Analysis of the historical collapse of an abandoned underground chalk mine in 1961 in Clamart (Paris,France)

In chalk mines, the method of room and pillar mining has been widely used in France. Yet, many large collapses have occurred during or after their exploitation, with the collapse of Clamart (south of Paris, France) being well known as one of the most catastrophic. Today, 50 years after the collapse, the main causes of the event are still not well understood. This paper presents a back-analysis of the case study, using both an empirical approach and a numerical approach. Based on a set of hypotheses, an empirical approach and 3D numerical modeling have indicated a plausible scenario that may explain the collapse: The large collapse may have been caused by a lack of bearing capacity of the pillars and the sudden rupture of a hard limestone bed on which the load would have accumulated until overload occurred. The bed weakness between the two mine levels may also have had an influence on the mine’s general lack of stability. Moreover, under the given hypothesis, the study shows a very low influence of the water table due to flooding of the mine.

     

A Reference Benchmark Solution for Free Convection in A Square Cavity Filled with A Heterogeneous Porous Medium

The Fourier-Galerkin (FG) method is used to produce a highly accurate solution for free convection in a square cavity filled with heterogeneous porous medium. To this end, the governing equations are reformulated in terms of the temperature and the stream function. These unknowns are then expanded in infinite Fourier series truncated at given orders. The accuracy of the FG solution is investigated for different truncation orders and compared to the results of an advanced finite-element numerical model using fine-mesh discretization. The obtained results represent a set of high-quality data that can be used for benchmarking numerical models.     

The physiological effects of pallidal deep brain stimulation in dystonia.

Dystonia is an involuntary movement disorder characterized by muscle contractions causing abnormal postures and spasms, affecting part or all of the body. Dystonia may be primary where an abnormal gene, most commonly DYT1, may be identified, or secondary to structural brain lesions or heredodegenerative disorders. The underlying defect is believed to be abnormal basal ganglia modulation of cortical motor pathways, and various motor and sensory physiological abnormalities have been demonstrated. The failure of medical treatment in many patients with the more severe and generalized forms of dystonia has led to renewed interest in neurosurgical treatment approaches. In recent years, deep brain stimulation (DBS) of globus pallidus internus (GPi) has emerged as an effective treatment for dystonia, particularly patients with primary generalized dystonia, where remarkable improvement may occur. In contrast to Parkinson's disease, the beneficial effects of DBS in dystonia are not immediate but progressive over weeks to months. Physiological and imaging studies in dystonia patients with GPi DBS have demonstrated both short and long-term effects of GPi DBS on motor cortex and subcortical circuits including progressive normalization of spinal and brainstem excitability after GPi DBS which correlate with clinical improvement. These effects, in light of existing physiological data in dystonia, suggest that GPi DBS acts primarily by major modification of basal ganglia output to brainstem, thalamus, and cortex resulting in neural reorganization, which may explain the characteristic progressive improvement in dystonia after GPi DBS.     

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter (called tweets). A dataset of the exchange rates between the United States Dollar (USD) and the Pakistani Rupee (PKR) was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words. The dataset was collected in raw form, and was subjected to natural language processing by way of data preprocessing. Response variable labeling was then applied to the standardized dataset, where the response variables were divided into two classes: “1” indicated an increase in the exchange rate and “ −1” indicated a decrease in it. To better represent the dataset, we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space. Clusters that were obtained using a sampling approach were then used for data optimization. Five machine learning classifiers—the simple logistic classifier, the random forest, bagging, naïve Bayes, and the support vector machine—were applied to the optimized dataset. The results show that the simple logistic classifier yielded the highest accuracy of 82.14% for the USD and the PKR exchange rates forecasting.     

Adaptive frequency‐hopping schemes for CR‐based multi‐link satellite networks

In this paper, we study two dynamic frequency hopping (DFH)–based interference mitigation approaches for satellite communications. These techniques exploit the sensing capabilities of a cognitive radio to predict future interference on the upcoming frequency hops. We consider a topology where multiple low Earth orbit satellites transmit packets to a common geostationary equatorial orbit satellite. The FH sequence of each low Earth orbit–geostationary equatorial orbit link is adjusted according to the outcome of out‐of‐band proactive sensing scheme, performed by a cognitive radio module in the geostationary equatorial orbit satellite. On the basis of sensing results, new frequency assignments are made for the upcoming slots, taking into account the transmit powers, achievable rates, and overhead of modifying the FH sequences. In addition, we ensure that all satellite links are assigned channels such that their minimum signal‐to‐interference‐plus‐noise ratio requirements are met, if such an assignment is possible. We formulate two multi‐objective optimization problems: DFH‐Power and DFH‐Rate. Discrete‐time Markov chain analysis is used to predict future channel conditions, where the number of states are inferred using k‐means clustering, and the state transition probabilities are computed using maximum likelihood estimation. Finally, simulation results are presented to evaluate the effects of different system parameters on the performance of the proposed designs.     

Applications of aeromagnetic data to detect the Basement Tectonics of Eastern Yemen region

The present study aims to throw light on the tectonic implications concerned with the distribution of the sedimentary sequence belts and the related basement complex zones, as well as to differentiate between the causative sources (contacts, dykes and faults) of Eastern Yemen region. The total intensity aeromagnetic map of the study area was first corrected by the application of the Reduction To the magnetic pole (for low latitude areas). The visual inspection of the RTP magnetic map defines a rapid change in the subsurface geologic conditions in the form of lithologic characters and tectonic inferences. On the other hand, this map showed different anomalies of varying frequencies and amplitudes that revealed various causative sources, as well as varying compositions and depths. At the interpretation stage, various techniques and software tools are available for extracting the geologic information from the data concerned. The magnetic fields of shallow sources can be separated from those of deeper causatives, using two processes known as power spectrum transformation and matched band pass filtering. Three methods for locating magnetic sources (Magnitude of Horizontal Gradients (HGM), the analytical signals (AS) and the local wavenumbers (LW)) in three dimensions and identifying the properties of their sources indicated that, the area was affected by some intrusions at various depths in sill or dyke forms, almost oriented in the NW–SE, NE–SW, E–W and N–S trends. Tectonically, the area is highly affected by the tectonics related to the Arabian Sea, Gulf of Aden and Red Sea. It is affecting both the basement and sedimentary rocks, dividing the study area into several faulted blocks.     

Compact low-power time-based conversion with noise immunity similar to digital conversion

An analog signal representation based on the inter-pulse-interval (IPI) time is presented. Voltage-to-IPI and IPI-to-voltage conversion circuits based on the representation are described. The circuits have been fabricated using a 0.35 μm mixed-signal CMOS process. Simulation and test results agree with the theory. Voltage-to-IPI conversion needs significantly less area and power than ADC and is significantly more immune to noise and other problems than using analog voltage/current signals.     

New approach to gas pressure profile prediction for high temperature AA5083 sheet forming

A new theoretical approach to the prediction of gas pressure profiles that vary smoothly with time in high temperature forming of fine-grained AA5083 sheet is presented. The required pressure-flow stress relationship, which couples the gas pressure profile and the material constitutive model, was implemented in ABAQUS implicit. Forming of a rectangular pan in a die with variable entry radii was simulated with a single creep mechanism model that accounts for hardening/softening in AA5083. Predicted sheet thickness and thinning in a die entry radius region at the end of forming are examined in detail. Results are compared with those from two additional gas pressure schemes. One of these is taken directly from experiments and the other is based upon an algorithm that is internal to ABAQUS. The effect of friction on forming time is explored in the absence of a stability criterion for necking.     

Magnetic alignment of cellulose nanowhiskers in an all-cellulose composite

Unidirectional reinforced nanocomposite paper was fabricated from cellulose nanowhiskers and wood pulp under an externally applied magnetic field. A 1.2 Tesla magnetic field was applied in order to align the nanowhiskers in the pulp as it was being formed into a sheet of paper. The magnetic alignment was driven by the characteristic negative diamagnetic anisotropy of the cellulose nanowhiskers. ESEM micrographs demonstrated unidirectional alignment of the nanowhiskers in the all-cellulose composite paper. Comparing with control paper sheets made from wood pulp only, the storage modulus in the all-cellulose nanocomposites increased dramatically. The storage modulus along the direction perpendicular to the magnetic field was much stronger than that parallel to the magnetic field. This new nanocomposite, which contains preferentially oriented microstructures and has improved mechanical properties, demonstrates the possibility of expanding the functionality of paper products and constitutes a promising alternative to hydrocarbon based materials and fibers.