Miracle-dz/UniFAR-FTUs huggingface dataset:
Jump training with blood flow restriction has no effect on jump performance

This study investigated whether jump training with blood flow restriction (BFR) improves jump performance compared to jump training without BFR under similar exercise intensity in healthy young humans. The participants were twenty healthy males who were assigned to either jump training with BFR (n = 10) or jump training without BFR [control (CON); n = 10] groups. All subjects completed five sets of 10 repetitions with one-minute intervals of half-squat jumps (SJ) at maximal effort, four days a week for four weeks. In the BFR group, circulatory occlusion around both thigh muscles was applied at a pressure of 200 mmHg, and physical characteristics, muscle strength and jump performance were evaluated before and after training. A significant main effect of training period on lean body mass, percentage of body fat and leg circumference in both groups was observed (P < 0.05). For jump training with BFR, only knee flexion strength increased (P < 0.05), while in the CON group, both knee extension and flexion strength increased (P < 0.05). BFR training did not improve SJ or counter-movement jumps (CMJ) (P > 0.05), whereas training without BFR (CON) improved the performance of both jumps (SJ: pre 35.7 ± 5.1 vs. post 38.9 ± 4.1 cm, P = 0.002: CMJ: pre 41.6 ± 3.6 vs. post 44.6 ± 3.8 cm, P < 0.001). These results indicate that jump training with BFR may not be an effective strategy for improving jump performance.
Improving Unsupervised Style Transfer in end-to-end Speech Synthesis with end-to-end Speech Recognition

End-to-end TTS model can directly take an utterance as reference, and generate speech from the text with prosody and speaker characteristics similar to the reference utterance. Ideally, the transcription of reference utterance does not need to match the text to be synthesized, so unsupervised style transfer can be achieved. However, in the previous model, because only the matched text and speech are used in training, given unmatched text and speech during testing would make the model synthesize blurry speech. In this paper, we propose to mitigate the problem by using the unmatched text and speech during training, and using the ASR accuracy of an end-to-end ASR model to guide the training procedure. The experimental results show that with the guidance of end-to-end ASR, both the ASR accuracy (objective evaluation) and the listener preference (subjective evaluation) of the speech generated by TTS model are improved. Moreover, we propose attention consistency loss as regularization, which is shown to accelerate the training.
Evaluation of Interface Resistance in a REBCO Tape at Different Temperatures by Contact-Probing Current Transfer Length Method

The multi-layered structure of a rare-earth barium copper oxide (REBCO) tape causes the resistance of Cu/Ag and Ag/REBCO interfaces (interface resistance), which is a major factor of the resistance of tape-to-tape joints (joint resistance). Interface resistance has been evaluated by the current transfer length (CTL) method. However, induced damage of a part of the measured REBCO tape in this method may cause variations in estimated values, as well as making the tape itself unable to be reused for applications. Therefore, this study proposes the contact-probing CTL method, which is a nondestructive evaluation method of interface resistance. We evaluated the interface resistance in a REBCO tape by the conventional CTL method and the contact-probing CTL method. The estimated interface resistance values by the contact-probing CTL method were more reliable and less varied than those by the conventional method. We also evaluated the temperature dependence of the interface resistance, which is important to analyze joint resistance for various applications of REBCO tapes. The result showed that the interface resistance remained constant at different temperatures.
Energy-Efficient Resource Allocation in Heterogeneous Cloud Radio Access Networks via BBU Offloading

Energy efficient resource allocation in heterogeneous cloud radio access networks (H-CRANs) is considered in this paper where a set of users within a specific region is served by femto-cell access points (FAPs) and remote radio heads (RRHs) connected to the base-band unit (BBU) pool through front-haul links. For orthogonal frequency division multiple access (OFDMA) based uplink H-CRANs, we formulate an energy efficient optimization problem with the novel utility function in order to jointly assign the access point (RRH/FAP), sub-carrier, transmit power, RRH, front-haul link and BBU. Due to highly complex relation between diverse optimization variables and their effects on each other, the formulated optimization problem is non-convex and NP-hard, suffering from high computational complexity. To tackle this issue, we develop an efficient two-step iterative algorithm with low computational complexity to solve the proposed problem based on the successive convex approximation (SCA) and complementary geometric programming (CGP). The simulation results reveal that our proposed approach can effectively offload the traffic from C-RAN to low-power femto-cells, and reduce the energy consumption cost by switching off the under-utilized BBUs.
Model-Predictive-Based Duty Cycle Control With Simplified Calculation and Mutual Influence Elimination for AC/DC Converter

The single-vector-based model-predictive-based direct power control (MPDPC) is commonly used for control of three-phase full-bridge ac/dc converters, but it could only select the best switching vector to be implemented. Due to the limited number of voltage vectors in a two-level three-phase converter, the sampling frequency needs to be high to achieve an acceptable performance. Also, it bears variable switching frequency that causes spread spectrum nature of harmonics. In this paper, a three-vector-based simplified model predictive duty cycle control (SMPDCC) is proposed. The adjacent two nonzero vectors are selected by evaluating the effects of each vector pairs based on the revised cost function. The duration calculation is simplified compared with the conventional predictive duty cycle control (CPDCC) by allocating a control period in reciprocal proportion with the corresponding cost function value of the selected vectors. Besides, the negative duration issue of CPDCC could be completely avoided and the mutual influence elimination ability could be realized. A comparative study with MPDPC and CPDCC has been conducted to verify the superiority of the proposed scheme by the simulation and experimental results. It shows that the SMPDCC has the advantages of lower power ripple, fixed switching frequency, lower total harmonic distortion, and mutual influence elimination ability.
A Wake-Up Radio-Based MAC Protocol for Autonomous Wireless Sensor Networks

Wireless sensor networks (WSNs) with energy harvesting capabilities have drawn increasing attention in the last few years, as they enable long-term monitoring applications. However, the level of power harvested is usually limited to few mW. To improve the energy efficiency of WSNs, many power management techniques have been proposed to adjust the quality of service according to the harvested energy fluctuations. As wireless communications consume a major fraction of the available energy, numerous medium access control (MAC) protocols have been proposed to minimize energy consumption, latency, and data collisions. In this paper, we present an innovative MAC protocol for energy-harvesting based WSNs exploiting ultralow-power wake-up radios. To overcome the limited range typical of wake-up radios, a multi-hop wake-up scheme based on a dual radio system is proposed enabling asynchronous communications between a base station and any node of the network while maintaining a low latency and a high energy efficiency. To reduce energy consumption, wake-up calls and data packets are transmitted using two distinct data rates. Combined with destination address decoding, using a higher data rate for data transmission also minimizes the risk of collisions. Our approach has been applied to monitoring applications composed of autonomous sensor nodes powered by indoor light energy. OMNeT++ simulation results demonstrate the benefits of our wake-up radio-based approach in terms of energy, latency, and collisions when compared with the state-of-the-art duty-cycled MAC protocols. Experiments performed with real WSN platforms equipped with a wake-up radio prototype confirm the efficiency of our approach.
Design and Characterization of a Fringing Field Capacitive Soil Moisture Sensor

This paper addresses the optimization and implementation of a fringing field capacitive soil moisture sensor using the printed circuit board technology. It includes the analysis of a novel configuration of an interdigital sensor for measuring soil moisture with two existing configurations. The optimized designs were simulated by using a 3-D finite-element method and fabricated by using a copper clad board. The performance of the fabricated sensors was evaluated using four soil samples collected from different locations. The observations were compared with the standard gravimetric method to evaluate the soil water content of the samples. The characterization method and the results of the whole sensing system are discussed in terms of calibration, dynamic test, and repeatability.
A Robust Inclusion Boundary Reconstructor for Electrical Impedance Tomography With Geometric Constraints

As a nondestructive, nonradioactive, and high temporal resolution technique, electrical impedance tomography (EIT) has potential applications in industrial and biomedical imaging. A novel geometrically constrained boundary reconstructor (GCBR) is presented for EIT. It can directly calculate the boundary of the target inclusions embedded into a homogeneous background conductivity and is robust to noise. The proposed GCBR depends on a specially designed energy function, which consists of the residual term and the geometric constraints’ term. The residual term drives the estimated inclusion boundary to the targets. The geometric constraints’ term regularizes the estimations and automatically excludes the meaningless boundary guesses from the candidate solution. A set of numerical tests are conducted to discuss the key factors influencing the boundary reconstruction performance. A set of experimental tests are conducted to evaluate the performance of the proposed GCBR. The position, size, and shape of the target inclusions are well reconstructed from the real data. The mean position error and mean Hausdorff distances between the target and reconstructed boundaries are 0.39% and 2.2% of the maximal observation diameter, respectively. The mean area errors and the mean area of the symmetric difference between the target and reconstructed inclusion regions are 0.32% and 2.54% of the total observation area, respectively.
Lung cancer assistant : a hybrid clinical decision support application in lung cancer treatment selection

We describe an online clinical decision support (CDS) system, Lung Cancer Assistant (LCA), which we have developed to aid the clinicians in arriving at informed treatment decisions for lung cancer patients at multidisciplinary team (MDT) meetings. LCA integrates rule-based and probabilistic decision support within a single platform. To our knowledge, this is the first time this has been achieved in the context of CDS in cancer care. Rule-based decision support is achieved by an original ontological guideline rule inference framework that operates on a domain-specific module of Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), containing clinical concepts and guideline rule knowledge elicited from the major national and international guideline publishers. It adopts a conventional argumentation-based decision model, whereby the decision options are listed along with arguments derived by matching the patient records to the guideline rule base. As an additional feature of this framework, when a new patient is entered, LCA displays the most similar patients to the one being viewed. Probabilistic inference is provided by a Bayesian Network (BN) whose structure and parameters have been learned based on the English Lung Cancer Database (LUCADA). This allows LCA to predict the probability of patient survival and lay out how the selection of different treatment plans would affect it. Based on a retrospective patient subset from LUCADA, we present empirical results on the treatment recommendations provided by both functionalities of LCA and discuss their strengths and weaknesses. Finally, we present preliminary work, which may allow utilising the BN to calculate survival odd ratios that could be translated into quantitative degrees of support for the guideline rule-based arguments. An online version of LCA is accessible on http://lca.eng.ox.ac. uk.
Alamouti Coding for DFT Spreading-Based Low PAPR FBMC

So far, the solutions for the two major issues of filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM), i.e., 1) high PAPR and 2) incompatibility with MIMO have been devised exclusive of each other; and thus, the signal formats and processings are also exclusive. Therefore, how to constructively combine their solutions into a single system is another challenging issue. As a specific solution to this, we extend the idea of DFT spreading-based low peak to average power ratio (PAPR) FBMC scheme to the ICI-free Alamouti-coded FBMC. In order to inherit the merits of the two schemes, we first provide the required modifications for combining the two schemes. A mathematical analysis confirms that even with frequency reversal Alamouti code arrangement and subblock partitioning, the proposed scheme still achieves the single carrier effect of DFT spreading. The simulation results reveal that the proposed scheme achieves substantially lower PAPR than the previous Alamouti-coded FBMC scheme. Moreover, owing to the additional frequency diversity via DFT spreading, the proposed scheme with properly chosen subblock size achieves even lower BER than the theoretical Alamouti-coded BER with a diversity order of two. Considering the PAPR, BER, and data rate loss, we conclude that the proposed scheme is a competitive solution for Alamouti-coded multicarrier system.
A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI

Olfactory dysfunction is related to several clinical neurodegenerative diseases, such as Alzheimer’s disease, multiple sclerosis, degenerative ataxias, Parkinson’s disease, and so on. Owing to the individual difference in the sensory adaption of human smell function, the olfactory responses usually exhibit large inter-individual difference and change over time after repeated stimulations. The traditional analysis tools, such as statistical parametric mapping (SPM) in functional magnetic resonance imaging technique (fMRI) analysis, utilize the paradigm-based linear correlation and statistical techniques to discriminate the activation areas from background activities. However, these traditional approaches extract olfactory-induced responses using the stereotypic template or paradigm generated model. The olfactory-induced hemodynamic responses affected by internal/external events, such as changes of smell fatigue and attention, are not considered and therefore could result in misleading results. In this paper, owing to the stochastic characteristic of olfactory-induced responses, we adopted multivariate empirical mode decomposition (MEMD) to extract olfactory-induced hemodynamic responses in MRI blood-oxygen-level dependent (BOLD) signals. The MEMD is an efficient data-driven approach to extract nonlinear and non-stationary signals, which is an improved method to expand traditional empirical mode decomposition (EMD) from one channel to multi-channel processing. We applied MEMD to decompose time series of BOLD signals from each slice into multivariate intrinsic mode functions (IMF). The MEMD enables common features of different scales in an image slice to be arranged in distinct IMFs. Each IMF is an analytic, self-constructed, well-defined, and data-driven function with time-varying frequencies. Each IMF was examined by checking its correlation with the paradigm-generated template. The task-related IMFs were chosen to reconstruct olfactory-induced hemodynamic responses. The group analysis of MEMD-processed data showed olfactory-induced activations in the anterior cingulate cortex and middle frontal gyrus.
The role of grid cells in spatial localisation and computation

From recording single neurons in rodents and primates, and observing the effects of lesions in humans, it is well recognised that the mammalian entorhinal cortex, and the broader hippocampal formation, perform cognitive tasks relating to both space and memory. In this work I propose a number of specific roles for grid cells and experimentally test aspects of their function. I also give an overview of a piece of software developed for streamlining the process of researching grid cells. The main theoretical chapter discusses how grid cells might be used to perform two-dimensional vector calculations. Simulations show that computing the vector to a goal can be performed accurately, and in a short time window, using a network topology that might conceivably be initialized during development. It is then suggested that such a network might also support vector addition. A short experimental chapter follows, in which some preliminary data is presented to test this hypothesis. The main experimental chapter examines how grid cells respond to variations in the level of spatial information present in the environment. In the data presented, grid cells appear to be more stably anchored in a cue rich arena, as compared to an otherwise identical cue poor arena. Differences in grid scale are examined in relation to the inaccuracy of self-localisation, as inferred from spatial tuning. The final chapter discusses the above findings further and makes suggestions as to their broader significance and the significance of other related recent findings.
Immediate effects of self-myofascial release on latent trigger point sensitivity: a randomized, placebo-controlled trial

Latent myofascial trigger points (MTrP) have been linked to several impairments of muscle function. The present study was conducted in order to examine whether a single bout of self-myofascial release using a foam roller is effective in reducing MTrP sensitivity. Fifty healthy, pain-free subjects (26.8±6 years, 21 men) with latent MTrP in the lateral gastrocnemius muscle were included in the randomized, controlled trial. One week after a familiarization session, they were randomly allocated to three groups: (1) static compression of the most sensitive MTrP using a foam roll, (2) slow dynamic foam rolling of the lateral calf and (3) placebo laser acupuncture of the most sensitive MTrP. Treatment duration in each group was 90 seconds. The pressure pain threshold (PPT) of the most sensitive MTrP was assessed using a handheld algometer prior to and after the intervention. A repeated measures analysis of variance (3x2) did not reveal significant between-group interactions (p>.05) but showed a significant time effect (F=7.715, p<.05). While placebo and dynamic self-myofascial release did not change MTrP sensitivity (p>.05), static compression of MTrP increased the PPT (2.6±0.8 to 3.0±1.1, d=.35; p<.05). Static self_myofascial release using a foam roller might represent an alternative to reduce pressure pain of latent MTrP. Additional research should aim to extend these findings to patients and athletes with myofascial pain syndromes.
Aggression or Aggressiveness? : A research hypothesis on aggression, videogames and executive functions in preschool age

The scientific evidence found in the literature shows that aggression is innate in humans, in a reactive and proactive form. This work proposes a distinction between an aggressive act and a predisposition to aggression, highlighting how this type of behavior is normative and natural in child’s development. Aggressiveness reaches its peak between two and four years old and tends to decrease sensibly and permanently throughout the life. This work traces genetic and temperamental correlates of aggressiveness, highlights the relationship between aggression and decision making and explores the elements that lead to the choice of implementing aggressive behavior. Finally, we discuss the preliminary research proposal for the development of a gaming software, structured with a set of decision tasks, including measurement of reaction times, in order to detect the predisposition to chose aggressive behavior. This will enable the correlation between the collected data and the results obtained from the temperamental tests.
Sample Survey Principles and Methods

Sample surveys play a vital role in modern society in allowing us to collect and process data about particular groups. Sample Survey: Principles and Methods aims to provide a digestible but comprehensive coverage of survey sampling to meet the needs of students, statisticians and other practitioners. This third edition has evolved to reflect the new demands for wider areas of application, and to keep up-to-date with developing methods of carrying out surveys, such as via email and the internet. New chapters introduce modern approaches to sampling methods for rare and sensitive events and for natural phenomena, with particular reference to contemporary biological, environmental and social issues. Wide-ranging topical examples on all topics are given throughout the text. Practical exercises are presented at chapter ends, and numerical answers are provided in all cases. As well as new chapters covering a wider range of fields in which sample surveys can be used, new features in this new edition include chapter summaries and an expanded bibliography and reference section.
Deep Neural Network-Based Digital Predistorter for Doherty Power Amplifiers

In this letter, measured adjacent channel leakage ratio (ACLR) results using a GaN Doherty power amplifier will show that for less than 2000 coefficients, sigmoid activated deep neural network (DNN)-based digital predistorter (DPD) outperforms rectified linear unit (ReLU) activation by up to 2 dB even when the number of layers of the network is increased. When the number of coefficients exceeds 2000 ReLU outperforms sigmoid activation with an improvement of up to 3–4 dB in ACLR suppression. Furthermore, to achieve an ACLR level of −54 dBc or better, the number of coefficients required to implement the DNN-DPD can be reduced by a factor of 150 when using ReLU rather than sigmoid activation.