Miracle-dz/UniFAR-FTUs huggingface dataset:
A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors

: This paper presents the ﬁrst complete approach to achieving environmental intelligence support in the management of vegetation within electrical power transmission corridors. Contrary to the related studies that focused on the mapping of power lines, together with encroaching vegetation risk assessment, we realised predictive analytics with vegetation growth simulation. This was achieved by following the JDL/DFIG data fusion model for complementary feature extraction from Light Detection and Ranging (LiDAR) derived data products and auxiliary thematic maps that feed an ensemble regression model. The results indicate that improved vegetation growth prediction accuracy is obtained by segmenting training samples according to their contextual simi-larities that relate to their ecological niches. Furthermore, efﬁcient situation assessment was then performed using a rasterised parametrically deﬁned funnel-shaped volumetric ﬁlter. In this way, RMSE ≈ 1 m was measured when considering tree growth simulation, while a 0.37 m error was estimated in encroaching vegetation detection, demonstrating signiﬁcant improvements over the ﬁeld observations.
Selection of Efficient and Accurate Prediction Algorithm for Employing Real Time 5G Data Load Prediction

In smart cities applications (i.e. intelligent transport systems, traffic management) cellular traffic load prediction is playing an essential role. The cellular data consumption can help to understand the road traffic patterns. In this context, the employment of predictive Machine Learning (ML)techniques can be useful for approximating the possible resource demands in cell towers. Therefore, cellular traffic data may very useful for finding trends and patterns of load of human activities in city traffic. In this paper, the main aim is to identify the suitable machine learning techniques, which can be used for traffic load prediction in a smart city application. The paper includes three main contributions, first providing an overview of 5G technology and their applications, second, a review on existing traffic load prediction techniques and finally, a comparative experimental study is performed among popular supervised and unsupervised learning approaches. In order to compare the performance of supervised learning algorithms, Support Vector Machine (SVM), Artificial Neural Network (ANN), Bays classifier, Linear Regression (LR), and Decision Tree (DT) are implemented. On the other hand, for comparing the performance of unsupervised learning algorithms the Self Organizing Map (SOM), Fuzzy C Means (FCM), and k-Means clustering algorithms have been involved. The experiments on publically available data on Kaggle for 4G (LTE Traffic Prediction) were used. According to the experimental analysis, SVM and ANN are accurate algorithms in the supervised learning algorithms. On the other side in unsupervised learning models, SOM shows superior accuracy. But after summarizing the results we found that the SVM and ANN algorithms are beneficial for the proposed application.
Three-dimensional tumour microenvironment reconstruction and tumour-immune interactions' analysis

Tumours arise within complex 3D microenvironments, but the routine 2D analysis of tumours often underestimates the spatial heterogeneity. In this paper, we present a methodology to reconstruct and analyse 3D tumour models from routine clinical samples allowing 3D interactions to be analysed at cellular resolution. Our workflow involves cutting thin serial sections of tumours followed by labelling of cells using markers of interest. Serial sections are then scanned, and digital multiplexed data are created for computational reconstruction. Following spectral unmixing, a registration method of the consecutive images based on a pre-alignment, a parametric and a non-parametric image registration step is applied. For the segmentation of the cells, an ellipsoidal model is proposed and for the 3D reconstruction, a cubic interpolation method is used. The proposed 3D models allow us to identify specific interaction patterns that emerge as tumours develop, adapt and evolve within their host microenvironment. We applied our technique to map tumour-immune interactions of colorectal cancer and preliminary results suggest that 3D models better represent the tumor-immune cells interaction revealing mechanisms within the tumour microenvironment and its heterogeneity.
An Electron and Hole Trap Energy Distribution Estimation Model and Its Application in Polypropylene Nanocomposites

To differentiate the electron and hole traps inside polymers, a model based on the pulsed electro-acoustic (PEA) method is proposed. It is premised that under short-term DC electric field polarization at relatively low temperature, the homocharge is injected into the sample to a shallow depth and dominates the space charge distribution. Thus, the homocharge near the ground electrode and the high voltage electrode can be separated. During depolarization, only the homocharge near the ground electrode is analyzed to reduce the influence of ultrasonic wave attenuation, which also results in unipolar space charge decay behavior being obtained. The isothermal relaxation current (IRC) theory is then employed to obtain the unipolar quasi-continuous trap energy distribution based on the space charge decay. Thereby, the electron and hole traps can be differentiated by applying positive and negative DC electric field polarization, respectively. The experimental results of low density polyethylene (LDPE) verify the validity of the proposed model. Based on the model, polypropylene (PP) and PP/MgO nanocomposites are studied to explore the effect of MgO doping on the trap characteristics. The results show that numerous electron deep traps are introduced into the nanocomposites due to the intrinsic properties of nanoparticles. Besides, the heterogeneous nucleation of the nanoparticles increases the crystal/amorphous and crystal/vacuum interfaces, which may be the main reason for the increase of hole trap density of nanocomposites.
IoT-oriented Secure Data Sharing Using Public Cloud

: The number of IoT devices is continuously increasing. Secure data sharing governed by appropriate access control is required to safely utilize data generated by IoT devices. Storing data in a public cloud is suitable for deploying services with distributed data sharing on a large scale. However, this raises security concerns since even when the data are encrypted, an adverse third party may access them if a decryption key is stored within the same environment (key escrow problem). Conventional methods are not supposed to be used in the IoT environment or have issues with security, key distribution, and changing access authority. We propose a novel approach to securely share the data generated by IoT devices within a public cloud. Our method enables 1) addressing the key escrow problem; 2) providing forward secrecy; 3) ensuring indistinguishability under Adaptive Chosen Ciphertext Attack (safety equivalent to IND-CCA2); 4) changing access authority easily; and 5) saving computational resources of IoT devices. We implemented this method and evaluated its performance. The experimental results show that it has comparable or better performance compared with conventional methods. Furthermore, we conﬁrm that resource consumption in our method is more practical even in the large-scale IoT environment.
Common key technology system of cloud manufacturing service platform for small and medium enterprises

Cloud manufacturing service platform for Small and Medium Enterprises(SME)provided effective support for the SME to facilitate utilization and sharing of social manufacturing resources as well as to enhance enterprises' general competitiveness.Features of cloud manufacturing service platform for SME were analyzed,common key technology system architecture of platform was constructed.Meanwhile,research idea and content of common key technology of platform were discussed,including core theory and technology,standards and specifications,system architecture,common engines and common management tools,service mode and operation mode,application architecture et al.Thus,the research laid foundation for the future research,development,implementation and application of cloud manufacturing platform for SME.
Headline Recognition from Hindi Newspaper for Visually Impaired People using Deep Learning

Numerous technologies have been developed for the provision of visually impaired people. Amongst the several technologies, the solution provided by deep learning for computer vision is evolving as one of the most capable options owing to their affordability and availability. In this paper, we propose an end - to-end deep learning model to read the headlines of Hindi newspapers for visually impaired people. A headline is a significant element in a newspaper that provides the summary of the newspaper article. Moreover, there are about 186 million Indian readers of Hindi newspapers. The proposed system first extracts the headlines from the scanned newspaper articles using the You Only Look Once v4 (YOLOv4) object detector. The text from the scanned headlines is recognized using Convolutional Recurrent Neural Network (CRNN). Further, the extracted text is converted to sound that helps visually impaired people get important information from the Hindi newspapers. The results obtained have a WER of 1.8 % and a CER of 0.8 %. We have successfully converted all the text recognized by the system to sound to assist blind people in reading newspaper headlines.
A Novel Motion Recognition Method Based on Force Myography of Dynamic Muscle Contractions

Surface electromyogram-based pattern recognition (sEMG-PR) has been considered as the most promising method to control multifunctional prostheses for decades. However, the commercial applications of sEMG-PR in prosthetic control is still limited due to the ambient noise and impedance variation between electrodes and skin surface. In order to reduce these issues, a force-myography-based pattern recognition method was proposed. In this method, a type of polymer-based flexible film sensors, the piezoelectrets, were used to record the rate of stress change (RSC) signals on the muscle surface of eight able-bodied subjects for six hand motions. Thirteen time domain features and four classification algorithms of linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) were adopted to decode the RSC signals of different motion classes. In addition, the optimal feature set, classifier, and analysis window length were investigated systematically. Results showed that the average classification accuracy was 95.5 ± 2.2% by using the feature combination of root mean square (RMS) and waveform length (WL) for the classifier of KNN, and the analysis window length of 300 ms was found to obtain the best classification performance. Moreover, the robustness of the proposed method was investigated, and the classification accuracies were observed above 90% even when the white noise ratio increased to 50%. The work of this study demonstrated the effectiveness of RSC-based pattern recognition method for motion classification, and it would provide an alternative approach for the control of multifunctional prostheses.
Network-Based Topological Exploration of the Impact of Pollution Sources on Surface Water Bodies

We developed a digital water management toolkit to evaluate the importance of the connections between water bodies and the impacts caused by pollution sources. By representing water bodies in a topological network, the relationship between point loads and basic water quality parameters is examined as a labelled network. The labels are defined based on the classification of the water bodies and pollution sources. The analysis of the topology of the network can provide information on how the possible paths of the surface water network influence the water quality. The extracted information can be used to develop a monitoring- and evidence-based decision support system. The methodological development is presented through the analysis of the physical-chemical parameters of all surface water bodies in Hungary, using the emissions of industrial plants and wastewater treatment plants. Changes in water quality are comprehensively assessed based on the water quality data recorded over the past 10 years. The results illustrate that the developed method can identify critical surface water bodies where the impact of local pollution sources is more significant. One hundred six critical water bodies have been identified, where special attention should be given to water quality improvement.
VGG16, ResNet-50, and GoogLeNet Deep Learning Architecture for Breathing Sound Classification: A Comparative Study

The traditional methods used by researchers when implementing breathing sounds classification systems involve two main steps - feature extraction and pattern classification. In recent years, the topic of interest in the field of breathing sound classification focuses on the use of deep neural networks, which have been proven to be effective for training large datasets. In this paper, we conducted a comparative study of three deep neural network architectures, the VGG16, ResNet-50, and GoogLeNet for breathing sounds classification. Digital recordings of cycle-based breathing sounds from the ICBHI database are processed to obtain gammatonegram images that are fed as an input to these three networks. The classification results, executed on the Google Colaboratory platform, indicated that these three networks yielded accuracies of 62.50%, 62.29%, and 63.69% respectively. Hence, the results provide initial evidence that GoogLeNet can significantly improve the accuracy and outperformed the VGG16 and Resnet-50 in our application.
MacAB-TolC Contributes to the Development of Acinetobacter baumannii Biofilm at the Solid–Liquid Interface

Acinetobacter baumannii has emerged as one of the most problematic bacterial pathogens responsible for hospital-acquired and community infections worldwide. Besides its high capacity to acquire antibiotic resistance mechanisms, it also presents high adhesion abilities on inert and living surfaces leading to biofilm development. This lifestyle confers additional protection against various treatments and allows it to persist for long periods in various hospital niches. Due to their remarkable antimicrobial tolerance, A. baumannii biofilms are difficult to control and ultimately eradicate. Further insights into the mechanism of biofilm development will help to overcome this challenge and to develop novel antibiofilm strategies. To unravel critical determinants of this sessile lifestyle, the proteomic profiles of two A. baumannii strains (ATTC17978 and SDF) grown in planktonic stationary phase or in mature solid–liquid (S-L) biofilm were compared using a semiquantitative proteomic study. Of interest, among the 69 common proteins determinants accumulated in the two strains at the S-L interface, we sorted out the MacAB-TolC system. This tripartite efflux pump played a role in A. baumannii biofilm formation as demonstrated by using ΔmacAB-tolC deletion mutant. Complementary approaches allowed us to get an overview of the impact of macAB-tolC deletion in A. baumannii physiology. Indeed, this efflux pump appeared to be involved in the envelope stress response occurring in mature biofilm. It contributes to maintain wild type (WT) membrane rigidity and provides tolerance to high osmolarity conditions. In addition, this system is probably involved in the maintenance of iron and sulfur homeostasis. MacAB-TolC might help this pathogen face and adapt to deleterious conditions occurring in mature biofilms. Increasing our knowledge of A. baumannii biofilm formation will undoubtedly help us develop new therapeutic strategies to tackle this emerging threat to human health.
Molecular Mechanisms of Epileptic Encephalopathy Caused by KCNMA1 Loss-of-Function Mutations

The gene kcnma1 encodes the α-subunit of high-conductance calcium- and voltage-dependent K+ (BK) potassium channel. With the development of generation gene sequencing technology, many KCNMA1 mutants have been identified and are more closely related to generalized epilepsy and paroxysmal dyskinesia. Here, we performed a genetic screen of 26 patients with febrile seizures and identified a novel mutation of KCNMA1 (E155Q). Electrophysiological characterization of different KCNMA1 mutants in HEK 293T cells, the previously-reported R458T and E884K variants (not yet determined), as well as the newly-found E155Q variant, revealed that the current density amplitude of all the above variants was significantly smaller than that of the wild-type (WT) channel. All the above variants caused a positive shift of the I-V curve and played a role through the loss-of-function (LOF) mechanism. Moreover, the β4 subunit slowed down the activation of the E155Q mutant. Then, we used kcnma1 knockout (BK KO) mice as the overall animal model of LOF mutants. It was found that BK KO mice had spontaneous epilepsy, motor impairment, autophagic dysfunction, abnormal electroencephalogram (EEG) signals, as well as possible anxiety and cognitive impairment. In addition, we performed transcriptomic analysis on the hippocampus and cortex of BK KO and WT mice. We identified many differentially expressed genes (DEGs). Eight dysregulated genes [i.e., (Gfap and Grm3 associated with astrocyte activation) (Alpl and Nlrp10 associated with neuroinflammation) (Efna5 and Reln associated with epilepsy) (Cdkn1a and Nr4a1 associated with autophagy)] were validated by RT-PCR, which showed a high concordance with transcriptomic analysis. Calcium imaging results suggested that BK might regulate the autophagy pathway from TRPML1. In conclusion, our study indicated that newly-found point E155Q resulted in a novel loss-of-function variant and the dysregulation of gene expression, especially astrocyte activation, neuroinflammation and autophagy, might be the molecular mechanism of BK-LOF meditated epilepsy.
Denoising EEG Signals for Real-World BCI Applications Using GANs

As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway between a brain and an external device, translating thought into action with suitable processing. EEG data is the most common signal source for such technologies. However, artefacts induced in BCIs in the real-world context can severely degrade their performance relative to their in-laboratory performance. In most cases, the recorded signals are so heavily corrupted by noise that they are unusable and restrict BCI's broader applicability. To realise the use of portable BCIs capable of high-quality performance in a real-world setting, we use Generative Adversarial Networks (GANs) that can adopt both supervised and unsupervised learning approaches. Although our approach is supervised, the same model can be used for unsupervised tasks such as data augmentation/imputation in the low resource setting. Exploiting recent advancements in Generative Adversarial Networks (GAN), we construct a pipeline capable of denoising artefacts from EEG time series data. In the case of denoising data, it maps noisy EEG signals to clean EEG signals, given the nature of the respective artefact. We demonstrate the capability of our network on a toy dataset and a benchmark EEG dataset developed explicitly for deep learning denoising techniques. Our datasets consist of an artificially added mains noise (50/60 Hz) artefact dataset and an open-source EEG benchmark dataset with two artificially added artefacts. Artificially inducing myogenic and ocular artefacts for the benchmark dataset allows us to present qualitative and quantitative evidence of the GANs denoising capabilities and rank it among the current gold standard deep learning EEG denoising techniques. We show the power spectral density (PSD), signal-to-noise ratio (SNR), and other classical time series similarity measures for quantitative metrics and compare our model to those previously used in the literature. To our knowledge, this framework is the first example of a GAN capable of EEG artefact removal and generalisable to more than one artefact type. Our model has provided a competitive performance in advancing the state-of-the-art deep learning EEG denoising techniques. Furthermore, given the integration of AI into wearable technology, our method would allow for portable EEG devices with less noisy and more stable brain signals.
Porphyromonas gingivalis Administration Induces Gestational Obesity, Alters Gene Expression in the Liver and Brown Adipose Tissue in Pregnant Mice, and Causes Underweight in Fetuses

Preventing adverse pregnancy outcomes is crucial for maternal and child health. Periodontal disease is a risk factor for many systemic diseases including adverse pregnancy outcomes, such as preterm birth and low birth weight. In addition, the administration of the periodontopathic bacterium Porphyromonas gingivalis exacerbates obesity, glucose tolerance, and hepatic steatosis and alters endocrine function in the brown adipose tissue (BAT). However, the effects of having periodontal disease during pregnancy remain unclear. Thus, this study investigates the effect of P. gingivalis administration on obesity, liver, and BAT during pregnancy. Sonicated P. gingivalis (Pg) or saline (Co) was injected intravenously and administered orally to pregnant C57BL/6J mice three times per week. Maternal body weight and fetal body weight on embryonic day (ED) 18 were evaluated. Microarray analysis and qPCR in the liver and BAT and hepatic and plasma triglyceride quantification were performed on dams at ED 18. The body weight of Pg dams was heavier than that of Co dams; however, the fetal body weight was decreased in the offspring of Pg dams. Microarray analysis revealed 254 and 53 differentially expressed genes in the liver and BAT, respectively. Gene set enrichment analysis exhibited the downregulation of fatty acid metabolism gene set in the liver and estrogen response early/late gene sets in the BAT, whereas inflammatory response and IL6/JAK/STAT3 signaling gene sets were upregulated both in the liver and BAT. The downregulation of expression levels of Lpin1, Lpin2, and Lxra in the liver, which are associated with triglyceride synthesis, and a decreasing trend in hepatic triglyceride of Pg dams were observed. P. gingivalis administration may alter lipid metabolism in the liver. Overall, the intravenous and oral administration of sonicated P. gingivalis-induced obesity and modified gene expression in the liver and BAT in pregnant mice and caused fetuses to be underweight.