Open Source Research Articles

Estimates of the accuracy of identification of a nonlinear dynamic system using step test signals

Machine learning (ML) has become widespread in everyday life. On its basis, programs with artificial intelligence work, on the basis of which many virtual assistants have evolved. ML plays an important role in various spheres of activity of many enterprises. ML helps to automate many processes, simplifying the functioning of the company. Named Entity Recognition (NER) models allow to automatically select and search for information according to certain criteria in mobile data extracted, for example, by the logistic method. Support for NER by Python models makes it possible to flexibly program specific requests that are generated in the forensic examination process. Open source creates a unique opportunity to continuously improve the model by training it on datasets. A powerful NER package is the spaCy framework, which helps to simplify data and extract detailed information from input data, train a model, perform model tuning, and more. spaCy is compatible with 64-bit CPython 3.7+ and runs on Unix/Linux, macOS/OS X and Windows. The latest spaCy releases are available over pip and conda.

A holistic analysis of environmental impacts and improvement pathways for the Brazilian electric sector based on long-term planning and life cycle assessment

Brazil presents a high share of renewable electricity generation, mainly due to its favorable hydroelectric potential. However, reports estimate that demand will grow substantially in the long term, requiring a significant diversification of the electricity mix. In this context, this paper analyzes how the environmental impacts of electricity generation tend to change over the horizon 2025–2050 in Brazil and identifies improvement opportunities accounting for environmental and economic factors. To do so, the Open Source Energy Modeling System (OSeMOSYS) is applied to estimate the composition of the electricity mix in 2050. Then, life cycle assessment (LCA) is used to evaluate the associated environmental impacts across ten categories. Lastly, hybrid weighted ɛ-constraint multi-objective optimization (MOO) is employed to evaluate which sources can be used to minimize climate change and the levelized cost of electricity while ensuring that other categories are reasonably restrained. The results indicate that the environmental performance of the Brazilian electricity matrix tends to deteriorate substantially, both when analyzing overall impacts and impacts per kWh. Moreover, the proposed MOO approach suggests that some sources, mainly hydropower, onshore wind, and centralized PV present an all-around economic/environmental performance and might be favorable for expanding the power system.

Applying systems engineering principles to develop an open source laser based metal powder bed fusion system

Purpose Conventional powder bed fusion systems, with their high costs, proprietary nature and restrictive fees, limit research opportunities. This study aims to unveil an affordable, open-source hardware, open-source software laser-based metal powder bed fusion system. Recognizing the distinction between DIY and open-source hardware is crucial for widespread acceptance. Design/methodology/approach The authors present a comprehensive system architecture using object process methodology for functions and architecture, a design structure matrix to model system dependencies and classical technical drawing exploded views for select subsystems. Modularization enables high adaptability, fostering potential adoption. Findings The fully open system enables unrestricted research, mirroring common industrial metal laser-based powder bed fusion (L-PBF) systems. While “open” systems are available for purchase, they remain closed-source, lacking source code and technical drawings sharing, hindering contribution and co-development. The authors’ is the pioneering and sole open-source metal L-PBF system, boasting 1,500+ print hours. A series of industrial and academic adopters are currently implementing the system. Originality/value The open system, slicer software and controller offer unique process control, supporting multimaterial printing. The authors shared the design on the OpenAM GitHub page under the CERN-OHL-P v2 Open Source Hardware license. While it is functional for additive manufacturing (e.g. aluminum, tool steel, titanium and stainless steel), the entire process chain is actively evolving, ideal for co-development with the additive manufacturing community.

A New Continuous Strength Method for Prediction of Strain-Hardening Performance of High-Strength Aluminum Alloy Cylindrical Columns

This paper aims to develop a new continuous strength method (CSM) to more accurately predict the strain-hardening characteristics of high-strength aluminum alloy circular hollow sections (CHSs) under axial compression. A total of 11 stub column specimens made of 7A04-T6 and 6061-T6 aluminum alloys underwent testing. Additionally, 16 sets of experiment data were gathered from open sources, encompassing various aluminum alloy types such as 6082-T6, 6061-T6, and 6063-T5. Validated by experimental result, the finite element (FE) model was applied in a series of comprehensive parameter studies, supplementing the limited test result of high-strength aluminum alloy stub columns. Based on the experiment and FE results, this paper proposes a new CSM relation to determine the cross-sectional resistance of high-strength non-slender CHS aluminum alloys under compression. The cross-sectional resistance obtained from tests are compared with predicted strengths determined using the European code, as well as the solution of the CSM proposed in a previous study and in this paper. The comparison illustrates that the strength predictions in the European code and the previous study are conservative. Compared with the European code and the previous study, the strength prediction formula proposed in this paper improves accuracy by 11% and 5%, respectively, while reducing scatter by 8.4% and 2%, respectively.

Bibliometric Profile of Research on Physical Activities in Special Education

The aim of the research is to understand the bibliometric profile of physical activity research in special education. The researchers first decided on the database to be searched. Then, keywords were determined and the data were converted into a form suitable for analysis. Then, data analysis was carried out. As a result of the research, a series of keywords were decided to ensure that publications containing research on physical activity in special education were included: "(AB=("special education")) AND AB=("sport" or "physical activity" or "exercise") In the first search, 255 articles were reached. After excluding the excluded articles, 137 articles were analysed. Using the restriction options of the WoS database, the search was restricted to consist only of articles and to be between 1992 and 2024. Articles and early access were selected and all languages were selected as the language of publication. VOSviewer, Microsoft Excel 2016 and the open source Biblioshiny package program created in R programming language for bibliometric analyses were used in the analysis of the research data. In this study, the number of publications was taken into account to determine the effective journals in the field, and the number of local and global citations was evaluated to determine the effective publications. In order to evaluate the historical development of research on physical activity in special education, the frequency of publication by years was used. Likewise, in order to determine the publication efficiency of the countries, the number of publications of the relevant authors according to their countries was evaluated. Common word analyses and strategic diagrams were used to identify thematic research areas.

Единые Государственные Информационные Системы Здравоохранения (ЕГИСЗ) в странах Востока.

The introduction of information technology (IT) in healthcare has led to a real revolution in the provision and management of medical care. However, they are developing with different intensity in different countries. The purpose of the study was to test criteria to determine the degree of IT development in healthcare, as well as to illustrate the assessment of IT development in different countries to compare them with each other. To achieve this goal, we have identified a set of criteria covering key aspects of the implementation and use of IT in medical practice. We have selected the countries of the East for this study. We searched information on the level of IT development in healthcare in the Eastern countries in Google open sources and PubMed databases manually, as well as using GPT-3.5 Turbo. According to the availability and development of criteria, the degree of IT development in healthcare was assessed and the categories «Highly», «Medium» and «Less» developed were assigned. As a result, within the study’s methodology framework, the UAE, Saudi Arabia, Qatar, Israel, Singapore, Indonesia, the Philippines, Laos, South Korea, Japan and China were classified as countries with highly developed IT in healthcare. Countries with medium-developed IT in healthcare included Jordan, Oman, Afghanistan, Iran, Iraq, Bahrain, Vietnam, Thailand, Myanmar, Cambodia, India and Bangladesh. Lebanon, Kuwait, East Timor and Brunei were among the countries with the least developed IT in healthcare.

Исследование причин и факторов изменения потребительского поведения населения

Public needs and tastes change due to global trends and personal transformations. In time of change, producers look for new ways to attract consumers. The research featured global trends that affect the behavior of Russian consumers, as well as the factors that altered the change in the structure and patterns of Russian consumption market during the pandemic of COVID-19. The authors analyzed global trends in consumer behavior in the period preceding the pandemic and studied the factors that affected the behavior of Russian consumers during the pandemic. They traced the evolution of consumer behavior patterns to identify effective tools that could help the market to meet the needs of the population. The research is relied on information from open sources, processed with the help of standard research methods and economic analysis. Unstable economy, poor economic well- being, and general uncertainty push people to the model of crisis consumption, which happened during the pandemic. The overconsumption in the acute phase of COVID-19 gradually gave way to rational choice and responsible consumption. Digital technologies that thrived during the pandemic shaped a new vector and patterns of consumer behavior that developed as part of pre-COVID global trends and during the pandemic. The article also describes tools that are able to improve the quality of public consumption.

Forecasting staffing needs in general education: HR-practices and staffing planning model for metropolitan schools

Introduction. The development of human capital is crucial for preparing an effective response of the Russian society to the great challenges related to ensuring sustainable economic growth, development and implementation of new promising technologies, increasing labour productivity, and improving the quality of life of citizens. The key role here is played by the state of the education sector, on which the population's mastering of competences, knowledge and skills demanded by the economy and the labour market directly depends. Ensuring the training of highly qualified specialists considering the changing staffing needs of society and trends in the development of the digital economy refers to the urgent problem of management and forecasting of staffing in the Russian education system. Materials and Methods. The methodological basis of the study was formed by reports of international organizations, national agencies in the field of education, reports of working groups, as well as domestic and foreign studies in the field of forecasting the staffing status; official web pages of federal and regional executive authorities that carry out management and control in the field of education; websites of educational organizations of general and higher education; information from open data sources. Qualitative analysis of data using general scientific methods of cognition was carried out on the obtained set of data. Results. The analysed solutions have limitations that make it difficult to directly transfer them to the staff forecasting model for Moscow schools. They do not consider the peculiarities of Moscow education, for example, the directions of the Moscow education development strategy, specifics of the nomenclature of positions and certification of managerial staff, city projects of pre-professional education, optimization of administrative staff, etc. When creating a staff forecasting service for Moscow schools, it is necessary to consider the advantages of platform solutions that provide an integrated interdisciplinary approach using large amounts of data. At the same time, the forecast should consider the peculiarities of the Moscow education system that can affect the staff composition of schools. Discussion and Conclusions. Based on the analysis, a model solution for forecasting the staffing status of educational organizations in the metropolitan education system has been developed, including methodological approaches to evaluation; directions for assessing the dynamics of changes in the staffing status of schools; the structure of the staffing composition of Moscow schools; the structure of primary data; and a set of indicators.

“James Baldwin, Return of the Prophet”: An Interview

Cornel West was interviewed by Christopher Lydon for Radio Open Source; the interview was originally broadcast in September of 2017. They discuss the works of Baldwin, the condition of America, and Baldwin’s relevance to that condition today. The interview is reprinted here by permission of the interviewee.

Pangenome-Informed Language Models for Privacy-Preserving Synthetic Genome Sequence Generation.

The public availability of genome datasets, such as The Human Genome Project (HGP), The 1000 Genomes Project, The Cancer Genome Atlas, and the International HapMap Project, has significantly advanced scientific research and medical understanding. Here our goal is to share such genomic information for downstream analysis while protecting the privacy of individuals through Differential Privacy (DP). We introduce synthetic DNA data generation based on pangenomes in combination with Pretrained-Language Models (PTLMs). We introduce two novel tokenization schemes based on pangenome graphs to enhance the modeling of DNA. We evaluated these tokenization methods, and compared them with classical single nucleotide and k -mer tokenizations. We find k -mer tokenization schemes, indicating that our tokenization schemes boost the model's performance consistency with long effective context length (covering longer sequences with the same number of tokens). Additionally, we propose a method to utilize the pangenome graph and make it comply with DP privacy standards. We assess the performance of DP training on the quality of generated sequences with discussion of the trade-offs between privacy and model accuracy. The source code for our work will be published under a free and open source license soon.

Numerical eigen-spectrum slicing, accurate orthogonal eigen-basis, and mixed-precision eigenvalue refinement using OpenMP data-dependent tasks and accelerator offload

Performing a variety of numerical computations efficiently and, at the same time, in a portable fashion requires both an overarching design followed by a number of implementation strategies. All of these are exemplified below as we present transitioning the PLASMA numerical library from relying on dependence-driven large tasks to achieving utilization of fine grain tasking and offload to hardware accelerators while keeping its core dependence sets: OpenMP source code pragmas and runtime for most system-level functionality and basic low-level numerical kernels provided directly by hardware vendors or open source projects with vendor contributions. We also present new algorithmic methods and their efficient parallel implementations including fine grained tasking for eigen-spectrum slicing and offload for mixed-precision eigenvalue refinement. We provide performance, scaling, and numerical results showing sizable gains over the available solutions from either the open source and vendor-provided packages.

Assessing the Reliability of OpenCap and OpenSim as Open Source Softwares for Biomechanical Analysis in Neurological Rehabilitation: A Case Study

The neurological rehabilitation focuses on enhancing functional recovery and improving the quality of life for people who have experienced injuries or diseases affecting the central or peripheral nervous system. This functional recovery includes a follow up of the kinematics of the patients limbs. The use of a open source software such as OpenSim, has been previously proposed as a tool for kinematic analysis, this software allows for highly specialized 3D musculoskeletal modeling, facilitates kinematic analysis and the assessment of force and angles in the lower and upper limbs of the human body. In this context, the propose of this research was to test the reliability of OpenSim for kinematic analysis during neurological rehabilitation. For this goal, the Motricity Index test was done by a group of three healthy participants, this values were used for comparison to the stroke patient who is currently undergoing neurological rehabilitation process. The results demonstrates all the limitations in the range of motion of the patient in comparison to the healthy group due his motor issues, such as muscle spasticity and weakness. This research shows the advantages and limitations of this software and its application in neurological rehabilitation. The goal is to contribute to the development of effective and personalized therapeutic strategies to improve the recovery process for these patients.

Good modelling software practices

Frequently in socio-environmental sciences, models are used as tools to represent, understand, project and predict the behaviour of these complex systems. Along the modelling chain, Good Modelling Practices have been evolving that ensure — amongst others — that models are transparent and their results replicable. Whenever such models are represented in software, Good Modelling meet Good Software Practices, such as a tractable development workflow, good code, collaborative development and governance, continuous integration and deployment; and they meet Good Scientific Practices, such as attribution of copyrights and acknowledgement of intellectual property, publication of a software paper and archiving. Too often in existing socio-environmental model software, these practices have been regarded as an add-on to be considered at a later stage only; modellers have shied away from publishing their model as open source out of fear that having to add good practices is too demanding. We here argue for making a habit of following a list of simple and not so simple practices early on in the implementation of the model life cycle. We contextualise cherry-picked and hands-on practices for supporting Good Modelling Practice, and we demonstrate their application in the example context of the Viable North Sea fisheries socio-ecological systems model.

Comparative Assessment of Otolaryngology Knowledge Among Large Language Models

ObjectiveThe purpose of this study was to evaluate the performance of advanced large language models from OpenAI (GPT‐3.5 and GPT‐4), Google (PaLM2 and MedPaLM), and an open source model from Meta (Llama3:70b) in answering clinical test multiple choice questions in the field of otolaryngology—head and neck surgery.MethodsA dataset of 4566 otolaryngology questions was used; each model was provided a standardized prompt followed by a question. One hundred questions that were answered incorrectly by all models were further interrogated to gain insight into the causes of incorrect answers.ResultsGPT4 was the most accurate, correctly answering 3520 of 4566 questions (77.1%). MedPaLM correctly answered 3223 of 4566 (70.6%) questions, while llama3:70b, GPT3.5, and PaLM2 were correct on 3052 of 4566 (66.8%), 2672 of 4566 (58.5%), and 2583 of 4566 (56.5%) questions. Three hundred and sixty‐nine questions were answered incorrectly by all models. Prompts to provide reasoning improved accuracy in all models: GPT4 changed from incorrect to correct answer 31% of the time, while GPT3.5, Llama3, PaLM2, and MedPaLM corrected their responses 25%, 18%, 19%, and 17% of the time, respectively.ConclusionLarge language models vary in their understanding of otolaryngology‐specific clinical knowledge. OpenAI's GPT4 has a strong understanding of core concepts as well as detailed information in the field of otolaryngology. Its baseline understanding in this field makes it well‐suited to serve in roles related to head and neck surgery education provided that the appropriate precautions are taken and potential limitations are understood.Level of EvidenceN/A Laryngoscope, 2024

Knowledge-based model generation for aircraft cabin noise prediction from pre-design data

AbstractThe climate targets set out in the “European Green Deal” call for the consideration and implementation of climate-friendly propulsion concepts and sustainable fuels in future aircraft configurations. This puts the use of very efficient propeller engines into the focus of aircraft design. However, these pose major challenges, especially to cabin acoustics, due to high, tonal sound pressure levels on the fuselage. The design of noise control measures requires the competence to predict the resulting interior noise as early as possible in the design process of the vehicle. This requires a high level of geometric and structural detail regarding the fuselage structure and cabin components. With increasing frequency range, the necessary structural details also increase, which have to be resolved in the simulation models due to the associated decreasing structural wavelength. Especially in the context of aircraft pre-design, there is usually not enough information available for a detailed vibro-acoustic modeling of the fuselage and cabin components, which allows a meaningful prediction of the vibrations and thus the cabin noise. Therefore, the knowledge-based tool Fuselage Geometry Assembler (FUGA) is developed for the targeted enrichment of preliminary design data with knowledge for detailed numerical analyses. This paper describes the knowledge-based geometry and model generation in FUGA, which can consider the necessary (increasing) level of detail for the vibro-acoustic prediction already in the preliminary design. For this purpose, aircraft data sets in the preliminary design data format Common Parametric Aircraft Configuration Schema (CPACS) form the modeling basis. Originating from the aircraft preliminary design, these initially describe the outer shell of the vehicle and are extended by detailed structural information that defines the geometric boundary conditions for component placement in cabin design. For the cabin components, the open-source geometry kernel Open Cascade Technology (OCCT) is used to provide geometries at the level of detail required for subsequent analyses. The geometry models are then discretized in open source (e.g. Gmsh) or commercial meshers and further used for numerical analysis. Finally, the prediction of cabin noise is demonstrated as a Proof of Concept using the example of a short-haul propeller-driven aircraft and the feasibility of the proposed method is indicated by investigating the sensitivity of resulting simulation models to the fuselage skin thickness.

Collective Translation as Forking (分岔)

Together with a Taiwanese working group, we have been producing a Chinese translation of Aesthetic Programming: A Handbook of Software Studies, a free and open source book first written by Winnie Soon and Geoff Cox in 2020, and released in English in a git repository, dynamic website, downloadable PDF and printed form. Apart from learning to code in p5.js, the book addresses the cultural and aesthetic dimensions of programming from its insides, as a means to think and act critically, and to understand the importance of programming as a cultural practice that can develop discussion of issues that are relatively under-acknowledged in technical subjects such as gender, race and sexuality. Importantly, the book is understood as a computational object, not released as a fixed and universal teaching resource, but rather a situated curriculum with the potential for extension and customization with other arts and coding communities. The use of Git has allowed the authors to formalize its production as an iterative process, allowing for reversioning and for others to fork a copy and customize with different references, examples, critical reflections and even new chapters. The interest is in forking a book like forking software, and incorporating local knowledge and examples, and how this resonates with a politics of cultural translation. This essay will elaborate on the process of running two open participatory workshops that were conducted in Taipei and London (in 2023) with the aim to challenge some of the normative social relations of production associated with translation, and explore other possibilities of collective practice. The politics of translation has been well-established in general, but what of the specifics of translating a book such as this? Aside from the technical and aesthetic challenges and implications, this raises the question of how the Chinese language model enforces particular hegemonic worldviews that occlude differences. With all the variants of Chinese language, how is this tied to expressions of colonial power that resonates with our use of English? Given the rich variations of Chinese and indigenous languages (not least in a Taiwanese context), we are curious how we might be sensitive to language diversity that challenges the Western-centrism of programming in English (and inherent nationalisms). We are also mindful of the way that “queer” politics has informed the way that terms can be appropriated/expropriated, as a means to “talk back” to the source codes of oppression. What are the implications of drawing the practices of forking and translating together?

Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing

Abstract. We provide a detailed description of the ice-sheet layer age tracer Englacial Layer Simulation Architecture (ELSA) – a model that uses a straightforward method to simulate the englacial stratification of large ice sheets – as an alternative to Eulerian or Lagrangian tracer schemes. ELSA's vertical axis is time, and individual layers of accumulation are modeled explicitly and are isochronal. ELSA is not a standalone ice-sheet model but requires unidirectional coupling to another model providing ice physics and dynamics (the “host model”). Via ELSA's layer tracing, the host model’s output can be evaluated throughout the interior using ice core or radiostratigraphy data. We describe the stability and resolution dependence of this coupled modeling system using simulations of the last glacial cycle of the Greenland ice sheet using one specific host model. Key questions concern ELSA's design to maximize usability, which includes making it computationally efficient enough for ensemble runs, as well as exploring the requirements for offline forcing of ELSA with output from a range of existing ice-sheet models. ELSA is an open source and collaborative project, and this work provides the foundation for a well-documented, flexible, and easily adaptable model code to effectively force ELSA with (any) existing full ice-sheet model via a clear interface.

Lazy Resampling: Fast and information preserving preprocessing for deep learning

Background and Objective:Preprocessing of data is a vital step for almost all deep learning workflows. In computer vision, manipulation of data intensity and spatial properties can improve network stability and can provide an important source of generalisation for deep neural networks. Models are frequently trained with preprocessing pipelines composed of many stages, but these pipelines come with a drawback; each stage that resamples the data costs time, degrades image quality, and adds bias to the output. Long pipelines can also be complex to design, especially in medical imaging, where cropping data early can cause significant artifacts. Methods:We present Lazy Resampling, a software that rephrases spatial preprocessing operations as a graphics pipeline. Rather than each transform individually modifying the data, the transforms generate transform descriptions that are composited together into a single resample operation wherever possible. This reduces pipeline execution time and, most importantly, limits signal degradation. It enables simpler pipeline design as crops and other operations become non-destructive. Lazy Resampling is designed in such a way that it provides the maximum benefit to users without requiring them to understand the underlying concepts or change the way that they build pipelines. Results:We evaluate Lazy Resampling by comparing traditional pipelines and the corresponding lazy resampling pipeline for the following tasks on Medical Segmentation Decathlon datasets. We demonstrate lower information loss in lazy pipelines vs. traditional pipelines. We demonstrate that Lazy Resampling can avoid catastrophic loss of semantic segmentation label accuracy occurring in traditional pipelines when passing labels through a pipeline and then back through the inverted pipeline. Finally, we demonstrate statistically significant improvements when training UNets for semantic segmentation. Conclusion:Lazy Resampling reduces the loss of information that occurs when running processing pipelines that traditionally have multiple resampling steps and enables researchers to build simpler pipelines by making operations such as rotation and cropping effectively non-destructive. It makes it possible to invert labels back through a pipeline without catastrophic loss of accuracy.A reference implementation for Lazy Resampling can be found at https://github.com/KCL-BMEIS/LazyResampling. Lazy Resampling is being implemented as a core feature in MONAI, an open source python-based deep learning library for medical imaging, with a roadmap for a full integration.

OPEN SOURCE TEXTBOOK AS A RESOURCE FOR THE TEACHING-LEARNING PROCESS OF ALGEBRA IN INITIAL TRAINING OF MATHEMATICS TEACHERS

Objective: To determine the implications of the use of open source text in the teaching-learning process of algebraic structures in the initial teacher training of the mathematics specialty in a state university in Peru. Theoretical Framework: The conceptual and epistemic bases of open learning are systematically addressed through interactive texts, as well as the aspects related to the teaching-learning process in university higher education. Method: The study has a mixed approach, pre-experimental design and correlational level, the target population was mathematics education students and a sample of 46 randomly chosen students, data collection is carried out through a diagnostic questionnaire, another multiple alternative and an interview, in the study sample classes are held through the open source book; then the learning achieved at the conceptual level is evaluated. procedural of algebraic structures. Results and Discussion: In this research it is evident that students did not know the open source online text for the study of mathematics, but they have skills in the use of digital technology; but with the use of the digital book they assimilate the concepts, perform demonstrations and solve problems efficiently, raising their level of motivation towards learning. Thus, the relevant use of open source books significantly improves learning and the development of mathematical competence in students. Research Implications: Strengthening of interactive and personalized activities for a comprehensive training of mathematics content mediated by digital technology, reinforcing in a sustained way the culture and mathematical competence of teachers in initial training. Originality/Value: Implementation of an innovative approach in pedagogical practice, through the use of open resources for the assimilation of mathematical concepts and procedures in a dynamic, efficient and effective way. The value of research is evidenced in the praxis of didactic strategies in the context of the digital age, oriented to the knowledge of algebraic structures by mathematics teachers.

Open source container orchestration for Industry 4.0 – requirements and systematic feature analysis

Container-based virtualization is a popular technique, e.g., to realize microservice architectures. Recently, containers became popular in Industry 4.0 / IIoT systems, which typically consist of hundreds of (edge) devices and machines. In such setups, efficient management of containers is essential as offered by container orchestrators like Kubernetes. However, currently no specific overviews discussing orchestrator capabilities for Industry 4.0 are available. In this paper, we analyze nine open source container orchestrators for their application in Industry 4.0 or IIoT settings as a basis for future research and development. We contribute a systematic literature review to identify 23 basic orchestration requirements. We complement this by insights from an intensive requirements collection in a research project on intelligent industrial production, as well as selected features from a published generic orchestrator analysis. From these 66 requirements, we derive a requirements/feature taxonomy, which we use to analyze the nine open source orchestrators including Kubernetes. We show that there is, e.g., still a lack of support regarding edge devices, IIoT protocols, security mechanisms, and specialized resources for artificial intelligence.