Urban heat exposure is intensifying due to climate change and urbanisation, with disproportionate impacts on vulnerable populations. Unfortunately, many urban areas, particularly informal settlements, lack sufficient data for detailed analysis to understand these impacts. Traditional air temperature measurement methods—such as meteorological stations—are sparsely distributed in African cities, typically located on city outskirts (e.g., airports), and fail to capture localized temperature variations. This study explores the use of low-cost sensors and citizen science initiatives to measure air temperature with higher spatial resolution in informal and surrounding formal settlements. A two-stage process is employed to evaluate data quality: first, statistically assessing biases in low-cost sensor (LCS) measurements, and second, employing Monte Carlo simulations to quantify uncertainties. The resulting data reveals significant temperature differences between informal settlements and surrounding formal areas, with informal settlements consistently exhibiting higher temperatures. This approach not only highlights the value of low-cost sensors and citizen science in generating high-resolution temperature data but also provides insights into thermal inequalities between different urban environments.

The existing literature on populism has seen numerous attempts to empirically quantify this somewhat ambiguous concept. Despite notable advances, continuous measures of populism with a clear theoretical background and a considerable coverage are still hard to come by. This paper proposes a novel approach to measuring party populism by combining several different expert-surveys via supervised machine learning techniques. Employing the random forest regression algorithm, we greatly expand the geographical and temporal coverage of two well-known populism indicators, which are based on the discursive and the ideational approach, respectively. The resulting multidimensional measures capture party-level populism on a continous 0–10 scale, covering 1920 parties in 169 countries from 1970 to 2019. Our measures accurately replicate both definitions of populism, although the indicators may be more suitable for predicting populist outcomes in Western countries, as compared to non-Western ones.

Deprived urban areas, commonly referred to as ‘slums,’ are the consequence of unprecedented urbanisation. Previous studies have highlighted the potential of Artificial Intelligence (AI) and Earth Observation (EO) in capturing physical aspects of urban deprivation. However, little research has explored AI’s ability to predict how locals perceive deprivation. This research aims to develop a method to predict citizens’ perception of deprivation using satellite imagery, citizen science, and AI. A deprivation perception score was computed from slum-citizens’ votes. Then, AI was used to model this score, and results indicate that it can effectively predict perception, with deep learning outperforming conventional machine learning. By leveraging AI and EO, policymakers can comprehend the underlying patterns of urban deprivation, enabling targeted interventions based on citizens’ needs. As over a quarter of the global urban population resides in slums, this tool can help prioritise citizens’ requirements, providing evidence for implementing urban upgrading policies aligned with SDG-11.

As a result of contemporary culture’s focus on continuous innovation and “change before you have to,” innovation has been identified with economic gains rather than with creating added value for society. At the same time, given current trends related to the automation of business models, workers seem all but destined to be replaced by machines in the labor market. In this context, we attempt to explore whether robots and Artificial Intelligence (AI) will be able to innovate, and the extent to which said activity is exclusively inherent to human nature. Following the need for a more anthropological view of innovation, we make use of MacIntyrean categories to present innovation as a domain-relative practice with creativity and practical wisdom as its corresponding virtues. We explain why innovation can only be understood within a tradition as it implies participating in inquiry about the principle and end of practical life. We conclude that machines and “intelligent” devices do not have the capacity to innovate and they never will. They may replicate the human capacity for creativity, but they squarely lack the necessary conditions to be a locus of virtue or engage with a tradition.

Many cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences in housing conditions and urban spaces, and these differences are linked to urban deprivation. However, the appropriate geospatial information for unravelling urban deprivation is typically not available for DUAs in LMICs, constituting an urgent knowledge gap. The objective of this study is to apply deep learning techniques and morphological analysis to identify degrees of deprivation in DUAs. To this end, we first generate a reference dataset of building footprints using a participatory community-based crowd-sourcing approach. Secondly, we adapt a deep learning model based on the U-Net architecture for the semantic segmentation of satellite imagery (WorldView 3) to generate building footprints. Lastly, we compute multi-level morphological features from building footprints for identifying the deprivation variation within DUAs. Our results show that deep learning techniques perform satisfactorily for predicting building footprints in DUAs, yielding an accuracy of F1 score = 0.84 and Jaccard Index = 0.73. The resulting building footprints (predicted buildings) are useful for the computation of morphology metrics at the grid cell level, as, in high-density areas, buildings cannot be detected individually but in clumps. Morphological features capture physical differences of deprivation within DUAs. Four indicators are used to define the morphology in DUAs, i.e., two related to building form (building size and inner irregularity) and two covering the form of open spaces (proximity and directionality). The degree of deprivation can be evaluated from the analysis of morphological features extracted from the predicted buildings, resulting in three categories: high, medium, and low deprivation. The outcome of this study contributes to the advancement of methods for producing up-to-date and disaggregated morphological spatial data on urban DUAs (often referred to as 'slums') which are essential for understanding the physical dimensions of deprivation, and hence planning targeted interventions accordingly.

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In an active motor unit (MU), the time intervals between the firings of its muscle fibers vary across successive MU activations. This variability is called jitter and is increased in pathological processes that affect the neuromuscular junctions or terminal axonal segments of MUs. Traditionally, jitter has been measured using single fiber electrodes (SFEs) and a difficult and subjective manual technique. SFEs are expensive and reused, implying a potential risk of patient infection; so, they are being gradually substituted by safer, disposable, concentric needle electrodes (CNEs). As CNEs are larger, voltage contributions from individual fibers of a MU are more difficult to detect, making jitter measurement more difficult. This paper presents an automatic method to estimate jitter from trains of motor unit potentials (MUPs), for both SFE and CNE records. For a MUP train, segments of MUPs generated by single muscle fibers (SF MUP segments) are found and jitter is measured between pairs of these segments. Segments whose estimated jitter values are not reliable, according to several SF MUP segment characteristics, are excluded. The method has been tested in several simulation studies that use mathematical models of muscle fiber potentials. The results are very satisfactory in terms of jitter estimation error (less than 10% in most of the cases studied) and mean number of valid jitter estimates obtained per simulated train (greater than 1.0 in many of the cases and less than 0.5 only in the most complicated). A preliminary study with real signals was also performed, using 19 MUP trains from 3 neuropathic patients. Jitter measurements obtained by the automatic method were compared with those extracted from a commercial system (Keypoint) and the edition and supervision of an expert electromyographer. From these measurements 63% were taken from equivalent interval pair sites within the time span of the MUP trains and, as such, were considered as compatible measurements. Differences in jitter of these compatible measurements were very low (mean value of 1.3 μs, mean of absolute differences of 2.97 μs, 25% and 75% percentile intervals of −0.85 and 3.82 μs, respectively). Although new tests with larger number of real recordings are still required, the method seems promising for clinical practice.

Abstract Johansen’s Cointegration Test (JCT) performs remarkably well in finding stable bivariate cointegration relationships. Nonetheless, the JCT is not necessarily designed to detect such relationships in presence of non-linear patterns such as structural breaks or cycles that fall in the low frequency portion of the spectrum. Seasonal adjustment procedures might not detect such non-linear patterns, and thus, we expose the difficulty in identifying cointegrating relations under the traditional use of JCT. Within several Monte Carlo experiments, we show that wavelets can empower more the JCT framework than the traditional seasonal adjustment methodologies, allowing for identification of hidden cointegrating relationships. Moreover, we confirm these results using seasonally adjusted time series as US consumption and income, gross national product (GNP) and money supply M1 and GNP and M2.

We present a new, automatic, correlation-based method for measuring the duration of motor unit action potentials (MUAPs). The method seeks to replicate the way an expert elctromyographer uses his or her eyes, calculating the start and end of the MUAP waveform on the basis of the degree of similarity of non-excluded discharges. We analysed 68 potentials from normal deltoid muscles during slight contraction. For each MUAP, two experienced electromyographers manually determined start and end marker positions, which were used as gold standard duration positions (GSP) in our subsequent tests. The novel method was compared with Nandedkar’s method and a wavelet transform-based method. To compare the three methods, the differences between the automatic marker positions and GSPs were statistically evaluated using one-factor ANOVA, the estimated mean square error, and a Chi-square test on the numbers of automatic marker placements with gross errors. All these parameters showed smaller values for the novel method and in most of the cases were statistically significant. In addition, the parameters of the new method were subjected to a sensitivity study, showing its good performance within a range of clinically useful parameter values. The new automatic method determined start and end markers in a more accurate and reliable manner than both of the acknowledged state-of-the art methods used in our comparison study. Graphical abstract The description of a new automatic duration measurement algorithm based on the similarity among discharges of the same MUAP. This method gave better results than the Nandedkar method and a highly regarded wavelet-based method. The new correlation-based method also had the lowest rate of gross aberrant errors in automatic placements

Objective: To evaluate the performance of a recently proposed motor unit action potential (MUAP) averaging method based on a sliding window, and compare it with relevant published methods in normal and pathological muscles. Methods: Three versions of the method (with different window lengths) were compared to three relevant published methods in terms of signal analysis-based merit figures and MUAP waveform parameters used in the clinical practice. 218 MUAP trains recorded from normal, myopathic, subacute neurogenic and chronic neurogenic muscles were analysed. Percentage scores of the cases in which the methods obtained the best performance or a performance not significantly worse than the best were computed. Results: For signal processing figures of merit, the three versions of the new method performed better (with scores of 100, 86.6 and 66.7%) than the other three methods (66.7, 25 and 0%, respectively). In terms of MUAP waveform parameters, the new method also performed better (100, 95.8 and 91.7%) than the other methods (83.3, 37.5 and 25%). Conclusions: For the types of normal and pathological muscle studied, the sliding window approach extracted more accurate and reliable MUAP curves than other existing methods. Significance: The new method can be of service in quantitative EMG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.

The present paper describes a global procedure for estimating all the synthesis parameters that generate a single fibre action potential (SFAP) in the Dimitrov-Dimitrova (D-D) convolutional model. We call this inverse problem Identification Procedure, and it is presented in two parts, this paper being the second. The procedure incorporates the candidate pair (CP) method developed in Part I, which provides the values of radial distance r and fibre diameter d of the simulated SFAP that best matches a potential under study. The CP-method required prior knowledge of all the excitation parameters. However, since the Identification Procedure makes no assumption about the excitation, multiple combinations of the synthesis parameters result in very similar SFAPs whose shape is close the signal under study. Analysis of the possible combinations reveals that r and d can be modelled as two jointly Gaussian random variables. The interest of the Identification Procedure is that, for a certain SFAP, it provides estimates of r and d, along with estimates of different parameters that determine the IAP waveform. Moreover, the procedure is able to determine the degree of error that accompanies the estimation of r and d.

This paper contributes to the literature on crude oil price behavior and examines how this affects mergers and acquisitions (M&A) in the petroleum industry in the US. The paper analyzes the relationship of these two series by studying its dynamic in the time-frequency domain. The novelty of this study's approach lies in the application of wavelet tools for its resolution. Monthly data are used in this study, covering the period January 1980-June 2012. It was observed that there was a shift to higher frequencies of the wavelet coherency during the mid-1990s and the late 2000s. The results also indicate that during the mid-1990s and the late-2000s, an increase in M&A took place that was led by the increase in West Texas Intermediate crude oil prices.

This study examines the relationship between oil prices and economic activity in the G-7 economies during the period 1960M1-2014M07 using a wavelet approach. The results show significant differences in the relationship between these two variables depending on the frequencies. Furthermore, we find that oil price shocks affect economic activity at low frequencies (long run) in all G-7 countries, while the effect at high frequencies (short run) is limited to a few countries.

We present a novel automatic method for measuring the duration of motor unit action potentials (MUAPs) and compare it with two state-of-the-art automatic duration methods on normal and pathological MUAPs. To this end we analyzed 313 EMG recordings from normal and pathological muscles during slight contractions. A “gold standard” of the duration positions (start and end markers) was obtained for each MUAP from the manual measurements determined by two expert electromyographists. The results of the novel method were compared to those obtained by the two automatic methods using the “gold standard” duration measures for the different groups of normal and pathological MUAPs. Several statistical tests were applied and showed that the novel method provided closer duration positions to the “gold standard” and fewer gross aberrant errors than those obtained by the two other methods in the four MUAP groups, being significantly different in many of the cases.

In quantitative electromyography (EMG), the set of potentials that constitute a motor unit action potential (MUAP) train are represented by a single waveform from which various parameters are determined in order to characterize the MUAP for diagnostic analysis. Several methods that extract such a waveform are currently available, and they are, in essence, based on two operations: averaging and selection, which are performed either sample-by-sample or on the whole-potential. We present a new approach that carries out selection and averaging on a local interval basis.We tested our algorithm with a dataset of MUAP records extracted from the tibialis anterioris muscle of healthy subjects and compared it with some of the most relevant state-of-the-art methods considered in a previous work (Malanda et al., J. Electromyogr. Kinesiol., 2015). The comparison covered general purpose signal processing figures of merit and clinically used MUAP waveform parameters. Significantly better results in both sets of figures of merit were obtained with the new approach. In addition, relative to the other algorithms tested, the new approach required fewer potentials from the MUAP set to obtain an accurate representative waveform. (C) 2016 The Authors. Published by Elsevier Ltd.