Online Documentation Research Articles

SmoQyDEAC.jl: A differential evolution package for the analytic continuation of imaginary time correlation functions

We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package’s output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.

Codebase release r1.1 for SmoQyDEAC.jl

We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package’s output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.

The CMS Statistical Analysis and Combination Tool: Combine

This paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide.

Teaching Linked Data Principles through Virtual Wikidata Edit‐a‐thons

ABSTRACTCataloging and metadata professionals in libraries have an interest in learning about the Semantic Web and Linked Data technologies. However, with limited release time and funding for continuing education, professional development opportunities need to be designed with learner needs in mind: free, flexible, and fun. Our regional Linked Data interest group approached this challenge by developing an annual program in the form of a five‐day edit‐a‐thon, providing training and hands‐on experience in creating and editing Wikidata. The flexible format makes use of synchronous video conferencing, detailed online documentation, independent editing days for hands‐on learning, and opt‐in community discussion via Slack. Individual and project progress was tracked with the Wikimedia Event Dashboard. This poster proposes the Wikidata edit‐a‐thon as an ideal method to fulfill a professional development need of library staff by introducing participants to the principles of Linked Data through this flexible and interactive event.

Advisory content in politics: the form of mitigation of the negative impact of identity politics on Instagram account @lensamu and @nuonline_id

This article aims to analyze efforts to mitigate the negative impacts of identity politics by two large mass organizations in Indonesia, Muhammadiyah and Nahdhatul Ulama (NU), through their Instagram accounts. Mitigation in this context refers to steps to recognize and increase awareness of identity politics risks often occurring in campaigns, especially before the 2024 election. This research is motivated by the political situation leading up to the 2024 election and vulnerability to identity politics, which perpetrators often use as a tool. The method used in this research is quantitative content analysis with content research objects discussing political themes and the 2024 election. Data was collected through virtual observation, literature research, and online documentation via the Instagram accounts @lensamu and @nuonline_id from September 2023 to February 2024. Based on the research results, it can be concluded that the percentage of forms of mitigating the negative impact of identity politics carried out by the @lensamu account is 0.91 or 91% and by @nu_online it is 0.86 or 86%. With these test results, it can be said that the form of mitigation of the negative impact of identity politics carried out by these two accounts is high in facing the 2024 election.

WASHBACK EFFECTS OF ONLINE ASSESSING STUDENTS’ READING COMPREHENSION IN NARRATIVE TEXT

The Covid-19 pandemic has altered the educational landscape. This is due to the fact that traditional classroom learning has been replaced with online learning. Even if the learning and assessment take place at home, teachers must be able to offer an effective learning process and assessment. The impact of testing on teaching and learning is known as washback. Teachers appear to play a significant part in developing different types of washback by examining the probable positive and negative washback that tests may cause at the micro and macro levels. In other words, the teachers' beliefs play an important role in influencing the washback effect. This study aims at explaining washback effect of online students’ assessment on reading comprehension in narrative text. Secondly, to describe students’ engagement in following online learning and assessing of reading comprehension. It employed qualitative method and descriptive research design. Data collection techniques used observation, online reading test, interview, and documentation. Meanwhile, data analysis techniques implemented data reduction, data display, data verification. The research participants were 32 the seventh graders of an Islamic secondary school in Semarang regency. The results show that narrative reading text used folklore digital storytelling namely The Crying Stone. The reading comprehension test consisted of forty items, they were short answer, close test, true or false, vocabulary development, matching, and references. Based on the data, there are two types of washback effects namely negative washback effect and positive washback effect. Positive and negative washback effect covered formative assessment and summative assessment. They are included assessment as learning, assessment for learning, and assessment of learning. Positive washback effects made students have eagerness to be autonomous learners. Students had high motivation and well-prepared in joining reading class. Meanwhile students had difficulties in learning and follow the reading assessment because of bad internet connection as negative washback. Sometimes some parents were not able to buy internet data package.

Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community.

The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures.

Benchmarking clustering, alignment, and integration methods for spatial transcriptomics

BackgroundSpatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development.ResultsIn this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets.ConclusionsOur analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.

LIME: A LIne MEasuring library for large and complex spectroscopic data sets

Context. The upcoming generation of telescopes, instruments, and surveys is poised to usher in an unprecedented “Big Data” era in the field of astronomy. Within this context, even seemingly modest tasks such as spectral line analyses could become increasingly challenging for astronomers. Aims. In this paper, we announce the release of LIME. This package is tailored for multidisciplinary observations with long-slit and integral field spectroscopy (IFS) support. LIME functions encompass the reading of observational files, detecting lines, conditioned line fitting, and the plotting and storage of results. Most importantly, these measurements are structured to support the subsequent chemical and kinematic analyses. Methods. To reduce the coding effort required from users, we introduced a notation system for atomic transitions that is accessible to humans and machine-readable. Along with this system, we present an extensive database of line bands, spanning from the ultraviolet to the infrared wavelength range. Additionally, we propose a model designed to train machine learning algorithms in line detection. LIME features a comprehensive online documentation, which details the command attributes and includes several tutorials. These tutorials range from measuring a single line to analyzing an entire IFS data cube. Results. This library functions and measurements are showcased in an online virtual observatory. The data in this interactive website come from the JWST NIRSpec observations of the CEERs survey. In this regard, LIME offers improvements related to the dissemination and accessibility of astronomical spectra.

Spexwavepy: an open-source Python package for X-ray wavefront sensing using speckle-based techniques.

In situ wavefront sensing plays a critical role in the delivery of high-quality beams for X-ray experiments. X-ray speckle-based techniques stand out among other in situ techniques for their easy experimental setup and various data acquisition modes. Although X-ray speckle-based techniques have been under development for more than a decade, there are still no user-friendly software packages for new researchers to begin with. Here, we present an open-source Python package, spexwavepy, for X-ray wavefront sensing using speckle-based techniques. This Python package covers a variety of X-ray speckle-based techniques, provides plenty of examples with real experimental data and offers detailed online documentation for users. We hope it can help new researchers learn and apply the speckle-based techniques for X-ray wavefront sensing to synchrotron radiation and X-ray free-electron laser beamlines.

Codebase release r0.3 for SmoQyDQMC.jl

We introduce the SmoQyDQMC.jl package, a Julia implementation of the determinant quantum Monte Carlo algorithm. SmoQyDQMC.jl supports generalized tight-binding Hamiltonians with on-site Hubbard and generalized electron-phonon (ee-ph) interactions, including non-linear ee-ph coupling and anharmonic lattice potentials. Our implementation uses hybrid Monte Carlo methods with exact forces for sampling the phonon fields, enabling efficient simulation of low-energy phonon branches, including acoustic phonons. The SmoQyDQMC.jl package also uses a flexible scripting interface, allowing users to adapt it to different workflows and interface with other software packages in the Julia ecosystem. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDQMC.jl or installed using the Julia package manager. The online documentation, including examples, can be obtained from our document page at https://smoqysuite.github.io/SmoQyDQMC.jl/stable/.

SmoQyDQMC.jl: A flexible implementation of determinant quantum Monte Carlo for Hubbard and electron-phonon interactions

We introduce the SmoQyDQMC.jl package, a Julia implementation of the determinant quantum Monte Carlo algorithm. SmoQyDQMC.jl supports generalized tight-binding Hamiltonians with on-site Hubbard and generalized electron-phonon (ee-ph) interactions, including non-linear ee-ph coupling and anharmonic lattice potentials. Our implementation uses hybrid Monte Carlo methods with exact forces for sampling the phonon fields, enabling efficient simulation of low-energy phonon branches, including acoustic phonons. The SmoQyDQMC.jl package also uses a flexible scripting interface, allowing users to adapt it to different workflows and interface with other software packages in the Julia ecosystem. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDQMC.jl or installed using the Julia package manager. The online documentation, including examples, can be obtained from our document page at https://smoqysuite.github.io/SmoQyDQMC.jl/stable/.

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis.

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.

GalaPy: A highly optimised C++/Python spectral modelling tool for galaxies

Bolstered by upcoming data from new-generation observational campaigns, we are about to enter a new era in the study of how galaxies form and evolve. The unprecedented quantity of data that will be collected from distances that have only marginally been grasped up to now will require analytical tools designed to target the specific physical peculiarities of the observed sources and handle extremely large datasets. One powerful method to investigate the complex astrophysical processes that govern the properties of galaxies is to model their observed spectral energy distributions (SEDs) at different stages of evolution and times throughout the history of the Universe. To address these challenges, we have developed GalaPy, a new library for modelling and fitting SEDs of galaxies from the X-ray to the radio band, as well as the evolution of their components and dust attenuation and reradiation. On the physical side, GalaPy incorporates both empirical and physically motivated star formation histories (SFHs), state-of-the-art single stellar population synthesis libraries, a two-component dust model for attenuation, an age-dependent energy conservation algorithm to compute dust reradiation, and additional sources of stellar continuum such as synchrotron, nebular and free-free emission, as well as X-ray radiation from low-and high-mass binary stars. On the computational side, GalaPy implements a hybrid approach that combines the high performance of compiled C++with the user-friendly flexibility of Python. Also, it exploits an object-oriented design via advanced programming techniques. GalaPy is the fastest SED-generation tool of its kind, with a peak performance of almost 1000 SEDs per second. The models are generated on the fly without relying on templates, thus minimising memory consumption. It exploits a fully Bayesian parameter space sampling, which allows for the inference of parameter posteriors and thereby facilitates the study of the correlations between the free parameters and the other physical quantities that can be derived from modelling. The application programming interface (API) and functions of GalaPy are under continuous development, with planned extensions in the near future. In this first work, we introduce the project and showcase the photometric SED fitting tools already available to users. GalaPy is available on the Python Package Index (PyPI) and comes with extensive online documentation and tutorials.

The Influence of Technology Users in Learning Towards Increasing Student Achievement in Primary School

Education in Indonesia is currently facing the fourth industrial revolution known as Industrial Revolution 4.0. Education 4.0 is education that is characterized by the use of digital technology in the learning process or known as a cyber system. This research aims to find out how digital technology is used in PAI subjects at Bina Sejahtera Elementary School in Medan, what is the learning achievement in PAI subjects at Darul Hikam Elementary School in Bandung, and how much influence the use of digital technology has in PAI subjects at Bina Sejahtera Elementary School in Medan. The method used in this research is a descriptive analysis method with a quantitative approach. The statistics use correlation analysis, simple linear regression analysis, and coefficient of determination and t test. The population in this study were 5th grade students at Bina Sejahtera Elementary School, Medan, totaling 103 students. The research sample was taken as many as 50 students. The data collection technique was carried out by means of an online questionnaire, interviews and documentation of the 2019/2020 odd semester PPKN report scores. Based on the research results, it was concluded that the influence of the use of digital technology on student learning achievement in Civics subjects is in the high category with a correlation coefficient of 0.701 and the contribution of the influence is 49.1% and the remaining 50.9% of learning achievement is influenced by other variables. There is a significant influence of the use of digital technology in Civics subjects on student learning achievement with a significance value of 0.000. This finding is very important, especially for Civics teachers so they can use and apply digital technology in the teaching and learning process because this media can improve students' learning achievement. For students, it is hoped that by implementing learning using digital technology, the learning achievements obtained by students can increase even higher.

Perilaku Prososial Pada Content Creator : Bentuk Bantuan Yang Nyata Kah?

After the Covid-19 outbreak, many people have changed professions to become creators. Not a few of them show us a high level of prosocial behavior. The objective of this study is to find out the existence of prosocial behavior carried out by creators in providing assistance. The research method of gathering data is by using interviews and online documentation assisted by the Google Form platform. This type of research is qualitative one with a descriptive design. The subjects in this study by using purposive sampling techniques are 4 creators who give help to the people whose economy level is low, in different social media (Instagram, Youtube and Tiktok). There are two results of the study obtained based on the analysis of researchers and interviews with the owners : (1) Creators did real prosocial behavior. They uploaded videos on social media just to motivate and inspire many people out there who always like helping everyone. There was a plan to always help people wherever it was, whether the videos were used as or not. However, there are some gains by number of followers, adsense, and endors fee obtained from the videos made are reciprocity that the creator does not really expect. (2) Creators who actually only expected reciprocal results from the videos they make, so their prosocial behavior is not real. Any videos displayed are arranged in such a way by the Creators that cause sympathy and empathy from the audience by expecting financial results for them.

Concrete experience: Parkour as a culture of agential learning

The field of performer training remains closely entwined with the authorial vision of twentieth Century practitioners. Performers in the context of theatre and dance are still trained to embody specific ways of being and doing, defined in relation the aesthetic and philosophical concerns of artists making work in the last century. However, as industry practices and scholarship move further away from paradigms of single authorship, or ensembles tied to the vision of auteurs, conventional, studio-based training practices have come under scrutiny. This has led educators and institutions to explore alternative models of training, prioritising student agency and choice. This article analyses the decentralised training practices of Parkour. It examines how Parkour practitioners use video sharing to spread innovations in practice and technique, establishing a training culture that promotes agential learning and experimentation. By looking at online documentation of training and video sharing from the Parkour team Storror, the article argues that Parkour training establishes models of experimentation and innovation that are decentralised and rooted in the agency and curiosity of the practitioner. The article concludes by considering how culture and practices from Parkour might inform the broader field of performer training – articulating strategies for encouraging student agency in learning.

Analisis Metode Dakwah Husain Basyaiban di Media Sosial

Da'wah is an activity of inviting and influencing people towards the straight path wisely in accordance with God's commands. This is done for every individual and community to want to improve themselves by doing good deeds and deepening the teachings of the Islamic religion. Da'wah must be carried out by people who are intelligent, civilized and have noble character. Da'wah media continues to evolve by following technology which is becoming more advanced day by day, one of the media used for preaching is social media. You Tube, Instagram, and Tik Tok are examples of social media that are currently widely used by preachers to preach. Husain Basyaiban is one of the young preachers who uses social media as a medium for his preaching. The preaching delivered by Husain had an influence on readers and viewers, because it was packaged well using simple language and strong arguments so that it was easy for his followers to understand. Therefore, researchers are interested in analyzing the preaching methods used by Husain. This research focuses on the da'wah methods and social media used by Husain Basyaiban. This research was carried out using a qualitative approach using the netnographic method where primary sources were obtained from posts and videos uploaded by Husain Basyaiban and secondary data sources were obtained from previous journals and articles. With data collection techniques by means of online documentation and observation. From the results of this research, it was found that Husain used da'wah methods which were categorized into three methods, namely the Al-Hikmah, Mauidzotul Hasanah, and Mujjadi methods. Then there is the development of Husain's social media at this time, with the username kadamsidik for 168 thousand subscribers on You Tube, the username basyasman for 1.9 million followers on Instagram, as well as the username @kadamsidik00 for 6 million followers and also 327.1 million likes on all videos which was uploaded from the beginning of 2020 until now on September 24 2023 on Tik Tok. Researchers hope that this research can be studied and researched more deeply by other researchers, both with other methods and objects.

Indigenous research ethics and Tribal Research Review Boards in the United States: examining online presence and themes across online documentation

Researchers seeking to engage in projects related to Tribal communities and their citizens, lands, and non-human relatives are responsible for understanding and abiding by each Tribal nation’s research laws and review processes. Few studies, however, have described the many diverse forms of Tribal research review systems across the United States (US). This study provides one of the most comprehensive examinations of research review processes administered by Tribal Research Review Boards (TRRBs) in the US. Through a systematic analysis, we consider TRRBs’ online presence, online documentation, and themes across documents, for five entity types: Tribal nations and Tribal consortiums, Tribal colleges and universities, Tribal health organizations, Indian Health Services, and other Tribal organizations. Results include an assessment of online presence for 98 potential TRRBs, identification of 118 publicly available online documents, and analysis of 41 themes across four document types: Tribal research codes and TRRBs’ guidelines, applications, and post-approval documents. Altogether, this research provides a macro-level analysis of the most common types of TRRBs in the US in an effort to increase researchers’ understanding of these important processes as they prepare to ethically engage Tribal communities in research. These results aim to empower Tribal decision makers as they align their TRRBs’ online visibility and documentation with community priorities and strengthen their protections for the rights and wellbeing of their citizens and community. Ultimately, by expanding our knowledge of TRRBs across the US, this contribution seeks to uphold Tribal sovereignty in research and promote ethical approaches to research with Indigenous communities.

ANALYSIS OF FINANCIAL REPORTS USING TREND ANALYSIS METHOD TO EVALUATE FINANCIAL PERFORMANCE AT PT. BUKIT ASAM TBK PERIOD 2018-2022

This research was carried out at PT. Bukit Asam Tbk. The phenomenon that occurs can be seen from the financial reports. Profits and losses from 2018-2022 tend to increase. The aim of this research is to determine the condition of the company's financial reports and financial performance. The research method used is a quantitative descriptive method with secondary data sources. The data collection technique used is literature study, online data browsing and documentation. The research results show that the percentage generated for the profit and loss financial report on net income for the 2018-2019 period tends to increase, whereas in 2019-2020 it decreased, then in 2021-2022 it increased. The cost of revenue for the 2018-2019 period tends to increase, while in 2019-2020 it decreases, then in 2021-2022 it increases. Gross profit for the 2018-2020 period tends to decrease and for the 2021-2022 period there is an increase. Furthermore, operating profit in the 2018-2020 period decreased and in the 2021-2022 period it increased. This increase was due to the increase in mining service costs and royalty costs after the COVID-19 pandemic.