Open Coding Research Articles

C anton MT: Investigating Back-Translation and Model-Switch Mechanisms for Cantonese-English Neural Machine Translation

This paper investigates the development and evaluation of machine translation models from Cantonese to English (and backward), where we propose a novel approach to tackle low-resource language translations. Despite recent improvements in Neural Machine Translation (NMT) models with Transformer-based architectures, Cantonese, a language with over 80 million native speakers, has below-par State-of-the-art commercial translation models due to a lack of resources. The main objectives of the study are to develop a model that can effectively translate Cantonese to English and evaluate it against state-of-the-art commercial models. To achieve this, a new parallel corpus has been created by combining different available corpora online with preprocessing and cleaning. In addition, a monolingual Cantonese dataset has been created through web scraping to aid the synthetic parallel corpus generation. Following the data collection process, several approaches, including fine-tuning models, back-translation , and model switch , have been used. The translation quality of models has been evaluated with multiple quality metrics, including lexicon-based metrics (SacreBLEU and hLEPOR) and embedding-space metrics (COMET and BERTscore). Based on the automatic metrics, the best model is selected and compared against the 2 best commercial translators using a new human evaluation framework HOPES . The best model proposed in this investigation (NLLB-mBART) with model switch mechanisms has reached comparable and even better automatic evaluation scores against State-of-the-art commercial models (Bing and Baidu Translators), with a SacreBLEU score of 16.8 on our test set. Furthermore, an open-source web application has been developed to allow users to translate between Cantonese and English, with the different trained models available for effective comparisons between models from this investigation and users. CantonMT is available at https://github.com/kenrickkung/CantoneseTranslation

Integrating ytopt and libEnsemble to autotune OpenMC

Ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations and progressively fitting a surrogate model over the input-output space until exhausting the user-defined maximum number of evaluations or the wall-clock time. libEnsemble is a Python toolkit for coordinating workflows of asynchronous and dynamic ensembles of calculations across massively parallel resources developed within the ECP PETSc/TAO project. libEnsemble helps users take advantage of massively parallel resources to solve design, decision, and inference problems and expands the class of problems that can benefit from increased parallelism. In this paper we present our methodology and framework to integrate ytopt and libEnsemble to take advantage of massively parallel resources to accelerate the autotuning process. Specifically, we focus on using the proposed framework to autotune the ECP ExaSMR application OpenMC, an open source Monte Carlo particle transport code. OpenMC has seven tunable parameters some of which have large ranges such as the number of particles in-flight, which is in the range of 100,000 to 8 million, with its default setting of 1 million. Setting the proper combination of these parameter values to achieve the best performance is extremely time-consuming. Therefore, we apply the proposed framework to autotune the MPI/OpenMP offload version of OpenMC based on a user-defined metric such as the figure of merit (FoM) (particles/s) or energy efficiency energy-delay product (EDP) on Crusher at Oak Ridge Leadership Computing Facility. The experimental results show that we achieve the improvement up to 29.49% in FoM and up to 30.44% in EDP.

CONSUMER CONFUSION AND ITS IMPACT ON DECISION MAKING AMONG FEMALE CONSUMERS IN INDONESIA’S ONLINE COSMETICS AND PERSONAL CARE INDUSTRY

This study examines consumer confusion and its impact on decision-making among female consumers in Indonesia's online cosmetics and personal care industry. With the rise of online shopping due to its convenience and extensive product information, consumer confusion—defined as the inability to correctly interpret various aspects of product information—has become a significant issue. A mixed-method approach was used, combining quantitative surveys and qualitative interviews. Data was collected through an online survey and analyzed using PLS-SEM, followed by semi-structured interviews that were manually coded through open, axial, and selective coding. The findings reveal that informational sources—overload, similarity, and ambiguity positively impact overall consumer confusion. Additionally, utilitarian decision-makers show higher levels of confusion compared to hedonic decision-makers. Previous brand experience was found to mitigate the negative effects of overload and ambiguity confusion on decision quality and enhance decision quality in the case of similarity confusion. Furthermore, overload and ambiguity confusion negatively impacts decision quality, whereas similarity confusion shows a positive impact. Finally, all informational sources of confusion positively impact decision postponement and cognitive dissonance. This study extends existing knowledge in marketing and consumer behavior, particularly regarding consumer confusion in the cosmetics sector. It offers practical insights for companies and brand managers to develop strategies that reduce consumer confusion, such as simplifying decision-making processes, providing clear product information, and leveraging previous brand experiences. These strategies aim to enhance decision quality, reduce decision postponement, and mitigate cognitive dissonance among consumers. Finally, the study suggests directions for future research in consumer confusion.

Rights Quest: A Game for Child Rights Awareness

The interactive video game "A Way Home!" is a serious game for promoting pro-social change by raising awareness of children's rights, as outlined in the United Nations Convention on the Rights of the Child treaty. All countries except the United States have ratified this treaty. Through a collaborative effort, faculty and students developed this game using an open-source platform to create an immersive first-person video game experience to disseminate crucial knowledge about children's rights and the need for ratification. Game design guides players through real-world comparative scenarios depicting environments that support or do not support those fundamental rights. Through engaging gameplay and storytelling, iterative development processes facilitated the game's effectiveness in conveying complex concepts, such as the distinction between rights and responsibilities. The significance of children's fundamental rights, such as access to food, a safe, clean home, the right to speech, and freedom of expression, as enshrined in the CRC, are emphasized by juxtaposing different environments and depicting the consequences of choices. Grounded upon the Diffusion of Innovations theory (Rogers 2010), the first step to adopting cultural change is education. Using gameplay elements, including rewards for positive choices and learner guidance instructions, "A Way Home!" rights quest motivates players to explore and understand the principles of children's rights. The player wins the game when they achieve the goal of being heard in a court of law. By combining education with entertainment, the game aims to inspire empathy and foster understanding, with the ultimate goal of teaching about the need for ratification of the CRC Treaty in the U.S. and empowering players to take action and advocate for children's rights. This paper depicts the methodology involving participants (n=17) from one southeastern university in the States to capture qualitative and quantitative feedback through a perception survey to evaluate the first demo model. Results support advancing data collection for the next version developed using virtual reality (V.R.) headsets to further immerse the learners in the experience.

A Parametrical Study on Hypervelocity Impact of Orbital Debris

A numerical method has been presented to simulate hypervelocity impacts on metal targets. The target is a rectangular prism and is positioned at various inclined angles relative to the impact direction, while four different projectiles such as square prism, triangular prism, truncated cone, and ogival shape are chosen. This numerical model employs an open-source code, MPM3D-F90, which is based on the Material Point Method. In order to enhance flexibility of the code for defining projectiles and target bodies in the material domain, a preprocessor is developed to create a variety of geometrical shapes for a given volume. In addition to supplementing and defining various geometrical bodies, this tool also simplifies the preprocessing process to create the user’s specific preferences for the problem. To demonstrate the utility of the preprocessor tool and investigate the influence of geometry on hypervelocity impacts, simulations are conducted using various projectile and target configurations. The analysis results reveal that the structure of the debris cloud formations, scattering behavior of the ejected particle from both front and rear faces, and penetration depth measures are significantly influenced by the projectile shape and impact angles.

Information needs in bug reports for web applications

Given the widespread popularity and increasing reliance on long-lived web applications (such as Netflix and Facebook), effective and efficient bug reproduction is essential to maintain functionality and user satisfaction throughout the application’s lifetime. Developers use bug reports to localize, reproduce, and eventually fix software bugs. However, the content of bug reports is not always helpful (e.g., due to incomplete or missing information). In this study, we explore what type of information is often missing in bug reports and how that information is presented in them. We manually analyzed the initial and final versions of 1000 bug reports from 10 popular open-source web-based applications. The analysis revealed that, regardless of the type of software (e.g., e-commerce software or personal tools), diagnostic suggestions from developers and end-user usage information are often missing in initial bug reports but only added later throughout the lifetime of a bug report. Also, textual descriptions and screenshots are used most to describe bugs, regardless of the type of bug (e.g., a functional or performance error). The study highlighted the need for improved bug reporting templates and tools to improve bug report quality and efficiency in web application development and maintenance.

Assessing the indoor soundscape approach among university students' home study places

University students spend a considerable time at study places. The acoustical quality of these study places is one of the indoor environmental qualities (IEQ) that can have an impact on student's health, comfort, and performance. The indoor soundscape approach has been introduced to better understand occupants' sound perception and experience of sounds in relation to the environment. This study aims to explore the indoor soundscapes of home study places of these students by conducting semi-structured interviews with 23 university students with different profiles. For qualitative analysis, open coding was used. Sub-categories, based on the codes, and categories were created and assigned to the soundscape themes that are defined in ISO 12913-1. An affinity diagram consisting of the themes, categories, and sub-categories was initially developed. Then, it was validated through two workshops with participants. The results showed that the interpretation of the sound environment, responses, and outcomes differed among the students. In a previous study, 451 students were clustered in 5 clusters with similar acoustical preferences (profiles). Therefore, it is recommended to consider making the indoor soundscape approach applicable for different profiles of occupants.

Surrogate Immunohistochemical Markers of Proliferation and Embryonic Stem Cells in Distinguishing Ameloblastoma from Ameloblastic Carcinoma

PurposeThe current study aimed to investigate the use of surrogate immunohistochemical (IHC) markers of proliferation and stem cells to distinguish ameloblastoma (AB) from ameloblastic carcinoma (AC).MethodsThe study assessed a total of 29 ACs, 6 ABs that transformed into ACs, and a control cohort of 20 ABs. The demographics and clinicopathologic details of the included cases of AC were recorded. The Ki-67 proliferation index was scored through automated methods with the QuPath open-source software platform. For SOX2, OCT4 and Glypican-3 IHC, each case was scored using a proportion of positivity score combined with an intensity score to produce a total score.ResultsAll cases of AC showed a relatively high median proliferation index of 41.7%, with statistically significant higher scores compared to ABs. ABs that transformed into ACs had similar median proliferation scores to the control cohort of ABs. Most cases of AC showed some degree of SOX2 expression, with 58.6% showing high expression. OCT4 expression was not seen in any case of AC. GPC-3 expression in ACs was limited, with high expression in 17.2% of ACs. Primary ACs showed higher median proliferation scores and degrees of SOX2 and GPC-3 expression than secondary cases. Regarding SOX2, OCT4 and GPC-3 IHC expression, no statistically significant differences existed between the cohort of ABs and ACs.ConclusionKi-67 IHC as a proliferation marker, particularly when assessed via automated methods, was helpful in distinguishing AC from AB cases. In contrast to other studies, surrogate IHC markers of embryonic stem cells, SOX2, OCT4 and GPC-3, were unreliable in distinguishing the two entities.

Analysis of thixotropy of cement grout based on a virtual bond model

Thixotropy of cementitious materials is a crucial intrinsic property that determines the flowability and workability of cement-based grout. A novel virtual bond model of cement particles is developed in this paper to depict the thixotropy of cement grout. A particulate description of the reversible and erasable interparticle bonds is established based on experimental observations with a focus on the non-contact interactions mainly contributed in practice by calcium silicate hydrates (C–S–H). The structural breakdown of the cement network is realized through bonds breakage under applied motion, and the bonding network recovers with regeneration of interparticle connections that involve reversible hydrate reactions in the mixture. The balance between bond rupture and rebuilding can be tuned by assigning different strength limits for bond breakage. We have implemented this model in the open-source code Yade to carry out 3D discrete element method simulations of a rotational vane system filled with spherical particles, and the results show good agreement with experimental data. The modelling results reveal the transition from a solid-like structure to a fluid-like medium within cement suspensions caused by the evolution of broken interparticle bonds. The results also provide a distinct view of thixotropic variation upon disturbance. This model is extendable to other cohesive materials providing an explicit physical definition of the interparticle interactions. It also provides a theoretical explanation for the empirical estimations of thixotropy common in engineering industries and a potential means of measuring cementitious granular flow that may be useful in future studies.

Person-Environment Fit and Socioeconomic Status in Medical School

Abstract Purpose This study examined the impact of socioeconomic status (SES) on medical education in the context of person-environment fit (PE fit) theory, and specifically focused on the medical school experiences of students from lower-SES backgrounds. Method A constructivist approach was used in this qualitative study of 48 medical students from 27 US medical schools, all of whom self-identified as first-generation college graduates and/or being from a lower-income background (30 were both). Semi-structured audio-only interviews were conducted with these demographically and geographically diverse students from November 2021 through April 2022. Themes were identified using open coding and content analysis software. Results Almost all, 44 of 48, interviews included themes related to PE fit. Medical students indicated three interacting domains in which PE fit is relevant for them: (1) school, (2) clinical, and (3) professional environments. Learners from lower-SES backgrounds describe struggling to navigate multiple environments that are unfamiliar, culturally complex, and both personally and financially costly. They also describe ways they are addressing gaps, generating positive changes, supporting underserved patients, and broadening the perspectives of peers and educators. Conclusions PE fit theory provides a lens to understand unique aspects of lower-SES medical student experiences, including navigation of professional identity formation. It is critical for medical schools, funders, peers, and professional communities to sustain learning environments that support the flourishing of medical students from lower-SES backgrounds. This support includes transferring the burden of addressing fit from individual learners and marginalized classes of learners to educational, clinical, and professional organizations.

Discovering API usage specifications for security detection using two-stage code mining

An application programming interface (API) usage specification, which includes the conditions, calling sequences, and semantic relationships of the API, is important for verifying its correct usage, which is in turn critical for ensuring the security and availability of the target program. However, existing techniques either mine the co-occurring relationships of multiple APIs without considering their semantic relationships, or they use data flow and control flow information to extract semantic beliefs on API pairs but difficult to incorporate when mining specifications for multiple APIs. Hence, we propose an API specification mining approach that efficiently extracts a relatively complete list of the API combinations and semantic relationships between APIs. This approach analyzes a target program in two stages. The first stage uses frequent API set mining based on frequent common API identification and filtration to extract the maximal set of frequent context-sensitive API sequences. In the second stage, the API relationship graph is constructed using three semantic relationships extracted from the symbolic path information, and the specifications containing semantic relationships for multiple APIs are mined. The experimental results on six popular open-source code bases of different scales show that the proposed two-stage approach not only yields better results than existing typical approaches, but also can effectively discover the specifications along with the semantic relationships for multiple APIs. Instance analysis shows that the analysis of security-related API call violations can assist in the cause analysis and patch of software vulnerabilities.

HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.

Predicting the stability and fitness effects of amino-acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutation effect prediction. Pre-trained to predict amino-acid propensities from their surrounding 3D structure atomic environments, HERMES can be efficiently fine-tuned to predict mutational effects, thanks to its symmetry-aware parameterization of the output space. Benchmarking against other models demonstrates that HERMES often outperforms or matches their performance in predicting mutation effects on stability, binding, and fitness, using either computationally or experimentally resolved protein structures. HERMES offers a versatile suit of tools for evaluating mutation effects and can be easily fine-tuned for specific predictive objectives using our open-source code.

Analysis of the Mammalian Lipidome by DMS Shotgun Lipidomics.

Shotgun Lipidomics is a robust methodology for the characterization of the lipidome of complex biological samples. This assay is among the most quantitative lipidomics methods and is capable of surveying a wide breadth of lipid subclasses, both neutral and polar. The shortfalls of the technique include limitations in lipid species characterization and computationally demanding data analysis requiring isotopic and isobaric overlap correction. Differential Mobility Spectrometry (DMS) has demonstrated its utility in enabling acyl tail characterization within a Shotgun Lipidomics experiment. Here, we present a workflow for DMS Shotgun Lipidomics that measures 1400 possible lipid species. It utilizes the Shotgun Lipidomics Assistant (SLA) application, an open-source application that supervises the data analysis for an expansive Shotgun Lipidomics experiment.

Environmental Psychological Characteristic Supporting the Football Player’s Development in Indonesia and Malaysia

Background. The development path of a football player toward becoming a professional player involves four main aspects: physical, technical, tactical, and psychological ones. Objectives. This study aimed to examine the psychological characteristics of the environment that influence and contribute to the development of football players under 17 years old in Indonesia and Malaysia. Materials and methods. This study used a qualitative approach, namely evolved grounded theory, and the data collection process was conducted using three distinct methods: semi-structured interviews, observation, and document analysis. Twenty-five coaches participated in this study, comprising fifteen coaches from various football academies in Malaysia and ten coaches from Indonesia. All coaches involved in this study have a license and at least five years of experience as coaches under 17. The data collected and analyzed began with open coding, axial coding, and selective coding, and the analysis was assisted by using qualitative research software, namely N-Vivo 14. Results. The findings revealed that several aspects influenced player development, such as policy support, including structured and competitive age group competitions, a healthy football ecosystem; other results have been defined as relationships and cooperation supported by family, friendship between clubs, and teamwork. Finally, the study underscored the significance of involving coaching factors, including passion, patience, and coach psychological treatment abilities. These are the psychological characteristics of the environment that can influence the development of football players. Conclusions. It is hoped that the results of this study can be used as a reference for coaches, the Indonesian Football Association, and the Malaysian Football Association to create a policy that can support football players under 17 years from all aspects, including psychology.

Origin of the Fourth Industrial Revolution: manufacturing predictions preceding Industrie 4.0

Purpose The Fourth Industrial Revolution is a prominent area of interest in the field of manufacturing that is bringing about revolutionary changes. In this study, the authors sought to determine whether professionals in academia or industry could have predicted the composition of advanced technologies associated with Industry 4.0 before Germany's Industrie 4.0 policy announcement. The purpose of this paper is to use the process for identifying technologies that can be included in industrial policy to improve national competitiveness in manufacturing. Design/methodology/approach Relevant documented research from 2000 to 2012 was identified and captured using a systematic literature review. The significant technological advancements during this period were analyzed to determine how technological innovations may have affected predictions about the future of manufacturing. The identified predictions were analyzed using an open-source natural language processing code that clustered relevant topics in the predictions that indicated common themes. The results were then compared to the ideas within “Industry 5.0”. Findings The results of this study showed that an aggregate analysis of manufacturing predictions would have preemptively revealed the Fourth Industrial Revolution and could have been used to inform industrial policy that could accelerate technology adoption. Also, contrary to popular belief, the popular Industry 5.0 is a sematic exemplification of a concept already embedded within the origins of the Fourth Industrial Revolution. Practical implications By examining the provenance of the Fourth Industrial Revolution, lessons are learned that bring light to Industry 4.0 and the measures that can be taken to enable the advancements that it brings. The results of this study show that is would be wise for government policymakers to enact programs that monitor the manufacturing predictions coming out of academia and to analyze them aggregately using natural language processing as a means to identify the next evolutions and revolutions and to mobilize policymakers to enhance outcomes of enacting policy. Originality/value Despite high hopes for the realization of Industry 4.0, there has been little discussion about the technological innovations and events that occurred to enable it. To the best of the authors’ knowledge, this is the first study that has determined that an aggregate analysis of manufacturing predictions would have preemptively revealed the Fourth Industrial Revolution. The success of the methodology used in this study has theoretical implications in support of natural language processing (NLP) being used to inform national policy.

ID: 332765 CARSS: an open-source closed-loop implantable neuromodulation device platform for bioelectronic medicine

ID: 332765 CARSS: an open-source closed-loop implantable neuromodulation device platform for bioelectronic medicine

A study of the electrostatic properties of the interiors of low-mass stars: Possible implications for the observed rotational properties

Context. In the partially ionized material of stellar interiors, the strongest forces acting on electrons and ions are the Coulomb interactions between charges. The dynamics of the plasma as a whole depend on the magnitudes of the average electrostatic interactions and the average kinetic energies of the particles that constitute the stellar material. An important question is how these interactions of real gases are related to the observable stellar properties. Specifically, the relationships between rotation, magnetic activity, and the thermodynamic properties of stellar interiors are still not well understood. These connections are crucial for understanding and interpreting the abundant observational data provided by space-based missions, such as Kepler/K2 and TESS, and the future data from the PLATO mission. Aims. In this study, we investigate the electrostatic effects within the interiors of low-mass main sequence (MS) stars. Specifically, we introduce a global quantity, a global plasma parameter, which allows us to compare the importance of electrostatic interactions across a range of low-mass theoretical models (0.7 − 1.4 M⊙) with varying ages and metallicities. We then correlate the electrostatic properties of the theoretical models with the observable rotational trends on the MS. Methods. We use the open-source 1D stellar evolution code MESA to compute a grid of main-sequence stellar models. Our models span the log g − Teff space of a set of 66 Kepler main-sequence stars. Results. We identify a correlation between the prominence of electrostatic effects in stellar interiors and stellar rotation rates. The variations in the magnitude of electrostatic interactions with age and metallicity further suggest that understanding the underlying physics of the collective effects of plasma can clarify key observational trends related to the rotation of low-mass stars on the MS. These results may also advance our understanding of the physics behind the observed weakened magnetic braking in stars.

MultIHeaTS: A Fast and Stable Thermal Solver for Multilayered Planetary Surfaces

A fully implicit scheme is proposed for solving the heat equation in 1D heterogeneous media, available as a computationally efficient open-source Python code. The algorithm uses finite differences on an irregular grid and is unconditionally stable due to the implicit formulation. The thermal solver is validated against a stiff analytical solution, demonstrating its robustness in handling stiff initial conditions. Its general applicability for heterogeneous cases is demonstrated through its use in a planetary surface scenario with nonlinear boundary conditions induced by blackbody thermal emission. MultIHeaTS's advantageous stability allows for computation times up to 100 times faster than Spencer’s explicit solver, making it ideal for simulating processes on large timescales. This solver is used to compare the thermal signatures of homogeneous and bilayer profiles on Europa. Results show that homogeneous materials cannot reproduce the thermal signature observed in bilayer profiles, emphasizing the need for multilayer solvers. In order to optimize the scientific return of a space mission, we propose a strategy made of three local time observations that is enough to identify bilayer media, for instance, for the next missions to the Jovian system. A second application of the solver is the estimation of the temperature profile of Europa’s near surface (first 10s m) throughout a 1 million yr simulation with varying orbital parameters. The probability distribution of temperature through depth is obtained. Among its various applications, MultIHeaTS serves as the core thermal solver in a multiphysics simulation model detailed in the companion article by C. Mergny & F. Schmidt.

TrojanProbe: Fingerprinting Trojan tunnel implementations by actively probing crafted HTTP requests

Trojan is a well-known hidden tunnel protocol widely used to bypass Internet censorship and thus presents a big challenge to transparent network management and forensics. As claimed by the protocol designer, Trojan maintains its anti-identifiability by proxying real HTTPS/TLS traffic to react to unauthenticated requests, eliminating any subtle differences between the Trojan traffic and the legitimate HTTPS. Despite such a protocol seeming unidentifiable by design, the diverse Trojan implementations adopting very different programming languages will likely have varied coding logic and networking API calls, opening a new door to be identified and fingerprinted from the implementation level. In this paper, we propose TrojanProbe, a new class of active probing methods that can be used to fingerprint Trojan implementations by triggering their identifiable responses. Our basic idea is to audit the source code of the Trojan programs and discover the subtle logic discrepancy compared with the legitimate HTTPS counterparts, to craft specific HTTP requests as probes to trigger these differences for fingerprinting. By this idea, we choose the five most popular open-source Trojan programs off-the-shelf as our targets to audit, covering the majority of Trojan market share and the mainstream programming languages from traditional C++ to the cutting-edge Go and Rust, and design a suite of novel HTTP probes to differentiate them from their web server masquerades. Our probes exploit either the different responding/buffering logic to the malformed HTTP requests and the different HTTP versions, or the varied timeouts set in the different networking APIs by default. To this end, we have conducted extensive experiments to evaluate the TrojanProbe against a comprehensive set of configuration and networking conditions. The experimental results show that our TrojanProbe can effectively fingerprint our selected Trojan targets in most conditions, but leave a single Rust implementation with a minority market occupied that can only be identified in some constraint cases. Despite such an exception, our research sheds light on a new kind of possibility to fingerprint Trojans at their implementation level, even if such a hidden tunnel is widely known as unidentifiable at the protocol level.

BracketMaker: Visualization and optimization of chemical protein synthesis.

Chemical protein synthesis (CPS), in which custom peptide segments of ~20-60 aa are produced by solid-phase peptide synthesis and then stitched together through sequential ligation reactions, is an increasingly popular technique. The workflow of CPS is often depicted with a "bracket" style diagram detailing the starting segments and the order of all ligation, desulfurization, and/or deprotection steps to obtain the product protein. Brackets are invaluable tools for comparing multiple possible synthetic approaches and serve as blueprints throughout a synthesis. Drawing CPS brackets by hand or in standard graphics software, however, is a painstaking and error-prone process. Furthermore, the CPS field lacks a standard bracket format, making side-by-side comparisons difficult. To address these problems, we developed BracketMaker, an open-source Python program with built-in graphic user interface (GUI) for the rapid creation and analysis of CPS brackets. BracketMaker contains a custom graphics engine which converts a text string (a protein sequence annotated with reaction steps, introduced herein as a standardized format for brackets) into a high-quality vector or PNG image. To aid with new syntheses, BracketMaker's "AutoBracket" tool automatically performs retrosynthetic analysis on a set of segments to draft and rank all possible ligation orders using standard native chemical ligation, protection, and desulfurization techniques. AutoBracket, in conjunction with an improved version of our previously reported Automated Ligator (Aligator) program, provides a pipeline to rapidly develop synthesis plans for a given protein sequence. We demonstrate the application of both programs to develop a blueprint for 65 proteins of the minimal Escherichia coli ribosome.