Miracle-dz/UniFAR-FTUs huggingface dataset:
A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles

—In this work, we propose a new learning-based iterative control (IC) framework that enables a complex soft-robotic arm to track trajectories accurately. Compared to traditional iterative learning control (ILC), which operates on a single ﬁxed reference trajectory, we use a deep learning approach to generalize across various reference trajectories. The resulting nonlinear mapping computes feedforward actions and is used in a two degrees of freedom control design. Our method incorporates prior knowledge about the system dynamics and by learning only feedforward actions, it mitigates the risk of instability. We demonstrate a low sample complexity and an excellent tracking performance in real-world experiments. The experiments are carried out on a custom-made robot arm with four degrees of freedom that is actuated with pneumatic artiﬁcial muscles. The experiments include high acceleration and high velocity motion.
Crowd behavioural simulation via multi-agent reinforcement learning

Crowd simulation can be thought of as a group of entities interacting with one another. Traditionally, an animated entity would require precise scripts so that it can function in a virtual environment autonomously. Previous studies on crowd simulation have been used in real world applications but these methods are not learning agents and are therefore unable to adapt and change their behaviours. The state of the art crowd simulation methods include flow based, particle and strategy based models. A reinforcement learning agent could learn how to navigate, behave and interact in an environment without explicit design. Then a group of reinforcement learning agents should be able to act in a way that simulates a crowd. This thesis investigates the believability of crowd behavioural simulation via three multi-agent reinforcement learning methods. The methods are Q-learning in multi-agent markov decision processes model, joint state action Q-learning and joint state value iteration algorithm. The three learning methods are able to produce believable and realistic crowd behaviours.
Plant EFFECT OF DIETHYL SULPHATE ON M1 ATTRIBUTES AND SDS-PAGE ANALYSIS IN GROUNDNUT ( Arachis hypogaea L.)

The present study was carried out to know the effect of diethyl sulphate on groundnut cultivar TMV-7 particularly in M 1 generation. The healthy and viable seeds of groundnut cultivar TMV-7 were exposed to different concentrations of diethyl sulphate (5 to 25 mM) and sown in the field. The various growth and morphological parameters like percentage of seed germination, survival percentage plant height, days to flowering, total height at time of harvest, root length and number of lateral roots, number of pods per plant and pod weight were decreased with increasing concentrations of diethyl sulphate. All the characteristics studied were inhibited when compared to control. The lower concentrations such as 5, 10 and 15 mM concentrations enhanced all the parameters. But there was a sudden decrease in 20 and 25 mM. The various concentrations of DES played a different role in the field condition itself. The shoots developed from different concentrations of DES showed variation in their number and height. The protein content also showed decreasing trend with increasing concentrations of DES. There was a steady increase of protein content up to 15 mM DES. After that, there was a drastic reduction in the protein content in 20 and 25 mM. From the diethyl sulphate treated seeds, the embryo axes were collected and subjected to SDS-PAGE analysis. The protein banding pattern also differentiated among various concentrations of DES. The protein band with 77.5 kDa was observed in 5 and 10mM treatments. Like that the 13.5 kDa was present in 10 and 25mM treatments. The 48.25 kDa recorded in 15 and 20 mM treatments.
Analysis of digital noise reduction methods on classifiers used in automated visual evaluation

Cervical cancer disproportionately hurts underserved women from disadvantaged communities. Automated visual evaluation (AVE), which analyzes white light cervical images using machine learning, is being considered for management of screen-positive patients. Gaussian noise was identified as degrading AVE performance. Two noise correction approaches were tested on images from historic data with added Gaussian noise. One denoising method (VDNet) was based on neural networks; the other used conventional Gaussian blur filtering. Images were evaluated by an object detection network (RetinaNet), and by a binary pathology ResNeSt classifier. VDNet filtering limited AVE performance degradation at higher noise levels, while Guassian blur only worked on low noise levels.
Isolation and Identification of Bacteria found in the milt of cultured Clarias gariepinus

Bacteria identification in fish is important; hence it provides update information on emerging and existing organisms thereby enriching the gene bank on fish disease. This study investigated bacteria associated with milt in cultured samples of African catfish- Clarias gariepinus. Catfish broodstock were collected from private Fish Farm in Ogun State, Nigeria. Fish samples were taken to the Microbiology laboratory, Federal University of Agriculture, Abeokuta Ogun State for milt microbial analyses, and amplicon were taken to International Institute of Tropical Agriculture, Ibadan for molecular characterization. Bio-edit was use for importing and mining nucleotide sequences into gene bank. The results revealed that the following bacterial organisms were present in the milt of Clarias gariepinus: Aeromonas caviae, Proteus mirabilis, Serratia rubidaea, Pseudomonas mosselii, Acinetobacter soli and Klebsiella variicola. The Basic Local Alignment Search Tools revealed the percentage similarity ranging from 86- 97.04% and their accession numbers. These bacteria indicated high levels of faecal contamination in the environment. In conclusion, bacteria were found in the milt of cultured catfish and are capable of being pathogenic to humans and may increase the vertical transfer to fry during breeding and rearing stage.
RustHornBelt: a semantic foundation for functional verification of Rust programs with unsafe code

Rust is a systems programming language that offers both low-level memory operations and high-level safety guarantees, via a strong ownership type system that prohibits mutation of aliased state. In prior work, Matsushita et al. developed RustHorn, a promising technique for functional verification of Rust code: it leverages the strong invariants of Rust types to express the behavior of stateful Rust code with first-order logic (FOL) formulas, whose verification is amenable to off-the-shelf automated techniques. RustHorn’s key idea is to use prophecies to describe the behavior of mutable borrows. However, the soundness of RustHorn was only established for a safe subset of Rust, and it has remained unclear how to extend it to support various safe APIs that encapsulate unsafe code (i.e., code where Rust’s aliasing discipline is relaxed). In this paper, we present RustHornBelt, the first machine-checked proof of soundness for RustHorn-style verification which supports giving FOL specs to safe APIs implemented with unsafe code. RustHornBelt employs the approach of semantic typing used in Jung et al.’s RustBelt framework, but it extends RustBelt’s model to reason not only about safety but also functional correctness. The key challenge in RustHornBelt is to develop a semantic model of RustHorn-style prophecies, which we achieve via a new separation-logic mechanism we call parametric prophecies.
Science center to go: a mixed reality learning environment of miniature exhibits

Modern science museums deliver exhibits that involve visitors in sessions of active playful learning. Mixed Reality may additionally be used as a catalyst for science centre exhibits to provide new perspectives and reveal otherwise hidden phenomena. Such sophisticated exhibits might well be qualified for improved learning at school – except they are hardly available outside of science centres. Our approach addresses this challenge by bringing miniaturized Augmented Reality exhibits out of the science centre into schools. The presented miniature exhibits uncouple science centre exhibits from their traditional venue and deliver natural ways of learning whenever and wherever it is desired. Therefore a set of miniature exhibits has been designed with the ambitious goal to meet the requirements of nowadays school curriculums. In this paper a science centre in a suitcase will be presented and discussed offering tailor-made learning experiences augmented through normal computers.
Toxic Workplaces: Worker and Firm Effects

Peer effects are pervasive in the workplace. Unfortunately, not all peer effects are positive and disruptive co-workers can create negative spillover effects on co-workers. In this paper, we consider an extreme indicator of a toxic workplace: violence amongst colleagues. Link-ing unique Finnish administrative data on all police reports to labour market outcomes, we provide the first evidence on the impact of workplace violence on worker and firm outcomes. Following a violent incident between colleagues, victims (perpetrators) are 10 (9) percentage points more likely to separate from their firms and experience a significant drop in earnings. In the five years following an incident, the firm experiences a change in its gender composition, becoming more male and less educated. We find stronger negative labour market impacts on victims and on the gender composition of firms when the incident occurs between a male perpetrator and a female victim.
UNCERTAINTY ESTIMATION THROUGH POLYNOMIAL MAP INVERSION

This paper investigates the estimation of the uncertainties of a system using poly- nomial map inversions. The measurement noise inﬂuence on the observations from the sensors can be mapped back into the initial state probability density function. Therefore, a new technique that analyzes which portion of the ‘a pri-ori’ distribution could have generated the measurements is proposed. The algo- rithm is tested in different applications, under either a square map and a case with measurement deﬁcit.
Homogenization models for the analysis of reflection properties of mushroom structures

1 Department of Radio Science and Engineering / SMARAD Center of Excellence, TKK Helsinki University of Technology, P.O. Box 3000, FI-02015 TKK, Finland, email: olli.luukkonen@tkk.fi 2 Department of Electrical Engineering Instituto de Telecomunicacoes, University of Coimbra, 3030 Coimbra, Portugal 3 Department of Electrical Engineering, University of Mississippi, University, MS 38677, USA Abstract
Jumpstart: Fast Critical Service Resumption for a Partitioning Hypervisor in Embedded Systems

Complex embedded systems are now supporting the co-existence of multiple OSes to manage services once assigned to separate embedded microcontrollers. Automotive systems, for example, now use multiple OSes to consolidate electronic control unit (ECU) functions on a centralized embedded computing platform. Such platforms have the complexity of an industrial embedded PC, with multiple cores and hardware virtualization capabilities. This enables a partitioning hypervisor to spatially and temporally share the physical machine with separate guest OSes, which manage services of different criticality levels. However, PC-class hardware incurs a large latency to bootstrap an OS and associated application-level services. A firmware BIOS performs a power-on-self-test, and then loads OS images into memory from a bootable storage device. This latency is unacceptable in time-critical embedded systems, where important services must be operational within milliseconds of starting the system. In this paper, we present Jumpstart, a PC-class power management approach that minimizes the wakeup delay of a partitioning hypervisor for use in embedded systems. We show how Jumpstart resumes critical OS services and tasks from a low power suspended state in approximately 600 milliseconds, and reduces full system startup delay by a factor of 23. Additionally, Jumpstart consumes minimal power compared to approaches requiring a system boot from a previously powered-off state. By comparison, an alternative firmware-optimized bootloader, called Slim, reduces boot latency by a factor of 1.8.
Project Neuron: Simulating superconducting nanowire neuron networks with Python and SPICE

In her 2020 thesis, Emily Toomey introduced a superconducting nanowire computing element for use in neuromorphic computing (3). Toomey demonstrated a circuit level model for the neuron, which required intricate knowledge of nanowire circuit dynamics to work with. To create spiking neural networks (SNNs) from this model required tuning individual circuit parameters in SPICE by hand; this method of creating networks was not generalizable and required knowledge of superconducting physics and circuit dynamics, which is not strictly necessary for the computer scientist creating spiking neural networks. To solve this issue, we created a layer of abstraction between the network and the hardware implementation by developing the Project Neuron Python package. This package acts as an interface for creating spiking neural networks by providing a graphical SNN framework which compiles into and evolves as a network composed of abstract superconducting neurons. This package allows a computer scientist to create SNNs using a standard graphical model and systematically compiles the network into superconducting hardware such that the hardware network evolves as expected by the graphical model. We used the basic compositional model for spiking neural networks created by Nancy Lynch and Cameron Musco (4) as the graphical model which the user would interface with. In order to systematically compile this graphical model into hardware, an abstract model for the nanowire neuron was developed through further characterizations of the neuron using python and SPICE simulations. The package was successfully used to create generalizable spiking neural networks which evolved as Lynch and Musco’s model would predict.
Continuously tunable ultra-thin silicon waveguide optical delay line

As light cannot be stopped or directly stored in any media, optical delay lines are usually used to temporally trap the optical signals. We report a wide-range continuously tunable optical delay line chip consisting of a ring resonator array and a Mach–Zehnder interferometer (MZI) switch array on the 60-nm-thick silicon waveguide platform. The ring delay line provides continuous delay tuning of more than 10 ps with a push–pull differential tuning method. The MZI switchable delay line provides digitally programmable delay tuning with a resolution of 10 ps upon reconfiguration of the MZI switches to establish different optical routing paths. Dual-stage MZI switches are used to ensure low crosstalk with an improved signal-to-noise ratio. The delay line chip can generate a maximum delay of >1 ns with an on-chip insertion loss of 12.4 dB. Optical pulse time-division multiplexing and quasi-arbitrary waveform generation are realized based on the delay line chip. These results represent a significant step towards the realization of highly reconfigurable optical signal processors enabled by optical delay manipulation with broad applications for optical communications and microwave photonics.
Investigations on Prediction of MRR and Surface Roughness on Electro Discharge Machine Using Regression Analysis and Artificial Neural Network Programming

Abstract Extensive work has been reported on optimization of process parameters for EDM process developing the unconventional techniques has across the world. In the present work, an effort has been made to explore the potentialities of application of Regression Analysis and Artificial Neural Network in EDM process for the case reported. The present paper discusses the investigations carried out on Electro Discharge Machine to find optimum setting of process parameters for a particular tool-work piece combination. Investigation has been focused using four levels of current and three levels of tool diameter to find the optimum values of output quality characteristics viz. Material Removal Rate (MRR) and Surface Roughness. The parameter combination is worked out using Full Factorial Design of Experiment methods (DOE). The experiments carried out using this method take on all the possible combinations of different levels across all the factors and hence allow studying the effect of each factor on the response variable. In present investigation, experiments are conducted for all possible combinations and based on the observed readings MRR and Surface Roughness are measured and regression equations are derived for the two output quality characteristics under study. The present investigation was further extended with the application of ANN. The ANN program is based on Multi layer perceptrons (MLPs) that are layered feed forward networks typically trained with static back propagation. The ANN analysis uses architecture that consists of 2 input and 2 output processing elements and a hidden layer. The network training is carried out and then trained network is tested with few experimental results which are not used during training. Prior to testing, network is also validated with the set of data. The results obtained during the study are critically discussed and reported.
Control procedures for falsework construction

Tbe majority of concrete flyover constructions involve falsework. In 1976 the Braggs Committee in England after intensive investigation of falsework failures, had concluded that technical reasons and procedural inadequacies were the two principal causes for the failures. The Committee also recommended the appointment of a Temporary Works Coordinator who is given responsibility for all aspects of a particular falsework from inception to removal. The British standard on falsework (BS5975:1982) renamed the personnel in charge as Falsework Coordinator and listed his principal activities. In Hong Kong an Independent Checking Engineer has been required in recent years responsible for the checking of temporary works designed by the Contractor. This paper reviews the control procedures of falsework design and construction and their relation with real cases of falsework failure in the region.
The internationalisation of HRM through reverse transfer: two case studies of French

Building on existing studies of reverse transfer (RT) of HRM and work organisation practices, this article reports on an investigation of reverse transfer of HR practices within two French multinational companies operating in Scotland. The study identifies limited evidence of RT despite the firms’ predisposing characteristics for RT. A number of factors inherent within the French national business system were found to create restrictions. In addition, France’s history of fostering national champions presented resistance to the transfer of localised HR ideas to the domestic economy. Finally, and to a lesser degree, market relations within one of the subsidiary’s locality were found to impede the RT of practices. The article discusses the theoretical implications for the conditions under which reverse transfer takes place.
Sensor selection based on principal component analysis for fault detection in wind turbines

Growing interest for improving the reliability of safety-critical structures, such as wind turbines, has led to the advancement of structural health monitoring (SHM). Existing techniques for fault detection can be broadly classified into two major categories: model-based methods and signal processing-based methods. This work focuses in the signal-processing-based fault detection by using principal component analysis (PCA) as a way to condense and extract information from the collected signals. In particular, the goal of this work is to select a reduced number of sensors to be used. From a practical point of view, a reduced number of sensors installed in the structure leads to a reduced cost of installation and maintenance. Besides, from a computational point of view, less sensors implies lower computing time, thus the detection time is shortened. The overall strategy is to firstly create a PCA model measuring a healthy wind turbine. Secondly, with the model, and for each fault scenario and each possible subset of sensors, it measures the Euclidean distance between the arithmetic mean of the projections into the PCA model that come from the healthy wind turbine and the mean of the projections that come from the faulty one. Finally, it finds the subset of sensors that separate the most the data coming from the healthy wind turbine and the data coming from the faulty one. Numerical simulations using a sophisticated wind turbine model (a modern 5MW turbine implemented in the FAST software) show the performance of the proposed method under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics.
Thin Film Fluid Flow Simulation on Rotating Discs

Prediction and analysis of complex industrial processes, for example wet chemical wafer etching, depend on accurate modelling of physical phenomena. This work presents a series of numerical studies of film flow with an impinging jet on rotating discs which is the basis for a high performance simulation tool that can be used in design and optimisation of such industrial processes. Numerical studies based on the Volume-of-Fluid (VoF) method were performed and evaluated against reported experimental data. The conclusion drawn is that a transient two-phase 3D free-surface VoF-simulation with a dynamically moving jet is impractical for an industrial use due to very long computational times required. The thin film model based on an integral method, which reduces the three-dimensional nature of the problem into a two-dimensional one by integrating the Navier-Stokes equations over the film thickness, is proposed as a possible remedy. An application of Reynolds decomposition and profile modelling of dependent variables allows capturing of the important inertial and centrifugal forces that would otherwise be lost during the equation transformation. An implementation of the derived thin film model was carried out in the open-source software toolbox OpenFOAM R ⃝ using the Finite Area method, a specialisation of the Finite Volume method for film flows on the arbitrary surfaces. The resulting code fulfils the requirements of a high-performance transient incompressible solver for the thin film and features a dynamic inlet implementation. The approach is validated with the ANSYS Fluent software and its VoF-implementation. An extension of the solver with a simple diffusion-controlled chemistry model for a wet chemical etching of silicon wafers is presented.