Newcomb-Benford Law in Neuromuscular Transmission: Validation at Hyperkalemic Conditions

Recently, we demonstrated the validity of anomalous numbers law, known as Newcomb-Benford's law, at the mammalian neuromuscular transmission, considering different extracellular calcium concentrations arXiv:2002.01986. The present work continues to examine how alterations in extracellular solution modulate the first digit law in the context of the spontaneous release of acetylcholine from the neuromuscular junction. We investigated if the intervals of miniature potentials collected at the neuromuscular junction obey the law in a hyperkalemic environment. The analysis showed that the interval between the miniature potentials at high potassium concentrations follows Newcomb-Benford's law. Also, our data allowed us to uncover a conformity fluctuation as a function of the number of intervals of the miniature potentials. Finally, we discuss the biophysical implications of the present findings.Comment: 25 pages, 6 figure

Execution time as a key parameter in the waste collection problem

Proper waste management has been recognized as a tool for the green transition towards a more sustainable economy. For instance, most studies dealing with municipal solid wastes in the literature focus on environmental aspects, proposing new routes for recycling, composting and landfilling. However, there are other aspects to be improved in the systems that deal with municipal solid waste, especially in the transportation sector. Scholars have been exploring alternatives to improve the performance in waste collection tasks since the late 50s, for example, considering the waste collection problem as static. The transition from a static approach to a dynamic is necessary to increase the feasibility of the solution, requiring faster algorithms. Here we explore the improvement in the performance of the guided local search metaheuristic available in OR-Tools upon different execution times lower than 10 seconds to solve the capacitated waste collection problem. We show that increasing the execution time from 1 to 10 seconds can overcome savings of up to 1.5 km in the proposed system. Considering application in dynamic scenarios, the 9 s increase in execution time (from 1 to 10 s) would not hinder the algorithm’s feasibility. Additionally, the assessment of the relation between performance in different execution times with the dataset’s tightness revealed a correlation to be explored in more detail in future studies. The work done here is the first step towards a shift of paradigm from static scenarios in waste collection to dynamic route planning, with the execution time established according to the conclusions achieved in this study.This work has been supported by FCT—Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/05757/2020, UIDP/05757/2020, UIDB/00690/2020, UIDB/50020/2020, and LA/P/0007/2021. Adriano Silva was supported by FCT-MIT Portugal Ph.D. grant SFRH/BD/151346/2021.info:eu-repo/semantics/publishedVersio

On Separating Environmental and Speaker Adaptation

This paper presents a maximum likelihood (ML) approach, concerned to the background model estimation, in noisy acoustic non-stationary environments. The external noise source is characterised by a time constant convolutional and a time varying additive components. The HMM composition technique, provides a mechanism for integrating parametric models of acoustic background with the signal model, so that noise compensation is tightly coupled with the background model estimation. However, the existing continuous adaptation algorithms usually do not take advantage of this approach, being essentially based on the MLLR algorithm. Consequently, a model for environmental mismatch is not available and, even under constrained conditions a significant number of model parameters have to be updated. From a theoretical point of view only the noise model parameters need to be updated, being the clean speech ones unchanged by the environment. So, it can be advantageous to have a model for environmental mismatch. Additionally separating the additive and convolutional components means a separation between the environmental mismatch and speaker mismatch when the channel does not change for long periods. This approach was followed in the development of the algorithm proposed in this paper. One drawback sometimes attributed to the continuous adaptation approach is that recognition failures originate poor background estimates. This paper also proposes a MAP-like method to deal with this situation

Spectral normalization MFCC derived features for robust speech recognition

This paper presents a method for extracting MFCC parameters from a normalised power spectrum density. The underlined spectral normalisation method is based on the fact that the speech regions with less energy need more robustness, since in these regions the noise is more dominant, thus the speech is more corrupted. Less energy speech regions contain usually sounds of unvoiced nature where are included nearly half of the consonants, and are by nature the least reliable ones due to the effective noise presence even when the speech is acquired under controlled conditions. This spectral normalisation was tested under additive artificial white noise in an Isolated Speech Recogniser and showed very promising results [1]. It is well known that concerned to speech representation, MFCC parameters appear to be more effective than power spectrum based features. This paper shows how the cepstral speech representation can take advantage of the above-referred spectral normalisation and shows some results in the continuous speech recognition paradigm in clean and artificial noise conditions

Spectral multi-normalisation for robust speech recognition

This paper presents an improved version of a spectral normalisation based method for extraction of speech robust features in additive noise. The baseline normalisation method was developed by taking into consideration that, while the speech regions with less energy need more robustness, since in these regions the noise is more dominant, the “peaked” spectral regions which are the most reliable due to the higher speech energy must also be preserved as much as possible by the feature extraction process. The additive noise effect tends to flatten the “peaked” spectral zones while the spectral zones of less energy are usually raised. The algorithm proposed in this paper showed to alleviate the noise effect by emphasising the voiced nature of the speech signal by raising the spectral “peaks”, which are “flatten” by the noise effect. The clean speech database is assumed as lightly contaminated, the additive noise is estimated in a frame by frame basis and then used to restore both the “peaked” and the flat spectral zones of the speech spectrum

Blind source separation by independent component analysis applied to electroencephalographic signals

Independent Component Analysis (ICA) is a statistical based method, which goal is to find a linear transformation to apply to an observed multidimensional random vector such that its components become as statistically independent from each other as possible. Usually the Electroencephalographic (EEG) signal is hard to interpret and analyse since it is corrupted by some artifacts which originates the rejection of contaminated segments and perhaps in an unacceptable loss of data. The ICA filters trained on data collected during EEG sessions can identify statistically independent source channels which could then be further processed by using event-related potential (ERP), event-related spectral perturbation (ERSP) or other signal processing techniques. This paper describes, as a preliminary work, the application of ICA to EEG recordings of the human brain activity, showing its applicability

Influence of lactose intolerance and physical activity level on bone mineral density in young women

The aim of this study was to verify the effect of physical activity level on bone mineral density (BMD) in pre-menopausal women with lactose intolerance. Sixty women was engaged in this study (age: 31.9±6.9 years) and were initially separated into two groups: 30 women with lactose intolerance (LI) and 30 controls (C). The groups were further subdivided into less and more active using the median of weekly total energy expenditure, estimated by the International Physical Activity Questionnaire (IPAQ-long version). The LI diagnosis was confirmed by lactose intolerance test (oral lactose overload with monitoring of blood glucose and associated clinical manifestations). BMD was assessed by dual energy X-ray absorptiometry (DXA). As expected, physical activity score was higher in both groups for women classified as more active (p>0.05). The BMD at hip and pelvis was lower in LI than in C group (p<0.05). In addition, there was a tendency for a lower BMD in L2, L4, femoral neck and total hip for LI compared to C group (p<0.10). However, there was no main effect of physical activity level or interaction for the BMD at any other bone sites (p<0.10). The LI group had lower (p<0.05) absolute free-fat mass, independently of physical activity level. Therefore, the results of the present study suggest that LI reduces BMD in pre-menopausal women and this reduction is independent of physical activity level