Graphical Interface Research Articles

The "Dry-Lab" Side of Food Authentication: Benchmark of Bioinformatic Pipelines for the Analysis of Metabarcoding Data.

Next Generation Sequencing Technologies (NGS), particularly metabarcoding, are valuable tools for authenticating foodstuffs and detecting eventual fraudulent practices such as species substitution. This technique, mostly used for the analysis of prokaryotes in several environments (including food), is in fact increasingly applied to identify eukaryotes (e.g., fish, mammals, avian, etc.) in multispecies food products. Besides the "wet-lab" procedures (e.g., DNA extraction, PCR, amplicon purification, etc.), the metabarcoding workflow includes a final "dry-lab" phase in which sequencing data are analyzed using a bioinformatic pipeline (BP). BPs play a crucial role in the accuracy, reliability, and interpretability of the metabarcoding results. Choosing the most suitable BP for the analysis of metabarcoding data could be challenging because it might require greater informatics skills than those needed in standard molecular analysis. To date, studies comparing BPs for metabarcoding data analysis in foodstuff authentication are scarce. In this study, we compared the data obtained from two previous studies in which fish burgers and insect-based products were authenticated using a customizable, ASV-based, and command-line interface BP (BP1) by analyzing the same data with a customizable but OTU-based and graphical user interface BP (BP2). The final sample compositions were compared statistically. No significant difference in sample compositions was highlighted by applying BP1 and BP2. However, BP1 was considered as more user-friendly than BP2 with respect to data analysis streamlining, cost of analysis, and computational time consumption. This study can provide useful information for researchers approaching the bioinformatic analysis of metabarcoding data for the first time. In the field of food authentication, an effective and efficient use of BPs could be especially useful in the context of official controls performed by the Competent Authorities and companies' self-control in order to detect species substitution and counterfeit frauds.

A Machine Learning System for Detection and Analysis of Sexist Content in Urban Music

A Machine Learning System for Detection and Analysis of Sexist Content in Urban Music

Graphical Interface in MATLAB for design and analysis of reflectors for lighting systems or solar collectors on parabolic surfaces

This project introduces the successful development of a MATLAB graphical interface for the design and analysis of reflectors and solar concentrators on parabolic surfaces. The integration of spline techniques for precise modeling of these surfaces has been implemented, along with the capability to generate STL files for 3D printing. The interface not only enables detailed and adaptable design of reflectors but also includes an advanced simulator for projecting incident rays and analyzing their reflection on the parabolic surface, focusing specifically on the focal point location. This has led to a significant increase in the efficiency of solar concentrators and lighting systems by enabling the precise placement of absorbers to maximize energy transfer. The obtained results confirm the initial hypothesis, demonstrating that the interface enhances the precision and efficiency in the design of these systems, thereby offering a valuable tool in both educational and professional realms in the fields of solar energy and lighting.

공작기계 이송축의 운동 물성치 분석 툴 개발

In this study, a tool is developed to analyze the motion properties of feed axes to minimize machining time and improve productivity in manufacturing fields such as aircraft component machining. The motion properties of jerk, acceleration, and velocity are used to calculate the feeding time under a certain distance for the axis of a CNC machine tool. This analysis tool categorizes feed distances based on acceleration and speed and implements a feed-time calculation algorithm based on each distance type. A user-friendly graphical user interface is developed for interaction with users. The use of this tool is expected to minimize the machining time and improve productivity in manufacturing processes such as aircraft component machining. Furthermore, applying this analytical tool to the identification of appropriate motion properties can increase the efficiency of manufacturing processes and improve product quality.

HMM-Based Blockchain Visual Automatic Deployment System

The traditional blockchain deployment process is too complicated and has high technical requirements for blockchain deployers. Deploying a blockchain requires building a complex software-dependent environment, being able to use Linux commands for cumbersome parameter configurations, as well as the need to consider whether the hardware meets the requirements for running a blockchain. To address these current challenges in blockchain deployment both domestically and internationally, a web-based automatic deployment system with an interactive front-end and back-end has been developed. This system streamlines the process by automatically configuring and deploying blockchains while providing deployers with a graphical interface to monitor the entire deployment procedure. Meanwhile, in order to improve the efficiency of blockchain deployment, a Hidden Markov Model has been designed for blockchain deployment, which can predict the best deployment method for blockchain deployment under the current software environment. As one of the excellent blockchain platforms in China, Chainmaker has the outstanding features of independent control, flexible assembly, software and hardware integration, open source, and openness. The system takes Chainmaker as an experimental object and after a lot of tests, it can easily build a blockchain network on the server.

Integration of electromagnetic and mechanical models for effective lightning protection in buildings

The study focused on the design of an advanced algorithm for the optimal sizing of protection systems against atmospheric discharges in architectural structures, applying the rolling sphere method. This technique facilitated the incorporation of user-specified parameters through an advanced graphical interface. The methodology began with the exhaustive accumulation of data relevant to the project. Risk indices were estimated through sophisticated risk analysis software applications. If adjustments were required, the process continued; If not, the building was considered to be adequately secured. The ground resistivity was evaluated according to IEEE Std. 81, and the rolling sphere method was implemented according to IEC 662305-3. The grounding systems were configured in accordance with IEEE Std. 142 and IEEE Std. 80. To analyze the interaction of electrical discharges with the protected building, the electrical equivalents of elements such as meshes, fused copper rods were computed. , and conductors positioned horizontally and vertically. Using these data, a model was built in ATPDraw, interconnected with Python for the generation of graphical representations of the current waves in the different protection subsystems. To conclude and corroborate the effectiveness of the process, the risk indices were reevaluated. The validation of the algorithm was achieved by minimizing the margin of error to insignificant levels by incorporating standardized data proposed by organizations such as IEC and IEEE, thus confirming the precision of the designed algorithm

Integration of electromagnetic and mechanical models for effective lightning protection in buildings

The study focused on the design of an advanced algorithm for the optimal sizing of protection systems against atmospheric discharges in architectural structures, applying the rolling sphere method. This technique facilitated the incorporation of user-specified parameters through an advanced graphical interface. The methodology began with the exhaustive accumulation of data relevant to the project. Risk indices were estimated through sophisticated risk analysis software applications. If adjustments were required, the process continued; If not, the building was considered to be adequately secured. The ground resistivity was evaluated according to IEEE Std. 81, and the rolling sphere method was implemented according to IEC 662305-3. The grounding systems were configured in accordance with IEEE Std. 142 and IEEE Std. 80. To analyze the interaction of electrical discharges with the protected building, the electrical equivalents of elements such as meshes, fused copper rods were computed. , and conductors positioned horizontally and vertically. Using these data, a model was built in ATPDraw, interconnected with Python for the generation of graphical representations of the current waves in the different protection subsystems. To conclude and corroborate the effectiveness of the process, the risk indices were reevaluated. The validation of the algorithm was achieved by minimizing the margin of error to insignificant levels by incorporating standardized data proposed by organizations such as IEC and IEEE, thus confirming the precision of the designed algorithm

ForestQB: Enhancing Linked Data Exploration through Graphical and Conversational UIs Integration

This paper introduces the Forest Query Builder (ForestQB), an innovative toolkit designed to enhance the exploration and application of observational Linked Data (LD) within the field of wildlife research and conservation. Addressing the challenges faced by non-experts in navigating Resource Description Framework (RDF) triplestores and executing SPARQL queries, ForestQB employs a novel integrated approach. This approach combines a graphical user interface (GUI) with a conversational user interface (CUI), thereby greatly simplifying the process of query formulation and making observational LD accessible to users without expertise in RDF or SPARQL. Developed through insights derived from a comprehensive ethnographic study involving wildlife researchers, ForestQB is specifically designed to improve the accessibility of SPARQL endpoints and facilitate the exploration of observational LD in wildlife research contexts. To evaluate the effectiveness of our approach, we conducted a user experiment. The results of this evaluation affirm that ForestQB is not only efficient and user-friendly but also plays a crucial role in eliminating barriers for users, facilitating the effective use of observational LD in wildlife conservation and extending its benefits to wider domains. (GitHub Link)

Advancement in graphical user interface tailored quality classification of sape soundboard

This research introduces an innovative methodology for evaluating and predicting soundboard quality in the intricate craftsmanship of sape instruments. Despite the sape’s profound cultural significance, the process of selecting soundboard wood has been inadequately explored, resulting in uncertainties within the crafting community. Addressing this research gap, this study integrates advanced machine learning techniques and devises a specialized Graphical User Interface (GUI) tailored for sape makers. The methodology encompasses a thorough acoustic analysis of three distinct hardwoods—adau, merbau, and tapang—employing machine learning classification through Support Vector Machine with a Gaussian kernel. The study culminates in the development of a user-friendly GUI for soundboard quality assessment. Results underscore the model’s proficiency for achieving an optimized accuracy of 87.8% in classifying sape audio samples. The MATLAB App Designer-based GUI streamlines the evaluation process, offering a practical and accessible tool for craftsmen. This integrated approach, harmonizing traditional craftsmanship with cutting-edge technology, holds the potential to revolutionize sape instrument manufacturing, ensuring the preservation and progressive evolution of this rich cultural heritage.

Animated designs revisited – a case study under EU design law

In view of the ongoing extensive review of EU design law and given the expected abandoning of the current seven-view limit, revisiting protection of animated designs under EU law and practice (and limits to such protection), particularly against the famous-infamous Candy Crush Graphical User Interface (GUI) dispute, allows for some fascinating conclusions with respect to a proper distinction between ‘partial designs’ and ‘parts of a design’, the correct identification of the relevant prior art, the determination of the subject matter of a design, and the finding of infringement. Overall, ‘less’ is apparently ‘more’.

Tongue Disease Prediction Based on Machine Learning Algorithms

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.

Counting nematodes made easy: leveraging AI-powered automation for enhanced efficiency and precision.

Counting nematodes is a labor-intensive and time-consuming task, yet it is a pivotal step in various quantitative nematological studies; preparation of initial population densities and final population densities in pot, micro-plot and field trials for different objectives related to management including sampling and location of nematode infestation foci. Nematologists have long battled with the complexities of nematode counting, leading to several research initiatives aimed at automating this process. However, these research endeavors have primarily focused on identifying single-class objects within individual images. To enhance the practicality of this technology, there's a pressing need for an algorithm that cannot only detect but also classify multiple classes of objects concurrently. This study endeavors to tackle this challenge by developing a user-friendly Graphical User Interface (GUI) that comprises multiple deep learning algorithms, allowing simultaneous recognition and categorization of nematode eggs and second stage juveniles of Meloidogyne spp. In total of 650 images for eggs and 1339 images for juveniles were generated using two distinct imaging systems, resulting in 8655 eggs and 4742 Meloidogyne juveniles annotated using bounding box and segmentation, respectively. The deep-learning models were developed by leveraging the Convolutional Neural Networks (CNNs) machine learning architecture known as YOLOv8x. Our results showed that the models correctly identified eggs as eggs and Meloidogyne juveniles as Meloidogyne juveniles in 94% and 93% of instances, respectively. The model demonstrated higher than 0.70 coefficient correlation between model predictions and observations on unseen images. Our study has showcased the potential utility of these models in practical applications for the future. The GUI is made freely available to the public through the author's GitHub repository (https://github.com/bresilla/nematode_counting). While this study currently focuses on one genus, there are plans to expand the GUI's capabilities to include other economically significant genera of plant parasitic nematodes. Achieving these objectives, including enhancing the models' accuracy on different imaging systems, may necessitate collaboration among multiple nematology teams and laboratories, rather than being the work of a single entity. With the increasing interest among nematologists in harnessing machine learning, the authors are confident in the potential development of a universal automated nematode counting system accessible to all. This paper aims to serve as a framework and catalyst for initiating global collaboration toward this important goal.

LUNAR: Automated Input Generation and Analysis for Reactive LAMMPS Simulations.

Generating simulation-ready molecular models for the LAMMPS molecular dynamics (MD) simulation software package is a difficult task and impedes the more widespread and efficient use of MD in materials design and development. Fixed-bond force fields generally require manual assignment of atom types, bonded interactions, charges, and simulation domain sizes. A new LAMMPS pre- and postprocessing toolkit (LUNAR) is presented that efficiently builds molecular systems for LAMMPS. LUNAR automatically assigns atom types, generates bonded interactions, assigns charges, and provides initial configuration methods to generate large molecular systems. LUNAR can also incorporate chemical reactivity into simulations by facilitating the use of the REACTER protocol. Additionally, LUNAR provides postprocessing for free volume calculations, cure characterization calculations, and property predictions from LAMMPS thermodynamic outputs. LUNAR has been validated via building and simulation of pure epoxy and cyanate ester polymer systems with a comparison of the corresponding predicted structures and properties to benchmark values, including experimental results from the literature. LUNAR provides the tools for the computationally driven development of next-generation composite materials in the Integrated Computational Materials Engineering (ICME) and Materials Genome Initiative (MGI) frameworks. LUNAR is written in Python with the usage of NumPy and can be used via a graphical user interface, a command line interface, or an integrated design environment. LUNAR is freely available via GitHub.

MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses.

Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open-source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at https://github.com/huizhanglab-jhu/ms-pycloud.

Fast vibrational analysis of molecular systems

AbstractThe development of infrared difference spectroscopy provides unprecedented insights on structures of complex molecules like metalloproteins. However, the relevant information can be hard to find among the many bands of the vibrational spectra. The ab initio modeling is very helpful to assign the frequencies to vibrational modes but it is a challenge to process the huge quantity of data into descriptors useful for experimentalists. To this end, we developed a new tool called VIBMOL allowing to analyze vibrational modes of molecules from hessian matrices calculated with common quantum chemistry codes. VIBMOL program runs on Unix machines. Through a new graphical interface, the users can calculate the normal modes of molecules, visualize them, simulate infrared spectra, and explore the Potential Energy Distribution of normal modes among any set of vibration coordinates. It is combined with an interface program (gosdmu) formatting relevant data from the GAUSSIAN program. VIBMOL code is available upon request to the authors. A discussion is provided to help the readers to choose between a large choice of different software and it shows how VIBMOL can make the IR assignment easier in the context of collaborations with experimentalists.

A Systematic Process to Engineer Dependable Integration of Frame-based Input Devices in a Multimodal Input Chain: Application to Rehabilitation in Healthcare

Designing new input devices and associated interaction techniques is a key contribution in order to increase the bandwidth between users and interactive applications. In the field of Human-Computer Interaction, research and development services in industry and research laboratories in universities have been, since the invention of the mouse and the graphical user interface, proposing multiple contributions including the integration of multiple input devices in multimodal interaction technique. Those contributions, most of the time, are presented as prototypes or demonstrators providing evidence of the bandwidth increase through user studies. Such contributions however, do not provide any support to software developers for integrating them in real-life systems. When done, this integration is performed in a craft manner, outside required software engineering good practice. This paper proposes a systematic process to integrate novel input devices and associated interaction techniques to better support users' work. It exploits most recent interactive systems architectural model and formal model-based approaches for interactive systems supporting verification and validation when required. The paper focusses on Frame-based input devices which support gesture-based interactions and movements recognition but also addresses their multimodal use. This engineering approach is demonstrated on an interactive application in the area of rehabilitation in healthcare where dependability of interactions and applications is as critical as their usability.

Multi-output neural network model for predicting biochar yield and composition

In biomass pyrolysis for biochar production, existing prediction models face computational challenges and limited accuracy. This study curated a comprehensive dataset, revealing pyrolysis parameters' dominance in biochar yield (54.8 % importance). Pyrolysis temperature emerged as pivotal (PCC = −0.75), influencing yield significantly. Artificial Neural Network (ANN) outperformed Random Forest (RF) in testing set predictions (R2 = 0.95, RMSE = 3.6), making it apt for complex multi-output predictions and software development. The trained ANN model, employed in Partial Dependence Analysis, uncovered nonlinear relationships between biomass characteristics and biochar yield. Findings indicated optimization opportunities, correlating low pyrolysis temperatures, elevated nitrogen content, high fixed carbon, and brief residence times with increased biochar yields. A multi-output ANN model demonstrated optimal fit for biochar yield. A user-friendly Graphical User Interface (GUI) for biochar synthesis prediction was developed, exhibiting robust performance with a mere 0.52 % prediction error for biochar yield. This study showcases practical machine learning application in biochar synthesis, offering valuable insights and predictive tools for optimizing biochar production processes.

An automation of Monte Carlo workflow for dosimetry study of an Elekta LINAC delivery system in radiotherapy

PurposeThis study aims to automate the Monte Carlo (MC) workflow utilized for radiotherapy dosimetry, focusing on an Elekta LINAC delivery system. It addresses the challenge of integrating MC simulations into routine clinical practice, making this accurate yet complex method more accessible and efficient for radiotherapy dosimetry. Methods and MaterialsWe developed a user-friendly software featuring a graphical user interface (GUI) that integrates EGSnrc for MC simulations. The software streamlines the process from retrieving Digital Imaging and Communications in Medicine (DICOM) data to executing dose calculations and comparing dose distributions. To validate our proposed tool, we compared its computed doses for IMRT and VMAT plans from the Pinnacle TPS for an Elekta Versa HD linear accelerator against MC simulation results. This comparison utilized our in-house software and GUI as the tool, covering various treatment sites and prescriptions. ResultsThe automated MC workflow demonstrated high accuracy in dose calculations and streamlined integration with clinical workflows. The comparison between the MC-simulated and TPS-calculated doses revealed excellent agreement, highlighting the reliability of MC for independent dose verification in complex treatment scenarios. ConclusionsThe automated MC workflow developed represents a substantial improvement in the practicality and efficiency of MC simulations in radiotherapy. This advancement not only simplifies the dosimetry process but also ensures high accuracy, establishing it as a valuable tool for routine patient-specific quality assurance and the development of specialized treatment procedures.

Rainfall-Runoff Simulation for Ungauged Watershed: A Case of Bessre Watershed, Duhok Province, Iraq

The scarcity of measured hydrological data poses a challenge in many developing countries, stemming from insufficiently established gauging stations. Due to the mentioned issue, it is crucial to develop models capable of conducting reliable simulations of runoff behavior, particularly for ungauged catchments. Understanding the intricate relationships in rainfall-runoff modeling is essential for estimating peak flows, a critical aspect in formulating water resources management strategies, which can aid in water resource management and planning. In areas prone to floods performing, an extensive hydrological study becomes necessary. This study determined the outflow discharge at the outlet point of the Bessre Valley Ungauged Catchment (41.4 km2) using the Watershed Modeling System, used by reliable hydrological standards as a graphical interface integrating with the Hydrologic Modeling System (HEC-HMS). Bessre Valley watershed is one of the flood-prone watersheds in the Duhok governorate, mainly due to the terrain’s steep slopes at the upper north and east of the catchment. The catchment was delineated by a Geographic Information System (GIS). Its properties were extracted from a 12.5 m × 12.5 m Digital Elevation Model (DEM), which evaluates the hydrological response of a watershed to two significant storm events: a real rainfall event in March 2020 and a hypothetical 100-year return period event by dividing the watershed into ten sub-basins. Achieving a Nash-Sutcliffe efficiency of 0.895 indicates a high accuracy between observed and simulated peak flows of the real rainfall event of March 2020, underscoring the model's reliability for hydrological predictions. Also, comparing the HEC-HMS model and the Rational Method of (100 YRP event) for calculating peak discharges revealed a mere 2.2% error. Furthermore, the study explores the potential for building additional dams based on discharge volumes from specific sub-basins to enhance flood control and water storage capabilities.

Artificial Intelligence for Automatic Analysis of Shunt Treatment in Presurgery and Postsurgery Computed Tomography Brain Scans of Patients With Idiopathic Normal Pressure Hydrocephalus.

Ventriculo-peritoneal shunt procedures can improve idiopathic normal pressure hydrocephalus (iNPH) symptoms. However, there are no automated methods that quantify the presurgery and postsurgery changes in the ventricular volume for computed tomography scans. Hence, the main goal of this research was to quantify longitudinal changes in the ventricular volume and its correlation with clinical improvement in iNPH symptoms. Furthermore, our objective was to develop an end-to-end graphical interface where surgeons can directly drag-drop a brain scan for quantified analysis. A total of 15 patients with 47 longitudinal computed tomography scans were taken before and after shunt surgery. Postoperative scans were collected between 1 and 45 months. We use a UNet-based model to develop a fully automated metric. Center slices of the scan that are most representative (80%) of the ventricular volume of the brain are used. Clinical symptoms of gait, balance, cognition, and bladder continence are studied with respect to the proposed metric. Fifteen patients with iNPH demonstrate a decrease in ventricular volume (as shown by our metric) postsurgery and a concurrent clinical improvement in their iNPH symptomatology. The decrease in postoperative central ventricular volume varied between 6 cc and 33 cc (mean: 20, SD: 9) among patients who experienced improvements in gait, bladder continence, and cognition. Two patients who showed improvement in only one or two of these symptoms had <4 cc of cerebrospinal fluid drained. Our artificial intelligence-based metric and the graphical user interface facilitate this quantified analysis. Proposed metric quantifies changes in ventricular volume before and after shunt surgery for patients with iNPH, serving as an automated and effective radiographic marker for a functioning shunt in a patient with iNPH.