Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Road Recognition Based in Multiple Artiﬁcial Neural Networks

—The development of autonomous vehicles is a highly relevant research topic in mobile robotics. Road recognition using visual information is an important capability for autonomous navigation in urban environments. Over the last three decades, a large number of visual road recognition approaches have been appeared in the literature. This paper propose a novel visual road detection system based on multiple ANN that can identify the road based on color and texture. Several features are used as inputs of the ANN such as: average, entropy, energy and variance from different color channels (RGB, HSV, YUV). As a result, our system is able to estimate the classiﬁcation and the conﬁdence factor of each part of the environment detected by the camera. Experimental tests have been performed in several situations in order to validate the proposed approach.
FINANCIAL STABILITY OF ELECTRICITY COMPANIES IN THE CONTEXT OF THE MACROECONOMIC INSTABILITY AND THE COVID-19 PANDEMIC

The electricity sector is an important part of any country’s economy as it holds a cross-sectoral importance and produces a socially signifi cant product for residents and industries. Economically, the sector is less vulnerable during world crises, receiving many variations of the state support. Both world electricity consumption and electricity generation have grown steadily over 2007-2019, with China, USA, India, Russia, Japan, Canada, South Korea, Germany, Brazil and France being world market leaders. This article analyzes the current state and the main trends of the development of the electricity industry as a whole and the fi nancial stability of its companies. The United States and Russia, with similar functioning market models, were chosen to assess. The analysis of the fi nancial stability of PJSC Inter RAO and Exelon Corp, two electricity giants in Russia and in the United States, has shown that they demonstrate stable results: Exelon Corp is more profi table while PJSC Inter RAO is less dependent on fi nancing from creditors. Overall, electricity companies and the industry as a whole should not suff er much from the COVID-19 pandemic: many fi nancial support measures have been developed in both countries, helping the sector to recover to 2019 levels by 2021.
Infant abnormal behavior classification through weakly supervised learning

— Nowadays, caregivers are focusing on the health information of newborn infants. It is very important to discover a neurological disease in infants. Especially, Early diagnosis must be made, in the case of premature infants as the possibility of neurological diseases is high. There are many systems for monitoring infants, but few systems classify the status of infants through artificial intelligence analysis. In this paper, the normal and abnormal movements were classified to help diagnose neurological diseases of infants by using Fully Convolutional Network (FCN) according to the skeleton information of infants. Besides, only the labels of normal and abnormal can produce the following weakly-supervised three characteristics. 1) whether the infant's condition is normal or abnormal) 2) which segment of the video shows the infant's movement is abnormal, 3) which body part of the infant is found to be abnormal. The proposed network is simple and suitable for application to robot devices that observe infants in the home environment. This study was conducted using the skeleton database extracted from the video of infants.
Two-particle azimuthal correlations in photonuclear ultraperipheral Pb + Pb collisions at 5 . 02 TeV with ATLAS

Citation for published version (APA): Aad, G. , Abbott, B. , Abbott, DC. , Abud, AA. , Abeling, K. , Abhayasinghe, D. K. , Abidi, S. H. , AbouZeid, O. S. A. , Abraham, NL. , Abramowicz, H. , Dam, M. , Camplani, A. , Ignazzi, R. , Hansen, J. B. , Hansen, P. H. , Hansen, J. D. , Wiglesworth, G. , Petersen, T. C. , Xella, S. , & ATLAS Collaboration (2021). Two-particle azimuthal correlations in photonuclear ultraperipheral Pb+Pb collisions at 5.02 TeV with ATLAS. Physical Review C, 104(1), [014903]. https://doi.org/10.1103/PhysRevC.104. 014903
Design of Intelligent Early Warning Robot System for Warehouse Autonomous Patrol based on Scale Estimation Algorithm of Probability Theory

In view of the backward monitoring status in the traditional safety management of hazardous chemicals warehouse, a patrol robot for hazardous chemicals warehouse with autonomous patrol stereo detection, intelligent early warning and remote monitoring functions is designed, and the design process of related functions of autonomous patrol navigation subsystem, environmental detection subsystem and remote monitoring subsystem is discussed from two aspects of hardware and software. Because both the camera and the target may be in motion, it is necessary to consider the scale change of target influence caused by relative distance. In order to overcome the shortcoming of invariable scale in Mean Shift algorithm, this paper adopts a scale adaptive method to estimate the size of the target, adjusts the parameters of kernel function by using ellipse feature, and takes the parameters of the current frame as the initial data of the next frame, so as to achieve the effect of adaptively adjusting the target scale. The test results show that the system can effectively traverse the map of hazardous chemicals warehouse, accurately assess the leakage of hazardous chemicals and the environmental safety of warehouse, and give early warning. The remote monitoring platform greatly facilitates the remote management of the warehouse.
Marszalkowski-1-Comparative study on tertiary contacts and folding of RNase P RNAs from a 1 psychrophilic , a mesophilic / radiation-resistant and a thermophilic bacterium 2 3

29 30 In most bacterial type A RNase P RNAs (P RNAs), two major loop-helix tertiary contacts 31 (L8-P4 and L18-P8) help to orient the two independently folding Sand C-domains for 32 concerted recognition of precursor tRNA substrates. Here, we analyze the effects of 33 mutations in these tertiary contacts in P RNAs from three different species: (i) the 34 psychrophilic bacterium Pseudoalteromonas translucida (Ptr), (ii) the mesophilic 35 radiation-resistant bacterium Deinococcus radiodurans (Dra), and (iii) the thermophilic 36 bacterium Thermus thermophilus (Tth). We show by UV melting experiments that 37 simultaneous disruption of these two interdomain contacts has a stabilizing effect on all 38 three P RNAs. This can be inferred from reduced RNA unfolding at lower temperatures 39 and a more concerted unfolding at higher temperatures. Thus, when the two domains 40 tightly interact via the tertiary contacts, one domain facilitates structural transitions in the 41 other. P RNA mutants with disrupted interdomain contacts showed severe kinetic defects 42 that were most pronounced upon simultaneous disruption of the L8-P4 and L18-P8 43 contacts. At 37°C, the mildest effects were observed for the thermostable Tth RNA. A 44 third interdomain contact, L9-P1, makes only a minor contribution to P RNA tertiary 45 folding. Furthermore, D. radiodurans RNase P RNA forms an additional pseudoknot 46 structure between the P9 and P12 of its S-domain. This interaction was found to be 47 particularly crucial for RNase P holoenzyme activity at near-physiological Mg2+ 48 concentrations (2 mM). We further analyzed an exceptionally stable folding trap of the 49 G,C-rich Tth P RNA. 50 Cold Spring Harbor Laboratory Press on August 26, 2021 Published by rnajournal.cshlp. org Downloaded from
Curva de aprendizagem de dacriocistorrinostomia endonasal endoscópica

Submitted for publication: July 20, 2020 Accepted for publication: October 22, 2020 Funding: This study received no specific financial support. Disclosure of potential conflicts of interest: None of the authors have any potential conflicts of interest to disclose. Corresponding author: Mustafa Vatansever. E-mail: vatansevermustafa@hotmail.com Approved by the following research ethics committee: University of Mersin (# 2020/255). ABSTRACT | Purpose: To compare the learning curves of the specialists in two different fields without previous endoscopic endonasal dacryocystorhinostomy experience as well as to reveal the related complications with surgical success rates. Methods: We retrospectively investigated 90 patients who received consecutive endoscopic endonasal dacryocystorhinostomy with mucosa preservation by an ophthalmologist (Group 1, n=45) and an otorhinolaryngologist (Group 2, n=45) between October 2017 and October 2019. Patients who were admitted with epiphora complaints and diagnosed with primary acquired nasolacrimal duct obstruction through lacrimal irrigation test and aged >18 years with at least 6 months of follow-up were included in the study. In all cases, additional pathologies such as septum deviation were evaluated by performing maxillofacial imaging. Patients’ medical records were evaluated in terms of surgery duration, complications, and functional achievements. Results: The mean surgical duration of the patients in Group-2 was 36.27 ± 11.61 min, while it was 43.62 ± 16.89 min in Group-1; the difference was statistically significant (p=0.018). Functional achievements in Group 1 was 84.4% (73.3% in the first set of 15 cases, 93.3% in the last set of 15 cases) in Group 2; this rate was 88.9% (80% in the first set of 15 cases, 93.3% in the last set of 15 cases), and the difference was not statistically significant (p=0.53). Septum intervention in addition to endoscopic surgery in both the groups (p=0.03, p=0.005, respectively) and intense bleeding during surgery (for both the groups, p<0.0001) significantly decreased the functional success. Conclusion: Endoscopic endonasal dacryocystorhinostomy, performed after the necessary training, can provide high success and low complication rates when even conducted by ophthalmologists who are unfamiliar with endoscopic surgery after an experience of 30 cases.
Anonymous and gene-linked microsatellite markers reveal no correlation between heterozygosity and song complexity in a wild population of song sparrows

Correlations between heterozygosity and fitness are commonly reported in the literature, but there is disagreement about the frequency of such correlations and the mechanisms which underlie them. Using a multi-year data set acquired from a wild population of song sparrows I investigated the relationship between heterozygosity and song complexity, an important sexually selected trait thought to be an indicator of genetic quality. Heterozygosity was determined at 17 putatively neutral microsatellites and 5 microsatellites derived from the expressed sequence tag of zebra finch brain proteins. Song and syllable repertoire size were taken as measures of song complexity. Neither genetic marker type was predictive of song complexity. A further test failed to support the hypothesis that heterozygosity-fitness correlations at marker loci are due to closely linked coding genes. These results indicate that heterozygosity is unlikely to play a major role in individual song complexity in this outbred population of song sparrows.
Helpful Automatic Development Email Summarization

Software development projects, especially if large-scale, require a remarkable coordination effort amongst the people involved. Coordination challenges are tackled by communication. In co-located development teams, unplanned informal face-to-face meetings are the favorite form of communication when developers need to coordinate or face program comprehension problems [1]. Personal meetings, besides disrupting de-velopers’ attention and retaining knowledge by a few developers [2], are inapplicable to distributed development projects. Developers, thus, replace face-to-face meetings with electronic communication means. Instant messaging, wikis, forums are viable options, but the decisive role is played by emails , indeed: “Mailing lists are the bread and butter of project communications” [3]. Emails are asynchronous, thus evade time zone barriers and do not disrupt the attention of developers; mailing lists broadcast discussions, announcements, and decisions to all participants, thus maintaining the awareness of developers; emails are not bound to speciﬁc abstraction levels (as opposed to commit messages, design documents, or code comments), thus they can be used to discuss issues ranging from low-level decisions ( e.g., implementation, bug ﬁxing) up to high-level considerations ( e.g., design rationales); mailing lists archive messages, thus offering a historical perspective.
Fog Assisted and IoT Based Real-Time Health Monitoring System Implementation

— With the proliferation of IoT devices in medical healthcare systems, IoT based health-monitoring systems and applications have brought about a ground-breaking breakthrough in modern healthcare facilities, medical data processing, and analysis. Meeting the challenge of co-operative and distributed IoT based healthcare systems; the problems like latency, network congestion, and data traffic in the systems can be overcome by fog computing, a decentralized cloud computing platform. In this study, we enhanced such an IoT-enabled real-time patient health monitoring system by exploiting the fog computing concept for extracting sensor data, visualizing them at reduced cost and power, storing at local storage, monitoring, and interacting remotely. Using three different sensor devices that extract health data, we developed a new type of fog computing interface using a combination of Raspberry Pi and Arduino. A dedicated local server called fog server was implemented as well for the storage and maintenance of the sensor extracted data and real-time notification. The implemented system portraits the efficacy of our proposed concept for monitoring patients’ vital parameters like temperature, pulse rate, and ECG simultaneously at low-cost, low power, simpler scheme, and real-time remote monitoring. The comparison between medical data, biosignals and sensor generated data, signals showed the feasibility of the system. The performance and compatibility of the system was evaluated in terms of cost, power consumption, and latency as well. The development and testing of the health monitoring system proves that it serves perfect in giving relief to medical caregivers while taking care of patients remotely irrespective of time and place.
Emotion Classiﬁcation using Recurrent Neural Network and Scalable Pattern Mining

—Emotions play an important role in everyday life. Analyzing these emotions or feelings from the social media platforms like Twitter, Facebook, Blogs, and Forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to beneﬁt the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to beneﬁt the end user. In this paper we propose automatic classiﬁcation of emotions in twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion, that help provide actionable suggestion on how to enhance user emotion from negative or neutral to more positive emotion ( Anticipation → Trust ) , and ( Sadness → Joy ) .
Leaf DiseaseDetectionUsing Machine LearningAlgorithm

—India is a nation of agriculture and over 70 per cent of our population relies on farming for income[1]. detection of disease present on plants is main concern in agriculture.therefor detection and classification of theses diseases plays an important role.Crop disease will be detected through various stages of image processing such as image acquisition, pre-processing of image, image feature extraction, featureclassification, disease prediction and fertilizer Recommendation[1]. In this paper plant leaf diseases are detected and classified using image processing and CNN.and when the result is obtain of classification ,depending on that result fertilizers are recommended.this will help farmers to maintain quality of their yeild.
The accountability influence on state capacity, allocation of public resources and socioeconomic development in Brazilian municipalities

The article aimed to verify the influence of accountability and state capacity in the allocation of public resources and socioeconomic development in Brazilian municipalities. By adopting state capacity as an intervener in this relationship, a system is formed in which accountability (in the general concept) and control (in a specific way) are understood to influence municipal capacity. The latter, in turn, prepares the budget and invests the resources in compliance with both tax legislation and social demand and, thus, generates positive results for society. For that, a Municipal Capacity Index was elaborated from a factor analysis and applied on the Structural Equation Modeling. The results thus prove that capacity is influenced by accountability and influences the allocation of public resources.
Gut microbial imbalance and neurodegenerative proteinopathies: from molecular mechanisms to prospects of clinical applications

The pathogenic basis behind some of the most prevalent neurodegenerative diseases in advanced societies, known as proteinopathies, deals with alterations in protein homeostasis. Despite the broad diversity of clinical symptoms, they share a remarkably common feature that is the serious neuronal loss in several disease-specific brain regions due to the presence of toxic aggregations of misfolded proteins. So far, research efforts have been insufficient to decipher the exact molecular mechanisms that trigger the conformational change from a functional healthy protein to its pathological version. This is a sine qua non condition to progress in developing new approaches and treatments for these diseases for which there is no cure. Currently, it is well accepted that perturbations in gut microbiota composition negatively impact a wide range of brain processes via the gut-brain axis which increases host susceptibility to neurodegenerative disorders. In this context, modulate the microbial ecosystem colonizing the gastrointestinal tract may be a promising therapeutic approach in the management of proteinopathies. This review aims to provide an updated view of the role that gut microbiota poses in the pathogenesis of Parkinson’s disease, Alzheimer’s disease and Huntington’s disease, the most common neurodegenerative proteinopathies, and of the possibility of translating this knowledge into effective and safe clinical microbiota-based interventions, especially those designed to afford neuroprotection.
Automating Online Hate Speech Detection: A Survey of Deep Learning Approaches

The problem of automating the detection of online hate speech is made more critical by the moral and legal consequences of its propagation in a social media network. This research surveys the current landscape of online hate speech detection, deep learning model architectures, embedding choices, and the inclusion of user behavior metrics, in order to improve performance on detecting the hateful class in a Twitter dataset. We find that Google’s pretrained BERT embeddings outperform the inclusion of user behavior metrics across many model choices and that a single-layer multiple input Convolutional Neural Network with many filters performs best. The contribution of this paper is a range of experiments that inform machine learning researchers on promising future paths and policymakers on the capabilities and limitations of automated systems on the task of detecting hate speech.
Serum 25-Hydroxyvitamin D Levels of Apparently Healthy Nigerian Children Aged 1-24 Months

Introduction: The role of vitamin D in promoting health and contributing to disease is an emerging area of research interest that has important health care and public health implications. As the health relevance of vitamin D outside bone health is now being explored globally, scanty data is available about the vitamin D status of healthy Nigerian children. Because Infants and young children have a relatively low supply of foods rich in vitamin D and may also have inadequate exposure to sunlight, they are at risk of vitamin D deficiency. We measured and described 25-hydroxyvitamin D levels of apparently healthy young Nigerian children. Subjects and methods: This was a cross sectional, descriptive study involving 112 apparently healthy children living in Zaria, North-West Nigeria which has adequate sunshine all through the year. Serum 25(OH)D concentrations were determined using a vitamin D ELISA kit. A level above 75 nmol/l was used to define vitamin D sufficiency in accordance with the Endocrine Society recommendations. Results: The mean 25-Hydroxyvitamin D level was 58.6 ± 30.5 nmol/l (range: 6.5-146 nmol/l). Only 31 (27.7%) of the children had 25-hydroxyvitamin D levels above 75 nmol/l, while 35 (31.3%) had insufficient (50-75 nmol/l) and 46 (41.0%) had deficient (< 50 nmol/l) serum vitamin D levels. There was no significant difference with regard to sex, age, nutritional and socioeconomical status between the groups. Conclusion: Apparently healthy young Northern Nigerian children had high prevalence of 25-hydroxyvitamin D insufficiency and deficiency. It is recommended that population studies be carried out across Nigeria to define the 25-hydroxyvitamin D levels of Nigerian children and establish predictors or determinants of vitamin D levels in them. This will assist in the development of governmental policies and strategies to prevent, detect, and treat vitamin D deficiency in Nigeria.
Classi(cid:28)cation Of Lung Diseases Using Deep Learning Models (cid:63)

In this paper we address the problem of medical data scarcity by considering the task of detection of pulmonary diseases from chest X-Ray images using small volume datasets with less than thousand samples. We implemented three deep convolutional neural networks (VGG16, ResNet-50, and InceptionV3) pre-trained on the ImageNet dataset and assesed them in lung disease classi(cid:28)cation tasks transfer learning approach. We created a pipeline that segmented chest X-Ray (CXR) images prior to classifying them and we compared the performance of our framework with the existing ones. We demonstrated that pre-trained models and simple classi(cid:28)ers such as shallow neural networks can compete with the complex systems. We also validated our framework on the publicly available Shenzhen and Montgomery lung datasets and compared its performance to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is a smaller number of trainable parameters. Furthermore, our InceptionV3 based model almost tied with the best performing solution on the Shen-zhen dataset despite being computationally less expensive.