GNU General Public License Research Articles

Environmental niche models improve species identification in DNA barcoding

Abstract Recent advances in DNA barcoding have immeasurably advanced global biodiversity research in the last two decades. However, inherent limitations in barcode sequences, such as hybridization, introgression or incomplete lineage sorting can lead to misidentifications when relying solely on barcode sequences. Here, we propose a new Niche‐model‐Based Species Identification (NBSI) method based on the idea that species distribution information is a potential complement to DNA barcoding species identifications. NBSI performs species membership inference by incorporating niche modelling predictions and traditional DNA barcoding identifications. Systematic tests across diverse scenarios show significant improvements in species identification success rates under the newly proposed NBSI framework, where the largest increase is from 4.7% (95% CI: 3.51%–6.25%) to 94.8% (95% CI: 93.19%–96.06%). Additionally, obvious improvements were observed when using NBSI on potentially ambiguous sequences whose genetic nearest neighbours belongs to another species or more than two species, which occurs commonly with species represented by single or short DNA barcodes. These results support our assertion that environmental factors/variables are valuable complements to DNA sequence data for species identification by avoiding potential misidentifications inferred from genetic information alone. The NBSI framework is currently implemented as a new R package, ‘NicheBarcoding’, that is open source under GNU General Public Licence and freely available from https://CRAN.R‐project.org/package=NicheBarcoding.

VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summaryProgram Title: VAN-DAMMECPC Library link to program files::https://doi.org/10.17632/zcgw2n5bjf.1Licensing provisions: GNU General Public License 3Programming language: C++ and CUDANature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability.Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

BAD2matrix: Phylogenomic matrix concatenation, indel coding, and more

AbstractPremiseCommon steps in phylogenomic matrix production include biological sequence concatenation, morphological data concatenation, insertion/deletion (indel) coding, gene content (presence/absence) coding, removing uninformative characters for parsimony analysis, recording with reduced amino acid alphabets, and occupancy filtering. Existing software does not accomplish these tasks on a phylogenomic scale using a single program.Methods and ResultsBAD2matrix is a Python script that performs the above‐mentioned steps in phylogenomic matrix construction for DNA or amino acid sequences as well as morphological data. The script works in UNIX‐like environments (e.g., LINUX, MacOS, Windows Subsystem for LINUX).ConclusionsBAD2matrix helps simplify phylogenomic pipelines and can be downloaded from https://github.com/dpl10/BAD2matrix/tree/master under a GNU General Public License v2.

SSLG4: A novel scintillator simulation library for Geant4

This study introduces a new Scintillator Simulation Library called SSLG4 for the Geant4 Monte Carlo simulation package. With SSLG4, we aim to enhance efficiency and accelerate progress in optical simulations within the Geant4 framework by simplifying scintillator handling and providing a rich repository of scintillators. The SSLG4 enables users to quickly include predefined scintillator materials in their simulations without requiring manual definition. The library initially contains 68 scintillators, consisting of 58 organic and 10 inorganic types. Most of these scintillators are selected from the catalogs of several scintillator manufacturers, notably Eljen and Luxium. Other scintillators are included based on their widespread use across various physics domains. The library stores optical data of scintillators in ASCII files with .mac and .txt extensions, enabling users to add, remove, or modify properties of scintillators at runtime of their applications. In addition, we made all the scintillator data available in the library on a dedicated page of our website to ensure convenient access for all users. Program summaryProgram title: SSLG4CPC Library link to program files:https://doi.org/10.17632/3zbwr5wf7z.1Developer's repository link:https://github.com/mkandemirr/SSLG4, https://neutrino.erciyes.edu.tr/SSLG4/Licensing provisions: GNU General Public License 3Programming language: C++External routines/libraries: Geant4, CMake, OPSimNature of problem: Defining a new scintillator in Geant4 is a cumbersome process for some users due to three main reasons: (1) It requires a lot of data input from users, (2) collecting the scintillator data requires an extensive literature review, and (3) the collected data needs to be converted into the desired format. In addition, the interfaces provided to define a scintillator direct users to embed scintillator data into their source code, resulting in increased code complexity, reduced code readability, and an inefficient working environment.Solution method: To solve the problems mentioned above, developing and introducing a new library consisting of fully parameterized and ready-to-use scintillators would greatly increase the useability of the Geant4 simulation package for scintillator studies and interest a wide range of scientific communities.

Scywalker: scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing.

Existing nanopore single-cell data analysis tools showed severe limitations in handling current data sizes. We introduce scywalker, an innovative and scalable package developed to comprehensively analyze long-read sequencing data of full-length single-cell or single-nuclei cDNA. We developed novel scalable methods for cell barcode demultiplexing and single-cell isoform calling and quantification and incorporated these in an easily deployable package. Scywalker streamlines the entire analysis process, from sequenced fragments in FASTQ format to demultiplexed pseudobulk isoform counts, into a single command suitable for execution on either server or cluster. Scywalker includes data quality control, cell type identification, and an interactive report. Assessment of datasets from the human brain, Arabidopsis leaves, and previously benchmarked data from mixed cell lines demonstrate excellent correlation with short-read analyses at both the cell-barcoding and gene quantification levels. At the isoform level, we show that scywalker facilitates the direct identification of cell-type-specific expression of novel isoforms. Scywalker is available on github.com/derijkp/scywalker under the GNU General Public License (GPL) and at https://zenodo.org/records/13359438/files/scywalker-0.108.0-Linux-x86_64.tar.gz.

Kinetic Diagram Analysis: A Python Library for Calculating Steady-State Observables of Biochemical Systems Analytically.

Kinetic diagrams are commonly used to represent biochemical systems in order to study phenomena such as free energy transduction and ion selectivity. While numerical methods are commonly used to analyze such kinetic networks, the diagram method by King, Altman and Hill makes it possible to construct exact algebraic expressions for steady-state observables in terms of the rate constants of the kinetic diagram. However, manually obtaining these expressions becomes infeasible for models of even modest complexity as the number of the required intermediate diagrams grows with the factorial of the number of states in the diagram. We developed Kinetic Diagram Analysis (KDA), a Python library that programmatically generates the relevant diagrams and expressions from a user-defined kinetic diagram. KDA outputs symbolic expressions for state probabilities and cycle fluxes at steady-state that can be symbolically manipulated and evaluated to quantify macroscopic system observables. We demonstrate the KDA approach for examples drawn from the biophysics of active secondary transmembrane transporters. For a generic 6-state antiporter model, we show how the introduction of a single leakage transition reduces transport efficiency by quantifying substrate turnover. We apply KDA to a real-world example, the 8-state free exchange model of the small multidrug resistance transporter EmrE of Hussey et al. (J. Gen. Physiol., 2020, 152, e201912437), where a change in transporter phenotype is achieved by biasing two different subsets of kinetic rates: alternating access and substrate unbinding rates. KDA is made available as open source software under the GNU General Public License version 3.

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins’ inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub (https://github.com/jvlehtonen/overlap-toolkit).Scientific contributionThis study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.Graphical

Kinetic Diagram Analysis: A Python Library for Calculating Steady-State Observables of Biochemical Systems Analytically.

Kinetic diagrams are commonly used to represent biochemical systems in order to study phenomena such as free energy transduction and ion selectivity. While numerical methods are commonly used to analyze such kinetic networks, the diagram method by King, Altman and Hill makes it possible to construct exact algebraic expressions for steady-state observables in terms of the rate constants of the kinetic diagram. However, manually obtaining these expressions becomes infeasible for models of even modest complexity as the number of the required intermediate diagrams grows with the factorial of the number of states in the diagram. We developed Kinetic Diagram Analysis (KDA), a Python library that programmatically generates the relevant diagrams and expressions from a user-defined kinetic diagram. KDA outputs symbolic expressions for state probabilities and cycle fluxes at steady-state that can be symbolically manipulated and evaluated to quantify macroscopic system observables. We demonstrate the KDA approach for examples drawn from the biophysics of active secondary transmembrane transporters. For a generic 6-state antiporter model, we show how the introduction of a single leakage transition reduces transport efficiency by quantifying substrate turnover. We apply KDA to a real-world example, the 8-state free exchange model of the small multidrug resistance transporter EmrE of Hussey et al (J General Physiology 152 (2020), e201912437), where a change in transporter phenotype is achieved by biasing two different subsets of kinetic rates: alternating access and substrate unbinding rates. KDA is made available as open source software under the GNU General Public License version 3.

ONeSAMP 3.0: estimation of effective population size via single nucleotide polymorphism data from one population.

The genetic effective size (Ne) is arguably one of the most important characteristics of a population as it impacts the rate of loss of genetic diversity. Methods that estimate Ne are important in population and conservation genetic studies as they quantify the risk of a population being inbred or lacking genetic diversity. Yet there are very few methods that can estimate the Ne from data from a single population and without extensive information about the genetics of the population, such as a linkage map, or a reference genome of the species of interest. We present ONeSAMP 3.0, an algorithm for estimating Ne from single nucleotide polymorphism data collected from a single population sample using approximate Bayesian computation and local linear regression. We demonstrate the utility of this approach using simulated Wright-Fisher populations, and empirical data from five endangered Channel Island fox (Urocyon littoralis) populations to evaluate the performance of ONeSAMP 3.0 compared to a commonly used Ne estimator. Our results show that ONeSAMP 3.0 is broadly applicable to natural populations and is flexible enough that future versions could easily include summary statistics appropriate for a suite of biological and sampling conditions. ONeSAMP 3.0 is publicly available under the GNU General Public License at https://github.com/AaronHong1024/ONeSAMP_3.

TRIQS/Nevanlinna: Implementation of the Nevanlinna Analytic Continuation method for noise-free data

We present the TRIQS/Nevanlinna analytic continuation package, an efficient implementation of the methods proposed by J. Fei et al. (2021) [53] and (2021) [55]. TRIQS/Nevanlinna strives to provide a high quality open source (distributed under the GNU General Public License version 3) alternative to the more widely adopted Maximum Entropy based analytic continuation programs. With the additional Hardy functions optimization procedure, it allows for an accurate resolution of wide band and sharp features in the spectral function. Those problems can be formulated in terms of imaginary time or Matsubara frequency response functions. The application is based on the TRIQS C++/Python framework, which allows for easy interoperability with other TRIQS-based applications, electronic band structure codes and visualization tools. Similar to other TRIQS packages, it comes with a convenient Python interface. Program summaryProgram Title:TRIQS/NevanlinnaCPC Library link to program files:https://doi.org/10.17632/4cbzfy5rds.1Developer's repository link:https://github.com/TRIQS/NevanlinnaLicensing provisions: GPLv3Programming language:C++/PythonExternal routines/libraries:TRIQS 3.2[1], Boost >= 1.76.0, Eigen >= 3.4.0, cmake >= 3.20.Nature of problem: Finite-temperature field theories are widely used to study quantum many-body effects and electronic structure of correlated materials. Obtaining physically relevant spectral functions from results in the imaginary time/Matsubara frequency domains requires solution of an ill-posed analytic continuation problem as a post-processing step.Solution method: We present an efficient C++/Python open-source implementation of the Nevanlinna/Caratheodory analytic continuation.

YADE - An extensible framework for the interactive simulation of multiscale, multiphase, and multiphysics particulate systems

This contribution presents the key elements of YADE, an extensible open-source framework for dynamic simulations. During the past 19 years, YADE has evolved from “Yet Another Dynamic Engine” to a versatile multiscale and multiphysics solver, counting a large, active, and growing community of users and developers. The computationally intense parts of the source code are written in C++, using flexible object models that allow for easy implementation of new features. The source code is wrapped in Python, equipping the software with an interactive kernel used for rapid and concise scene construction, simulation control, post-processing, and debugging. The project, including documentation and examples, is hosted on https://yade-dem.org, while the source code is freely available on GitLab. Over the last decade, YADE has expanded in terms of capabilities thanks to the contribution of many developers from different fields of expertise, including soil and rock mechanics, chemical engineering, physics, bulk material handling, and mineral processing. The rapid growth of YADE can be attributed to (1) the careful and robust design of the framework core, (2) a continuous integration pipeline with fully embedded thorough tests which are executed upon each merge request, ensuring stable compilation for various operating systems, and (3) user-friendliness, facilitated by the Python interface, detailed documentation, and rigorous user support. In this paper, we review the main features of YADE, highlighting its versatility in terms of applications, its flexibility in terms of code development, as well as recent improvements in terms of computational efficiency. Program summaryProgram Title: YADE - Yet Another Dynamic EngineCPC Library link to program files:https://doi.org/10.17632/n4f5fw97rd.1Developer's repository link:https://gitlab.com/yade-dev/trunkLicensing provisions: GNU General Public License 2Programming language: C++, PythonNature of problem: Numerical simulation of many-particle systems requires accurate models for particle-to-particle interactions, efficient contact detection between objects of various shapes, and robust time integration. In addition, the flow of fluids, thermal effects, as well as other coupled problems in the presence of particles are found in many fundamental and practical applications and they need dedicated computational tools. YADE provides a computational framework to perform such simulations using the discrete element method and multiple extensions of it.Solution method:YADE simulates particulate systems using the Discrete Element Method (DEM) in a flexible platform combining C++ and Python. It provides a large variety of shape and interaction models, an explicit time-integration scheme, and many post-processing tools. YADE features dedicated solvers for both fluid and heat fluxes at the pore scale, and it supports couplings with third-party software such as Escript (finite element method) and OpenFOAM (computational fluid dynamics).

PM2D: A parallel GPU-based code for the kinetic simulation of laser plasma instabilities at large scales

Laser plasma instabilities (LPIs) have significant influences on the laser energy deposition efficiency and therefore are important processes in inertial confined fusion (ICF). Numerical simulations play important roles in revealing the complex physics of LPIs. Since LPIs are typically a three wave coupling process, the precise simulations of LPIs with kinetic effects require to resolve the laser period (around one femtosecond) and laser wavelength (less than one micron). In the typical ICF experiments, however, LPIs are involved in a spatial scale of several millimeters and a temporal scale of several nanoseconds. Therefore, the precise kinetic simulations of LPIs in such scales require huge computational resources and are hard to be carried out by present kinetic codes like particle-in-cell (PIC) codes. In this paper, a full wave fluid model of LPIs is constructed and numerically solved by the particle-mesh method, where the plasma is described by macro particles that can move across the mesh grids freely. Based upon this model, a two-dimensional (2D) GPU code named PM2D is developed. The PM2D code can simulate the kinetic effects of LPIs self-consistently as normal PIC codes. Moreover, as the physical model adopted in the PM2D code is specifically constructed for LPIs, the required macro particles per grid in the simulations can be largely reduced and thus overall simulation cost is considerably reduced comparing with PIC codes. More importantly, the numerical noise in the PM2D code is much lower, which makes it more robust than PIC codes in the simulation of LPIs for the long-time scale above 10 picoseconds. After the distributed computing is realized, our PM2D code is able to run on GPU clusters with a total mesh grids up to several billions, which meets the requirements for the simulations of LPIs at ICF experimental scale with reasonable cost. Program summaryProgram Title: PM2DCPC Library link to program files:https://doi.org/10.17632/xscj6vnkkw.1Licensing provisions: GNU General Public License v3.0.Programming language: C++, CUDA.Nature of problem: Although the large scale simulations of laser plasma instabilities (LPIs) is of great significance for the inertial confinement fusion (ICF), there is still no suitable code to simulate these problems. PM2D code based on a GPU platform provides an effective method to simulate these large scale problems in ICF.Solution method: A fluid model for LPIs is established firstly, which contains wave equations that describe the laser propagating process, electron and ion fluid equations that describe the plasma motions, and a Poisson's equation that describes the electrostatic field induced by charge separation. The wave equation is solved on a rectangular region using absorption boundary conditions on all of four boundaries. The absorption boundary condition on the left boundary is further extended to allow the incidence of driven lasers and absorption of scattering lasers simultaneously. The fluid equations in the physical model are solved by the particle-mesh method, in which the macro particles are driven to move by fluid forces. Since macro particles can move freely within the fixed fluid grids, the PM2D code can capture the kinetic effects self-consistently. The Poisson's equation for the electrostatic field is solved by a Fourier decomposition method in the y direction, which helps to decrease the simulation cost greatly. The PM2D code is developed on a GPU platform base on CUDA toolkit, which largely increase the computational speed.

PH-NODE: A DFPT and finite displacement supercell based python code for searching nodes in topological phononic materials

Exploring the topological physics of phonons is fundamentally important for understanding various practical applications. Here, we present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material through a first-principles approach. The present version of the code is interfaced with the WIEN2k, Elk, and ABINIT packages. In order to benchmark the code, six different types of materials are considered, which include (i) FeSi, a well-known double-Weyl point; (ii) LiCaAs, a half-Heusler single-type-I Weyl topological phonon (TP); and (iii) ScZn, coexisting nodal-line and nodal-ring TPs; (iv) TiS, six pairs of bulk Weyl nodes; (v) CdTe, type-II Weyl phonons; (vi) CsTe, coexisting TP and quadratic contact TP. In FeSi, the node points are found at Γ(0,0,0) and R(0.5,0.5,0.5) high symmetric points. Also, there are 21 energy values at which the node points are situated, corresponding to the full Brillouin zone. For LiCaAs, the previously reported literature claims that there is a node point along the W-X high symmetry direction between the highest longitudinal acoustic and the lowest transverse optical branch, while in our DFT calculations, a gap of 0.17 meV is found. Furthermore, ScZn hosts six nodal-ring TPs phonons at the boundary planes of the Brillouin zone in the vicinity of the M high-symmetric point. In addition to this, straight-line TPs are also found along the Γ-X and Γ-R high symmetric directions. Moreover, for TiS, six Weyl node points (WP1, WP2, WP3, WP4, WP5 and WP6) are found along H-K high-symmetric direction. In CdTe, it is found that Weyl points are located along the X-W high-symmetry direction. In the case of CsTe, a TP and a quadratic contact TP are found along the Γ-X direction and at the R high-symmetry point, respectively. The results obtained from the PH-NODE code are in good agreement with the experimentally and theoretically reported data for each material. Program summaryProgram title: PH-NODECPC Library link to program files:https://doi.org/10.17632/sjydzn49nw.1Licensing provisions: GNU General Public License 3.0Programming language: Python 3External routines/libraries: Math, Time, NumPy, SciPyNature of problem: Searching for the phonon-node points corresponding to the given number of phonon-branch using Nelder-Mead's simplex approach.Solution method: We present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material through a first-principles approach.

The hybrid anti-symmetrized coupled channels method (haCC) for the tRecX code

We present a new implementation of the hybrid antisymmetrized Coupled Channels (haCC) method in the framework of the tRecX (Scrinzi, 2022 [6]). The method represents atomic and molecular multi-electron functions by combining CI functions, Gaussian molecular orbitals, and a numerical single-electron basis. It is suitable for describing high harmonic generation and the strong-field dynamics of ionization. Fully differential photoemission spectra are computed by the tSurff method. The theoretical background of haCC is outlined and key improvements compared to its original formulation are highlighted. We discuss control of over-completeness resulting from the joint use of the numerical basis and Gaussian molecular orbitals by pseudo-inverses based on the Woodbury formula. Further new features of this tRecX release are the iSurff method, new input features, and the AMOS gateway interface. The mapping of haCC into the tRecX framework for solving the time-dependent Schrödinger equation is shown. Use, performance, and accuracy of haCC are discussed on the examples of high-harmonic generation and strong-field photo-emission by short laser pulses impinging on the Helium atom and on the linear molecules N2 and CO. Program summaryProgram title: tRecX — time-dependent Recursive indeXing (tRecX=tSurff+irECS)CPC Library link to program files:https://doi.org/10.17632/m9g2jc82sw.1Developer's repository link:https://gitlab.physik.uni-muenchen.de/AG-Scrinzi/tRecXLicensing provisions: GNU General Public License 2Programming language: C++External libraries: Eigen, arpack, lapack, blas, boost, FFTW (optional)Journal Reference of previous version: A. Scrinzi, Comp. Phys. Comm., 270:108146, 2022.Does the new version supersede the previous version: YesReasons for the new version: Major new functionality: haCC — hybrid antisymmetrized coupled channels methodSummary of revisions: Main additions are haCC and iSurff. Code usage and compilation were improved.Nature of problem: tRecX is a general solver for time-dependent Schrödinger-like problems, with applications mostly in strong field and attosecond physics. There are no technical restrictions on the spatial dimension of the problem with up to 6 spatial dimensions realized in the strong-field double ionization of Helium. Gaussian-based quantum chemical multi-electron atomic and molecular structure can be combined with the numerical basis. A selection of coordinate systems is available and any Hamiltonian involving up to second derivatives and arbitrary up to three dimensional potentials can be defined on input by simple scripts.Solution method: The method of lines is used with spatial discretization by a flexible combination of one dimensional basis sets, DVR representations, discrete vectors, expansions into higher-dimensional eigenfunctions of user-defined operators, and Gaussian based molecular orbitals. Multi-electron Gaussian-based CI (configuration interaction) functions for neutrals and ions are combined with the numerical basis. Photo-emission spectra are calculated using the time-dependent surface flux method (tSurff) in combination with infinite range exterior complex scaling (irECS) for absorption. The code is object oriented and makes extensive use of tree-structures and recursive algorithms. Parallelization is by MPI. Code design and performance allow use in production as well as for graduate level training.

MAM-STM: A software for autonomous control of single moieties towards specific surface positions

In this publication we introduce MAM-STM, a software to autonomously manipulate arbitrary moieties towards specific positions on a metal surface utilizing the tip of a scanning tunneling microscope (STM). Finding the optimal manipulation parameters for a specific moiety is challenging and time consuming, even for human experts. MAM-STM combines autonomous data acquisition with a sophisticated Q-learning implementation to determine the optimal bias voltage, the z-approach distance, and the tip position relative to the moiety. This then allows to arrange single molecules and atoms at will. In this work, we provide a tutorial based on a simulated response to offer a comprehensive explanation on how to use and customize MAM-STM. Additionally, we assess the performance of the machine learning algorithm by benchmarking it within a simulated stochastic environment. PROGRAM SUMMARYProgram title: MAM-STMCPC Library link to program files: https://doi.org/10.17632/gtf3bt4v47.1Developer's repository link: https://gitlab.tugraz.at/software_public/mam_stm.gitLicensing provisions: GNU General Public License 3 (GPL)Programming language: Python 3Nature of problem: Achieving precise control over the arrangement of individual molecules on surfaces is essential for advancing nanofabrication and understanding molecular interaction processes. While self-assembly offers a method for forming nanostructures, achieving arbitrary arrangements of moieties remains difficult. Current approaches, such as scanning probe microscopy (SPM), require extensive manual intervention and precise control is difficult to achieve consistently due to the stochastic nature of quantum mechanical systems at the nanoscale. Thus, learning to manipulate several moieties in order to build even relatively small structures is challenging and time consuming and the automation through conventional expert systems is hindered by the lack of prior knowledge about the surface-moiety interaction processes.Solution method: This scenario is ideal for machine learning algorithms, like reinforcement learning (RL), which do not require an underlying model but are able to autonomously learn the optimal manipulation parameters by performing manipulations directly at the machine. Introducing MAM-STM, which stands for Molecular and Atomic Manipulation via Scanning Tunneling Microscopy. MAM-STM allows to control molecules and atoms by learning the manipulation parameters for either vertical or lateral manipulations. However, the vast number of manipulation parameter combinations and the inefficient learning procedure of RL agents exhibit several challenges. MAM-STM overcomes these challenges with an autonomous masking routine that eliminates manipulation parameters that induce structural changes to the moiety or lift it off the surface. Additionally, a sophisticated Q-learning approach is developed that speeds up the learning procedure, enabling molecular manipulations within one day of training.

Quality of Service (QoS) Pada Jaringan Wi-Fi (Wireless Fidelity) FKIP UNTIRTA Menggunakan Software Wireshark

Wi-Fi (Wireless Fidelity) networks have become vital infrastructure in the era of modern wireless communications. With the rapid development of technology, monitoring and analyzing Wi-Fi networks has become increasingly important to ensure optimal performance and guaranteed security. Wireshark software has become one of the most popular and reliable tools in analyzing computer networks, including Wi-Fi networks. Wireshark is a very popular and powerful open source network analysis software. Originally known as Ethereum, Wireshark was developed by a team of volunteers and released under the GNU General Public License (GPL). The software can run on multiple platforms, including Windows, macOS, and Linux, making it a flexible tool for network analysis. The method used shows how to solve an analysis or analysis of a problem. This method section uses a literature study which is packaged briefly, clearly and concisely, so that it can be understood well. Another method used is to carry out simulation practices on Wireshark software to get results from a network. This abstract discusses the use of Wireshark in the context of Wi-Fi networks, covering basic Wi-Fi network concepts, monitoring and analysis methods, and security applications. Through careful monitoring and analysis with Wireshark, users can identify network performance issues, detect security threats, and optimize use of their Wi-Fi network. This abstract provides a comprehensive overview of Wireshark's role in understanding and effectively managing Wi-Fi networks.

MISTER-T: An open-source software package for quantum optimal control of multi-electron systems on arbitrary geometries

We present an open-source software package, MISTER-T (Manipulating an Interacting System of Total Electrons in Real-Time), for the quantum optimal control of interacting electrons within a time-dependent Kohn-Sham formalism. In contrast to other implementations restricted to simple models on rectangular domains, our method enables quantum optimal control calculations for multi-electron systems (in the effective mass formulation) on nonuniform meshes with arbitrary two-dimensional cross-sectional geometries. Our approach is enabled by forward and backward propagator integration methods to evolve the Kohn-Sham equations with a pseudoskeleton decomposition algorithm for enhanced computational efficiency. We provide several examples of the versatility and efficiency of the MISTER-T code in handling complex geometries and quantum control mechanisms. The capabilities of the MISTER-T code provide insight into the implications of varying propagation times and local control mechanisms to understand a variety of strategies for manipulating electron dynamics in these complex systems. Program summaryProgram Title: MISTER-TCPC Library link to program files:https://doi.org/10.17632/psymy4ddnw.1Licensing provisions: GNU General Public License 3Programming language: MATLABSupplementary material: animated movies of total electron densities under the influence of optimal control fields for (1) an asymmetric double-well potential for long propagation times, (2) an asymmetric double-well potential for short propagation times, and (3) a triple-well potential with a position-dependent effective mass.Nature of problem: The MISTER-T code solves quantum optimal control problems for interacting electrons within a time-dependent Kohn-Sham formalism. It can handle two-dimensional systems with arbitrary cross-sectional geometries within the effective mass formulation. The user-friendly code uses forward and backward propagator integration methods to evolve the Kohn-Sham equations with a pseudoskeleton decomposition algorithm for enhanced computational efficiency.Solution method: iterative solution of the quantum optimal control equations using finite element methods, effective mass formulation, pseudoskeleton decomposition, sparse matrix linear algebra, and nonuniform fast Fourier transforms.

SynergyAnalyzer: A Matlab toolbox implementing mixed-matrix factorization to identify kinematic-muscular synergies

Background and ObjectiveA new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach. MethodsMMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies. ResultsAs an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License. ConclusionsThanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.

GeoTaichi: A Taichi-powered high-performance numerical simulator for multiscale geophysical problems

This study introduces GeoTaichi, an open-source high-performance numerical simulator designed for addressing multiscale geophysical problems. By leveraging the power of the Taichi parallel language, GeoTaichi maximizes the utilization of modern computer resources on multicore CPU and GPU architectures. It offers robust and reliable modules for the discrete element method (DEM), material point method (MPM), and coupled material point-discrete element method (MPDEM). These modules enable efficient solving of large-scale problems while being implemented in pure Python. The design philosophy of GeoTaichi focuses on creating a framework that is readable, extensible, and user-friendly. This paper highlights the coupling procedure of MPDEM, the code structures, and the most important features of GeoTaichi. Rigorous benchmark tests have been conducted to verify the validity and robustness of GeoTaichi. Additionally, the performance of GeoTaichi is compared with similar software tools in the field, underscoring a notable improvement in both computational efficiency and memory savings when compared to existing alternatives. Program summaryProgram title: GeoTaichiCPC Library link to program files:https://doi.org/10.17632/858bmcf7j6.1Developer's repository link:https://github.com/Yihao-Shi/GeoTaichiLicensing provisions: GNU General Public License v3.0Programming language: PythonNature of problem: The simulations of large-deformation geophysical flows and their interaction with structures play a crucial role in the field of geophysics. To address the complexities of these nonlinear problems, the discrete element method (DEM), material point method (MPM), and their coupling (MPDEM) have proven to be highly suitable numerical schemes. However, these schemes impose substantial computational demands, necessitating the development of an efficient framework that can harness modern computer resources on multicore CPU and GPU architectures.Solution method: The open-source code GeoTaichi implements the DEM, MPM, and coupled MPDEM, encompassing a range of constitutive models and contact laws for different geologic materials. The clump particle model is also introduced in DEM to solve granular mechanics involving complex-shaped particles. One significant advantage of GeoTaichi is its utilization of the Taichi parallel language, which is designed to be user-friendly and easily extensible for customized applications.

DUGKS-GPU: An efficient parallel GPU code for 3D turbulent flow simulations using Discrete Unified Gas Kinetic Scheme

This paper presents a parallel implementation of the Discrete Unified Gas Kinetic Scheme (DUGKS) on the GPU system using the CUDA Fortran and CUDA C++ programming languages. Firstly, we conducted an extensive revision of our original CPU-based code, resulting in a threefold decrease in memory usage. This new implementation is also paired with a novel approach to compute cell face flux using trilinear interpolation. It is shown analytically that the interpolation-based approach to flux calculation is more accurate compared to the one used in the original DUGKS. The initial simulation results using this new approach suggest that trilinear interpolation can reduce numerical errors on a coarse mesh. For example, in the case of the decaying Taylor-Green vortex flow at a 1283 mesh resolution, the relative numerical error in the energy dissipation rate at t⁎=2, using the spectral simulation result as the benchmark, is approximately 30% lower than that of the original implementation. The improved GPU DUGKS method is applied to laminar and turbulent flows in periodic and wall-bounded boundary configurations. A performance comparison of the GPU implementation is also presented and compared to the previous CPU implementation. A maximum speedup of 7.64x was achieved on a desktop-level GPU compared to a 32-core CPU. The strong scaling test, conducted on an eight-GPU node, demonstrated the efficient utilization of available multiple GPU resources by the code. Program summaryProgram Title: DUGKS-GPUCPC Library link to program files:https://doi.org/10.17632/yykv5s9g2n.1Developer's repository link:https://github.com/kairzhan/DUGKS-GPUCode Ocean capsule:https://codeocean.com/capsule/3b1e4f74-cdd3-4781-8923-8514d5923dfb/Licensing provisions: GNU General Public License 3Programming language: CUDA C++, CUDA FortranSupplementary material:Nature of problem: DUGKS-GPU is an advanced numerical code designed to accelerate the simulation of turbulent fluid flows through the application of the kinetic method known as the Discrete Unified Gas Kinetic Scheme (DUGKS). This computational tool comprises a CUDA Fortran version, tailored for a single GPU, as well as a CUDA C++ version developed for multi-GPU platforms.Solution method: The code employs a second-order finite-volume discretization of the discrete velocity Boltzmann equation with the BGK collision model. It can be used to simulate small to medium-scale problems on modern multi-GPU platforms with CUDA architecture.