Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlates of Sedentary Behaviour in Adults with Intellectual Disabilities—A Systematic Review

Individuals with intellectual disabilities (ID) are at high risk for high levels of sedentary behaviour. To inform the development of programmes to reduce sedentary behaviour, insight into the correlates is needed. Therefore, the aim of this study is to review the evidence on correlates of sedentary behaviour in adults with ID. We performed a systematic literature search in Ovid Medline, Ovid Embase, Web of Science and Google Scholar up to 19 January 2018, resulting in nine included studies that were published from 2011 to 2018. Correlates were categorized according to the ecological model. Studies predominantly focused on individual level correlates. Of those correlates studied in more than one study, having epilepsy was associated with less sedentary behaviour and inconsistent results were found for sex, genetic syndromes, weight status, physical health, mobility, level of ID, and mental health. Of the few interpersonal and environmental factors studied, only living arrangements were studied in more than one study, with inconsistent results. To date, we have limited and inconclusive evidence about correlates of sedentary behaviour in adults with ID. Only when future studies unravel correlates and determinants, across all domains of the ecological model, will the potential opportunities to improve health by reducing sedentary behaviour come within reach.
Applying Machine Learning Techniques for Speech Emotion Recognition

Emotion is an instinctive or intuitive feeling which is distinguished from reasoning or knowledge, it is a strong feeling derived from one's circumstance or surroundings. The main idea of the paper is to apply Deep Neural Network (DNN) and k-nearest neighbor (k-NN) in recognition of emotion from speech-especially scary state of mind. The area of application of the system is mainly concerned over the health care units. The foundation of this research has its main firm applications in palliative care. Under most precise outcome the alert signals are made through cloud. Many raw data are collected under special emphasis techniques. The acoustic voice signals are converted to wave form, uttereance level feature extraction emotion classification, existing database recognition, alert signal creation through cloud is the sequence of steps to be followed. The findings of the paper lays a fruitful contribution to palliative care system.
DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters

Edge computing has emerged as a trend to improve scalability, overhead, and privacy by processing large-scale data, e.g., in deep learning applications locally at the source. In IoT networks, edge devices are characterized by tight resource constraints and often dynamic nature of data sources, where existing approaches for deploying Deep/Convolutional Neural Networks (DNNs/CNNs) can only meet IoT constraints when severely reducing accuracy or using a static distribution that cannot adapt to dynamic IoT environments. In this paper, we propose DeepThings, a framework for adaptively distributed execution of CNN-based inference applications on tightly resource-constrained IoT edge clusters. DeepThings employs a scalable Fused Tile Partitioning (FTP) of convolutional layers to minimize memory footprint while exposing parallelism. It further realizes a distributed work stealing approach to enable dynamic workload distribution and balancing at inference runtime. Finally, we employ a novel work scheduling process to improve data reuse and reduce overall execution latency. Results show that our proposed FTP method can reduce memory footprint by more than 68% without sacrificing accuracy. Furthermore, compared to existing work sharing methods, our distributed work stealing and work scheduling improve throughput by <inline-formula> <tex-math notation="LaTeX">$1.7\times -2.2\times$ </tex-math></inline-formula> with multiple dynamic data sources. When combined, DeepThings provides scalable CNN inference speedups of <inline-formula> <tex-math notation="LaTeX">$1.7\times$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$3.5\times$ </tex-math></inline-formula> on 2–6 edge devices with less than 23 MB memory each.
Placental inflammation, lifestyle, maternal and early child sensitisation to allergens – the assessment of lifestyle and allergic disease during infancy birth cohort

To investigate (i) whether maternal sensitisation to allergens, and lifestyle can influence the risk of acute and chronic inflammation of the placenta, in the forms of chorioamnionitis and villitis, respectively, and (ii) whether these placental inflammations are associated with the outcome of sensitisation for the child during preschool age.
A Novel MIMO Channel State Feedback Scheme and Overhead Calculation

The value of the channel state feedback becomes more significant in the upcoming 5G era due to the application of the millimeter-wave (mm-Wave) technologies in 5G. In the meantime, the feedback is challenging in the mm-Wave systems especially with massive MIMO antenna for two reasons: first, the channel states change faster at the millimeter band; second, in the massive antenna system, there are a large number of channel coefficients to report which means the feedback caused overhead will be large. Thus, optimization of the feedback system becomes necessary in the 5G era. In our optimization work, the perfect channel states are assumed to be not accessible at user equipments. Under this assumption, we propose a novel codebook-based-feedback scheme within which the differential channel information is first projected to the codebook, and then the index of the projection in the codebook is fed back. We compare the new scheme with other feedback schemes, such as analog feedback of original states and differential states. From the comparison, we analytically and numerically obtain the insight of the advantage of the proposed scheme. We can also observe that the advantage of the proposed scheme is even more significant in low-SNR conditions.
Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks

In this paper, we propose a hierarchical building detection framework based on deep learning model, which focuses on accurately detecting buildings from remote sensing images. To this end, we first construct the generation model of the multilevel training samples using the Gaussian pyramid technique to learn the features of building objects at different scales and spatial resolutions. Then, the building region proposal networks are put forward to quickly extract candidate building regions, thereby increasing the efficiency of the building object detection. Based on the candidate building regions, we establish the multilevel building detection model using the convolutional neural networks (CNNs), from which the generic image features of each building region proposal are calculated. Finally, the obtained features are provided as inputs for training CNNs model, and the learned model is further applied to test images for the detection of unknown buildings. Various experiments using the Datasets I and II (in Section V-A) show that the proposed framework increases the mean average precision values of building detection by 3.63%, 3.85%, and 3.77%, compared with the state-of-the-art methods, i.e., Method IV. Besides, the proposed method is robust to the buildings having different spatial textures and types.
A Novel Approach to Investigate the Core Vibration in Power Transformers

The vibration and noise are a serious problem for large power transformers. The core vibration, as the noise source, is very important to calculate accurately. For the transformers with tanks, vibration calculation is very complicated, considering the complicated propagation path of vibration in the tank and the transformer oil. This paper provides a novel approach to investigate the core vibration of the DFP-270000/500 type super high-voltage generator transformer. Based on the vibration acoustic coupling calculation, the damping coefficient of the core vibration of the transformer is obtained, and the magnetic structural coupling analysis is used for the vibration of the transformer core. To validate the feasibility and applicability of the proposed method, the numerical results obtained from the proposed method are compared with those of experimental ones.
Working County Lines: Child Criminal Exploitation and Illicit Drug Dealing in Glasgow and Merseyside

This article explores recent developments within the U.K. drug market: that is, the commuting of gang members from major cities to small rural urban areas for the purpose of enhancing their profit from drug distribution. Such practice has come to be known as working “County Lines. ” We present findings drawn from qualitative research with practitioners working to address serious and organized crime and participants involved in street gangs and illicit drug supply in both Glasgow and Merseyside, United Kingdom. We find evidence of Child Criminal Exploitation (CCE) in County Lines activity, often as a result of debt bondage; but also, cases of young people working the lines of their own volition to obtain financial and status rewards. In conclusion, we put forward a series of recommendations which are aimed at informing police strategy, practitioner intervention, and wider governmental policy to effectively address this growing, and highly problematic, phenomenon.
Analytical Performance Evaluation of MRC Receivers in Massive MIMO Systems

In recent years, massive or large-scale multiple-input multiple-output (MIMO) systems that rely on very large antenna arrays have become a hot topic of research in the field of wireless communications. This is in part due to the nearly optimum performance and relative simplicity of massive MIMO linear receivers and transmitters. This paper investigates the performance of maximum-ratio combining linear receivers in massive MIMO communication systems in terms of signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), and outage probability. The probability density function (PDF) of SINR is analytically calculated for the first time and verified by simulation. Due to its complexity, the use of obtained analytical expression for performance evaluation purposes is prohibitive. Hence, this PDF is approximated by a gamma distribution and the resulting expression is used to evaluate the outage probability and the BER performance of the receiver.
Halloween genes in panarthropods and the evolution of the early moulting pathway in Ecdysozoa

Moulting is a characteristic feature of Ecdysozoa—the clade of moulting animals that includes the hyperdiverse arthropods and less speciose groups, such as onychophorans, tardigrades and nematodes. Moulting has been best analysed in arthropods, specifically in insects and crustaceans, in which a complex neuroendocrine system acts at the genomic level and initiates the transcription of genes responsible for moulting. The key moulting hormones, ecdysone and 20-hydroxyecdysone, are subsequently synthesized from cholesterol ingested with food. Their biosynthesis is regulated by the Rieske-domain protein Neverland and cytochrome P450 enzymes encoded by the so-called ‘Halloween’ genes. Ecdysone is then released into the haemolymph and modified into 20-hydroxyecdysone, which binds to the nuclear receptor EcR/USP and initiates transcription of the Early genes. As little is known about the moulting pathway of other ecdysozoans, we examined the occurrence of genes involved in ecdysteroid biosynthesis and the early moulting cascade across ecdysozoan subgroups. Genomic and transcriptomic searches revealed no Halloween genes in cycloneuralians, whereas only shadow (CYP315A1) is present in onychophorans and tardigrades, suggesting that the Halloween genes evolved stepwise in panarthropods. These findings imply that the genes which were responsible for the ecdysteroid biosynthesis in the last common ancestor of Ecdysozoa are currently unknown.
An SI-MISO Boost Converter With Deadbeat-Based Control for Electric Vehicle Applications

This paper presents a single-inductor multiple-input single-output (SI-MISO) boost converter with a deadbeat-based control method. The proposed deadbeat-based control has the capability to simultaneously conduct the power regulation from multiple inputs and the output voltage regulation for the SI-MISO boost converter. The advantages of the proposed method are less component count, lower system cost, compatibility for arbitrary number of inputs, fast response, and simplicity for implementation. Hence, the SI-MISO boost converter is a promising interface for the regulation of multiple energy sources as a standalone power system, which can be utilized in electric vehicle applications. To verify the performance of the proposed deadbeat-based control method, a laboratorial prototype of the SI-MISO boost converter is built. The steady-state and dynamic operations are investigated based on the prototype. The experimental results demonstrate that input power and output voltage of the SI-MISO boost converter can be effectively regulated by the proposed control method. The comprehensive comparison with the existing methods is conducted to further illustrate the advantages of the proposed deadbeat-based method.
Osteogenic niche in the regulation of normal hematopoiesis and leukemogenesis

The bone marrow microenvironment, also known as the bone marrow niche, is a complex network of cell types and acellular factors that supports normal hematopoiesis. For many years, leukemia was believed to be caused by a series of genetic hits to hematopoietic stem and progenitor cells, which transform them to preleukemic, and eventually to leukemic, cells. Recent discoveries suggest that genetic alterations in bone marrow niche cells, particularly in osteogenic cells, may also cause myeloid leukemia in mouse models. The osteogenic niche, which consists of osteoprogenitors, preosteoblasts, mature osteoblasts, osteocytes and osteoclasts, has been shown to play a critical role in the maintenance and expansion of hematopoietic stem and progenitor cells as well as in their oncogenic transformation into leukemia stem/initiating cells. We have recently shown that acute myeloid leukemia cells induce osteogenic differentiation in mesenchymal stromal cells to gain a growth advantage. In this review, we discuss the role of the osteogenic niche in the maintenance of hematopoietic stem and progenitor cells, as well as in their transformation into leukemia cells. We also discuss the signaling pathways that regulate osteogenic niche-hematopoietic stem and progenitor cells or osteogenic niche-leukemic stem/initiating cell interactions in the bone marrow, together with novel approaches for therapeutically targeting these interactions.
A group I metabotropic glutamate receptor controls synaptic gain between rods and rod bipolar cells in the mouse retina

The canonical mGluR6‐Trpm1 pathway that generates the sign‐inverting signal between photoreceptors and ON bipolar cells has been well described. However, one type of ON bipolar cell, the rod bipolar cell (RBC), additionally is thought to express the group I mGluRs whose function is unknown. We examined the role of group I mGluRs in mouse RBCs and here provide evidence that it controls synaptic gain between rods and RBCs. In dark‐adapted conditions, the mGluR1 antagonists LY367385 and (RS)‐1‐Aminoindan‐1,5‐dicarboxylic acid, but not the mGluR5 antagonist 2‐Methyl‐6‐(phenylethynyl)pyridine hydrochloride reduced the light‐evoked responses in RBCs indicating that mGluR1, but not mGluR5, serves to potentiate RBC responses. Perturbing the downstream phospholipase C (PLC)‐protein kinase C (PKC) pathway by inhibiting PLC, tightly buffering intracellular Ca2+, or preventing its release from intracellular stores reduced the synaptic potentiation by mGluR1. The effect of mGluR1 activation was dependent upon adaptation state, strongly increasing the synaptic gain in dark‐, but not in light‐adapted retinas, or in the presence of a moderate background light, consistent with the idea that mGluR1 activation requires light‐dependent glutamate release from rods. Moreover, immunostaining revealed that protein kinase Cα (PKCα) is more strongly expressed in RBC dendrites in dark‐adapted conditions, revealing an additional mechanism behind the loss of mGluR1 potentiation. In light‐adapted conditions, exogenous activation of mGluR1 with the agonist 3,5‐Dihydroxyphenylglycine increased the mGluR6 currents in some RBCs and decreased it in others, suggesting an additional action of mGluR1 that is unmasked in the light‐adapted state. Elevating intracellular free Ca2+, consistently resulted in a decrease in synaptic gain. Our results provide evidence that mGluR1 controls the synaptic gain in RBCs.
Carbon fiber-reinforced chassis equipped with four-wheel torque vectoring and steering system

The need to improve the quality of the air in European cities has produced increased interest in the powertrains of electric vehicles. Electric mobility has increased tremendously in the last ten years so that, today, more than a million electric vehicles are in use in North America and Europe. Pure electric vehicles must be very efficient to maximize their driving range in order to reduce drivers’ anxiety about being stranded. In this paper, we present the design and future development of an optimized platform for an electric vehicle. This study considers the production of a very light chassis and a four-wheel drive (4WD) powertrain equipped with torque vectoring (TV) and rear wheel steering (RWS) systems. Both TV and RWS techniques were tested by real-time simulations, and the results were compared to previous studies available in the literature.
Device-to-Device Data Transfer Through Multihop Relay Links Underlaying Cellular Networks

With the increasing demand for high-data mobile traffic and applications, Device-to-Device (D2D) communication has emerged as a promising technique for next generation of cellular networks to increase the network capacity. In this paper, to offload traffic from network backhaul, we investigate the problem of relaying data through multihop nodes, using D2D communication, for a set of transmitter-receiver pairs, by sharing cellular resource blocks. Furthermore, the problem is extended to schedule the transmission links within a maximum time frame to obey a delay constraint. If the transmission path could not be scheduled within the maximum time frame for any D2D communication pair, the communication is redirected through the base station similar to cellular mode. Our problem aims at minimizing the overall D2D transmission power by giving priority to maximizing the number of paths that go through D2D relaying. We mathematically formulate the above problem and find optimal solutions for small networks. We show the NP-hardness of the problem, and owing to its complexity, we propose an algorithmic method for more extensive networks. Through numerical results, the performance of our mathematical optimization model and algorithm is compared and the efficiency of the proposed algorithmic method is shown.
Infinitesimal Weak Rigidity and Stability Analysis on Three-Agent Formations

In this paper, new concepts of weak rigidity matrix and infinitesimal weak rigidity for planar frameworks are introduced. The weak rigidity matrix is used to directly check if a framework is infinitesimally weakly rigid while previous work can check a weak rigidity of a framework indirectly. An infinitesimal weak rigidity framework can be uniquely determined up to a translation and a rotation (and a scaling also when the framework does not include any edge) by its inter-neighbor distances and angles. We propose applying the new concepts to a three-agent formation control problem with a gradient control law, and further we finally propose a modified Henneberg construction which is a technique to generate minimally rigid (or weakly rigid) graphs.
Vowel Articulation Dynamic Stability Related to Parkinson's Disease Rating Features: Male Dataset

Neurodegenerative pathologies as Parkinson's Disease (PD) show important distortions in speech, affecting fluency, prosody, articulation and phonation. Classically, measurements based on articulation gestures altering formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been proposed to measure speech distortion, but these markers are based mainly on static positions of sustained vowels. The present study introduces a measurement based on the mutual information distance among probability density functions of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the jaw and tongue articulation gestures is estimated and modeled statistically. The distribution of this feature may differentiate PD patients from normative speakers during sustained vowel emission. The study is based on a limited database of 53 male PD patients, contrasted to a very selected and stable set of eight normative speakers. In this sense, distances based on Kullback-Leibler divergence seem to be sensitive to PD articulation instability. Correlation studies show statistically relevant relationship between information contents based on articulation instability to certain motor and nonmotor clinical scores, such as freezing of gait, or sleep disorders. Remarkably, one of the statistically relevant correlations point out to the time interval passed since the first diagnostic. These results stress the need of defining scoring scales specifically designed for speech disability estimation and monitoring methodologies in degenerative diseases of neuromotor origin.
Estimation of the Noise Spectral Covariance Matrix in Hyperspectral Images

Accurate estimation of the underlying noise is vital in the processing of hyperspectral images (HSIs). Previous studies have shown that many HSI processing algorithms perform poorly if the noise is not correctly estimated. The classic residual (CR) method is commonly employed for the estimation of variance of the noise in different spectral bands. However, noise estimates as per the CR method ignore the presence of spectral or spatial correlation amongst noise samples. Some studies have been conducted in the past to investigate the spectral and spatial correlation present in the noise but there are very few methods available to estimate the correlations present in the noise. In this paper, we present a reliable method for the estimation of the spectral correlation in the noise. Recently, it was shown that the CR method performs poorly when estimating the spectral correlation in the noise. By using both artificial and real datasets, we show that the proposed new method estimates the noise spectral correlation significantly more accurately than the CR method.
Methods for Long-Distance Crack Location and Detection of Concrete Bridge Structures

In order to improve the efficiency of crack detection of concrete bridge structures, a new method based on computer vision technology and coordinate mapping is proposed. In this research, this crack measurement system is integrated mainly with a high magnification image acquisition system, a two-dimensional electric cradle head device and a laser ranging system. It has a set of observing coordinate system. Firstly, the marking points' image coordinates are mapped to the observation coordinates. Secondly, according to the marking points' observation coordinates, the measured crack's coordinates are mapped to a same world coordinates so as to realize the spatial location of the measured cracks regardless of different test cycles or instrument's setup positions, which is a great convenience for the review detection of surface cracks of concrete bridge structures. The experiments show that this method is efficient and convenient. It can automatically locate the measured cracks within 16 s, and the deviation is not more than ± 0.07 °. At a distance of 100 m, the measurement accuracy of crack width is better than ± 0.12 mm.