Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligibility and Listening Effort of Spanish Oesophageal Speech

Communication is a huge challenge for oesophageal speakers, be it for interactions with fellow humans or with digital voice assistants. We aim to quantify these communication challenges (both human–human and human–machine interactions) by measuring intelligibility and Listening Effort (LE) of Oesophageal Speech (OS) in comparison to Healthy Laryngeal Speech (HS). We conducted two listening tests (one web-based, the other in laboratory settings) to collect these measurements. Participants performed a sentence recognition and LE rating task in each test. Intelligibility, calculated as Word Error Rate, showed significant correlation with self-reported LE ratings. Speaker type (healthy or oesophageal) had a major effect on intelligibility and effort. More LE was reported for OS compared to HS even when OS intelligibility was close to HS. Listeners familiar with OS reported less effort when listening to OS compared to nonfamiliar listeners. However, such advantage of familiarity was not observed for intelligibility. Automatic speech recognition scores were higher for OS compared to HS.
Thermal Modeling and Analysis of a Novel Transverse Flux HAPM Generator for Small-Scale Wind Turbine Application

This article focuses on a Transverse Flux Permanent Magnet generator which benefits from Halbach-array permanent magnets. In this topology, a sinusoidal air-gap flux is achieved and thus, lower THD content and cogging torque is resulted. The Transverse flux generators can be a proper choice for direct drive, low speed, and high torque density applications such as wind turbines. Regarding the available small scale turbines in the market, an operating point is chosen for the proposed machine. The problem of a high torque density generator is thermal stresses which should be taken into consideration in the design and modeling process. Precise thermal modeling at the design stage can take the risk of the increase in machine temperature. Using an active component meshed model by considering the localized losses along with a lumped parameter circuit for the unmeshed parts is proposed for the understudy machine. A 3 kW, 320 rpm generator is designed and analyzed by considering the electromagnetic and thermal properties. It is shown that the magnetic-thermal modeling of the proposed machine is highly consistent with the experimental results.
Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis

Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN.
A Modular Underactuated Gripper with Force Control System

A design of an underactuated electromechanical gripper with force control algorithm is presented in this paper. The key feature of the gripper is the ability to grasp fragile objects and objects of a complex shape. Such advantages are due to the usage of elastic joints and force sensitive resistors embedded in modules of gripper’s fingers. Also low cost and mass of the presented device makes its application rational for a larger number of robotic systems. Proposed force control system is based on PI control and passification approaches that provide tuning simplicity and good performance in the case of unknown environments. Experimental results show the efficiency of proposed solution.
Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot

PurposeThis paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot.Design/methodology/approachIn ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R2) for the experimental data were determined based on conjugate gradient back propagation, Levenberg–Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagation feed forward back propagation training algorithm with logistic transfer function.FindingsIn the ANN architecture established for the surface roughness (Ra), three neurons [cutting speed (V), feed rate (f) and depth of cut (a)] were contained in the input layer, five neurons were included in its hidden layer and one neuron was contained in the output layer (3-5-1). Trials showed that LM learning algorithm was the best learning algorithm for the surface roughness. The ANN model obtained with the LM learning algorithm yielded estimation training valuesR2(97.5 per cent) and testing valuesR2(99 per cent). TheR2for multiple regressions was obtained as 96.1 per cent.Originality/valueThe result of the surface roughness estimation model showed that the equation obtained from the multiple regressions with quadratic model had an acceptable estimation capacity. The ANN model showed a more dependable estimation when compared with the multiple regression models. Hereby, these models can be used to effectively control the milling process to reach a satisfactory surface quality.
Effects of Fuel Injection Pressure on Combustion and Emission Characteristics under Low Speed Conditions in a Diesel Engine Fueled with Palm Oil Biodiesel

In this study, the effect of injection pressure on combustion and emission characteristics was evaluated on a common rail direct injection diesel engine fueled with palm oil biodiesel. Recently, many studies have been conducted to utilize biodiesel produced from various sources to prevent environmental pollution and the depletion of petroleum resources. The oxygen content and high cetane number of biodiesel can reduce the production of exhaust pollutants by improving the combustion, but its high viscosity deteriorates the atomization of the injected fuel. Particularly at low engine speed conditions like idle, poor atomization and low airflow in the cylinder deteriorates the combustion efficiency. Increasing the fuel injection pressure is one of the effective methods to improve the atomization of biodiesel without mechanical modification of the current diesel engine. In this study, combustion characteristics and emission levels of pollutants were measured by varying the fuel injection pressure applying palm oil biodiesel. As a result, it was confirmed that increasing the injection pressure to apply palm oil biodiesel at low engine speed can reduce ignition delay and improve combustion efficiency so that nitrogen oxides (NOx) is increased but soot formation is reduced. Carbon monoxide (CO) and hydrocarbon (HC) are slightly reduced but these are increased again when using 100% palm oil biodiesel. The increased NOx due to increased injection pressure can be reduced by applying exhaust gas recirculation (EGR).
Modeling and Stability Analysis of Inverter-Based Microgrid Under Harmonic Conditions

Microgrids (MGs) operate under harmonic conditions due to the integration of nonlinear loads. The autonomous harmonic compensation control of inverter-interfaced DG has been proposed to successfully mitigate the harmonics. However, the small-signal analysis of harmonic compensation controls has not been investigated in the microgrid with multiple inverters. This paper develops the modeling and analysis of the inverter-based MGs under harmonic conditions. The concept of dynamic phasor (DP) is used to describe the fundamental and harmonic components of an ac waveform via dc variables. The developed model consists of droop-controlled distributed generators (DGs), diodes rectifiers (working as nonlinear load) and resistance loads. Virtual impedance control is considered in the droop-controlled DGs for the autonomous harmonic compensation. Based on the developed DP model, the dynamic behavior of the microgrid is investigated via small-signal analysis. It is observed that the virtual impedance for harmonic compensation brings inter-inverter oscillations on harmonic domain. Participation and eigenlocus analysis are performed to investigate the influence of parameter tuning of harmonic compensation on microgrid stability. Numerical simulations are carried out to validate the effectiveness of the proposed modeling method and the analysis results.
Electromyography Signal Analysis to Obtain Knee Joint Angular Position

Knee injuries are due to several causes and affect a large part of the population. In all of the cases, rehabilitation is required to recover the joint mobility and strength. In this context, the use of technology, especially the development of assistive devices may offer advantages to the patients, e.g. allow to perform correctly the exercises, adapt to the users’ needs and help to comply with the prescribed physical therapy. These devices may have specific requirements focused on not harming the patient. This is why control strategies are needed, and therefore feedback sensing is highly important. In this paper we present an algorithm to determine the knee joint angular position from surface Electromyography (EMG) measurements, using a curve fit from a polynomial adjustment method and a Locally Weighted Projection Regression (LWPR) method. We validate our approach, comparing the data obtained from the curve fitting with the measurements obtained with position sensors. In this way, results show that indeed we can explain the joint angular position with the EMG data taken in knee flexion-extension motion, applying a polynomial adjustment approach and the LWPR method.
Energy Peer-to-Peer Trading in Virtual Microgrids in Smart Grids: A Game-Theoretic Approach

Traditionally, energy consumers pay non-commodity charges (e.g., transmission, environmental and network costs) as a major component of their energy bills. With the distributed energy generation, enabling energy consumption close to producers can minimize such costs. The physically constrained energy prosumers in power networks can be logically grouped into virtual microgrids (VMGs) using telecommunication systems. Prosumer benefits can be optimised by modelling the energy trading interactions among producers and consumers in a VMG as a Stackelberg game in which producers lead and consumers follow. Considering renewable (RES) and non-renewable energy (nRES) resources, and given that RES are unpredictable thus unschedulable, we also describe cost and utility models that include load uncertainty demands of producers. The results show that under Stackelberg equilibrium (SE), the costs incurred by a consumer for procuring either the RES or nRES are significantly reduced while the derived utility by producer is maximized. We further show that when the number of prosumers in the VMG increases, the CO2 emission cost and consequently the energy cost are minimized at the SE. Lastly, we evaluate the peer-to-peer (P2P) energy trading scenario involving noncooperative energy prosumers with and without Stackelberg game. The results show that the P2P energy prosumers attain 47% higher benefits with Stackelberg game.
Online Hysteresis Identification and Compensation for Piezoelectric Actuators

Hysteresis in piezoelectric actuators (PEAs) causes significant position errors and limits the application of PEAs. Compensating hysteresis via inverse models is one of the most popular approaches to reduce its effects. However, the construction of an accurate model is very challenging since hysteresis is coupled with creep and high-order dynamics, and hysteresis properties are also affected by operating conditions and aging effects. To overcome these challenges, this article proposes an adaptive generalized Maxwell-slip (AGMS) algorithm to identify and compensate for hysteresis online. By evenly distributing the saturation deformations throughout the desired range, a linear relation between the output force and the spring stiffness is generated, which enables a low-computational-complexity algorithm, such as the recursive least-squares algorithm, to update the stiffness online. Since the GMS model is self-invertible, an inverse model can be further analytically constructed in real time and utilized to compensate for the hysteresis. Experimental and comparison studies are carried out. The results show that this approach relaxes the requirement on the precision of the model and is robust to the operating conditions due to the capability of updating model parameters online. The normalized root mean square tracking error is approximately 0.4%.
Acute perforation of small intestine due to blunt trauma directly to the inguinal region: A rare cause of acute abdomen -

: Perforation of intestines due to blunt abdominal trauma in patients with inguinal hernia is as a rare situation. We present a case of perforation of small intestines due to blunt trauma to the inguinal region. The patient had been previously diagnosed with an inguinal hernia. At his abdominal examination there was tenderness. He had no abnormal laboratory findings. Abdominal ultra-sound examination revealed free fluid in the pelvic space. Abdominal CT revealed free abdominal fluid and free abdominal air. An exploratory laparotomy had been planned. Laparotomy revealed a perforation of 1 cm in an ileal segment which was 100 cm far from the treitz ligament. The perforation was closed primary and herniorraphy was performed at the same session. His recovery was uneventful. We think that blunt trauma to the inguinal region in a patient with inguinal hernia can cause acute intestinal perforation. This rare situation shows us the importance of hernia repair.
Sampled‑data H ∞ Fuzzy Observer Design Under Time‑varying Sampling Rates and its Applications to the Attitude and Heading Reference System

In this paper, we design a sampled-data H ∞ fuzzy observer for nonlinear systems under variable sampling periods. To do so, a linear matrix inequality-based design condition is derived to stabilize the error vector consisting of the difference of the state vectors of the observer and the system. When deriving the condition, we propose a time-dependent fuzzy Lyapunov–Krasovskii functional with discrete time membership functions to obtain numerically less conservative condition. Also, since the proposed method allows the sampling time to be variable, it can be used in low-cost hardwares that do not meet the strict sampling period. Finally, in the hardware experiment, the proposed method is applied to design the attitude and heading reference system that estimates the Euler angles from inertial sensor data, and the results demonstrate the effectiveness of the proposed method.
A PGD-based Method for Robot Global Path Planning: A Primer

: The present paper shows, for the ﬁrst time, the technique known as PGD-Vademecum as a global path planner for mobile robots. The main idea of this method is to obtain a Vademecum containing all the possible paths from any start and goal positions derived from a harmonic potential ﬁeld in a predeﬁned map. The PGD is a numerical technique with three main advantages. The ﬁrst one is the ability to bring together all the possible Poisson equation solutions for all start and goal combinations in a map, guaranteeing that the resulting potential ﬁeld does not have deadlocks. The second one is that the PGD-Vademecum is expressed as a sum of uncoupled multiplied terms: the geometric map and the start and goal conﬁgurations. Therefore, the harmonic potential ﬁeld for any start and goal positions can be reconstructed extremely fast, in a nearly negligible computational time, allowing real-time path planning. The third one is that only a few uncoupled parameters are required to reconstruct the potential ﬁeld with a low discretization error. Simulation results are shown to validate the abilities of this technique.
Risks in the chemotherapy process and possibilities for improvement

Medications are of considerable help if healthcare providers are able to administer them to patients safely and appropriately. The medication use process is complex and a team of professionals – doctors, nurses, and pharmacists – work together with the patient for optimal treatment effect. The process involves many sub-processes and in every step there is a possibility for an error. Cytotoxic drugs have high toxicity and a narrow therapeutic index, which means that there is little difference between a lethal and therapeutic dose. The use of such drugs may entail great risks for the patient. The general aim of the research presented in this thesis is to identify these risks and to identify actions and strategies for improving safety in the chemotherapy process. The research focuses on errors in the medication use process of parenteral cytotoxic drugs and how to prevent them. The main objectives are: o To proactively disclose system weaknesses in the process of prescribing and administering cytotoxic drugs in order to suggest interventions. o To identify the characteristics of the medication errors involving cytotoxic drugs in Sweden in order to gain increased knowledge about them. Two approaches have been applied: a proactive one using the disturbance effect barrier (DEB) method, and a retrospective one using a qualitative analysis of the data. The proactive method revealed system weaknesses mainly at the stage of prescribing and the medication use process was found to involve great risks for the patient. In the retrospective analysis, the most commonly involved drug was fluorouracil and the platinum containing drugs were the ones that often caused serious consequences for the patients. The most common error types were too high doses and use of the wrong drug. Fully 40% of the errors occurred at the prescribing and transcribing stage. All of these were delivered to the patient causing temporary or life-threatening harm. Fully 40% of the errors occurred at the preparation stage and were made by pharmacists. The rest of the errors occurred during preparation or administration by nurses. The stage of prescribing by doctors was identified as a major risk. The most commonly identified error types were wrong dose, drug or patient. This indicates that there are possibilities for improvements, such as by using computerised prescriber order entry (CPOE) systems and bar coding for identification of drug and patient. Other strategies for improvement of the chemotherapy process have been suggested in the literature. Among these are standardisation of prescribing vocabulary, multidisciplinary co-operation, working with pharmaceutical manufacturers, and education of the patients. As described in this thesis, the chemotherapy process is complex and involves a great deal of risks for the patients. There is great awareness of the risks and there are many suggestions for actions and strategies for improvements. Some of hese needs to be further evaluated and implemented for better safety for the patients. (Less)
Parameter Analysis and Optimization of Class-E Power Amplifier Used in Wireless Power Transfer System

In this paper, a steady-state matrix analysis method is introduced to analyze the output characteristics of the class-E power amplifier used in a wireless power transfer (WPT) system, which takes the inductance resistance, on-resistance and leakage current of metal-oxide-semiconductor field effect transistor (MOSFET) into account so that the results can be closer to the actual value. On this basis, the parameters of the class-E power amplifier are optimized, and the output power is improved under the premise of keeping the efficiency unchanged. Finally, the output characteristics of the amplifier before and after optimization are compared by an experiment, while the B-field strength around the WPT system is studied through simulation. The experimental results verify the correctness and feasibility of the optimization method based on steady-state matrix analysis.
Investigating the Behaviour of Machine Learning Techniques to Segment Brain Metastases in Radiation Therapy Planning

This work aimed to investigate whether automated classifiers belonging to feature-based and deep learning may approach brain metastases segmentation successfully. Support Vector Machine and V-Net Convolutional Neural Network are selected as representatives of the two approaches. In the experiments, we consider several configurations of the two methods to segment brain metastases on contrast-enhanced T1-weighted magnetic resonance images. Performances were evaluated and compared under critical conditions imposed by the clinical radiotherapy domain, using in-house dataset and public dataset created for the Multimodal Brain Tumour Image Segmentation (BraTS) challenge. Our results showed that the feature-based and the deep network approaches are promising for the segmentation of Magnetic Resonance Imaging (MRI) brain metastases achieving both an acceptable level of performance. Experimental results also highlight different behaviour between the two methods. Support vector machine (SVM) improves performance with a smaller training set, but it is unable to manage a high level of heterogeneity in the data and requires post-processing refinement stages. The V-Net model shows good performances when trained on multiple heterogeneous cases but requires data augmentations and transfer learning procedures to optimise its behaviour. The paper illustrates a software package implementing an integrated set of procedures for active support in segmenting brain metastases within the radiotherapy workflow.
Effects of gradation on the mobilized friction angle for the instability and steady states of sand-silt mixtures: experimental evidence

This paper deals with a laboratory study of the influence of low-plastic-fines content and grading characteristics on the instability and steady-state friction angle of sand-silt mixtures under monotonic loading conditions. For this purpose, a series of compression undrained triaxial tests were carried out on Chlef sand (Algeria) mixed with low plastic fines (Ip=5%) ranging from Fc=0% to Fc=30%. The samples were prepared in the laboratory with the dry-funnel-pluviation method and subjected to three different confining pressures (Pc’=100 kPa, 200 kPa and 300 kPa). The obtained results indicate that the fines content, void ratio and gradation have a significant influence on the mobilized friction angle for the instability and steady state of the sand-silt mixtures and confirm the existence of simple correlations between the mobilized instability Wiebke Baille Bochum Ruhr University, Laboratory of Foundation Engineering, Soil and Rock Mechanics Bochum, Germany Mostefa Belkhatir University of Chlef, Laboratory of Material Sciences & Environment Algeria