Miracle-dz/UniFAR-FTUs huggingface dataset:
Game Theoretic Reinforcement Learning Framework For Industrial Internet of Things

The fifth-generation (5G) wireless net-work provides high-rate, ultra-low latency, and high-reliability connections that can meet the industrial IoT requirements in factory automation especially for swarm robotics communication. In this paper, we address 5G service provisioning in an automated warehouse scenario where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the co-ordinated multipoint (CoMP), we formulate a joint CoMP clustering and 5G ultra-reliable low-latency communication (URLLC) beamforming design problem to control the robots that move around the automated warehouse for goods storage with the planed reference tracks. Traditional iterative optimization approaches are impractical in such dynamic wireless environments due to high computational time. We propose a game-theoretic CoMP clustering algorithm combined with the Proximal Policy Optimization method to obtain a stationary solution closed to that of the exhaustive search algorithm considered as the global optimal solution.
Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes

Abstract. In climate models, subgrid parameterizations of convection and clouds are one of the main causes of the biases in precipitation and atmospheric circulation simulations. In recent years, due to the rapid development of data science, machine learning (ML) parameterizations for convection and clouds have been demonstrated to have the potential to perform better than conventional parameterizations. Most previous studies were conducted on aqua-planet and idealized models, and the problems of simulation instability and climate drift still exist. Developing an ML parameterization scheme remains a challenging task in realistically configured models. In this paper, a set of residual deep neural networks (ResDNNs) with a strong nonlinear fitting ability is designed to emulate a super-parameterization (SP) with different outputs in a hybrid ML–physical general circulation model (GCM). It can sustain stable simulations for over 10 years under real-world geographical boundary conditions. We explore the relationship between the accuracy and stability by validating multiple deep neural network (DNN) and ResDNN sets in prognostic runs. In addition, there are significant differences in the prognostic results of the stable ResDNN sets. Therefore, trial and error is used to acquire the optimal ResDNN set for both high skill and long-term stability, which we name the neural network (NN) parameterization. In offline validation, the neural network parameterization can emulate the SP in mid- to high-latitude regions with a high accuracy. However, its prediction skill over tropical ocean areas still needs improvement. In the multi-year prognostic test, the hybrid ML–physical GCM simulates the tropical precipitation well over land and significantly improves the frequency of the precipitation extremes, which are vastly underestimated in the Community Atmospheric Model version 5 (CAM5), with a horizontal resolution of 1.9∘ × 2.5∘. Furthermore, the hybrid ML–physical GCM simulates the robust signal of the Madden–Julian oscillation with a more reasonable propagation speed than CAM5. However, there are still substantial biases with the hybrid ML–physical GCM in the mean states, including the temperature field in the tropopause and at high latitudes and the precipitation over tropical oceanic regions, which are larger than those in CAM5. This study is a pioneer in achieving multi-year stable climate simulations using a hybrid ML–physical GCM under actual land–ocean boundary conditions that become sustained over 30 times faster than the target SP. It demonstrates the emerging potential of using ML parameterizations in climate simulations.
Imparare a rendere visibile l’invisibile in un laboratorio di emodinamica

This essay documents the practical activity consisting in seeing and learning to see the invisibility of the heart ramifications within an hemodynamic laboratory exploiting imaging technologies. The ethnographic observation shows that the visibility of the images is socially constructed and negotiated among laboratory members and involves the use of heterogeneous artefacts and multimodal semiotic resources. Their professional vision is social, dynamic, situated and implies the mastery of a practical, tacit and body knowledge. The ethnographer herself, like a novice, learns step by step to see what was for her initially invisible and confused, by succeeding to notice the pertinent elements – progressively standing out on the screen’s background– for the purpose of activities, while some practitioners perform the role of instructors guiding her through gesture, words and gazes, in the vision of these elements.
Breakdown of synthetic air under nanosecond pulsed voltages in quasi-uniform electric fields

The electrical breakdown of synthetic air under ultrashort high-voltage pulses (50 ns duration), as well as slowly increasing ("DC") voltages, is experimentally studied in a well-defined quasi-uniform electrode configuration (sphere–plane). The electrode spacing is varied from 0.1 to 1 mm, and the gas pressure is varied from 1.5 to 8 bar. The study’s goal is to provide experimental data to develop breakdown models that predict the breakdown probability of small gas gaps under arbitrary voltage excitations. In particular, this analysis is intended for applications where statistical and formative time lags need to be considered, such as nanosecond pulsed ignition or partial discharges in inverter-fed motors. The influence of the electrode material and the presence of UV illumination are investigated for both DC and pulsed voltages. The results highlight the important role of seed electron provision for breakdown under short transient voltages. Evidence for a field-assisted emission of seed electrons with a pressure-dependent onset field is found from time lag measurements. An empirical expression is derived based on the Fowler–Nordheim formula to quantify the seed electron generation rate. The expected dependence of the breakdown threshold on the cathode material (work function) was confirmed for breakdown under slowly increasing voltages (Townsend mechanism). Interestingly, a dependence of the breakdown voltage was also found for nanosecond pulsed voltages (dominated by the streamer mechanism). This suggests that the field emission of electrons from the cathode is the dominant source of seed electrons in large cathode electric fields.
A Deep Learning Approach to Mapping Irrigation Using Landsat: IrrMapper U-Net

Accurate maps of irrigation are essential for understanding and managing water resources. We present a new method of mapping irrigation based on an ensemble of convolutional neural networks that use reflectance information from Landsat imagery to classify irrigated pixels. The methodology does not rely on extensive feature engineering and does not condition the classification with land-use information from existing geospatial datasets. The ensemble does not need exhaustive hyperparameter tuning, and the analysis pipeline is lightweight enough to be implemented on a personal computer. Furthermore, the proposed methodology provides an estimate of the uncertainty associated with the classification. We evaluated our methodology and the resulting irrigation maps using a highly accurate novel spatially explicit ground-truth dataset, using county-scale United States Department of Agriculture (USDA) surveys of irrigation extent, and using cadastral surveys. We demonstrate the accuracy of the method by mapping irrigation over the state of Montana from 2000 to 2019. We found that our method outperforms other methods that use satellite remote-sensing information in terms of overall accuracy (OA) and precision. We found that our method agrees better statewide with the USDA National Agricultural Statistics Survey estimates of irrigated areas compared to other methods and has far fewer errors of commission in rainfed agriculture areas. This methodology has the potential to be applied across the entire United States and for the complete Landsat record.
Monoclonal Antibodies and Invasive Aspergillosis: Diagnostic and Therapeutic Perspectives

Invasive aspergillosis (IA) is a life-threatening fungal disease that causes high morbidity and mortality in immunosuppressed patients. Early and accurate diagnosis and treatment of IA remain challenging. Given the broad range of non-specific clinical symptoms and the shortcomings of current diagnostic techniques, most patients are either diagnosed as “possible” or “probable” cases but not “proven”. Moreover, because of the lack of sensitive and specific tests, many high-risk patients receive an empirical therapy or a prolonged treatment of high-priced antifungal agents, leading to unnecessary adverse effects and a high risk of drug resistance. More precise diagnostic techniques alongside a targeted antifungal treatment are fundamental requirements for reducing the morbidity and mortality of IA. Monoclonal antibodies (mAbs) with high specificity in targeting the corresponding antigen(s) may have the potential to improve diagnostic tests and form the basis for novel IA treatments. This review summarizes the up-to-date application of mAb-based approaches in assisting IA diagnosis and therapy.
Drug resistant epilepsy of the insular lobe: A review and update article

Background: Epilepsy is a chronic disease that affects millions of people around the world generating great expenses and psychosocial problems burdening the public health in different ways. A considerable number of patients are refractory to the drug treatment requiring a more detailed and specialized investigation to establish the most appropriate therapeutic option. Insular epilepsy is a rare form of focal epilepsy commonly drug resistant and has much of its investigation and treatment involved with the surgical management at some point. The insula or the insular lobe is a portion of the cerebral cortex located in the depth of the lateral sulcus of the brain; its triangular in shape and connects with the other adjacent lobes. The insular lobe is a very interesting and complex portion of the brain related with different functions. Insula in Latin means Island and was initially described in the 18th century but its relation with epilepsy was first reported in the 1940–1950s. Insular lobe epilepsy is generally difficult to identify and confirm due to its depth and interconnections. Initial non-invasive studies generally demonstrate frustrating or incoherent information about the origin of the ictal event. Technological evolution made this pathology to be progressively better recognized and understood enabling professionals to perform the correct diagnosis and choose the ideal treatment for the affected population. Methods: A literature review was performed using MEDLINE/PubMed, Scopus, and Web of Science databases. The terms epilepsy/epileptic seizure of the insula and surgical treatment was used in various combinations. We included studies that were published in English, French, or Portuguese; performed in humans with insular epilepsy who underwent some surgical treatment (microsurgery, laser ablation, or radiofrequency thermocoagulation). Results: Initial search results in 1267 articles. After removing the duplicates 710 remaining articles were analyzed for titles and abstracts applying the inclusion and exclusion criteria. 70 studies met all inclusion criteria and were selected. Conclusion: At present, the main interests and efforts are in the attempt to achieve and standardize the adequate management of the patient with refractory epilepsy of the insular lobe and for that purpose several forms of investigation and treatment were developed. In this paper, we will discuss the characteristics and information regarding the pathology and gather data to identify and choose the best therapeutic option for each case.
Global transcriptional response of oral squamous cell carcinoma cell lines to health-associated oral bacteria - an in vitro study

ABSTRACT Background We have recently demonstrated that health-associated oral bacteria Streptococcus mitis, Neisseria flavescens, and Haemophilus parainfluenzae induce cytotoxicity in oral squamous cell carcinoma (OSCC) cell lines and downregulate CD36, a cancer-assocaited gene. Aim To explore the effect of these three species on global transcriptome of OSCC cell lines. Methods Gene expression of cell lines CAL27, SCC4 and SCC25 cocultured with the test species was assessed with Clariom-S Human microarray. Porphyromonas gingivalis was included as a pathogenic control. Data were analyzed using Ingenuity Pathway Analysis. Results The results differed by species and cell line. Overall, the transcriptional changes by S. mitis were predominantly anti-cancer including inhibition of HOTAIR regulatory pathway, JAK/Stat signaling, cyclins/cyclin-dependent kinases, and endothelin1 signaling. H. parainfluenzae and N. flavescens resulted in a mix of pro- and anti-cancer responses including activation of acute phase response, pro-inflammatory interleukins signaling, TREM-1 signaling, and tumor microenvironment pathway; but downregulation of cell cycle by inhibition of cyclins and cyclin-dependent kinases. P. gingivalis had a predominantly pro-cancer effect limited to SCC4, including upregulation of inflammatory pathways, phospholipases and PI3K signaling. Conclusion These findings provide a new insight into the role of commensal oral bacteria in OSCC. Animal studies are required to further explore them.
Estimation of Stress Concentration Factor of Plate with Hole using Piezoelectric Actuator and Finite Element Method

In this paper, the effect of piezoelectric actuators placed above a circular hole of a rectangular plate subjected to uniform uniaxial tension is studied. The core idea is to investigate the stress (compression/tension) produced by the piezoelectric actuators on the stress distribution around the hole and along the width of the host plate. For this purpose, Finite Element Analysis (FEA) was carried out through parametric study in ANSYS software. The results demonstrated that the positive electric field would decrease and change the state of the stress distribution along the width of the host plate in contrast to the negative applied electric filed which increases the stress distribution smoothly without affecting its behaviour. The results also indicated that the reduction of the stress concentration factor increases with the decrease of the ratio (D/W) for the same applied positive electric field.
Evolution of Pluto's Impact‐Deformed Ice Shell Below Sputnik Planitia Basin

Sputnik Planitia basin, the dominant surface feature of the dwarf planet Pluto, is located very close to the far point of Pluto‐Charon tidal axis. This position is currently believed to be a result of whole body reorientation driven by the combination of (a) the uplift of a subsurface ocean in response to a basin‐forming impact and (b) the nitrogen layer accumulated inside the basin. Since an ice shell made of pure water ice cannot maintain the uplift on timescales of billions of years, the presence of an insulating and highly viscous layer of methane clathrates at the base of the shell has recently been proposed. In this study, we solve the thermo‐mechanical evolution of the ice shell in a 2D spherical axisymmetric geometry and evaluate the gravity anomaly associated with the evolving ice shell shape. Taking into account the effect of impact heating and stress‐dependent rheology of both ice and clathrates, we show that a thick shell (≥200 km) loses the impact heat slowly which leads to fast uplift relaxation of the order of hundreds of million years. On the contrary, a thin shell (∼100 km) cools down quickly (∼10 Myr), becoming rigid and more likely to preserve the ocean/shell interface uplift till the present. These results suggest that a thick ocean may be present beneath Pluto's ice shell.
Effect of Agricultural Cooperative Membership on Willingness to Pay for Non-fatal Risk Reduction from COVID-19

The recurring nature of the COVID-19 pandemic and the recent discovery of the Omicron variant in Nigeria, coupled with concerns about the specific roles and contributions of social institutions in redressing the spread of the virus, prompted this study. Thus, the study determined the willingness to pay for health risk reduction from Corona Virus (COVID-19); estimated the value of statistical case (VSC) and ascertained the effects of cooperative membership on the willingness to pay (WTP) for COVID-19 morbidity health risk reduction. Multi-stage sampling technique was deployed in selecting 149 cooperative and 130 non-cooperative members for the study, in Edu and Patigi Local Government Areas of Kwara State, Nigeria. To reach its conclusion, descriptive statistics, Levene’s test, t-test, chi-square, Probit analysis, propensity score matching, WTP and the value of statistical case were employed for data analysis. The study revealed that the typical cooperative member is willing to pay N1,638 per farming season to decrease his or her chance of falling sick from COVID-19 or averting non-fatal cases of COVID-19 by 1 in 10,000. In addition, the propensity score matching (PSM) estimation indicated that agricultural cooperative societies had effect on members, with the average member willing to pay N561.32 per season higher than the non-members. Arising from these results, it becomes imperative to consider integrating cooperative societies as complementary instrument for the prevention and control of COVID-19 pandemic. The estimated VSC further provides pecuniary basis and policy guidance for efficient allocation of resources for the mitigation and control of COVID-19.
Dropout Rate Prediction of Massive Open Online Courses Based on Convolutional Neural Networks and Long Short-Term Memory Network

Massive open online courses (MOOC) is characterized by large scale, openness, autonomy, and personalization, attracting increasingly students to participate in learning and gaining recognition from more and more people. This paper proposes a network model based on convolutional neural networks and long short-term memory network (CNN-LSTM) for MOOC dropout prediction task. The model selects 43-dimensional behavioral features as input from students’ learning activity logs and adopts the CNN model to automatically extract continuous features over a period of time from students’ learning activity logs. At the same time, considering the time sequence of students’ learning behavior characteristics, a MOOC dropout prediction model was established by using long short-term memory network to obtain students’ learning status at different time steps. The algorithm proposed in this chapter was trained and evaluated on the public dataset provided by the KDD Cup 2015 competition. Compared with the dropout prediction methods based on LSTM and CNN-RNN, the model improved the AUC by 2.7% and 1.4%, respectively. The result in this paper is a good predictor of dropout rates and is expected to provide teaching aid to teachers.
Deep Reinforcement Learning for Joint User Association and Resource Allocation in Factory Automation

We consider the problem of joint user association, channel assignment, and power allocation for mobile robot application in factory automation system that require ultrareliable and low latency communications (URLLC). The aim is to deliver control commands from the controller to mobile robots with stringent requirements of latency and reliability. To achieve URLLC, we develop a two-phase communication scheme. The robots work close to each other in a factory environment and can form clusters for reliable device-to-device (D2D) communications. Within the latency requirements, the combined payload of a cluster is transmitted to the leader in Phase I. In Phase II, the leader broadcasts the payload to its members. Under this strategy, we use multi-agent reinforcement learning (MARL) for resource allocation. The cluster leaders in Phase I act as the agents and interact with the environment to optimally select the Access Point (AP) for connection along with the sub-band and power level. The objective is to maximize the successful payload delivery probability to all the robots. Illustrative simulation results indicate that the proposed scheme can offer average successful payload delivery probability close to that of centralized exhaustive search algorithm.
Lower Network Degrees Promote Cooperation in the Prisoner’s Dilemma With Environmental Feedback

Cooperation is a fundamental aspect in nature, as it determines many levels of biological organization. Examples include single cells, but also social insects, such as ants and honeybees, and groups of animals, such as vampire bats and bird flocks. In unstructured populations, where individuals interact with each other with equal probability, the dynamics have been thoroughly investigated and results indicate that the predominant strategy to be favored by natural selection is defection. The focus of this research is to study these evolutionary dynamics in structured population, where the structure is captured by a regular graph. A recent line of research investigated the impact of the population dynamics onto an environmental resource and the mutual effects that the changes in the quantity of this resource have on the game dynamics. In this framework the impact takes the form of game-environment feedback on the population dynamics. The contributions of this paper are as in the following. Firstly, we study the impact of a regular network in the prisoner’s dilemma (PD) game and provide a threshold on the degree of the network below which cooperation is favored. Secondly, we derive the corresponding structured model with environmental feedback. Lastly, we carry out the stability analysis of this system and discuss the impact of the network on the environmental resource.