Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Resolution Gabor Descriptor for Corrosion Detection in Pipeline Video Sequences

The work, in this paper, is dealing with texture analysis of internal pipeline video sequences in order to detect the corrosion indications. Our application concerns internal pipeline videos, that we have acquired using a pipeline endoscope named ”Pipe-Explorer”. The video processing consists of: recovering the input video into different frames; each frame is divided into different patches and each patch has to be classified as corroded region or safe one, using as image features a set of well-known and powerful texture descriptors such as FLBP, Haralick, HOG and Gabor descriptors. In this paper, a new texture descriptor based on the application of 1-D wavelet transform on the Gabor descriptor, which is called Wavelet Transform-Based Gabor descriptor (WTBG). Through the experiments, it has been shown that the new descriptor outperforms the other descriptors, in terms of classification rate of corrosion indications. The different processed frames are assembled to build an output video on which the corroded regions appear in red frames, as shown in Experiments.
A Hybrid Deep Learning Model Based on LSTM for Long-term PM2.5 Prediction

With the acceleration of industrialization and urbanization, problem of air pollution becomes an urgent problem to be solved. PM2.5 is a major pollutant in the atmosphere which is harmful to human health. Existing models like RNN and LSTM could make instant predictions while long-term prediction is difficult for them. In response to this problem, we propose a hybrid CNN-LSTM PM2.5 forecasting system (CLPFS), which combines the CNN structure to the LSTM model. Data in Beijing from January 1st, 2010 to December 31st, 2014 are used to train the proposed model. Experiments are conducted on both univariate and multivariate datasets. For multivariate datasets, multiple meteorological factors are considered. The result indicates that our model outperforms the LSTM model whether on univariate or multivariate datasets. Based on multivariate datasets, our model achieves a great result with the NRMSE metric is 3.66%, which means with addition of CNN structure, our model can extract more information of value and thus improving the accuracy of prediction.
Identity-Based Public Auditing for Cloud Storage of Internet-of-Vehicles Data

The Internet of Vehicles (IoV), with the help of cloud computing, can provide rich and powerful application services for vehicles and drivers by sharing and analysing various IoV data. However, how to ensure the integrity of IoV data with multiple sources and diversity outsourced in the cloud is still an open challenge. To address this concern, this paper first presents an identity-based public auditing scheme for cloud storage of IoV data, which can fully achieve the essential function and security requirements, such as classified auditing, multi-source auditing and privacy protection. Particularly, we design a new authenticated data structure, called data mapping table, to track the distribution of each type of IoV data to ensure fine and rapid audits. Moreover, our scheme can reduce the overheads for both the key management and the generation of block tags. We formally prove the security of the presented scheme and evaluate its performance by comprehensive comparisons with the state-of-the-art schemes designed for traditional scenarios. The theoretical analyses and experimental results demonstrate that our scheme can securely and efficiently realize public auditing for IoV data, and outperforms the previous ones in both the computation and communication overheads in most cases.
Machine learning-informed predictions of nanoparticle mobility and fate in the mucus barrier

Diffusion and transport of nanomaterials through mucus, is of critical importance to many basic and applied areas of research such as drug delivery and infectious disease. However, it is often challenging to interpret the dynamics of nanoparticles within the mucus gel due to its inherently heterogeneous microstructure and biochemistry. In this study, we measured the diffusion of densely PEGylated nanoparticles (NP) in human airway mucus ex vivo using multiple particle tracking and utilized machine learning to classify NP movement as either traditional Brownian motion (BM) or one of two different models of anomalous diffusion, fractional Brownian motion (FBM) and continuous time random walk (CTRW). Specifically, we employed a physics-based neural network model to predict the modes of diffusion experienced by individual NP in human airway mucus. We observed rapidly diffusing NP primarily exhibit BM whereas CTRW and FBM exhibited lower diffusion rates. Given the use of muco-inert nanoparticles, the observed transition from diffusive (BM) to sub-diffusive (CTRW/FBM) motion is likely a result of patient-to-patient variation in mucus network pore size. Using mathematic models that account for the mode of NP diffusion, we predicted the percentage of nanoparticles that would cross the mucus barrier over time in human airway mucus with varied total solids concentration. We also applied this approach to explore the transport modes and predicted fate of influenza A virus within human mucus. These results provide new tools to evaluate the extent of synthetic and viral nanoparticle penetration through mucus in the lung and other tissues.
The Importance of Big Data Visualisations for Auditors’ Decisions

Many achievements using big data are recorded in the vast majority of industrial sectors, accounting is no different, so big data is ubiquitous. The presence of big data within the practice of auditing is still at an early stage. A reliance upon technological tools, however, has resulted in the implementation of computer-assisted auditing techniques. This study, then, based on qualitative analyses, highlights how big data visualisations may assist the evaluation of auditors’ evidence so that data can be retrieved in ways that help produce professional judgments.
PROMETHEUS - Strategy

The European automotive industry has joined forces in the PROMETHEUS research and development project. The purpose of PROMETHEUS is to increase traffic safety, economy and efficiency and to reduce the environmental impacts of road traffic by applying modern information and communication technology. Working in close cooperation with the electronics and supplier industries, basic research and public authorities, the Prometheus team is now well on the way to designing the integrated European road traffic system for the next century.
Tyrosine phosphorylation tunes chemical and thermal sensitivity of TRPV2 ion channel

Transient receptor potential vanilloid 2 (TRPV2) is a multimodal ion channel widely regulating central and peripheral functions. Its important involvement in immune responses has been suggested such as in the macrophages’ phagocytosis process. However, the endogenous signaling cascades controlling the gating of TRPV2 remain to be understood. Here, we report that enhancing tyrosine phosphorylation remarkably alters the chemical and thermal sensitivities of TRPV2 endogenously expressed in rat bone marrow-derived macrophages. We identify that the protein tyrosine kinase JAK1 mediates TRPV2 phosphorylation at the molecular sites Tyr(335), Tyr(471), and Tyr(525). JAK1 phosphorylation is required for maintaining TRPV2 activity and the phagocytic ability of macrophages. We further show that TRPV2 phosphorylation is dynamically balanced by protein tyrosine phosphatase non-receptor type 1 (PTPN1). PTPN1 inhibition increases TRPV2 phosphorylation, further reducing the activation temperature threshold to ∼40 °C. Our data thus unveil an intrinsic mechanism where the phosphorylation/dephosphorylation dynamic balance sets the basal chemical and thermal sensitivity of TRPV2. Targeting this pathway will aid therapeutic interventions in physiopathological contexts.
Lung injury epigenetically primes mesenchyme for amplified activation upon re-injury

The lungs have a remarkable capacity to repair. However, repetitive injury can lead to progressive fibrosis and end-stage organ failure. Whether tissue-resident mesenchymal cell populations retain epigenetic memory of prior injuries that contribute to this pathological process is unknown. Here we used a genetic lineage labeling approach to mark the lung mesenchyme prior to injury, then performed multi-modal analyses on isolated lung mesenchyme during the initiation, progression and resolution of the fibrotic response. Our results demonstrate the remarkable epigenetic and transcriptional plasticity of the lung mesenchyme during fibrogenic activation and de-activation. Despite this plasticity, we also find that the lung mesenchyme retains specific epigenetic traits (memory) of prior activation, resulting in amplified induction of a fibrogenic program upon re-injury. We identify Runx1 as a critical driver of both fibrogenic activation and epigenetic memory. Comparison of fresh isolated and cultured lung mesenchyme demonstrates that Runx1 is spontaneously activated in standard culture conditions, previously masking these roles of Runx1. Genetic and pharmacological targeting of Runx1 dampens fibrogenic mesenchymal cell activation in cell and tissue models, confirming its functional importance. Finally, publicly available scRNAseq data reveal selective expression of Runx1 in the fibrogenic cell subpopulations that emerge in mouse and human fibrotic lung tissue. Collectively, our findings implicate Runx1 in both the initiation and memory of fibrogenic mesenchymal cell activation that together prime amplified mesenchymal cell responses upon repeated injury.
Infant brain regional cerebral blood flow dynamics supporting emergence of vital functional networks

Human infancy is characterized by most rapid cerebral blood flow (rCBF) increase across lifespan and emergence of a fundamental brain system default-mode network (DMN). However, how infant rCBF change spatiotemporally across the brain and how the rCBF dynamics support emergence of vital functional networks such as DMN remains unknown. Here, by acquiring cutting-edge multi-modal MRI including pseudo-continuous arterial-spin-labeled perfusion MRI and resting-state functional MRI of infants aged 0 to 24 months, we elucidated unprecedented 4D spatiotemporal infant rCBF framework and region-specific physiology-function coupling across infancy. We found faster rCBF increases in the DMN than other regions. We also found strongly coupled increases of rCBF and network strength specifically in the DMN, suggesting faster local blood flow increase to meet extra neuronal metabolic demands in the DMN maturation. These results offer insights into physiological mechanism of brain functional network emergence and have important implications in altered network maturation in brain disorders.
A Communication Complex Approach to Autism Awareness Training Within First Response Systems in Indiana

Providing Autism and communication training to facilitate better interactions in emergency situations between first responders and individuals with Autism is a critical need. Although trainings exist for first responders, “typical training already being provided on the job is not sufficient” (IBCCES). In addition, while resources—such as YouTube videos—are available, the process for viewing, discussing, and debriefing over the material along with the development of more effective conversational practices is an unexplored space. This case study describes the use of the Communication Complex (CC) perspective for training Volunteer Firefighters in Indiana, emphasizing the development of knowledge and skills necessary for effectively managing this often challenging context. It extends the body of CC research that relates to first responders and other health care providers. An elaboration of the theoretical framing is provided, followed by a brief introduction to Autism and a discussion of safety concerns in the community. Finally, a detailed analysis of the case study is presented. Reports from the participants indicate that Autism awareness training through a CC approach can enable more desirable interaction patterns in emergency situations involving first responders and those with Autism. The findings suggest that the CC approach is effective for enhancing understanding, which can positively impact future scenarios involving individuals with Autism and their families and increase feelings of safety.
Post-Infectious Autoimmunity in the Central (CNS) and Peripheral (PNS) Nervous Systems: An African Perspective

The direct impact and sequelae of infections in children and adults result in significant morbidity and mortality especially when they involve the central (CNS) or peripheral nervous system (PNS). The historical understanding of the pathophysiology has been mostly focused on the direct impact of the various pathogens through neural tissue invasion. However, with the better understanding of neuroimmunology, there is a rapidly growing realization of the contribution of the innate and adaptive host immune responses in the pathogenesis of many CNS and PNS diseases. The balance between the protective and pathologic sequelae of immunity is fragile and can easily be tipped towards harm for the host. The matter of immune privilege and surveillance of the CNS/PNS compartments and the role of the blood-brain barrier (BBB) and blood nerve barrier (BNB) makes this even more complex. Our understanding of the pathogenesis of many post-infectious manifestations of various microbial agents remains elusive, especially in the diverse African setting. Our exploration and better understanding of the neuroimmunology of some of the infectious diseases that we encounter in the continent will go a long way into helping us to improve their management and therefore lessen the burden. Africa is diverse and uniquely poised because of the mix of the classic, well described, autoimmune disease entities and the specifically “tropical” conditions. This review explores the current understanding of some of the para- and post-infectious autoimmune manifestations of CNS and PNS diseases in the African context. We highlight the clinical presentations, diagnosis and treatment of these neurological disorders and underscore the knowledge gaps and perspectives for future research using disease models of conditions that we see in the continent, some of which are not uniquely African and, where relevant, include discussion of the proposed mechanisms underlying pathogen-induced autoimmunity. This review covers the following conditions as models and highlight those in which a relationship with COVID-19 infection has been reported: a) Acute Necrotizing Encephalopathy; b) Measles-associated encephalopathies; c) Human Immunodeficiency Virus (HIV) neuroimmune disorders, and particularly the difficulties associated with classical post-infectious autoimmune disorders such as the Guillain-Barré syndrome in the context of HIV and other infections. Finally, we describe NMDA-R encephalitis, which can be post-HSV encephalitis, summarise other antibody-mediated CNS diseases and describe myasthenia gravis as the classic antibody-mediated disease but with special features in Africa.
Circadian Rhythms and Melatonin Metabolism in Patients With Disorders of Gut-Brain Interactions

Circadian rhythms are cyclic patterns of physiological, behavioural and molecular events that occur over a 24-h period. They are controlled by the suprachiasmatic nucleus (SCN), the brain’s master pacemaker which governs peripheral clocks and melatonin release. While circadian systems are endogenous, there are external factors that synchronise the SCN to the ambient environment including light/dark cycles, fasting/fed state, temperature and physical activity. Circadian rhythms also provide internal temporal organisation which ensures that any internal changes that take place are centrally coordinated. Melatonin synchronises peripheral clocks to the external time and circadian rhythms are regulated by gene expression to control physiological function. Synchronisation of the circadian system with the external environment is vital for the health and survival of an organism and as circadian rhythms play a pivotal role in regulating GI physiology, disruption may lead to gastrointestinal (GI) dysfunction. Disorders of gut-brain interactions (DGBIs), also known as functional gastrointestinal disorders (FGIDs), are a group of diseases where patients experience reoccurring gastrointestinal symptoms which cannot be explained by obvious structural abnormalities and include functional dyspepsia (FD) and irritable bowel syndrome (IBS). Food timing impacts on the production of melatonin and given the correlation between food intake and symptom onset reported by patients with DGBIs, chronodisruption may be a feature of these conditions. Recent advances in immunology implicate circadian rhythms in the regulation of immune responses, and DGBI patients report fatigue and disordered sleep, suggesting circadian disruption. Further, melatonin treatment has been demonstrated to improve symptom burden in IBS patients, however, the mechanisms underlying this efficacy are unclear. Given the influence of circadian rhythms on gastrointestinal physiology and the immune system, modulation of these rhythms may be a potential therapeutic option for reducing symptom burden in these patients.
Photosynthetic Carbon Assimilation and Electron Transport Rates in Two Symbiont-Bearing Planktonic Foraminifera

Photosymbiosis is one of the key features characterizing planktonic foraminifera; the number of symbiont cells within a single host has been reported to be well over thousands, meaning that photosynthesis by photosymbiosis may be a “hot spot” for primary production, especially in oligotrophic oceans. As microenvironmental conditions around foraminifera are greatly affected by rapid biological activities—such as photosynthesis and respiration—information on the photosynthetic activities of symbionts is essential to interpret the geochemical proxies recorded in foraminiferal tests (e.g., δ13C and δ18O). Recently, active chlorophyll fluorometry has been increasingly employed as a useful tool for immediate estimation of photosynthesis. However, carbon assimilation rates are the only direct indicator of the photosynthetic carbon flux. Therefore, before utilizing active fluorescence methods to understand carbon dynamics in foraminiferal symbiosis, it is necessary to confirm the relationship between the fluorescence-based photosynthetic rate [electron transport rate (ETR)] and carbon assimilation rate (P). Here, these two rates were compared for two species, Trilobatus sacculifer and Globigerinella siphonifera Type II, using 14C-tracer experiments and active fluorometric measurements by fast repetition rate fluorometry. The results showed a significant positive correlation between the P and ETR of the two species, indicating that carbon assimilation can be estimated by the fluorometric method. However, the regression slopes, which represent the apparent electron requirement for carbon assimilation (e–/C), were significantly different in the two species, and were estimated at 26.2 for T. sacculifer and 96.5 for G. siphonifera. These are strikingly high, considering the theoretically and empirically realistic e–/C values. We hypothesized that the high e–/C observed may be due in part to the use of unlabeled respiratory carbon (underestimation of P). A simple mass balance calculation suggests that a significant amount of carbon should derive from the host’s respired CO2, whose contribution is higher in G. siphonifera than in T. sacculifer. Within the context of using test geochemical parameters, such as δ13C, as paleoceanographic proxies, it is important to note that the potential magnitude of the photosynthetic effect varies among species. This attempt to couple ETR and P could comprehensively reveal an interesting perspective on the intimate interactions existing within photosymbiotic systems.
Apprentissage multi-vues pour la recommandation dans le domaine du pneumatique

Résumé. Nous utilisons constamment des systèmes de recommandation, sou-vent sans même nous en apercevoir. Ils construisent un proﬁl nous correspon-dant aﬁn de nous recommander le contenu le plus susceptible de nous intéresser. Les données représentant les utilisateurs, leurs interactions avec le système ou encore les produits peuvent provenir de sources différentes et être de natures di-verses. Notre objectif est d’appliquer les approches d’apprentissage multi-vues pour améliorer notre système de recommandation dans le domaine du pneuma-tique et lui permettre de gérer au mieux des données organisées en de multiples vues. Nous présentons une étude comparative entre plusieurs modèles multi-vues de l’état de l’art appliqués à nos données industrielles. Celle-ci nous a per-mis de démontrer la pertinence de l’utilisation de l’apprentissage multi-vues au sein de systèmes de recommandation.
Structure for multi-input multi-output dc–dc boost converter

In this study, a new structure for multi-input multi-output (MIMO) dc-dc boost converter is proposed. The number of inputs and outputs of the converter are arbitrary and independent from each other. The proposed topology has the advantages of both dc-dc boost and switched-capacitor converters. This converter is proper to use in applications like photovoltaic or fuel cell systems. The main advantages of the proposed structure are possibility of using energy supplies with different voltage-current characteristics, continuous input current, high voltage gain without high duty cycle, and possibility of performing at high switching frequencies. First, the different operating modes of the proposed converter are explained. Then, the effect of equivalent series resistance (ESR) of the inductor and voltage drop of diodes and switches on the voltage gain is investigated. Finally, the correctness operation of the proposed converter is reconfirmed by the simulation and experimental results.
How Does Social Insurance Affect the Social Interactions of Rural Residents in China: Study on the Impact of Rural Formal Social Security System on Informal Social Security Mechanism

With the increasing mobility of the rural population in China and the growing number of residents moving to the cities for work or study, rural society is forming a pluralistic, interest-centered, “open” social networks relations that follows the modern rule of law contract. Based on Chinese General Social Survey (CGSS) data, the results of the empirical study finds that social insurance can significantly enhance the social interactions of rural residents in China, that is, formal social security system in rural areas promotes informal social security mechanisms such as social interactions. The results of the grouping test show that rural residents in China’s different regions were affected by social insurance in different ways. Social insurance has a greater impact on the social interactions in the eastern region than in the middle and western regions. The propensity score matching method was used to reduce the sample selection bias, and the findings of the paper were found to be robust.