User Interface Research Articles

Developing interoperable, accessible software via the atomic, molecular, and optical sciences gateway: A case study of the B-spline atomic R-matrix code graphical user interface.

The Atomic, Molecular, and Optical Science (AMOS) Gateway is a comprehensive cyberinfrastructure for research and educational activities in computational AMO science. The B-Spline atomic R-Matrix (BSR) suite of programs is one of several computer programs currently available on the gateway. It is an excellent example of the gateway's potential to increase the scientific productivity of AMOS users. While the suite is available to be used in batch mode, its complexity does not make it well-suited to the approach taken in the gateway's default setup. The complexity originates from the need to execute many different computations and to construct generally complex workflows, requiring numerous input files that must be used in a specific sequence. The BSR graphical user interface described in this paper was developed to considerably simplify employing the BSR codes on the gateway, making BSR available to a large group of researchers and students interested in AMO science.

HERALD: A domain-specific query language for longitudinal health data analytics

BackgroundLarge-scale health data has significant potential for research and innovation, especially with longitudinal data offering insights into prevention, disease progression, and treatment effects. Yet, analyzing this data type is complex, as data points are repeatedly documented along the timeline. As a consequence, extracting cross-sectional tabular data suitable for statistical analysis and machine learning can be challenging for medical researchers and data scientists alike, with existing tools lacking balance between ease-of-use and comprehensiveness. ObjectiveThis paper introduces HERALD, a novel domain-specific query language designed to support the transformation of longitudinal health data into cross-sectional tables. We describe the basic concepts, the query syntax, a graphical user interface for constructing and executing HERALD queries, as well as an integration into Informatics for Integrating Biology and the Bedside (i2b2). MethodsThe syntax of HERALD mimics natural language and supports different query types for selection, aggregation, analysis of relationships, and searching for data points based on filter expressions and temporal constraints. Using a hierarchical concept model, queries are executed individually for the data of each patient, while constructing tabular output. HERALD is closed, meaning that queries process data points and generate data points. Queries can refer to data points that have been produced by previous queries, providing a simple, but powerful nesting mechanism. ResultsThe open-source implementation consists of a HERALD query parser, an execution engine, as well as a web-based user interface for query construction and statistical analysis. The implementation can be deployed as a standalone component and integrated into self-service data analytics environments like i2b2 as a plugin. HERALD can be valuable tool for data scientists and machine learning experts, as it simplifies the process of transforming longitudinal health data into tables and data matrices. ConclusionThe construction of cross-sectional tables from longitudinal data can be supported through dedicated query languages that strike a reasonable balance between language complexity and transformation capabilities.

DIRA: A model of the user interface

The user interface is a central concept in human–computer interaction, but peculiarly fuzzy. This affects discussions of the fundamentals of our discipline and the positioning of our work. We propose a model of the user interface that consists of four elements: Devices, Interaction Techniques, Representations, and Assemblies (DIRA). We explain their roles in the user interface and discuss some associated concerns about evaluation and design. We then show how to use the model to describe the elements of user interfaces (with examples that include a menu, a fisheye interface, and notifications) and to analyze the central characteristics of user interface paradigms (including tangible user interfaces and mixed reality). Finally, we discuss how describing user interfaces with the model can drive their design and evaluation.

Protocol for Designing De Novo Noncanonical Peptide Binders in OSPREY.

D-peptides, the mirror image of canonical L-peptides, offer numerous biological advantages that make them effective therapeutics. This article details how to use DexDesign, the newest OSPREY-based algorithm, for designing these D-peptides de novo. OSPREY physics-based models precisely mimic energy-equivariant reflection operations, enabling the generation of D-peptide scaffolds from L-peptide templates. Due to the scarcity of D-peptide:L-protein structural data, DexDesign calls a geometric hashing algorithm, Method of Accelerated Search for Tertiary Ensemble Representatives, as a subroutine to produce a synthetic structural dataset. DexDesign enables mixed-chirality designs with a new user interface and also reduces the conformation and sequence search space using three new design techniques: Minimum Flexible Set, Inverse Alanine Scanning, and K*-based Mutational Scanning.

Usability Evaluation of the Revised Color Me Healthy Symptom Assessment App: Perspectives of Children and Parents.

The Color Me Healthy symptom reporting app was co-designed with school-age children with cancer, their parents, and clinicians. Preliminary studies demonstrated its feasibility and acceptability; however, children and parents identified the need for additional refinements. Guided by the Technology Acceptance Model and principles of user-centered design, this study supported refinements to the Color Me Healthy user interface and evaluation of its usability. As the programming team completed builds of the app, school-age children with cancer and their parents participated in cognitive walkthrough usability evaluations and qualitative interviews. Usability logs documented the completion of key tasks related to reporting pain and review of child-reported data. Parents completed the Technology Acceptance Model Perceived Usefulness Scale (TAM-PUS). Interview responses were analyzed using qualitative content analysis. Fourteen children (median age 8.5 years; range 6-12) and 14 parents (median age 38.5 years; range 34-49) participated in one of three usability evaluation cycles. After the third cycle, children and parents navigated the app and completed key tasks independently. Median TAM-PUS scores were 6 (range 6-8), indicating high perceived usefulness. Qualitative analyses indicated that children regarded the app as easy and fun to use. Parents emphasized the app's developmental relevance for their child and for themselves as the child's caregiver. This study demonstrates the perceived usefulness and perceived ease of use of the revised Color Me Healthy app. Optimizing the usability of the Color Me Healthy app with attention to the user needs of children and parents positions the app for wider-scale clinical implementation.

Evaluation of a semi-supervised self-adjustment fine-tuning procedure for hearing aids for asymmetrical hearing loss

Objective This study investigated a previously evaluated self-adjustment procedure with respect to its applicability for asymmetrical hearing loss (AHL). Self-adjusted settings were evaluated for speech recognition in noise and sound preference. Design Participants were given the possibility to adjust the left and right hearing aid separately using a two-dimensional user interface. Two different adjustment sequences were tested. Realistic everyday sound scenes in a laboratory environment were presented. The difference between the ears regarding their speech recognition in noise was tested with two spatial conditions, unaided as well as with the prescriptive formula and the self-adjusted setting. Study sample Nineteen experienced hearing aid users (median age 76 years) with different degrees of AHL were invited to participate in this study. Results Participants adjusted a higher gain slope across frequency in the worse ear than in the better one. The two adjustment sequences resulted in significantly different adjustment durations and gain settings. The difference between the ears regarding speech recognition in noise did not change with the self-adjustment. Overall, group-mean effect sizes were small compared to the parameter space. Conclusions The adjustment procedure can be used also by hearing aid users with AHL to find a possibly preferred gain setting.

Hybrid extreme gradient boosting regressor models for the multi-objective mixture design optimization of cementitious mixtures incorporating mine tailings as fine aggregates

The design of cementitious mixtures incorporating mine tailings as fine aggregates is a multi-objective optimization (MOO) problem, in which both the uniaxial compressive strength (UCS) and cost of the mixtures need to be considered simultaneously. Given that data-driven methods have shown promising results when solving similar MOO problems, this study developed an extreme gradient boosting regressor (XGBR) model on a dataset extracted from the literature to predict the UCS. Among the efforts taken to improve the models, a genetic algorithm (GA)-based XGBR model demonstrated the optimal prediction performance, with an R2 of 0.959. Next, the GA-XGBR model and a cost equation were used as objective functions in the MOO problem. The non-dominated sorting genetic algorithm with elite strategy (NSGA-II) was selected to solve the optimization problem. A case study was conducted, generating mixture designs that offered improved trade-offs between cost and UCS compared to experimental designs. Finally, a graphical user interface was developed to provide access to the prediction model and optimization method. Overall, this work can be used as a guide for optimal mixture designs as it facilitates informed decision-making before the actual applications.

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.

CIEC: Cross-tissue Immune Cell Type Enrichment and Expression Map Visualization for Cancer.

Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell type or state. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell-type/state map for each context and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell type/state. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar genes detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.

EEG Dataset for the Recognition of Different Emotions Induced in Voice-User Interaction

Electroencephalography (EEG)-based open-access datasets are available for emotion recognition studies, where external auditory/visual stimuli are used to artificially evoke pre-defined emotions. In this study, we provide a novel EEG dataset containing the emotional information induced during a realistic human-computer interaction (HCI) using a voice user interface system that mimics natural human-to-human communication. To validate our dataset via neurophysiological investigation and binary emotion classification, we applied a series of signal processing and machine learning methods to the EEG data. The maximum classification accuracy ranged from 43.3% to 90.8% over 38 subjects and classification features could be interpreted neurophysiologically. Our EEG data could be used to develop a reliable HCI system because they were acquired in a natural HCI environment. In addition, auxiliary physiological data measured simultaneously with the EEG data also showed plausible results, i.e., electrocardiogram, photoplethysmogram, galvanic skin response, and facial images, which could be utilized for automatic emotion discrimination independently from, as well as together with the EEG data via the fusion of multi-modal physiological datasets.

REVEALS: an open-source multi-camera GUI for rodent behavior acquisition.

Deciphering the rich repertoire of mouse behavior is crucial for understanding the functions of both the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent Behavior Multi-Camera Laboratory Acquisition), a graphical user interface for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the graphical user interface implementation, including the camera control software and the video recording functionality. We validate results demonstrating the graphical user interface's stability, reliability, and accuracy for capturing rodent behavior using DeepLabCut in various behavioral tasks. REVEALS can be incorporated into existing DeepLabCut, MoSeq, or other custom pipelines to analyze complex behavior. In summary, REVEALS offers an interface for collecting behavioral data from single or multiple perspectives, which, when combined with deep learning algorithms, enables the scientific community to identify and characterize complex behavioral phenotypes.

A model-informed clinical trial simulation tool with a graphical user interface for Duchenne muscular dystrophy.

Quantitative model-based clinical trial simulation tools play a critical role in informing study designs through simulation before actual execution. These tools help drug developers explore various trial scenarios in silico to select a clinical trial design to detect therapeutic effects more efficiently, therefore reducing time, expense, and participants' burden. To increase the usability of the tools, user-friendly and interactive platforms should be developed to navigate various simulation scenarios. However, developing such tools challenges researchers, requiring expertise in modeling and interface development. This tutorial aims to address this gap by guiding developers in creating tailored R Shiny apps, using an example of a model-based clinical trial simulation tool that we developed for Duchenne muscular dystrophy (DMD). In this tutorial, the structural framework, essential controllers, and visualization techniques for analysis are described, along with key code examples such as criteria selection and power calculation. A virtual population was created using a machine learning algorithm to enlarge the available sample size to simulate clinical trial scenarios in the presented tool. In addition, external validation of the simulated outputs was conducted using a placebo arm of a recently published DMD trial. This tutorial will be particularly useful for developing clinical trial simulation tools based on DMD progression models for other end points and biomarkers. The presented strategies can also be applied to other diseases.

A deep learning approach to case prioritisation of colorectal biopsies.

To create and validate a weakly supervised artificial intelligence (AI) model for detection of abnormal colorectal histology, including dysplasia and cancer, and prioritise biopsies according to clinical significance (severity of diagnosis). Triagnexia Colorectal, a weakly supervised deep learning model, was developed for the classification of colorectal samples from haematoxylin and eosin (H&E)-stained whole slide images. The model was trained on 24 983 digitised images and assessed by multiple pathologists in a simulated digital pathology environment. The AI application was implemented as part of a point and click graphical user interface to streamline decision-making. Pathologists assessed the accuracy of the AI tool, its value, ease of use and integration into the digital pathology workflow. Validation of the model was conducted on two cohorts: the first, on 100 single-slide cases, achieved micro-average model specificity of 0.984, micro-average model sensitivity of 0.949 and micro-average model F1 score of 0.949 across all classes. A secondary multi-institutional validation cohort, of 101 single-slide cases, achieved micro-average model specificity of 0.978, micro-average model sensitivity of 0.931 and micro-average model F1 score of 0.931 across all classes. Pathologists reflected their positive impressions on the overall accuracy of the AI in detecting colorectal pathology abnormalities. We have developed a high-performing colorectal biopsy AI triage model that can be integrated into a routine digital pathology workflow to assist pathologists in prioritising cases and identifying cases with dysplasia/cancer versus non-neoplastic biopsies.

Dynamic Spatio-Temporal Modeling for Enhanced Air Quality Prediction: Implications for Information Management and Public Health Decision Support Systems

Air quality forecasting has significant implications for environmental monitoring, public health, and information management systems. This study proposes three advanced deep learning models: Graph Neural Networks with Dynamic Spatio-Temporal Attention (GNN-DSTA), Multi-Resolution Convolutional Recurrent Neural Networks (MRC-RNN), and Variational Autoencoders with Spatio-Temporal Latent Embeddings (VAE-STLE). These models address the challenges of capturing complex spatio-temporal dependencies in environments with sparse data samples. The GNN-DSTA model introduces a temporal attention mechanism that dynamically captures evolving spatial-temporal dependencies. MRC-RNN combines CNN's spatial pattern recognition with RNN's temporal modeling across multiple spatial resolutions. VAE-STLE provides a probabilistic framework for robust and interpretable forecasting. Experimental results demonstrate significant improvements in prediction accuracy: GNN-DSTA reduces RMSE by 15-20%, MRC-RNN improves accuracy by 12-15%, and VAE-STLE shows a 10-12% improvement with enhanced uncertainty estimation. These models advance AQI predictions through dynamic attention mechanisms, multi-resolution analysis, and probabilistic forecasting. Furthermore, this study explores the implications of these advanced predictions for information management and public health decision support systems. We discuss the integration of real-time data, scalability considerations for large-scale deployments, user interface design for effective communication of predictions, and ethical considerations in using AI-driven models for public health decision-making. The proposed approach not only enhances the accuracy and reliability of AQI predictions but also provides a framework for developing more effective and responsive public health interventions and environmental policies.

LLM Based Chatbot for Farm-to-Fork Blockchain Traceability Platform

Blockchain technology has been used with great effect in farm-to-fork traceability projects. However, this technology has a steep learning curve when it comes to its user interface. To minimize this difficulty, we created a solution based on a Large Language Model (LLM) conversational agent. Our implementation, starting with an existing knowledge base that is prepared and processed with an embedding model to be stored in a vector database, follows a Retrieval-Augmented Generation (RAG) approach. Other non-textual media like images and videos are aggregated with the embeddings to enrich the user experience. User queries are combined with a proximity search in the vector database and feed into an LLM that considers the conversation history with the user in its replies. Given the asynchronous nature of these models, we implemented a similarly asynchronous scheme using Server-Sent Events that deliver the models’ replies to a UI that supports multimodal media types such as images and videos by providing the visualization of these resources. The end solution allows users to interact with advanced technologies using a natural language interface; this in turn empowers food traceability projects to overcome their natural difficulty in engaging early adopters.

B-023 Workflow evaluation of the new Siemens Atellica CI Analyzer

Abstract Background The Atellica® CI Analyzer, Model 1900 is an integrated clinical chemistry and immunoassay analyzer that has the same user interface, general workflow, and planned test menu to that of the larger Atellica® Solution. Atellica CI Analyzer also shares the same reagents and consumables as the Atellica Solution. The purpose of this study was to evaluate workflow performance of the Atellica CI Analyzer in a routine-like clinical laboratory setting. Methods The study was conducted at four international laboratory facilities over a four-month period. Basic throughput of the Atellica CI Analyzer was evaluated at each site by testing quality control (QC) and remnant anonymized patient samples using an 8:2 mixture of chemistry (ion selective electrolytes [IMT], clinical chemistry analytes) and immunochemistry assays. Additionally, the impact of interrupting routine testing for STAT high-sensitivity cardiac troponin samples was also evaluated. Daily, weekly, and monthly maintenance activities were observed, and data collected through direct observations, time and motion studies, and system run logs. Results The Atellica CI Analyzer aspirated patient samples in as little as two minutes (mins) with the time to first result for the mixed assay worklist ranging from 1 to 12 mins from the time of first barcode read. Across the four sites, the time to last result for the mixed assay worklist (average n=495 test results per site) ranged from 52 to 65 mins. The average turn around time (TAT) for high-sensitivity troponin was 9 mins from barcode read. Daily maintenance and QC activities for the Atellica CI Analyzer are comprised of both automated and manual activities. The average daily maintenance hands-on time was 3.6 min, ranging from 1.6 to 6.4 mins across the four sites. Daily QC preparation required <5 mins of hands-on time. The total average hands-on time to complete weekly maintenance for the Atellica CI Analyzer was 7.5 min. Conclusions The Atellica CI Analyzer demonstrated consistent system performance across all four study sites. Implementation of the Atellica CI Analyzer has the potential to increase laboratory efficiency given the minimal hands-on time required for daily and weekly maintenance activities and short TATs.

Agriculture Robot Using Image Processing

Background: Agriculture is a cornerstone of the economy, but traditional farming is labor-intensive and time-consuming. Advances in technology, such as Convolutional Neural Networks (CNNs) and robotics, can help address challenges like disease detection, weed control, and labor shortages. Tomatoes, a key crop, benefit from proper management using these innovations. Objective: This project aims to design and develop an agriculture robot using IoT and Machine Learning to automate farming tasks, detect tomato plant diseases, and manage weeds more efficiently. Methods: The robot is controlled by an ESP32 microcontroller connected via Wi-Fi for real-time monitoring. The system uses CNNs for disease detection in tomato plants, along with a custom-trained dataset using the TensorFlow framework. Mechanical arms perform tasks like seeding, watering, spraying fertilizers, and harvesting. A user interface for disease detection was designed for real time disease detection. Results: The model achieved an accuracy of 95.0% for disease detection, 95.56% for fruit detection, and 95.14% for plant detection. These results highlight the effectiveness of the robot in managing tomato crops. Conclusion: The agriculture robot provides a comprehensive solution to key farming challenges, enhancing efficiency through automation and improving crop health monitoring. The integration of IoT and ML offers a pathway toward sustainable and profitable agriculture, particularly in managing tomatoes. Further improvements and scaling could expand its application to other crops and farming tasks. Keywords: Machine Learning, Internet of Things, Agricultural Robot, Disease Detection, Convolutional Neural Networks, Mechanical Tasks.

Barriers and facilitators to the uptake of electronic collection and use of patient-reported measures in routine care of older adults: a systematic review with qualitative evidence synthesis.

The aims of this systematic review were to (1) synthesize the available qualitative evidence on the barriers and facilitators influencing implementation of the electronic collection and use of patient-reported measures (PRMs) in older adults' care from various stakeholder perspectives and (2) map these factors to the digital technology implementation framework Non-adoption, Abandonment, challenges to the Scale-up, Spread, Sustainability (NASSS) and behavior change framework Capability, Opportunity, Motivation, Behaviour (COM-B). A search of MEDLINE, CINAHL Plus, and Web of Science databases from 1 January 2001 to 27 October 2021 was conducted and included English language qualitative studies exploring stakeholder perspectives on the electronic collection and use of PRMs in older adults' care. Two authors independently screened studies, conducted data extraction, quality appraisal using the Critical Appraisal Skills Programme (CASP), data coding, assessed confidence in review findings using Grading of Recommendations Assessment, Development, and Evaluation Confidence in the Evidence from Reviews of Qualitative Research (GRADE CERQual), and mapped the findings to NASSS and COM-B. An inductive approach was used to synthesize findings describing the stakeholder perspectives of barriers and facilitators. Twenty-two studies were included from the 3368 records identified. Studies explored older adult, caregiver, healthcare professional, and administrative staff perspectives. Twenty nine of 34 review findings (85%) were graded as having high or moderate confidence. Key factors salient to older adults related to clinical conditions and socio-cultural factors, digital literacy, access to digital technology, and user interface. Factors salient to healthcare professionals related to resource availability to collect and use PRMs, and value of PRMs collection and use. Future efforts to implement electronic collection and use of PRMs in older adults' care should consider addressing the barriers, facilitators, and key theoretical domains identified in this review. Older adults are more likely to adopt electronic completion of PRMs when barriers associated with digital technology access, digital literacy, and user interface are addressed. Future research should explore the perspectives of other stakeholders, including those of organizational leaders, digital technology developers and implementation specialists, in various healthcare settings and explore factors influencing implementation of PREMs. CRD42022295894.

A Survey of Medical Visualization Through the Lens of Metaphors.

We provide an overview of metaphors that were used in medical visualization and related user interfaces. Metaphors are employed to translate concepts from a source domain to a target domain. The survey is grounded in a discussion of metaphor-based design involving the identification and reflection of candidate metaphors. We consider metaphors that have a source domain in one branch of medicine, e.g., the virtual mirror that solves problems in orthopedics and laparoscopy with a mirror that resembles the dentist's mirror. Other metaphors employ the physical world as the source domain, such as crepuscular rays that inspire a solution for access planning in tumor therapy. Aviation is another source of inspiration, leading to metaphors, such as surgical cockpits, surgical control towers, and surgery navigation according to an instrument flight. This paper should raise awareness for metaphors and their potential to focus the design of computer-assisted systems on useful features and a positive user experience. Limitations and potential drawbacks of a metaphor-based user interface design for medical applications are also considered.

Predicting solvation free energies for neutral molecules in any solvent with openCOSMO-RS

In this study, we introduce openCOSMO-RS 24a, an improved version of the open-source COSMO-RS model parameterized using quantum chemical calculations from ORCA 6.0, leveraging a comprehensive dataset that includes solvation free energies, partition coefficients, and infinite dilution activity coefficients for various solutes and solvents mainly at 25 °C. This is the first version of the model also capable of predicting solvation free energies based on ORCA calculations. Additionally, we develop a Quantitative Structure-Property Relationships model to predict molar volumes of the solvents, an essential requirement for predicting solvation free energies and partition coefficients from structure alone. Our results show that openCOSMO-RS 24a achieves an average absolute deviation of 0.45 kcal mol1 for solvation free energies, 0.76 for the logarithm of the partition coefficients, and 0.51 for the logarithm of infinite dilution activity coefficients, demonstrating improvements over the previous openCOSMO-RS 22 parameterization and comparable results to COSMOtherm 24 BP-TZVP. The user interface was extended to be able use it as solvation model directly from within ORCA 6.0 or from the command line to provide researchers with a robust tool for applications in chemical and materials science.