Miracle-dz/UniFAR-FTUs huggingface dataset:
Pulse stretching and shape control by compound feedback in a Q-switched ruby laser

Feedback control is employed to stabilize the output of a Q -switched ruby laser. A computational model of the rate equations plus feedback terms is used to examine pulse shapes with changing feedback parameters. It is shown that a lengthened flat-top pulse cannot be obtained with negative feedback alone, a compound feedback arrangement is suggested to facilitate this. A practical laser system is constructed with an associated feedback loop, good agreement is found between theory and experiment. Pulses flat to within ±5 percent are obtained with durations up to 600 ns and are amplified with no apparent saturation. Output powers of 15 MW are achieved for hundreds of nanoseconds with a divergence of 7 mrad and a spectrum consisting of transform-limited lines separated by the axial mode spacing.
Model‐reference impedance and admittance control of linear systems

A variable structure model‐reference adaptive control (VS‐MRAC) of impedances and admittances (driving‐point (DP) functions) is proposed. Only voltage and current measurements are required to implement the controllers. The inclusion of a prefilter in the reference model allows the synthesis of quite general DP functions, even with nonminimum phase zeros and unstable poles. It is shown that the stability of the closed‐loop system depends only on the source DP function and the chosen reference model. The VS‐MRAC guarantees global exponential stability properties, robustness to parameter uncertainties, disturbances, and unmodelled dynamics. Simulation results illustrate the performance and robustness of the VS‐MRAC. Copyright © 2016 John Wiley & Sons, Ltd.
Robotized additive manufacturing of silicone for skeleton-reinforced linear soft actuators

This paper presents a method for manufacturing a soft pneumatic linear actuator. The linear actuator is based on a deformable chamber reinforced by a cylindrical auxetic structure. The objective of this work is to create a hermetic silicone chamber inside the auxetic structure previously machined in PVC. The manufacturing process is based on 3D silicone printing using an anthropomorphic robotic arm. The proposed strategy increases the versatility of the process compared to overmolding strategies, especially in regard to the dimensions of the actuator. In this paper we present an experimental setup integrating a robotic arm, the system for the registration of the different elements and the control of the print head trajectories. The actuator has been designed, built and implemented, allowing us to evaluate its performances and life span.
Electronic health record data quality variability across a multistate clinical research network

Abstract Background: Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However, there has yet to be a consensus on the best practice. We used a rule-based approach to assess the variability of EHR data quality across multiple healthcare systems. Methods: To quantify data quality concerns across healthcare systems in a PCORnet Clinical Research Network, we used a previously tested rule-based framework tailored to the PCORnet Common Data Model to perform data quality assessment at 13 clinical sites across eight states. Results were compared with the current PCORnet data curation process to explore the differences between both methods. Additional analyses of testosterone therapy prescribing were used to explore clinical care variability and quality. Results: The framework detected discrepancies across sites, revealing evident data quality variability between sites. The detailed requirements encoded the rules captured additional data errors with a specificity that aids in remediation of technical errors compared to the current PCORnet data curation process. Other rules designed to detect logical and clinical inconsistencies may also support clinical care variability and quality programs. Conclusion: Rule-based EHR data quality methods quantify significant discrepancies across all sites. Medication and laboratory sources are causes of data errors.
Mediating Effect of External Factors to the Relationship Between Leadership Styles and Organizational Performance

Inappropriate leadership style harms employees and the organization's overall performance. Lack of leadership likely causesunethical practises, high turnover, and employee disengagement. Many of the organization's problems are attributed to incompetent or mismatched administrators. They may use an inappropriate leadership style with project subordinates. There is lack of studies on leadership styles and organisational performance, especially in oil and gas organisations. Hence, this research examines the relationship leadership styles and organisational performanceof ADNOC company. This study involved questionnairesurveyfor data collection andanalysis of 398 questionnaire forms was carried out through structuralequation modelling. Thestudy established a mediationmodelwhere the external factors act as mediator to the relationship. Based on the assessment on the model, it was found that Charismatic and Democratic leadership styles havea significant impact on organisational performance while Transformational and Transactional do not have significant effect on the organization performance. Also, external environment is proven as mediator of Charismatic and Democratic leadership styles. Overall, the developed model can assist for adopting the leadership style in achievingthe successful organizational performanceof ADNOC Company.
Compression of three-dimensional surfaces by means of chain coding

Abstract. Studies for representing three-dimensional (3-D) objects are an important subject in different fields, such as computer vision, data compression, creation of virtual scenes, and others. Any surface can be seen as a 3-D object and studied as such. 3-D voxel-based surfaces are described using a 3-D tree structure known as an enclosing tree. A modified version of this structure is used to describe the surface. In order to describe the surfaces, we used the enclosing trees that are represented via the orthogonal direction change chain code. The representation obtained is compared with the original to verify if proper data compression is achieved.
Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters

The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible–Exposed–Infected–Quarantined–Recovered–Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation.
A New Methodology to Precisely Induce Wake-Up for Reliability Assessment of Ferroelectric Devices

Studies on ferroelectric (FE) device degradation are performed on “woken up” devices. The process of waking up a device is typically done by applying a logarithmically increasing number of pulsed, alternating bipolar switching voltage cycles. However, this method has low resolution in precisely achieving the wake-up state, resulting in ambiguity in the current stage of the life cycle of the device. Furthermore, ferroelectric device performance depends heavily on the spatio-temporal distribution of defects in the device stack, which are very different in the wake-up and fatigue phases of the life cycle. The standard bipolar pulsed stressing scheme as well as asymmetric device structure further complicate the analysis of the effects of voltage stressing on defect drift and subsequent device degradation. Here, we propose a new stressing methodology leveraging on an alternating stress-sense scheme using CVS/RVS and positive-up-negative-down (PUND) waveforms to better control the extent of wake-up in the device. Wake-up and the associated changes to the spatio-temporal mapping of the charged defect concentrations can be more confidently ascertained using the proposed methodology, thereby enabling better understanding of the reliability physics governing wake-up and fatigue for FE devices in the future for lifetime prediction from accelerated life tests.
Development of metformin HCl granules using brown flaxseed mucilage as a retardant polymer: effects of polymer and drug ratio

Flaxseed ( Linum usitatissimum L.) is the seed of a multipurpose plant of pharmaceutical interest, as its mucilage can be used as a natural matrix to develop extended-release dosage forms and potentially replace synthetic polymers. In this study, a 3² factorial design with two replicates of the central point was applied to optimize the development of extended-release granules of metformin HCl. The total fiber content of the mucilage as well as the friability and dissolution of the formulations were evaluated. The lyophilized mucilage presented a high total fiber content (42.63%), which suggests a high efficiency extraction process. Higher concentrations of the mucilage and metformin HCl yielded less friable granules. In addition, lower concentrations of metformin HCl and higher concentrations of the mucilage resulted in slower drug release during the dissolution assays. The release kinetics for most formulations were better represented by the Hixson-Crowell model, while formulations containing a higher concentration of the mucilage were represented by the Korsmeyer-Peppas model. Nonetheless, five formulations showed a longer release than the reference HPMC formulation. More desirable results were obtained with a higher concentration of the mucilage (13–18%) and a lower concentration of metformin (40%).
Professional digital competence (PDC) in teacher education – teacher candidates reasoning about programming when involved in problem-solving activities with digital tools

Abstract The increased emergence of digital tools in educational contexts have changed the conditions for teaching and learning which, in turn, has meant a need for the development of professional digital competence (PDC) among teachers worldwide. Simultaneously, and aligned with this, programming has become an increasingly important part of schooling in many parts of the world and a step toward developing computational thinking. In this paper we investigate teacher candidates reasoning about programming when involved in problem-solving activities with digital tools. The unit of analysis is programming activities conducted by a group of 17 teacher candidates in a Swedish primary teacher education. The research question posed is: How do digital tools co-determine teacher candidates’ reasoning about programming and its pedagogical application during problem-solving activities with digital tools designed for teaching? Drawing from a sociocultural approach, the results of the present study imply that the teacher candidate’s appropriation of knowledge is co-determined by the digital tool itself and as such it does not provide any deeper understanding for its pedagogical use.
A VCO-Based ADC With Inherent Mixing Capability and Local Oscillator Suppression in 55-nm CMOS

This brief introduces a new circuit that can be used as a mixer and as a VCO-ADC simultaneously. The power supply nodes of the inverters in a ring oscillator are sorted in two sets, and connected to two different input signals. We prove mathematically that the oscillator frequency of such configuration reacts to the product of the two analog inputs. Therefore, the ring oscillator can be used as an ADC to directly digitize the low pass down converted component. A first system-level approach is made to minimize the non desired beat components by a proper number of taps in the ring oscillator. To confirm the concept of the Mixer VCO-ADC and its performance, a 55nm CMOS chip has been designed and fabricated. Measurements show a SNDR of 51.3 dB in a 100 kHz bandwidth in the down converted signal from a 1.001 MHz input signal mixed with a 1 MHz local oscillator.
Applications of Physics to Finance and Economics: Returns, Trading Activity and Income

This dissertation reports work where physics methods are applied to financial and economical problems. The first part studies stock market data (chapter 1 to 5). The second part is devoted to personal income in the USA (chapter 6). We first study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. For mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an exponential law. At longer times, the exponential law continuously evolves into Gaussian distribution. After characterizing the stock returns at mesoscopic time lags, we study the subordination hypothesis. The integrated volatility V_t constructed from the number of trades process can be used as a subordinator for a Brownian motion. This subordination is able to describe approximatly 85% of the stock returns for time lags that start at 1 hour but are shorter than one day. Finally, we show that the CIR process describes well enough the empirical V_t process, such that the corresponding Heston model is able to describe the log-returns x_t process, with approximately the maximum quality that the subordination allows. Finally, we study the time evolution of the personal income distribution. We find that the personal income distribution in the USA has a well-defined two-income-class structure. The majority of population (97-99%) belongs to the lower income class characterized by the exponential Boltzmann-Gibb(``thermal'') distribution, whereas the higher income class (1-3% of population) has a Pareto power-law (``superthermal'') distribution. We show that the ``thermal'' part is stationary in time.
Design of Energy-Efficient RFET-Based Exact and Approximate 4:2 Compressors and Multipliers

The ever-increasing demand for low-power and area-efficient circuits for use in battery-powered devices and the CMOS scaling problems have attracted the attention of VLSI designers to beyond-CMOS technologies like Reconfigurable Field-Effect Transistors (RFETs). Improving the efficiency of multipliers is critical as the core component of many applications such as image processing and Machine Learning (ML). This brief proposes a compact and energy-efficient RFET-based architecture for the 4:2 compressor and Dadda multiplier, leveraging transistor-level reconfigurability and multi-input support of the RFET. Moreover, we propose a novel approximate 4:2 compressor based on efficient RFET logic cells to cater to the needs of error-resilient applications. Extensive circuit-level simulations with 14nm germanium nanowire (GeNW) RFET technology show that the proposed RFET-based exact multiplier improves the power consumption and power-delay product (PDP) by 65% and 45%, respectively, compared to the conventional CMOS-based counterpart in 14nm FinFET technology. Besides, we show that utilizing the proposed approximate compressor, the area and PDP of the multiplier reduce by 46% and 42%. The effectiveness of the approximate multiplier is evaluated in the image multiplication, and the average PSNR and SSIM values are 31.39 and 0.87, respectively.
Analysis and Modeling of Leakage Currents in Stacked Gate-All-Around Nanosheet Transistors

For efficient use of the upcoming Stacked Gate-all-around Nanosheet Field Effect Transistors (GAAFET), identifying and mitigating leakage current components are essential. This paper comprehensively investigates the leakage components not only in the nanosheets but also through the substrate, including effects such as Gate-Induced Drain Lowering (GIDL) and parasitic substrate leakage. We thoroughly investigate the impact of device geometry on the device leakage current and propose device design guidelines for mitigation of the substrate leakage current for these devices. In addition, we have modeled the GIDL current of GAAFETs using BSIM-CMG code.
Reduced finite element modelling and closed-loop control of pneumatic-driven soft continuum robots

The introduction of soft robots has led to the development of inherently safe and flexible interventional tools for medical applications, when compared to their traditionally rigid counterparts. In particular, robot-assisted surgery is one of the medical applications that benefits from the inherent properties of soft instruments. However, robust control and reliable manipulation of soft tools remain challenging. In this paper, we present a new method based on reduced finite element method model and closed-loop inverse kinematics control for a fiber-reinforced soft robot. The highly flexible, pneumatically driven soft robot has three fully fiber-reinforced chamber pairs. The outer diameter is 11.5 mm. An inner working channel of 4.5 mm provides a free lumen for in-vivo cancer imaging tools during minimally invasive interventions. Here, the manipulator is designed in order to retrieve a tissue biopsy which can then be investigated for cancerous tissue. Simulation and experimental results are compared to validate the model and control methods, using one-module and two-module robots. The results show a real-time control is achievable using the reduced model. Combing the closed-loop control, the median position tracking errors are generally less than 2 mm.
Design of an Exoskeleton for Hand Rehabilitation

An Exoskeleton is a device used to assist a person with disabilities to perform routine tasks in daily life. In the present work, an exoskeleton is designed in the laboratory using an in-house facility of 3D printing for a prosthetic hand that helps an amputee with hand loss and a person having a disability in moving palm and fingers. In the designed prototype, an EMG muscle sensor is used to acquire the signals from the muscles of the hand and convert them into voltages. These low-level voltages are then fed to a signal conditioning circuit and subsequently to the microcontroller. The microcontroller is programmed in such a way that servo motors rotate according to the voltages received from these sensors. In this way, movements of the fingers are directed by servo motors which rotate according to muscle activities, thereby enabling a person to perform routine tasks very effectively.
Inverted SOA and Transient Non-Linearity of LDMOS Devices With RESURF-Implant

Reduced surface field (RESURF) technique in the laterally double-diffused MOS (LDMOS) devices offers a promising solution of high voltage alternative over conventional LDMOS devices with improved figure of merits. This paper addresses the safe operating area (SOA) concerns and ON-state breakdown driven limitations of RESURF technique, highlighting the inverted SOA behavior. Using electrothermal simulations on TCAD tool, this work also investigates the influence of RESURF-implant on the lattice heating in the LDMOS devices.
NMPC-Based Integrated Thermal Management of Battery and Cabin for Electric Vehicles in Cold Weather Conditions

One of the major obstacles along the way of electric vehicles' (EVs') wider global adoption is their limited driving range. Extreme cold or hot environments can further impact the EV's range as a significant amount of energy is needed for cabin and battery temperature regulation while the battery's power and energy capacity are also impeded. To overcome this issue, we present an optimal control strategy based on nonlinear model predictive control (NMPC) for integrated thermal management (ITM) of the battery and cabin of EVs, where the proposed NMPC simultaneously optimizes the EV range and cabin comfort in real time. Firstly, the components of the designed ITM system are introduced and control-oriented modeling is done. Secondly, to demonstrate and validate the benefits of the proposed ITM, an optimal control problem is defined and dynamic programming (DP) is employed to find the global optimal solution. Thirdly, for practical implementation, NMPC-based control strategy is developed, where the cost function design and weights calibration are done in comparison with DP global optimal solution. Weight-tuning results show that our NMPC-based approach can achieve close driving range maximization as compared to the DP benchmark while ensuring cabin comfort. The developed NMPC-based ITM strategy is further illustrated by comparing its performance to two additional benchmark strategies, i.e., rule-based control and cabin heating only. Finally, our simulation results also identify several important factors that impact the benefits of the proposed NMPC-based ITM, which are used to summarize the operating conditions under which the proposed ITM is critically needed.
Trajectory Generation for Space Manipulators Capturing Moving Targets Using Transfer Learning

In a debris mitigation mission, a crucial phase of the proximity operation for a space manipulator is chasing a capture point on a noncooperative target satellite. Knowing the uncertain position and velocity of the target, a learning-based online trajectory planner offers a robust solution to the chasing problem. This paper uses the concept of transfer learning to develop an online trajectory generator for the task of capturing a moving target with an uncertain space manipulator. We divide this complex task into multiple sub-tasks and order them based on their difficulty level. We employ the Deep Deterministic Policy Gradient (DDPG) algorithm to learn each sub-task individually. The DDPG is a deep reinforcement learning approach that provides the ability to work with continuous states and actions by approximating the action-value function and the policy with neural networks. We propose a novel method to transfer the knowledge gained in an easier sub-task to a more difficult one in the form of expert policy and transition memories. State and action representation has a crucial impact on learning performance, which is comprehensively studied in this paper for the task of capturing a moving target. Considering the learning performance, we show the existence of an optimal state repre-sentation, which is not necessarily the minimal representation of the system. We compare different action representations of a manipulator, i.e., joint space and workspace velocities, and demonstrate the superiority of the workspace actions. Finally, the developed transfer learning approach is implemented on a planar space manipulator with an onboard 2-link arm to generate trajectories that can capture a target randomly moving with the maximum speed of the manipulator's end effector. To show the efficacy of the approach, its results are compared with the case where the agent learns the task from scratch.