Miracle-dz/UniFAR-FTUs huggingface dataset:
Torque Adaptive Hexagon Voltage Extension Method For PMSM Flux-Weakening Control Based on Dual PI Cascade Structure

Hexagon voltage extension is an effective method to improve the maximum output torque of permanent magnet synchronous motors (PMSMs) in flux-weakening (FW) region. But, the hexagon voltage extension can cause the voltage harmonic inevitably, leading to torque ripple. In this article, it is proved that the increasing rate of voltage harmonic gradually increases during the extension from the inscribed circle voltage to the hexagon voltage. To avoid the voltage harmonic at low torque demand conditions, it is expected that the hexagon voltage extension is only employed at the maximum torque demand conditions. However, the conventional methods employ the nonadjustable hexagon voltage in the whole torque range. To realize the torque-adaptive hexagon voltage extension and suppress the voltage harmonic at low torque demand conditions, this article proposed the dual PI cascade voltage control structure. Two PI controller are designed for adjustments of the extended voltage trajectory and the flux amplitude, respectively. By establishing the Rouse table, the stable conditions of the proposed method are calculated. The effect of suppressing voltage harmonic is theoretically demonstrated. Finally, the comparative experiments are conducted to verify the effectiveness of the proposed method.
Tracking the Evolution of Clusters in Social Media Streams

Tracking the evolution of clusters in social media streams is becoming increasingly important for many applications, such as early detection and monitoring of natural disasters or pandemics. In contrast to clustering on a static set of data, streaming data clustering does not have a global view of the complete data. The local (or partial) view in a high-speed stream makes clustering a challenging task. In this paper, we propose a novel density peak based algorithm, <monospace>TStream</monospace>, for tracking the evolution of clusters and outliers in social media streams, via the evolutionary actions of cluster adjustment, emergence, disappearance, split, and merge. <monospace>TStream</monospace> is based on a temporal decay model and text stream summarisation. The decay model captures the decreasing importance of textual documents over time. The stream summarisation compactly represents them with the help of cells (<italic>aka</italic> micro-clusters) in the memory. We also propose a novel efficient index called <italic>shared dependency tree</italic> (<italic>aka</italic> SD-Tree) based on the ideas of density peak and shared dependency. It maintains the dynamic dependency relationships in <monospace>TStream</monospace> and thereby improves the overall efficiency. We conduct extensive experiments on five real datasets. <monospace>TStream</monospace> outperforms the existing state-of-the-art solutions based on <monospace>MStream</monospace>, <monospace>MStreamF</monospace>, <monospace>EDMStream</monospace>, <monospace>OSGM</monospace>, and <monospace>EStream</monospace>, in terms of cluster mapping measure (CMM) by up to 17.8%, 18.6%, 6.9%, 16.4%, and 20.1%, respectively. It is also significantly more efficient than <monospace>MStream</monospace>, <monospace>MStreamF</monospace>, <monospace>OSGM</monospace>, and <monospace>EStream</monospace>, in terms of response time and throughput.
An Enhanced Data Augmentation Approach to Support Multi-Class Code Readability Classification

— Context: Code readability plays a critical role in software maintenance and evolvement, where a metric for classifying code readability levels is both applicable and desired. However, most prior research has treated code readability classification as a binary classification task due to the lack of labeled data. Objective: To support the training of multi-class code readability classification models, we propose an enhanced data augmentation approach. Method: The approach includes the use of domain-specific data transformation and GAN-based data augmentation. By virtue of this augmentation approach, we could generate sufficient readability data and well train a multi-class code readability model. Result: A series of experiments are conducted to evaluate our augmentation approach. The experimental re-sults show that a state-of-the-art multi-class code readability classification accuracy of 68.0% is reached with a significant improvement of 6.3% compared to only using the original data. Conclusion: As an innovative work of proposing multi-class code readability classification and an enhanced code readability data augmentation approach, our method is proved to be effective.
Comparison of three types of closed-loop control for the 2D trajectory of a magnetic microswimmer

This work is about the comparison of three types of closed-loop controllers for the 2D navigation of magnetic microswimmers within a simulated environment that takes real physical parameters from the literature. We modeled the physical system in state space, then trajectories were generated using the A* algorithm. Discrete PID, LQR and MPC controllers were implemented in our simulations to be compared in terms of settling time, overshoot and error. Finally, the implementation of the LQR control algorithm is recommended due to its better overall performance. However, to be implemented, we also propose a redesign of the electromagnetic coils, that actuate the microswimmers, due to LQR’s demand in control effort and current intensity.
Forward-Douglas–Rachford splitting and forward-partial inverse method for solving monotone inclusions

We provide two weakly convergent algorithms for finding a zero of the sum of a maximally monotone operator, a cocoercive operator, and the normal cone to a closed vector subspace of a real Hilbert space. The methods exploit the intrinsic structure of the problem by activating explicitly the cocoercive operator in the first step, and taking advantage of a vector space decomposition in the second step. The second step of the first method is a Douglas–Rachford iteration involving the maximally monotone operator and the normal cone. In the second method, it is a proximal step involving the partial inverse of the maximally monotone operator with respect to the vector subspace. Connections between the proposed methods and other methods in the literature are provided. Applications to monotone inclusions with finitely many maximally monotone operators and optimization problems are examined.
A Comparative Evaluation of ODL and ONOS Controllers in Software-Defined Network Environments

The SDN controller is a fundamental component of Software-Defined Networking. There is a variety of SDN controllers created by industry and academic research. Consequently, its selection is critical as it influences the success or failure of network connectivity. This study compared Open Network Operating System (ONOS) and Open Daylight (ODL) controllers. These open-source controllers have been installed in virtual machines to evaluate Performance using three types of network topologies that were emulated with the Mininet tool. The Performance was evaluated in terms of Throughput, Jitter, and Latency. The results showed that the ONOS controller achieved a high throughput of 79.200 Mbits, with a lower jitter of 0.022 ms and a minimum latency of 0.117 ms compared to the ODL controller.
Improved RRT*-A*-based Three-Dimensional Path Planning Algorithm for the Robotic Dolphin

The dolphins are fairly well endowed with high turn maneuverability in vertical and horizontal planes. This paper proposes a modified path planning algorithm fusions of rapidly-exploring random tree (RRT) and graph-based methods for a developed robotic dolphin. Considering simultaneously the both minimum yaw radius and minimum pitch radius constraints, a method of calculating a three-dimensional (3D) Dubins curve from two 2D Dubins curves by interpolation is proposed. The 3D Dubins curves and the length of the curves are utilized as the paths and costs of path planning to satisfy the motion constraints of the robotic dolphin. Furthermore, in order to meet the speed and optimization of planned path, a variant RRT algorithm combined with A* algorithm is employed to generate feasible path for the robotic dolphin. The path cost and calculation time of this method is lower. Finally, a tendon-driven continuum robotic dolphin is presented to provide the simulation platform for verifying the effectiveness of the proposed methods.
Online Automatic Gain Tuning for Geometric Attitude Control

We present a framework to automatically tune the gains of a geometric attitude controller based on operating conditions (distance from equilibrium). We propose a two-thread architecture: on the one hand, a computationally simple geometric control law provides real-time control inputs to globally stabilize the system; on the other hand, an optimization procedure continuously varies the gains of the control law to improve the convergence rate guarantees when possible. In particular, we use Control Barrier and Lyapunov functions to find rates of change for the gains that improve bounds on the convergence rate while guaranteeing exponential stability for all subsequent times. The advantage of our architecture is that the gain updates can be computed at a rate possibly much slower than the control law; thanks to the constraints used, even if the gain updates were to stop, exponential stability would still be guaranteed for all future times.The resulting controller is compared via simulations against a static feedback controller and a version based on traditional discontinuous gain scheduling. While we focus on attitude control, our framework could be generalized to any static feedback controller equipped with explicit convergence conditions.
SWAMI: A SWARM-INTELLIGENT OPTIMIZATION TECHNIQUE FOR VOLTAGE COLLAPSE MITIGATION

In this paper, a voltage collapse optimization system based on comparative studies of swarm-intelligent techniques is proposed for voltage collapse mitigation in power system network. The approach draws inspiration from the idea of utilizing the intelligent behavior of swarm-based artificial machine intelligence technique coined SWAMI for voltage collapse minimization or prevention through dynamic shunt compensation of overloaded power network buses. Several simulation studies have been conducted considering three very popular and successful SWAMI agents – the PSOM, BCOM and ACOM on an IEEE benchmark power network with promising results. Simulation studies showed that the PSOM SWAMI exhibited the most stable response in terms of voltage profile collapse and recovery from voltage collapse state after voltage sensitivity studies. Safe margins of loading and optimal shunt compensations are determined based on the SWAMI techniques.
The Methods for Direct Detection of WIMP with Mass <0.5 GeV

The chamber for direct detection of WIMP with mass <0.5 Gev/c2 was developed. The chamber is filled with gas mixture Ne+10%H2 (0-1bar)+0,15ppm Ge(CH3)4. For events detection used GEM+pin-anodes , which provides the energy threshold about eV. The electron background is suppressed owing to photosensitive addition Ge(CH3)4 . It is proposed also for direct detection of WIMP the liquid argon chamber with H2 dissolved in liquid argon at a concentration 100ppm+0,015ppm Ge(CH3)4 .
A Distributed Newton Algorithm for Optimal Resource Allocation in Multi-Agent Systems

This paper considers the solution of a multi-agent, multi-resource allocation problem using a distributed Newton consensus approach. This technique integrates a Newton consensus method to address the optimization task. The solution requires consensus on each vector of the Lagrange multipliers of the local optimization problems. The agents in the system are expected to collaborate and communicate local information with neighbouring agents over a communication network. In contrast to leading resource allocation techniques, the proposed method avoids the need to impose feasible initial conditions. In addition, the technique does not require any coordination or communication of network wide Lagrange multipliers. A simulation study demonstrates the effectiveness and robustness of this algorithm.
Estimation of Electron Density and Temperature by Continuum Spectrum in a Moving Atmospheric-Pressure Nonthermal-Equilibrium Argon Plasma Bullet

A moving atmospheric-pressure nonthermal-equilibrium argon plasma bullet was investigated by emission spectrum and plasma current. Moving plasma changes spatially and temporally. Therefore, it is necessary to consider the time variation of plasma in measurement. Thus, the validity of the electron density estimated by the two types of optical and electrical measurement methods was examined. The electron density and temperature are estimated from the analysis of continuum spectrum (CS). Moreover, the electron density was also estimated from the relationship between the current and the propagation velocity of the plasma luminescence. The plasma current was estimated from the power-line currents with and without plasmas. The propagation velocity of the plasma luminescence was measured by applying the time-of-flight (ToF) method on the observed emission intensity. The electron density estimated from the analysis of CS is comparable to that estimated from the effective peak plasma current. Consequently, the analysis of CS is applicable to a moving plasma bullet.
An overview on synthesis procedures of nanoparticles applied to enhanced oil recovery

Abstract Current technologies have not been able to completely remove all the oil from reservoirs, which are often abandoned with a considerable amount of remaining oil in place. To improve the extraction of oil from reservoirs, nanotechnology has recently gained an important role in the enhanced oil recovery, since nanoparticles have unique characteristics that can improve the efficiency of oil recovery. This study reviews articles published in this field during the past 5 years, highlighting the most important characteristics of nanoparticles for applications in oil recovery concerning different synthesis methods used for their preparation to the efficiency of oil recovery.
Laparoscopic radical cystectomy with pelvic lymph node dissection and ileal orthotopic neobladder by a total extraperitoneal approach: Our initial technique and short-term outcomes

Purpose With the increasing application of laparoscopic or robot-assisted radical cystectomy, a reliable and promising method is needed for reducing postoperative complications. We describe the short-term outcomes of totally extraperitoneal laparoscopic radical cystectomy (TELRC) with extraperitoneal pelvic lymph node dissection (EPLND) and extraperitoneal ileal orthotopic neobladder (EION) techniques. Materials and Methods From January 2020 to December 2021, we performed TELRC and EPLND with EION in 72 patients in our center. The accompanying video highlights our novel techniques. The patients’ demographic data, intraoperative data, and perioperative complications were collected, and short-term oncological and functional results are reported. Results All procedures were technically successful without conversion to open surgery. The patients’ mean body mass index was 26.22±5.71. Median age was 57.51±12.34 years. Average hospital stay was 13.78±4.62 days. Median intraoperative blood loss was 112.92±88.56 mL. No blood transfusion was needed during the operations and only one blood transfusion was performed during the perioperative period. Mean operating time was 259.44±49.84 minutes. Average cost was US$9,875.71±1,873.08. Postoperative short-term complications included short-term ileus (n=3), infection (n=13), leakage of urine (n=11), and lymph fistula (n=7). One late complication of unilateral vesicoureteral anastomotic stenosis occurred. The mean follow-up was 13.42±8.77 months, and no patient developed local or systemic recurrence. The short-term follow-up and small cohort of patients limited our evaluation of outcomes. Conclusions TELRC with PLND and EION was technically feasible and clinically promising, with a reduced potential harm of postoperative complications. Long-term follow-up and a larger cohort of patients are needed for further study.
Qualitative System Dynamics Modeling to Support Community Planning in Opioid Overdose Prevention

Purpose: We aimed to help community stakeholders develop a shared understanding of the opioid crisis through qualitative system dynamics (SD) modeling to inform local strategies for prevention and treatment. Methods: As part of the HEALing Communities Study-New York State, we used secondary qualitative data from community stakeholder interviews and coalition meeting notes to develop qualitative SD models that elucidate the interdependencies and feedback structures underlying the opioid epidemic in each community. Results: The synthesized model revealed multiple balancing and reinforcing feedback loops that influenced the adoption and reach of evidence-based practices to reduce opioid overdose and fatality. Conclusion: SD modeling is a novel approach to helping community stakeholders to see the inter-connectedness of actors, factors and sectors and the need for multiple mutually reinforcing strategies to avert opioid overdose and fatality. Social workers could play a key role in linking actions across sectors in such a complex system.
Determinants of Barley Output Supply Response in Ethiopia: Application of Ardl Bound Cointegration Approach

This study investigated barley output supply response determinant factors in Ethiopia. An ARDL bound test approach was employed as method using secondary data from 1981-2020. The study demonstrated that barley output supply was affected positively and significantly by zero-order lagged seasonal rainfall and crop growing period temperature. The study supports the findings of researchers who reported that warming temperature followed by an increase in the amount of rainfall had a positive impact on barley output supply. The positive impact of temperature was induced because of a rise in the ocean and earth’s surface average temperature, causing more evaporation that increases overall rainfall while reaching over the highland areas. Studies confirm that ENSO and moist winds coming from the Atlantic and Indian Oceans influence the occurrence of rainfall in the western, southeastern, central, and northern highlands of Ethiopia. The study further exhibited that CSMRR and CGPMT had a positive effect on barley output both in the long-run and short-run, implying that climate parameters have minimal effect on barley production. Nonclimatic variables demonstrated that both lagged and current year’s producer prices had a positively significant effect on barley output supply in both the long-run and short-run, implying that barley output supply is highly responsive to any price incentive strategies announced before re-allocation of the area towards barley cultivation. Conversely, the study explored that use of fertilizer in first-order lag had negatively significant impact on barley output supply in both seasons; implying that increased use of fertilizer in lagged period may reduce barley output as a result of inappropriate fertilizers application by farmers. The results generated by this study are useful addendum to the repository of knowledge on elasticity of crop supply at an aggregate level, which can be used in designing strategies and measures for mitigation and adaptation of climate change.
Optimal Bayesian Biomarker Selection for Gene Regulatory Networks under Regulatory Model Uncertainty

Gene regulatory networks (GRNs) are large and complex dynamical systems often monitored through RNA sequencing or microarray technologies. Genomics studies often focus on a small subset of genes and analyze only these genes due to the huge cost and time-limit constraints. Therefore, selecting a small subset of genes that carries the highest information about the underlying process of these complex systems is highly desired. The existing biomarker selection techniques rely on unrealistic assumptions such as direct observability of genes’ states as well as the availability of perfect knowledge about the modeling process. To address the aforementioned issues, this paper models GRNs with uncertain regulatory models with the signal model of partially-observed Boolean dynamical systems (POBDS) and derives the optimal Bayesian biomarker selection framework given the noisy available gene-expression data. The proposed framework is built on the multiple-model adaptive estimation (MMAE) framework and the optimal minimum mean-square error (MMSE) state estimator for POBDS, called Boolean Kalman smoother (BKS). The proposed framework is an optimal solution relative to the uncertainty class, and its high performance is demonstrated using the mammalian cell-cycle Boolean network model and the p53-MDM2 negative feedback loop observed through gene-expression data.
A 1.5 V 2 GS/s 82.1 dB-SFDR Track and Hold Circuit Based on the Time-Divided Post-Distortion Cancelation Technique

This brief presents a track and hold (T/H) circuit for time-interleaved analog-to-digital converters based on a new linearization technique—time-divided post-distortion cancellation. Fabricated in 65 nm CMOS, it achieves 82.1 dB SFDR with a 700-mV near-Nyquist input at a clock speed of 2 GS/s, while drawing only 9 mW power from a 1.5-V supply. Compared to the previous state-of-the-art circuits, this T/H circuit exhibits an energy-efficiency figure-of-merit improvement of 9.5 dB.
Decision-making System based on Finite State Machine for Low-speed Autonomous Vehicles in the Park

Park is an important population and industry gathering area in a city, and a popular scene in theoretical research and technical application of automatic driving. Decision-making, path planning and control are the core technologies. Due to cost and safety issues, automatic driving has not been widely used for park applications. In this paper, a decision-making model is devised based on finite state machine (FSM), a local path planner is designed based on Bézier curve and the lateral and longitudinal control method is realized on GAC Aion LX. This decision-making system for low speed automatic driving in the park is tested. Results demonstrate its accuracy and reliability.
Acetylacetone modulated TiO2 nanoparticles for low‐temperature solution processable perovskite solar cell

TiO2 has been widely adopted as an electron transport layer during the fabrication of efficient metal halide perovskite solar cells (MHP‐SCs). In this study, we prepared acetylacetone (Acac)‐modulated TiO2 nanoparticles (NPs) suitable for low‐temperature solution processing via the sol‐gel method. Acac coordinates with TiO2 NPs and preferably passivates surface trap states which are detrimental to photovoltaic performance. Moreover, the Acac‐TiO2 NPs establish a favorable band alignment for charge extraction by donating the lone pair electron. As a result, Acac‐TiO2 NPs based MHP‐SCs exhibit an outstanding power conversion efficiency (PCE) of 21.20% attributed to facilitated charge extraction and reduced defect density. Furthermore, by implementing low‐temperature solution‐processable Acac‐TiO2 NPs, we successfully demonstrated a remarkable PCE of 18.01% along with excellent mechanical stability, retaining 74.5% and 67.1% PCE after 5000 bending cycles in outward and inward directions from Acac‐TiO2 NPs based MHP‐SCs on a flexible substrate.