthening the case for worldwide HPV immunization.
Landscape-scale perspectives on Stone Age behavioural change from the Tankwa Karoo, South Africa

ABSTRACT Southern Africa is an ecologically highly varied region, yet many generalisations about past human behaviour are drawn from rock shelter sites in coastal and montane Fynbos Biome environments. The Tankwa Karoo region offers the opportunity to extend our archaeological knowledge from the well-researched Western Cape into the arid interior Karoo in order to better capture behavioural variability and identify specific adaptations to more marginal conditions. This research presents the results of off-site surveys in the Tankwa Karoo, which spans the Cape to Karoo transition, mapping surface stone artefacts from the Earlier and Middle Stone Ages. The observed patterns in landscape use and lithic technology for each time-period were tested against a set of expectations based on previous research in the Western Cape and the Upper Karoo. The results indicate that in the Earlier Stone Age the most arid parts of the Tankwa Karoo saw only ephemeral use, with the better-watered mountain fringes preferred. In contrast, various strategies in the Middle Stone Age allowed groups to occupy these marginal parts of the landscape, including new kinds of technological behaviour suggestive of specific adaptations to this environment.
miR-513b-5p inhibits the proliferation and promotes apoptosis of retinoblastoma cells by targeting TRIB1

Abstract MicroRNAs are involved in the pathogenesis of various human malignant tumors. This study aims to explore the role of miR-513b-5p in the malignant proliferation of retinoblastoma (RB) cells and its potential molecular mechanisms. The function-gain and function-loss experiments were performed in Weri-RB1 cells using miR-513b-5 mimics and inhibitors. miR-513b-5p mimics inhibited the proliferation and clone formation and promoted apoptosis of Weri-RB1 cells. In contrast, the miR-513b-5p inhibitor promoted the proliferation and clone formation of Weri-RB1 cells and inhibited cell apoptosis. miR-513b-5p can directly bind to the 3′UTR region of TRIB1 mRNA, and inhibit its protein expression. Overexpression of TRIB1 promoted the proliferation and cloning of Weri-RB1 cells but inhibited their apoptosis. The knockdown of TRIB1 inhibited the proliferation and clone formation of Weri-RB1 cells and promoted cell apoptosis. In addition, miR-513b-5p mimics neutralized the effects of TRIB1 overexpression on the proliferation and apoptosis of Weri-RB1 cells. Finally, miR-513b-5p can inhibit the phosphorylation level of AKT, mTOR, and p70, while TRIB1 played the opposite role. miR-513b-5p inhibits the malignant proliferation of Weri-RB1 cells by repressing the expression of TRIB1. miR-513b-5p and TRIB1 may be the biomarkers and/or key targets for clinical diagnosis and treatment of RB.
Guided-mode resonance on pedestal and half-buried high-contrast gratings for biosensing applications

Abstract Optical sensors typically provide compact, fast and precise means of performing quantitative measures for almost any kind of measurand that is usually probed electronically. High-contrast grating (HCG) resonators are known to manifest an extremely sharp and sensitive optical resonance and can constitute a highly suitable sensing platform. In this paper we present two advanced high-contrast grating designs improving the sensing performances of conventional implementations. These configurations, namely pedestal and half-buried HCGs, allow to enhance the shift of the photonic resonance while maintaining the spectral features of the standard configuration. First, the spectral feature of the HCGs was numerically optimized to express the sharpest possible resonance when the structure is immersed in serum. Second, the sensing properties of conventional and advanced HCG implementations were studied by modelling the biological entities to be sensed as a thin dielectric coating layer of increasing thickness. Pedestal HCGs were found to provide a ∼12% improvement in sensitivity and a six-fold improvement in resonance quality factor (Q-factor), while buried HCGs resulted in a ∼58% improvement in sensitivity at the expense of a slightly broader resonance. Such structures may serve as an improved sensitive biosensing platform for near-infrared spectroscopy.
One Source to Detect them All: Gender, Age, and Emotion Detection from Voice

Gender, age, and emotion detection from the speech are essential in machine and human interaction. Sometimes it is required to categorize audios by age and gender from speech. Sometimes it is required to predict age, gender, and emotion from audio clips for investigation purposes. Most telecommunication companies need to analyze audio calls to predict customer demography and recommend offers based on demographic segments. Several researchers have focused on detecting gender, age, and emotion from different types of sources. But according to the best of our knowledge, none of them use a single type of source to detect all of them. We have introduced a system to detect gender, age, and emotion from audio speech. In our system, all audio files were converted into 20 statistical features, and the converted numerical datasets were used to create the different prediction models to attain the objective. The different prediction models are Random Forest, CatBoost, Gradient Boosting, K-nearest neighbors (KNN), XGBoost, AdaBoost, Decision Tree, Artificial neural networks (ANN), Naive Bayes, and Support vector machine (SVM). All the prediction models were evaluated and compared based on their test accuracy. In predicting gender, CatBoost performs best among all predictive models with 96.4% test accuracy. On the other hand, Random Forest performs best for predicting age among all predicting models with 70.4% test accuracy. For emotion prediction, XGBoost performs best with 66.1% test accuracy. It was also analyzed among 20 features which features are most influential for the effective prediction models. We believe that our findings will be beneficial to future researchers in this area.
Reduction of Motion Artifacts From Remote Photoplethysmography Using Adaptive Noise Cancellation and Modified HSI Model

Remote photoplethysmography (rPPG) is a method to measure cardiac activities without any contact sensors. Non-contact sensors include radar, laser, and digital cameras, and there have been wide developments regarding the measurement of rPPG signals using continuous face frames. However, non-contact sensors are quite sensitive to the subject’s motion, which causes motion artifacts. In this paper, two hypotheses are proposed: a) the motion artifacts are caused by unevenly reflected light due to the curvature of the subject’s face; and b) melanin and residuals in the continuous face frames are time-varying values whenever the subject’s movement is triggered. Adaptive noise cancellation based on recursive least square (ANS based on RLS) using the Lambert-Beer law and the hue–saturation–intensity (HSI) model were applied. The former is used for skin modeling, and the latter is used to reduce noises derived by the curvature of the face. Furthermore, the proposed algorithm is directly applied to two-dimensional continuous face frames and results in the rPPG signal and rPPG image, respectively. To evaluate proposed algorithm, two different experiments (e.g., static and dynamic situation) were conducted. Furthermore, in a study with 15 participants, the performances of heart rate estimation and heart rate variability (HRV) were evaluated by comparing the proposed method with previously developed methods. The results showed that a) the artifacts derived by head movement are efficiently removed, compared to previous methods; and b) rPPG images describing the spread of facial blood flow are acquired in real-time.
Reducing CO₂ Emissions of an Airport Baggage Handling Transport System Using a Particle Swarm Optimization Algorithm

Optimizing the design of an airport baggage handling transport system (BHTS) with respect to the minimization of the total costs and energy consumption is essential to reduce costs and Carbon dioxide (chemical formula CO2) emissions in airport operations. This paper introduces a mathematical model that comprehensively considers relevant costs regarding the operation of belt conveyors in a BHTS. Specifically, the Capital Expenditure (CapEx) and Operational Expenditure (OpEx) are considered in the airport BHTS cost function. Furthermore, to include the impact of CO2 emissions, the offsetting costs of CO2 emissions are included in the airport BHTS cost function. This function forms the basis of an objective function that can be used to optimize the airport BHTS’s design by metaheuristic algorithms. Three state-of-the-art particle swarm optimization (PSO) algorithms are utilized to solve the airport BHTS optimization problem. The results of experiments show that the three PSO variants can solve the optimization problem effectively and efficiently. The self-regulation PSO algorithm performed the best in terms of CPU time and has been used for the case studies. Extensive tests of the impact of key parameters, e.g., capacity and system length, on the optimized solutions have been conducted. Experiments show that a system with several belt conveyors of shorter lengths performs better than a system with one long conveyor. In reality however, more parameters play a role like the varying baggage throughput per hour and therefore the BHTS problem needs to be optimized case-by-case. Optimizing an airport BHTS design leads to a significant reduction in CO2 emission and thus costs.
A vehicle classification system based on a non-intrusive sensor's binary image and convolutional neural networks

Express highways are increasingly adopting Intelligent Transportation Systems. Automatic vehicle classification is one of the most critical subsystems, as it is responsible for the validation of vehicle categories on electronic toll collection. Vehicle classification needs to reach a high success rate, and in real applications, it is achieved by using several intrusive sensors and sophisticated algorithms. This paper proposes a novel classification method based on binary images of vehicle profiles extracted from a non-intrusive optical barrier sensor. We used a modified AlexNet convolutional neural network as a vehicle classification algorithm. The last layers of the network were modified to the vehicle classification domain. We used transfer learning and data augmentation techniques. The method was tested using 11233 images grouped into a set of 11 categories. Results achieved 98.02% accuracy, which indicates that replacing a set of intrusive sensors by only an optical barrier may be a feasible vehicle classification alternative.
An Empirical Study of Vulnerabilities in Robotics

The ubiquitous usage of robots in modern society necessitates secure development of robotics systems. Practitioners who engage in robot development can benefit from a systematic study that investigates the categories of vulnerabilities that appear in robotics systems. The goal of this paper is to help practitioners mitigate vulnerabilities in robotics systems by conducting an empirical study of vulnerabilities in robotics systems. We conduct an empirical study where we analyze 176 robotics-related vulnerabilities collected from the Robot Vulnerability Database (RVD). Our findings show that: (i) robotics-related vulnerabilities can be classified into nine categories; (ii) memory-related vulnerabilities are the most frequent category, (iii) 92.6% of the reported vulnerabilities are software-related, and (iv) software components in robotics systems include more critical vulnerabilities compared to that of hardware components. Based on our findings, we provide a list of development activities that can be used to mitigate vulnerabilities for robotics systems.
Convolutional neural network applied for object recognition in a warehouse of an electric company

This paper presents a computational tool that analyzes the quality of captured colored images and classifies products of an electrical company’s warehouse using Convolutional Neural Network (CNN). The tool is part of a prototype aiming at automated flow control and inventory, saving time and cost. In the data acquisition process, the tool examines the quality of images through an Image Quality Assessment algorithm (IQA) named BRISQUE. Two classes of materials were chosen to compose the dataset which went through a dataset resampling and data augmentation. The binary object classification is performed by a residual neural network (Resnet). Using a pre-trained model, a transfer learning method called feature extraction was applied, adjusting the network to respond to the addressed task by updating the final layer’s weights, biases, and number of neurons. An extensive test was conducted to define the best set of hyperparameters for the application. The neural network was tested 10 times on every combination of these hyperparameters values and the average accuracy on test dataset defined the best set. Adaptive moment estimation (ADAM) optimizer, a learning rate of 0.001, and batch size of 16 proved to have an improvement over other combinations, achieving an average accuracy on the test set of 92.876%. The feature extraction proved to be powerful, once the accuracy on training was close to 90% even at the first epochs and it did not require full architecture training. The tool is a combination of automation, deep learning, and computer vision applied to a real engineering problem.
Digital Twin Design for Thermal Power Plant Cooling System using Fuzzy System

With technological advances, the industrial sector is using enabling technologies based on the concepts of Industry 4.0, among them the modeling of systems based on Digital Twins, which allow building models of real physical systems from field measurements, with the ability to follow the dynamic system changes. This article describes the development of a digital twin for a cooling system based on data from the sensors of a thermal power plant. The digital twin created used data from a plant currently in operation at the major system, and the automatic rules extraction approach from the test database was used to form the model's knowledge database. The principal component analysis technique was used to reduce the dimensionality of the system to reduce the computational effort of the model. An algorithm for automatic update of rules during the operation of the digital twin was also proposed, making the model learn during the operation even without the need to retrain and, consequently, reduce error of the model's response in the short term.
Tunable 24-GHz Antenna Arrays Based on Nanocrystalline Graphite

In this work, we present a tunable 24-GHz antenna array based on a CMOS-compatible 110-nm-thick nanocrystalline graphite film grown by plasma enhanced chemical vapor deposition. The film has a nominal bulk conductivity exceeding 16000 S/m (hence, greater than any graphene monolayer or industrially available graphene multilayer) but still able to show an outstanding modulation of its charge carrier density in the upper microwave spectrum. The manufactured layer was used to design, simulate, fabricate, and test a 24-GHz patch antenna array, with each radiating element having overall dimensions of just $\lambda _{0}/8\times \lambda _{0}$ /7. The fabricated array exhibits a measured maximum gain of about 3 dBi around 24 GHz (unbiased state), with a half-power beam width of only 14.5° (suitable in wireless links where a high directivity is envisaged). Spanning the dc bias voltage between -25 V and 25 V at 24 GHz, the gain can be tuned continuously between -1.5 dBi and 4 dBi, whereas the resonance frequency undergoes a maximum shift of 166 MHz. This voltage-dependent tuning of the gain represents a first step in developing carbon-based applications to control amplitude and phase simultaneously and independently. These results (never reported) demonstrate the big potential of nanocrystalline graphite for high-performance microwave components that are CMOS compatible (a highly desirable characteristic for high fabrication yield and large-scale production), with unprecedented tunability for next-generation high-capacity communications.
SimRouter: Message routing based on Similarity and Relative Probable Positions of nodes in DTNs

Delay tolerant networks (DTNs) have the major challenge of unpredictable topology due to nodes’ mobility and intermittent connections between nodes. Researchers have proposed various routing schemes to route packets effectively and efficiently within the given constraints, but we are still far from a perfect protocol. We propose a novel multi-copy protocol SimRouter for such networks, leveraging the additional information about the network, to make the routing decisions. SimRouter uses relative probable positions of the nodes and their similarity to route messages effectively with low overhead. Each node estimates and stores the relative probable positions of other nodes and a vector containing the number of times other nodes came in contact with it. The decision whether to replicate a message to another node or not is taken considering the two factors. Firstly, how similar the two nodes are in terms of their contact history and secondly, the relative positions of the nodes with respect to the destination node. The proposed protocol outperforms the current state of art multi-copy protocols specifically for high traffic loads.
Adsorption and desorption of boron in cultivated soils of Himachal Pradesh

The boron (B) concentration in the soil solution is generally controlled by B adsorption and desorption reactions. With this background, a study was carried out to know the adsorption and desorption of boron using Freundlich, Langmuir and Temkin isotherms in cultivated soil of Himachal Pradesh. Eighty soil samples (0-0.15 m depth) were collected randomly from different locations across different districts of Himachal Pradesh and their B adsorption-desorption behaviour was studied. Among three different adsorption isotherms i.e. Freundlich, Langmuir and Temkin used to determine the B retention capacity of soils, Freundlich and Temkin isotherms represented better fit for fine textured soils, while Langmuir isotherm showed better fit for coarse textured soils. The results emanating from the study revealed that with increase in boron concentration, the boron adsorption by soils increases.
CGAN-IRB: A Novel Data Augmentation Method for Apple Leaf Diseases

At present, the identification of apple leaf diseases plays an important role in controlling apple leaf diseases and improving apple yield. CNNs(Convolutional Neural Networks) have been widely used in apple leaf diseases identification, but the training of the CNNs requires a large number of images. The lack of images would make the CNNs hard to generalize. Thus the CNNs are unable to recognize new disease images. Focusing on this problem, this paper proposes a new model named CGAN-IRB(Conditional Generative Adversarial Network with the Improved Residual Block) for data augmentation. Firstly, various improvements have been made based on CGAN to generate high-quality, robust, and specific-category images of apple leaf diseases. Among which the embedding of the residual block has been found to significantly improve the model performance. Then the interpolation algorithm is used instead of deconvolution to increase the image size. Finally, the TTUR(Two-Timescale Update Rule) training strategy is employed and all the convolutional layers of the network are spectrally normalized to stabilize the training of the network. The performance of CGAN-IRB was tested both on image generation and classification tasks. Experiment results show that the images generated by the network possess high quality and robust features, pro-viding a novel solution for the data augmentation of apple leaf diseases. The new GAN-based data augmentation method leads to significant improvements in the classification accuracy of CNNs. In the case of all tested CNNs, the classification accuracy improvements are 11.75% and 2.17% on average over non-augmented and traditional-augmented, respectively. Among them, the classification accuracy of GoogLeNet V2 and ShuffleNet V2 is 99.34% and 99.67%, respectively. The data augmentation approach proposed in this paper can be used more widely in the field of disease identification, solving the problem of insufficient data sets, and can be extended to related fields where data sets are difficult to obtain.