Graphical Interface Research Articles

Prototype Implementation of a Digitizer for Earthquake Monitoring System.

A digitizer is considered one of the fundamental components of an earthquake monitoring system. In this paper, we design and implement a high accuracy seismic digitizer. The implemented digitizer consists of several blocks, i.e., the analog-to-digital converter (ADC), GPS receiver, and microprocessor. Three finite impulse response (FIR) filters are used to decimate the sampling rate of the input seismic data according to user needs. A graphical user interface (GUI) has been designed for enabling the user to monitor the seismic waveform in real time, and process and adjust the parameters of the acquisition unit. The system casing is designed to resist harsh conditions of the environment. The prototype can represent the three component sensors data in the standard MiniSEED format. The digitizer stream seismic data from the remote station to the main center is based on TCP/IP connection. This protocol ensures data transmission without any losses as long as the data still exist in the ring buffer. The prototype was calibrated by real field testing. The prototype digitizer is integrated with the Egyptian National Seismic Network (ENSN), where a commercial instrument is already installed. Case studies shows that, for the same event, the prototype station improves the solution of the ENSN by giving accurate timing and seismic event parameters. Field test results shows that the event arrival time and the amplitude are approximately the same between the prototype digitizer and the calibrated digitizer. Furthermore, the frequency contents are similar between the two digitizers. Therefore, the prototype digitizer captures the main seismic parameters accurately, irrespective of noise existence.

CEMPRI, a primary cementing software for vertical onshore wells as a tool for petroleum engineering education

The use of simulation software applicable to the various stages of petroleum engineering facilitates decision-making and, at the same time, minimizes possible failures, problems, and incidents during each well intervention. In addition, a computer tool provides the user with instant and accurate results that can be used during personnel training and higher education. The objective of the present work was to develop an open-source computational tool with a graphical, numerical, and schematic interface to facilitate the teaching and learning of operations related to primary cementing. The tool considers four sections: (a) wellbore diagram, (b) identification of the relation between volumetry and the geometric design of the well, (c) integration of the mechanical state with the number of intervals, slurry design, and operating characteristics of the pumps, and (d) the wellbore diagram integrated by drilling, displacement and slurry fluids, according to each of the cementing stages. Among the results, it was combined programming with specialized technical and scientific material, considering academic and field experience characteristics. The program is a versatile tool that integrates the general mechanical state and each of the five stages with a maximum depth of 5,000 m.

Metis: a python-based user interface to collect expert feedback for generative chemistry models

One challenge that current de novo drug design models face is a disparity between the user’s expectations and the actual output of the model in practical applications. Tailoring models to better align with chemists’ implicit knowledge, expectation and preferences is key to overcoming this obstacle effectively. While interest in preference-based and human-in-the-loop machine learning in chemistry is continuously increasing, no tool currently exists that enables the collection of standardized and chemistry-specific feedback. Metis is a Python-based open-source graphical user interface (GUI), designed to solve this and enable the collection of chemists’ detailed feedback on molecular structures. The GUI enables chemists to explore and evaluate molecules, offering a user-friendly interface for annotating preferences and specifying desired or undesired structural features. By providing chemists the opportunity to give detailed feedback, allows researchers to capture more efficiently the chemist’s implicit knowledge and preferences. This knowledge is crucial to align the chemist’s idea with the de novo design agents. The GUI aims to enhance this collaboration between the human and the “machine” by providing an intuitive platform where chemists can interactively provide feedback on molecular structures, aiding in preference learning and refining de novo design strategies. Metis integrates with the existing de novo framework REINVENT, creating a closed-loop system where human expertise can continuously inform and refine the generative models.Scientific contributionWe introduce a novel Graphical User Interface, that allows chemists/researchers to give detailed feedback on substructures and properties of small molecules. This tool can be used to learn the preferences of chemists in order to align de novo drug design models with the chemist’s ideas. The GUI can be customized to fit different needs and projects and enables direct integration into de novo REINVENT runs. We believe that Metis can facilitate the discussion and development of novel ways to integrate human feedback that goes beyond binary decisions of liking or disliking a molecule.

A Fuzzy Inference System for Predicting Air Traffic Demand based on Socioeconomic Drivers

The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a Graphical User Interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed Air Passenger Demand Prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Model Predictive Control for Level Control of a Conical Tank

Conical tanks have a high application rate in industrial processes, especially in colloidal mills, chemical processes, and food processing. The use of conical tanks presents significant benefits because they contribute to sedimentation and reduce the accumulation of impurities compared to conventional cylindrical tanks. However, level control of a conical tank due to its shape requires advanced strategies to guarantee efficient control. In this research, a model predictive control (MPC) method was designed and implemented for the level control of a conical tank on a laboratory scale. To evaluate the performance of the controller, it was compared with a traditional proportional–integral (PI) controller, and the rise time, settling time, overshoot, and error in the steady state were analyzed when different set point changes were tested. In addition, the system was subjected to disturbances, and the MPC demonstrated better performance in a transient state, as well as smooth and stable action controls that allowed for an increase in the useful life of the actuator. In addition, an interactive graphical interface was developed that allowed a dynamic response in a real plant to be experienced; this provides an academic tool for designing control strategies before implementation in a real process.

Intelligent evaluation of interference effects between tall buildings based on wind tunnel experiments and explainable machine learning

The interference effects of taller high-rise buildings significantly impact the nearby shorter high-rise buildings in dense urban areas, potentially leading to severe wind-induced disasters. This study aims to develop a universal intelligent assessment method to predict the aerodynamic forces and wind-induced responses of buildings, considering various interfering and coupling factors. A database containing 61440 samples is established through a series of synchronous pressure measurement wind tunnel tests. The aerodynamic forces and wind-induced responses were predicted using six machine learning models under varying wind fields, interfering building locations, interfering building sizes, and dynamic characteristics of the principal building. The performance of the models was enhanced through Bayesian hyperparameter optimization and evaluated using the R2 and NRMSE. Additionally, the SHapley Additive exPlanations (SHAP) method was applied to evaluate the impact of each factor on the aerodynamic force and wind-induced response coefficients. Eight specific cases were examined using local SHAP explanations to explore the influence of seven input variables on maximum wind-induced response coefficients. Results indicated that the XGB model exhibited superior predictive performance, with R2 exceeding 0.99 in both training and testing sets. The SHAP analysis revealed significant impacts from wind direction angle, reduced wind speed, and damping ratio on wind-induced response. Consequently, irrelevant or minimally impactful input variables were removed from the XGB models. The R2 and NRMSE variation rate with reduced inputs for the optimized XGB model was less than 0.45 %. The graphical user interface (GUI) was designed can effectively guide intelligent urban planning and building design.

Gamemasters of the Playground

In this work, we explore how programmable playground artefacts can affect social dynamics and power structures in an outdoor play setting. A set of re-programmable artefacts and a graphical programming interface were designed and developed for the study. Twenty children were invited to co-design and explore the interactivity of the re-programmable devices. They tested how they could play with them, and were asked about how they would re-design and repurpose the artefacts and the scripting interface for their way of playing. Through a thematic analysis of the observations and group interviews, it could be seen that an implicit social role emerged, centered around the use of the programming device. This role took on a guiding and supporting role, rather than a leading role. By deliberately designing for this ‘gamemaster’ role, this understanding may be useful in future design of technology for public and outdoor play.

Development of a robust and generalizable algorithm "gQuant" for accurate normalizer gene selection in qRT-PCR analysis

The emergent role of nucleic acid-based biomarkers—microRNAs(miRNAs), long non-coding RNAs(lncRNAs), and messenger RNAs(mRNAs), is becoming increasingly prominent in disease diagnostics and risk assessment. qRT-PCR is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, an absolute requirement for accurate quantification. Current tools normalizer selection are fraught with statistical limitations and suboptimal graphical user interface for data visualisation. These deficiencies underscore the necessity for a balanced tool tailored to handle qRT-PCR datasets. Addressing the identified challenges, we have developed 'gQuant' tool crafted to address these limitations. We employed voting classifiers that combine predictions from multiple statistical methods. Tool's efficacy was validated through different available and in house data derived from urinary exosomal miRNAs datasets. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas 'gQuant' consistently yielded rankings characterised by lower standard-deviation, reduced covariance, and enhanced kernel density estimation values. Given 'gQuant's' promising performance, normalizer gene identification will be greatly improved, improving precision of gene expression quantification in a variety of research scenarios. The gQuant tool developed for this study is available for public use and can be accessed at [https://github.com/ABHAYHBB/gQuant-Tool]."

Usability Assessment of Voice-Enabled Technologies for Users with Visual Disabilities

Voice-enabled technologies such as VoiceOver (screen reader) and the Seeing AI app (image recognition) have revolutionized daily tasks for people with visual disabilities, fostering greater independence and information access. However, a gap remains in understanding the user experience (UX) of these technologies. This study investigated how those with visual disabilities interacted with VoiceOver and the Seeing AI app. A convenience sample of eight participants with visual disabilities engaged in direct observations while using these technologies. The study utilized the System Usability Scale (SUS) to assess perceived usability and analyzed findings using descriptive statistics. Results indicated a poorer UX with VoiceOver compared to the Seeing AI app, with challenges identified in graphical user interfaces (GUIs), voice and gesture commands. Relevant recommendations were made to enhance usability. The study emphasizes the need for more intuitive GUIs and optimized voice/gesture interactions for users with visual disabilities.

Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas

This paper introduces the web application-type Graphical User Interface that has been developed and also presents an application example. The introduced simulator conducts hydrodynamics and ecosystems in coastal and estuarine areas. It consists of (1) a hydrodynamic model that can simulate the current velocity, water temperature, salinity, and water level; (2) an ecosystem model that can simulate dissolved oxygen, phytoplankton, zooplankton, nutrients, fish, and bivalves; and (3) a benthic ecosystem model that can simulate elution. Web GUI is the first web system of aquatic environment simulation system that can both prepare calculation conditions and visualize them. Another significant feature is that it requires no installation and can be easily used by anyone to perform calculations. Thus, the proposed system helps fill the expertise gap experienced by potential users of the model. The use of standard systems, such as those discussed in this study, will facilitate evidence-based policymaking (EBPM).

Controller design and remote piloting training module development for unmanned cropped delta reflex wing configuration

In this study, a cost-effective high-fidelity six-degree of freedom module augmented with an onboard controller is developed. The unmanned aerial vehicle (UAV) under consideration is a wing-alone configuration with a cropped delta planform and reflex airfoil cross-section designed to perform manoeuvres at high angles of attack. The six-degree of freedom dynamics for the aforementioned configuration is developed based on Newtonian mechanics incorporating linear and nonlinear aerodynamic models to expand flight simulations in corresponding regimes. Aerodynamic stability and control parameters for the simulations are obtained from full-scale wind tunnel measurements and flight tests. It is observed that the linear aerodynamic model is valid in angle of attack (α) domain −5°≤α≤10°, followed by nonlinearity up to α≈21°, post which lift coefficient remains almost constant with angle of attack till 45°. The simulation model is validated with the corresponding flight data obtained from flight tests. A user-friendly graphical interface with real-time animation of UAV has been developed for the aforementioned configuration by integrating MATLAB and Flightgear environment with real-time control input from the hardware to enable the pilot for visual and tactile feedback, respectively. A controller based on total energy control is designed for velocity and altitude hold modes, while an attitude hold controller is designed for pitch and roll angle tracking to enable controller-in-the-loop remote piloting simulations. In order to enhance practical applicability, the designed module has been tested under various simulated wind conditions. It is noticed from the results that the steady-state error in attitude remains below 2% with a maximum overshoot of 5° from reference. The total energy controller achieves a zero steady-state error with a maximum overshoot of 2 m/s for velocity and 6 m for altitude, respectively. The developed module enhances remote piloting training, flight response assessment, and control algorithm testing for cropped delta planform UAVs.

OOPS: Object-Oriented Polarization Software for analysis of fluorescence polarization microscopy images.

Most essential cellular functions are performed by proteins assembled into larger complexes. Fluorescence Polarization Microscopy (FPM) is a powerful technique that goes beyond traditional imaging methods by allowing researchers to measure not only the localization of proteins within cells, but also their orientation or alignment within complexes or cellular structures. FPM can be easily integrated into standard widefield microscopes with the addition of a polarization modulator. However, the extensive image processing and analysis required to interpret the data have limited its widespread adoption. To overcome these challenges and enhance accessibility, we introduce OOPS (Object-Oriented Polarization Software), a MATLAB package for object-based analysis of FPM data. By combining flexible image segmentation and novel object-based analyses with a high-throughput FPM processing pipeline, OOPS empowers researchers to simultaneously study molecular order and orientation in individual biological structures; conduct population assessments based on morphological features, intensity statistics, and FPM measurements; and create publication-quality visualizations, all within a user-friendly graphical interface. Here, we demonstrate the power and versatility of our approach by applying OOPS to punctate and filamentous structures.

Advancing scanning probe microscopy simulations: A decade of development in probe-particle models

The Probe-Particle Model combine theories designed for the simulation of scanning probe microscopy experiments, employing non-reactive, flexible tip apices to achieve sub-molecular resolution. In the article we present the latest version of the Probe-Particle Model implemented in the open-source ppafm package, highlighting substantial advancements in accuracy, computational performance, and user-friendliness. To demonstrate this we provide a comprehensive review of approaches for simulating non-contact Atomic Force Microscopy. They vary in complexity from simple Lennard-Jones potential to the latest full density-based model. We compared those approaches with ab initio calculated references, showcasing their respective merits. All parts of the ppafm package have undergone acceleration by 1-2 orders of magnitude using OpenMP and OpenCL technologies. The updated package includes an interactive graphical user interface and seamless integration into the Python ecosystem via pip, facilitating advanced scripting and interoperability with other software. This adaptability positions ppafm as an ideal tool for high-throughput applications, including the training of machine learning models for the automatic recovery of atomic structures from nc-AFM measurements. We envision significant potential for this application in future single-molecule analysis, synthesis, and advancements in surface science in general. Additionally, we discuss simulations of other sub-molecular scanning-probe imaging techniques, such as bond-resolved scanning tunneling microscopy and kelvin probe force microscopy, all built on the robust foundation of the Probe-Particle Model. Altogether this demonstrates the broad impact of the model across diverse domains of on-surface science and molecular chemistry.

Thermal Modeling of Li-Ion Cylindrical Cells at Scale

The electric vehicle industry has created a demand for batteries with higher power densities. The current design choice for low-weight lithium-ion battery packs for electric vehicles is to create stacks arranged in parallel or series configurations to match the power demands. The individual cells are separated and placed in a cooling duct to pull heat away from the longitudinal surface convective. The amount of cooling is dependent on the internal temperature of batteries core. The current design of the cylindrical cell is to roll the cathode, anode, and separators into a jelly roll together and attach a tab to the cathode and anode at opposite corners of the sheet. The tabs connect to the positive and negative terminals of the cell to draw current away from the cell. The tabs may act as an agent for drawing heat away from the center of the battery. Currently, commonly used cylindrical cells have a form factor of 18650, 21700, or 26650. The thermal, geometrical, and electrical properties of 18650 and 21700 cylindrical cells are studied and it is found that the 21700 cells showed additional degradation and stronger heating. Recently, companies like Tesla have produced cylindrical cells with a 4680-form factor, which is a decent way to increase the ratio of active material vs. inactive material in a cylindrical cell. However, large diameter thick cylindrical cells suffer from thermal issues. Even large format pouch cells suffer through heat trapped within the stack.Since the thermal conductivity of the anode and cathode electrodes is often near 1 W/m-K, it becomes difficult for the heat to be extracted from the center of a large-diameter cylindrical cell without forming a considerable thermal gradient. PyBaMM, an open-source mathematical modeling software written in Python programming language, has the capability to simulate different electrochemical models such as the Doyle-Fuller-Newman (DFN), Single particle model (SPM), and SPMe, and varying chemistry. The software provides easy access to the cell parameters modification and graphical user interface capabilities. The PyBaMM framework also allows to simulation lumped, isothermal, and base thermal models which are electrochemically coupled. PyBaMM uses CasADi (computer algebra systems algorithmic differentiation). The thermal gradient and heat dynamics within a porous electrode are modeled using PoreSpy and openpnm, two open-source Python libraries used to design porous networks and model physical equations such as governing heat flow, concentration gradient, and movement of water/oil through porous shale media.We have explored and combined heat dynamic models for Li-Ion cylindrical cells at both cell and electrode levels with reduced-order Doyle-Fuller-Newman electro-chemical model frameworks implemented in the PyBaMM python library. Radial temperature gradient at cell and electrode levels were both generated from simulations with a distributed parameter model as a function of electrolyte and separator thickness and thermodynamic properties. Figure 1

Machine Learning-Based Strategies for Detecting Cyberbullying in Online Chats

This study employed the stacking of three machine learning techniques: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression algorithms to develop a model for detecting cyberbullying using a post dataset acquired from the X Platform. The proposed model's task is to extract keywords from the post dataset and then classify them as either 1 ("cyberbullying word") or 0 ("not cyberbullying word"). The model generated an accuracy of 85.52%, and it was deployed using a simple Graphical User Interface (GUI) web application. This study recommends that the model be included on social media platforms to help reduce the growing use of cyberbullying phrases.

High-throughput image processing software for the study of nuclear architecture and gene expression

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.

DiSSCover the Potential of FAIR Digital Object Annotations and How You Can Use Them!

The infrastructure for the Distributed System of Scientific Collections (DiSSCo) is in full development. Work within the DiSSCo Transition Project has been focused on building infrastructure, creating data models, and setting up Application Programming Interfaces (APIs) (Koureas et al. 2024). In the past years, DiSSCo has presented this work at different Biodiversity Information Standards (TDWG) conferences (Leeflang and Addink 2023, Leeflang et al. 2022, Addink et al. 2021). In this year’s session, we would like to focus on the human-facing application: DiSSCover. DiSSCover is the graphical user interface through which users can interact with Findable, Accessible, Interoperable and Reusable (FAIR) Digital Objects (FDOs), facilitating the curation and enhancement of specimen data (Islam 2024). Development started in 2022 and is ongoing. The interface acts as a gateway into the DiSSCo infrastructure, providing access to digital specimens and media. Extracted from the core DiSSCo API, the data is converted into an easily readable format and made discoverable through a diverse set of filters. DiSSCover’s main focus is to allow users to make annotations upon the data. Through the concept of annotations, we connect expert and machine-generated information, to create extended digital specimens (Hardisty et al. 2022), e.g, by creating linkages to other infrastructures, correcting or adding new information, or by triggering machine annotation services. Machine annotation services are automated scalable tools that run in the background and automatically curate and extend the specimen (Addink et al. 2023). Human users will remain important, as all annotations made by machine annotation services can be reviewed by a trusted person. Annotations are Fair Digital Objects and target a specific part of a specimen, be it a data fragment or an associated media file. At the heart of DiSSCover lies the Open Digital Specimen data specification (Leeflang and Addink 2023). It tries to harmonise multiple data standards into one generic specification based on the new Global Biodiversity Information Facility (GBIF) Unified Model (Robertson et al. 2022). The data is stored as JavaScript Object Notation (JSON) based on JSON Schemas (Anonymous 2024). Annotations are linked to specific data attributes using a JSON-path as the identifier. Data attributes can be individual terms, collections of terms called classes, or the whole object. This creates a flexible but complex data structure, the basis for which we used the World Wide Web Consortium (W3C) web annotation data model (Sanderson et al. 2017). The W3C Web annotation data model contains two main components: the target and body. The target specifies which data attribute the annotation is made on, for example, the term: ‘ods:specimenName’. This is a local term within the open Digital Specimen namespace (ods), which holds the accepted name of the digital specimen. The annotation body holds the value(s) that are appended to the digital specimen, and differ based upon the annotation motivation. DiSSCo recognises five different annotation motivations: addition, modification, comment, assessment and deletion, each of which has its own unique function. This creates a flexible structure that should be able to handle any information the user wants to add to the object. The challenge of DiSSCover is to preserve the complex structure of annotations, whilst making it convenient for users to work with. The session will provide a look at the different kinds of annotations and their use from a practical perspective. A demonstration of DiSSCover will show how users can create annotations, providing knowledge about the process that will give shape to DiSSCo’s main goal of enriching natural history data.

Semisupervised Learning to Boost hERG, Nav1.5, and Cav1.2 Cardiac Ion Channel Toxicity Prediction by Mining a Large Unlabeled Small Molecule Data Set.

Predicting drug toxicity is a critical aspect of ensuring patient safety during the drug design process. Although conventional machine learning techniques have shown some success in this field, the scarcity of annotated toxicity data poses a significant challenge in enhancing models' performance. In this study, we explore the potential of leveraging large unlabeled small molecule data sets using semisupervised learning to improve drug cardiotoxicity predictive performance across three cardiac ion channel targets: the voltage-gated potassium channel (hERG), the voltage-gated sodium channel (Nav1.5), and the voltage-gated calcium channel (Cav1.2). We extensively mined the ChEMBL database, comprising approximately 2 million small molecules, and then employed semisupervised learning to construct robust classification models for this purpose. We achieved a performance boost on highly diverse (i.e., structurally dissimilar) test data sets across all three targets. Using our built models, we screened the whole ChEMBL database and a large set of FDA-approved drugs, identifying several compounds with potential cardiac ion channel activity. To ensure broad accessibility and usability for both technical and nontechnical users, we developed a cross-platform graphical user interface that allows users to make predictions and gain insights into the cardiotoxicity of drugs and other small molecules. The software is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred2.

Smart Scheduling: A Pilot Project of Workforce Scheduling in Radiation Oncology

Introduction: In the case of the radiation oncology department, the large number of visits faced by patients illuminates the critical need for optimal time management. Focused on three central themes: daily waiting times, diagnosis-to-treatment waiting times, and appropriate staffing for the present workload, the research highlights the impact of inefficient time management on patient satisfaction and overall operational efficiency. The time and energy invested in a schedule are high and frequently many scheduling conflicts occur even after the schedule is made. The ability to schedule different employees in the most optimal manner would increase the productivity of the radiation oncology department. Methods:The scheduling software was constructed using Python language and importations of libraries from the Tkinker software for the Graphical User Interface. The software is a constraint-based algorithm that allocates staff to different sites based on each radiation therapy clinic’s staffing requirements. Results and Discussion: This work developed a basic software that creates a randomized schedule of employees. While this would benefit the team by curating a schedule that has no functional mistakes, the algorithm provides a foundation for the data collection that will facilitate the future incorporation of artificial intelligence (AI). This would allow for deeper learning overtime of the software to develop a schedule that is optimal for the success of the individual and, thus, the entire team. This pilot project aimed to generate interest regarding the introduction of AI to current scheduling software in the context of the radiation oncology department.

APPLICATION DEVELOPMENT IN PYTHON FOR CONCRETE MIX DESIGN ACCORDING TO SNI 7656-2012

Concrete mix design is an important aspect in concrete construction process as it determines the quality of produced concrete. In practice, the process of getting a suitable concrete mix for a certain project is repetitive process. For repetitive work, it is an advantage to be carried out by using an application. This study focuses on the development of the application that can used to design the concrete mixes. Python programming language was employed in the development because it offers easiness and supports a rapid application development. The work is started by studying the procedure outlined SNI 7656-2012. The pseudocode of the application was then developed and implemented in Python. Tkinter, the Python package, was used in the development of Graphical User Interface for the application. The application was successfully developed and tested to carry out the concrete mix design.