Structure and dimension of global branches of solutions to multiparameter nonlinear equations

A general study of the dimension and connectivity properties of branches of solutions of equations depending on n-parameters are studied using the homotopy properties of 0-epi maps

Rett Syndrome and Other Neurodevelopmental Disorders Share Common Changes in Gut Microbial Community : a Descriptive Review

In this narrative review, we summarize recent pieces of evidence of the role of microbiota alterations in Rett syndrome (RTT). Neurological problems are prominent features of the syndrome, but the pathogenic mechanisms modulating its severity are still poorly understood. Gut microbiota was recently demonstrated to be altered both in animal models and humans with different neurodevelopmental disorders and/or epilepsy. By investigating gut microbiota in RTT cohorts, a less rich microbial community was identified which was associated with alterations of fecal microbial short-chain fatty acids. These changes were positively correlated with severe clinical outcomes. Indeed, microbial metabolites can play a crucial role both locally and systemically, having dynamic effects on host metabolism and gene expression in many organs. Similar alterations were found in patients with autism and down syndrome as well, suggesting a potential common pathway of gut microbiota involvement in neurodevelopmental disorders

Diverse Effects of Women’s Employment on Fertility: Insights From Italy and Poland

In this paper, we look into how country-specific factors shape the interrelationship between childbearing and women’s labor supply. To this end, we compare Italy and Poland, two low-fertility countries where the country-specific obstacles to work and family reconciliation are similarly strong but which differ in the history of women’s labor supply and the extent to which couples’ material aspirations are satisfied by men’s earnings. Our findings show that women’s employment clearly conflicts with childbearing in Italy, while in Poland women tend to combine the two activities, despite the similar difficulties they face. These results challenged the standard microeconomic explanations and point to the importance of other country-specific factors, apart from conditions for work and family reconciliation, in shaping women’s employment and fertility decisions, such as economic incentives or culturally rooted behavioral patterns. Overall, our study provides thus foundations for explaining the variation in the relationship between women’s employment and fertility in an enlarged Europe

Multiple-point statistical simulation for hydrogeological models: 3D training image development and conditioning strategies

Most studies about the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level, structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2D or quasi-3D training images. In the present study, we demonstrate a novel strategy for 3D MPS modelling characterized by: (i) realistic 3D training images, and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed sand/clay spatial trends. The training image is constructed as a small 3D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, the study underlines that it is important to consider both the geological environment, and the type and quality of input information in order to achieve optimal results from MPS modelling. In this study we present a possible workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modellin

A Low Complexity Rolling Bearing Diagnosis Technique Based on Machine Learning and Smart Preprocessing

In this work, we present a diagnosis system for rolling bearings that leverages simultaneous measurements of vibrations and machine rotation speed. Our approach combines the robustness of simple time domain methods for fault detection with the potential of machine learning techniques for fault location. This research is based on a neural network classifier, which exploits a simple and novel preprocessing algorithm specifically designed for minimizing the dependency of the classifier performance on the machine working conditions, on the bearing model and on the acquisition system set-up. The overall diagnosis system is based on light algorithms with reduced complexity and hardware resource demand and is designed to be deployed in embedded electronics. The fault diagnosis system was trained using emulated data, exploiting an ad-hoc test bench thus avoiding the problem of generating enough data, achieving an overall classifier accuracy larger than 98%. Its noteworthy ability to generalize was proven by using data emulating different working conditions and acquisition set-ups and noise levels, obtaining in all the cases accuracies greater than 97%, thereby proving in this way that the proposed system can be applied in a wide spectrum of different applications. Finally, real data from an on-line database containing vibration signals obtained in a completely different scenario are used to demonstrate the distinctive capability of the proposed system to generalize

Development of a Self-Sufficient LoRaWAN Sensor Node with Flexible and Glass Dye-Sensitized Solar Cell Modules Harvesting Energy from Diffuse Low-Intensity Solar Radiation

This paper aims to demonstrate the viability of energy harvesting for wide area wireless sensing systems based on dye-sensitized solar cells (DSSCs) under diffuse sunlight conditions, proving the feasibility of deploying autonomous sensor nodes even under unfavorable outdoor scenarios, such as during cloudy days, in the proximity of tall buildings, among the trees in a forest and during winter days in general. A flexible thin-film module and a glass thin-film module, both featuring an area smaller than an A4 sheet of paper, were initially characterized in diffuse solar light. Afterward, the protype sensor nodes were tested in a laboratory in two different working conditions, emulating outdoor sunlight in unfavorable lighting and weather to reconstruct a worst-case scenario. A Li-Po battery was employed as a power reserve for a long-range wide area network (LoRaWAN)-based sensor node that transmitted data every 8 h and every hour. To this end, an RFM95x LoRa module was used, while the node energy management was attained by exploiting a nano-power boost charger buck converter integrated circuit conceived for the nano-power harvesting from the light source and the managing of the battery charge and protection. A positive charge balance was demonstrated by monitoring the battery trend along two series of 6 and 9 days, thus allowing us to affirm that the system’s permanent energy self-sufficiency was guaranteed even in the worst-case lighting and weather scenario