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User interactivity can be enhanced in agricultural measurement and control systems, especially with the continuous advancements in natural language semantic processing. It is necessary to improve user-friendliness in control and query operations within the agricultural measurement and control field, in order to reduce the user operating costs. Firstly, a precise interface of human-computer interaction can be constructed to tailor for the agricultural domain, in order to efficiently translate the user's natural language input into understandable commands for the computer system. The current agricultural field has relied mainly on graphical user interfaces to meet human-computer interaction. But some limitations still remained over time, e.g., the high complexity of human-computer interaction and the low efficiency. Therefore, natural language interface (NLI) has been designed to establish the mapping between natural language from the nature of human-computer interaction. Agricultural measurement and control systems have been considered as the efficient strategy. Among them, the primary task of natural language understanding (NLU) is often used to transform the human language into computer-understandable structured expressions, in order to accurately capture the user's intention and semantics. Deep learning has been utilized to name entity recognition tasks in recent years. Relational components of sentences can be extracted to identify the sentence actions, and then incorporate the annotations of semantic roles, in order to understand the utterances for the computers. Entity recognition has distinctly realized the entity features in the specific domains. Commonly-named entities are usually characterized by fuzzy boundaries in the field of agricultural measurement and control systems. Some challenges remain in the quality of data and the accuracy of annotations, due to the relatively scarce data. It is important to directly apply to the agricultural measurement and control system. In this study, the agricultural measurement and control natural language interface (AMC-NLI) was presented to serve as the natural language interface for the agricultural measurement and control. The users were allowed to operate and control systems using natural language commands. These commands were interpreted using OPERATE, PLACE, and OBJECT attributes within the operate-place-object (OPO) ternary structure, and then transmitted to the gateways, nodes, or devices. Significant semantic information was previously lost using conventional methods when extracting entities from natural language commands, particularly when the commands contained multiple entities of the same type. Additionally, the entity order was confounded on the semantic relationships. A semantic parsing model called BERT-BiLSTM-ATT-CRF-OPO was proposed for the recognition tasks of the named entity in the command parsing of the measurement and control system. BERT pre-trained language models were utilized for the word embedding to enhance contextual understanding. The bidirectional long short-term memory networks (BiLSTM) were employed to capture the semantic features of long sentences and long-distance dependent information. An attention mechanism was incorporated to prioritize the features related to named entities for better local feature extraction. Conditional Random Field (CRF) was utilized to learn the labeling constraints and output globally optimal labeled sequences. The experimental results show that the BERT-BiLSTM-ATT-CRF-OPO model achieved a recognition accuracy of 92.13%, a recall of 93.12%, and an F1 score of 92.76% for the three types of entities. The improved model performed well in the AMC-NLI agricultural measurement and control command interaction, with the accuracy, precision, recall, F-value, and average maximum response time reaching 91.63%, 92.77%, 92.48%, 91.74%, and 2.45s, respectively. The human-computer interaction was enhanced in the agricultural measurement and control system, in order to improve the recognition accuracy of command entity. The finding can offer novel insights into Chinese command parsing, indicating the potential application of natural language processing in agriculture. A more user-friendly and efficient humancomputer interaction was provided for future agricultural measurement and control systems. [ABSTRACT FROM AUTHOR]

This study aims to make a unique contribution to the existing body of knowledge about rock strength and deformation parameters and crack stress thresholds through intelligent and statistical approaches applied to a database comprising various rock types (i.e., sedimentary, igneous, and metamorphic rocks). The database contains physical–mechanical and ultrasonic parameters. Six distinct machine learning (ML) algorithms— artificial neural network (ANN), random forest (RF), decision tree (DT), K-nearest neighbor (KNN), support vector regression (SVR), and bagging regressor (BR)— along with the conventional linear regression techniques, were employed to develop predictive models. These models estimate uniaxial compressive strength ( σ c ) and Tangent Young's modulus ( E t ) based on bulk density (ρ ) and P-wave ultrasonic velocity ( V p ). Furthermore, they predict crack stress thresholds (i.e., crack closure stress σ cc , crack initiation stress σ ci , and crack damage stress σ cd ) as a function of σ c , E t , ρ , V p , axial strain at failure ( ε 1 f ), and lateral strain at failure ( ε 3 f ). Various performance indices were utilized to evaluate and compare the performance of these models. The results indicated that the RF method outperformed other ML-based and linear regression-based approaches in predicting the output parameters. Additionally, the multiple parametric sensitivity analysis (MPSA) was carried out to determine the significance of input parameters in predicting the output variables. This analysis revealed that V p and ρ have the highest and lowest impact on predicting σ c and E t , respectively. On the other hand, σ c was identified as the most influential parameter in predicting σ ci and σ cd , while parameters ε 3 f and V p showed the least impact on the foregoing outputs, respectively. This is while ε 1 f and ρ were, respectively, found as the most important and least important factors in predicting σ cc . Finally, to facilitate easy access to the prediction results and enhance the practicality of the proposed RF model, a graphical user interface (GUI) was developed, which enables the practical application of the most performing developed prediction model. Highlights: A comprehensive database including different rock types was prepared to evaluate the rock strength parameters and crack stress thresholds. The used six robust machine learning algorithms effectively unveiled the latent complex nonlinear relationship between the parameters. The random forest algorithm outperformed the traditional regression and machine learning algorithms in predicting the output variables. P-wave velocity, uniaxial compressive strength, and axial strain at the failure point were identified as the most influential parameters in predicting outputs. The developed user-friendly graphical user interface brings the power of the random forest model to practitioners without the need for complex analyses and destructive laboratory tests. [ABSTRACT FROM AUTHOR]

Gene cloning is an important step in investigating gene structure and function. To verify gene sequence, Sanger sequencing is used, which may produce several overlapping sequencing files that need to be merged before alignment to the target gene sequence is performed. Previously, we reported the Python program to Merge Sanger sequences (), which ran in command line and relied heavily on EMBOSS suite. In this updated version of the program, we have made several remarkable improvements. It provides a graphical user interface (GUI) written with tkinter, which is convenient and stable. It does not require users to rename the input sequences before performing merging. With regard to the implementation, the updated version utilizes Python function (Align.PairwiseAligner) to align adjacent sequences, which is more flexible (can adjust program parameter i.e., the number of first-time consecutive matching bases). The new version of the program makes merging Sanger sequences much more convenient and facilitates gene study. [ABSTRACT FROM AUTHOR]

This article is an overview of the design, implementation and testing of a tool to visualise and interact with probability density functions. The tool is a desktop application implemented entirely in Python using the tkinter library for the graphical user interface. The project was undertaken as part of a collaboration between Mathematics and Computer Science. The goal of the application is to provide a simple user interface for teaching staff and students to visualise and interact with probability density functions, and we investigate whether such a simple visualisation tool can improve staff and student engagement. The application should help improve students' understanding of the concepts involved and its simple design should reduce the complexity barrier that often faces users when using technology in the classroom. Following initial testing, a variety of teaching staff were involved with trialling the tool, together with student volunteers from a first-year and second-year statistics module at Swansea University. Feedback was obtained and evaluated from all participants. For the teaching staff group, we found that all four participants strongly agreed that the application is easy to use and that the user interface was not distracting. Furthermore, all teaching staff stated that they would consider using the application in their own teaching and all would recommend using the application to a colleague/friend. For the student volunteer group, all twelve participants either agreed or strongly agreed with the statements that the application is easy to use, useful and not distracting. Similar to the teaching staff group, all the student participants stated that they would consider using the application in their own learning and all would recommend the application to a friend. A full analysis of the survey results is provided in the Feedback section. [ABSTRACT FROM AUTHOR]

A human–machine interface (HMI)‐based visual interface along with an embedded system was developed to real‐time measure, display, and store the various tractor–implement performance parameters, that is, geoposition, depth, speed, slip, fuel consumption, draft, and power take‐off (PTO) torque. The developed system consists of various commercially available (global positioning system, rotary potentiometer, tension/compression load cell, Hall‐effect sensor, flow meter, and strain gauge) sensors/transducers to measure the performance parameters. A strain‐gauge‐based special type of transducer was also developed for measuring the PTO torque acting on the implement and the output of the transducer was transferred to the virtual interface‐based data acquisition system using radio frequency‐based modules. Along with the sensors, the developed system is composed of a microcontroller to process the data received from sensors, an HMI‐assisted smart touch screen to display the output, and a secure digital card module to store the processed data. The developed visual interface of the embedded system comprises multiple operator‐friendly touch screens and each screen was designed with a graphical user interface for the visual presentation of the tractor–implement performance parameters. The system was installed in the TAFE Samrat 4410 tractor and tested under various field operations. Sensors employed in the system were calibrated for obtaining precise measurement, and excellent linearity with a high correlation between actual and measured variables. Under various field operations (plowing and tillering), a maximum error of 15% (except PTO torque) was found between parameters measured with the developed system and manual/predicted measurement. The field results fortify the acceptable accuracy of the developed system. The developed system will be helpful for researchers or students to study the matching of tractor–implement combinations. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) is a branch of artificial intelligence technologies that has made interacting with computers more akin to natural language. This study aimed to define NLP, present its history from the sixties to the present, clarify the fundamental terms used in the field of NLP, as well as identify the constituent elements of NLP technology, the linguistic levels in the field of NLP, the stages involved in NLP technology, and the applications of NLP in library and information science. The study relied on a descriptive-analytical approach in reviewing the intellectual production related to the field of NLP, based on available foreign databases on the Egyptian Knowledge Bank (EKB). The study reached several conclusions, notably anticipating the emergence of numerous graphical user interface software that will allow the use of NLP techniques without the need for encoding, thereby facilitating beginners in applying NLP techniques easily without relying on algorithms. [ABSTRACT FROM AUTHOR]

Bacteriophages are the most prolific organisms on Earth, yet many of their genomes and assemblies from metagenomic sources lack protein sequences with identified functions. While most bacteriophage proteins are structural proteins, categorized as Phage Virion Proteins (PVPs), a considerable number remain unclassified. Complicatingmatters further, traditional lab-basedmethods for PVP identification can be tedious. To expedite the process of identifying PVPs, machine-learning models are increasingly being employed. Existing tools have developed models for predicting PVPs from protein sequences as input. However, none of these efforts have built software allowing for both genomic and metagenomic data as input. In addition, there is currently no framework available for easily curating data and creating new types of machine learning models. In response, we introduce PhageScanner, an open-source platform that streamlines data collection for genomic and metagenomic datasets, model training and testing, and includes a prediction pipeline for annotating genomic and metagenomic data. PhageScanner also features a graphical user interface (GUI) for visualizing annotations on genomic and metagenomic data. We further introduce a BLAST-based classifier that outperforms ML-based models and an efficient Long Short-Term Memory (LSTM) classifier. We then showcase the capabilities of PhageScanner by predicting PVPs in six previously uncharacterized bacteriophage genomes. In addition, we create a new model that predicts phage-encoded toxins within bacteriophage genomes, thus displaying the utility of the framework. [ABSTRACT FROM AUTHOR]

The growing impact of the built environment on the natural environment, human health, and its economic importance has emphasized the importance of green building design. It is widely recognized that the most significant benefits can be obtained if the green design initiatives can be implemented at the earliest stages of the design processes. This requires designers to consider embodied impacts (embodied energy and emission) among other criteria in selecting building systems and materials during the early design phase of a building. However, this endeavor is not a simple task, as the absence of appropriate tools to facilitate such considerations becomes apparent. The aim of this research is to develop an innovative system, which can support designers in selecting the structure and envelope systems of a building considering the embodied impacts and costs during the early design stage. A graphical user interface empowers designers to scrutinize diverse building structures and exterior envelope assemblies and compare their embodied energy, embodied carbon emissions, and costs, thereby making an informed decision about the most desirable systems. To validate its utility, a case study is used to illustrate the application of the system and demonstrate the feasibility of its use in selecting structure and envelope systems for educational buildings. [ABSTRACT FROM AUTHOR]

Background/Objectives: In perioperative nursing practice, nursing students can engage in direct, in-person clinical experiences in perioperative environments; however, they face limitations due to infection and contamination risks. This study aimed to develop and evaluate educational content for perioperative clinical practice for nursing students using virtual reality (VR) and haptic technology. Methods: The program, based on the Unity Engine, was created through programming and followed the system development lifecycle (SDLC) phases of analysis, design, implementation, and evaluation. This program allows nursing students to engage in perioperative practice using VR and haptic technology, overcoming previous environmental limitations and enhancing practical and immersive experiences through multi-sensory stimuli. Results: Expert evaluations indicated that the developed content was deemed suitable for educational use. Additionally, a usability assessment with 29 nursing students revealed high levels of presence, usability, and satisfaction among the participants. Conclusions: This program can serve as a foundation for future research on VR-based perioperative nursing education. [ABSTRACT FROM AUTHOR]

Round-ended concrete-filled steel tubes (RECFSTs) present very different performances between the primary and secondary axes, which renders them particularly suitable for use as bridge piers and arches. In recent years, research into RECFST heavily relies on experimental procedures restricting the parameter range under consideration, which narrows the far-reaching applicability of RECFST. This study employs advanced machine learning methods to predict the axial load-bearing capacity of RECFST with a wide parameter range. Firstly, a machine learning database comprising 2400 RECFSTs is established, which covers a wider range of commonly used material strengths and cross-sectional dimensions. Three machine learning prediction models of this database are then developed, respectively, using different algorithms. The robustness of the machine learning models is evaluated by predicting the axial load-bearing capacity of 60 RECFST specimens from existing references. The results demonstrated that the machine learning models provided superior predictive accuracy compared to theoretical or code-based formulas. A graphical user interface (GUI) is ultimately developed based on the machine learning prediction models to predict the axial load-bearing capacity of RECFST. This tool facilitates rapid and accurate RECFST design. [ABSTRACT FROM AUTHOR]

This paper presents an artificial neural network-based graphical user interface, designed to automate finite element model updating using data from operational modal analysis. The approach aims to reduce the uncertainties inherent in both the experimental data and the computational model. A key feature of this method is the application of a discrete wavelet transform-based approach for denoising OMA data. The graphical interface streamlines the FEMU process by employing neural networks to automatically optimize FEM inputs, allowing for real-time adjustments and continuous structural health monitoring under varying environmental and operational conditions. This approach was validated with OMA results, demonstrating its effectiveness in enhancing model accuracy and reliability. Additionally, the adaptability of this method makes it suitable for a wide range of structural types, and its potential integration with emerging technologies such as the Internet of Things further amplifies its relevance. [ABSTRACT FROM AUTHOR]

Background Robust venous outflow (VO) profiles, measured by degree of venous opacification on pre-thrombectomy CT angiography (CTA) studies, are strongly correlated with favorable outcomes in patients with large vessel occlusion acute ischemic stroke treated by thrombectomy. However, VO measurements are laborious and require neuroimaging expertise. Objective To develop a semi-automated method to measure VO using CTA and CT perfusion imaging studies. Methods We developed a graphical interface using The Visualization Toolkit, allowing for voxel selection at the confluence and bilateral internal cerebral veins on CTA along with arterial input functions (AlFs) from both internal carotid arteries. We extracted concentration-time curves from the CT perfusion study at the corresponding locations associated with AIF and venous output function (VOF). Outcome analyses were primarily conducted by the Mann-Whitney U and Jonckheere-Terpstra tests. Results Segmentation at the pre-selected AIF and VOF locations was performed on a sample of 97 patients. 65 patients had favorable VO (VO+) and 32 patients had unfavorable VO (VO-). VO+ patients were found to have a significantly shorter VOF time to peak (8.26; 95% CI 7.07 to 10.34) than VO- patients (9.44; 95% CI 8.61 to 10.91), P=0.007. No significant difference was found in VOF curve width and the difference in time between AIF and VOF peaks. Conclusions Time to peak of VOF at the confluence of sinuses was significantly associated with manually scored venous outflow. Further studies should aim to understand better the association between arterial inflow and venous outflow, and capture quantitative metrics of venous outflow at other locations. [ABSTRACT FROM AUTHOR]

This research presents a pioneering approach in virtual control systems, focusing on developing a highly intuitive Graphical User Interface (GUI) for real-time PID control of LED brightness using Arduino IDE. By embracing a robust pedagogical framework, the study meticulously outlines specific learning objectives that adhere to the SMART criteria, ensuring precise educational outcomes. The key to this research is the tailored design of the GUI, which considers diverse audience needs, prior knowledge, and learning styles. The interfaces are meticulously crafted for optimal user experience, ensuring seamless navigation and engagement. Interactive elements, encompassing simulations, virtual experiments, problem-solving scenarios, and quizzes, foster active participation and hands-on learning. Multimedia elements, such as text, images, videos, animations, and audio, are thoughtfully integrated to cater to varied learning preferences. This paper underscores the critical role of collaborative learning facilitated through discussion forums, chat functionalities, and collaborative virtual environments. The study emphasizes continuous improvement through regular assessments and detailed user feedback analysis. Notably, the research highlights the significant educational impact of the designed GUI. By offering a practical platform where students can dynamically adjust PID parameters and observe immediate effects on LED brightness, the GUI enhances the learning experience in classroom settings. This approach equips students with a tangible understanding of complex PID principles, preparing them for real-world applications. In summary, this research showcases the creation of engaging, practical virtual control system tools, fostering profound and meaningful learning experiences for users within educational contexts. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is a method of electrical neuromodulation used to treat a variety of neuropsychiatric conditions including essential tremor, Parkinson's disease, epilepsy, and obsessive–compulsive disorder. The procedure requires precise placement of electrodes such that the electrical contacts lie within or in close proximity to specific target nuclei and tracts located deep within the brain. DBS electrode trajectory planning has become increasingly dependent on direct targeting with the need for precise visualization of targets. MRI is the primary tool for direct visualization, and this has led to the development of numerous sequences to aid in visualization of different targets. Synthetic inversion recovery images, specified by an inversion time parameter, can be generated from T1 relaxation maps, and this represents a promising method for modifying the contrast of deep brain structures to accentuate target areas using a single acquisition. However, there is currently no accessible method for dynamically adjusting the inversion time parameter and observing the effects in real-time in order to choose the optimal value. In this work, we examine three different approaches to implementing an application for real-time optimal synthetic inversion recovery image selection and evaluate them based on their ability to display continually-updated synthetic inversion recovery images as the user modifies the inversion time parameter. These methods include continuously computing the inversion recovery equation at each voxel in the image volume, limiting the computation only to the voxels of the orthogonal slices currently displayed on screen, or using a series of lookup tables with precomputed solutions to the inversion recovery equation. We find the latter implementation provides for the quickest display updates both when modifying the inversion time and when scrolling through the image. We introduce a publicly available cross-platform application built around this conclusion. We also briefly discuss other details of the implementations and considerations for extensions to other use cases. [ABSTRACT FROM AUTHOR]

This study presents a real-time fraud detection system for online payment platforms, leveraging machine learning techniques to identify suspicious transactions. The system analyses historical transaction data to uncover patterns commonly associated with fraudulent activity. By applying algorithms such as decision trees, random forests, and logistic regression, it distinguishes between legitimate and fraudulent transactions. The system offers both user and admin interfaces: users can securely transfer funds and review their transaction history, while admins can monitor transactions and manage potential threats. Experimental results demonstrate high accuracy in fraud detection, effectively reducing false positives and issuing real-time alerts. This model, when integrated into online payment systems, enhances security and boosts user confidence in digital transactions. [ABSTRACT FROM AUTHOR]

In the streaming era, the very thing that defines it is what threatens to impede access to important media history and cultural heritage. Streaming's barriers to entry and its interim content catalogs challenge the actual collection and preservation of it for research and teaching purposes. If researchers and libraries do not work together to document and preserve these, we will keep losing important sources and data. From a collection perspective, we argue that streaming services consist of their catalog, metadata, and graphical user interfaces. First, we map the large-scale legal deposit collection of streaming at a national library as well as a media researcher's small-scale targeted collection. Second, we compare the resulting collections of web sites and graphical user interfaces in order to discuss methodological challenges. The findings of this comparative analysis indicate the existing deficiencies in both collections and suggest potential improvements in the collection and preservation of streaming services. [ABSTRACT FROM AUTHOR]

Previous research largely concentrated on predicting load‐carrying capacities of concrete‐filled steel tubes (CFST) confined with fiber‐reinforced polymer (FRP) wraps under pure concentric loads, neglecting the more complex failure mechanisms that occur under real‐life eccentric loading conditions. This study, therefore, employs both finite element modeling (FEM) and machine learning methods to accurately predict the load‐bearing capacities under both concentric and eccentric loading conditions. This research analyzed a comprehensive dataset comprising 128 experimental tests and an equivalent number of FEM simulations designed to evaluate their eccentric performance. These models have been thoroughly generated and validated against existing literature. Additionally, the developed ML models, particularly a hybrid deep learning model, demonstrated significant predictive accuracy, with an average R2 value of 0.969 across all model folds. Partial dependence analysis further highlighted the significant influence of concrete strength and the interactive effects of steel tube area and FRP wrap thickness on the load‐carrying capacity of the columns. Furthermore, to enhance cost‐efficiency and resource management compared with traditional laboratory testing, a user‐friendly graphical user interface (GUI) has been developed and hosted on an open‐source platform such as GitHub. This interface supports real‐time, precise predictive capabilities and promotes a collaborative environment for ongoing model refinement and improvement. [ABSTRACT FROM AUTHOR]

Airway diseases cause significant challenges while diagnosing it accurately as well as timely. In current years, the combination of deep learning techniques with Explainable Artificial Intelligence (XAI), has emerged as a promising possibility to enhance the understanding as well as interpretability of complex medical data which are associated with these conditions. The study explores the usage of advanced convolutional neural networks (CNNs) on large-scale of Chest X-ray or CT scan image datasets and their application in the domain of medical imaging to detect as well as classify airway diseases such as pneumoconiosis, pulmonary embolism, Covid-19, tuberculosis, and Lung cancer. The visualization of the images have been enhanced by applying sharpening techniques and are later subsequently used to train various deep learning models which include InceptionResNetV2, MobileNet, Xception, ResNet152, EfficientNetV2M, and DenseNet169. Further, various parameters have been used for the evaluation of the performance of these models in determining the most optimal choice. During training the model, it has been found that the highest accuracy, precision, recall, and F1 score has been computed by InceptionResNetV2 with 99.15%, 98% and 99% (both) respectively on a loss of 0.0275. Besides this, a graphical user interface (GUI) has been also developed to improvise the interaction between the AI based model and end users. The GUI is based on the theory of XAI which after predicting the diseases provides in detail explanation of treatment as well as preventive measures of the disease. The motto of the research is to escalate the knowledge of deep learning techniques for various lung based issues as well as to enlighten with the significance of XAI in promoting interpretability as well as transparency. The findings of the research implies towards the advancement of decision support systems at various medical sectors to promote effective as well as personalized interventions in the realm of pulmonology. [ABSTRACT FROM AUTHOR]

This study investigates the key factors influencing the usability of smartphone graphical user interfaces (GUIs) among elderly users in Shanxi Province, China. A total of 1,111 healthy individuals aged 60-75 years participated in the study. Quantitative analysis methods, including descriptive statistics, Pearson correlation, regression analysis, and mediation and moderation analysis, were employed to examine the interactions between user characteristics (age, education, experience, health status) and GUI design factors (interface layout, text and icon design, feedback mechanisms, information presentation) about usability metrics (cognitive load, interaction barriers, user experience). The analysis revealed that both user characteristics and GUI design factors significantly impact usability outcomes. Specifically, GUI design factors were found to mediate the relationship between user characteristics and usability, while user characteristics moderated the effects of GUI design on usability. These findings underscore the necessity of tailored interface design to enhance user experience and promote digital inclusion among elderly populations. Practical recommendations include the use of larger and more readable text, simplified iconography, and clear, immediate feedback to users. Such adjustments can significantly improve usability and foster greater digital inclusion. This research contributes valuable insights to both theoretical understanding and practical applications in the design of age-appropriate mobile interfaces. [ABSTRACT FROM AUTHOR]

Although much information can be gained about thermally induced microstructural changes in metals through the measurement of their thermophysical properties using a differential scanning calorimeter (DSC), due to competing influences on the signal, not all microstructural changes can be fully characterised this way. For example, accurate characterisation of recrystallisation, tempering, and changes in retained delta ferrite in alloyed steels becomes complex due to additional signal changes due to the Curie point, oxidation, and the rate (and therefore the magnitude) of transformation. However, these types of microstructural changes have been shown to invoke strong magnetic and electromagnetic (EM) responses; therefore, simultaneous EM measurements can provide additional complementary data which can help to emphasise or deconvolute these complex signals and develop a more complete understanding of certain metallurgical phenomena. This paper discusses how a DSC machine has been modified to incorporate an EM sensor consisting of two copper coils printed onto either side of a ceramic substrate, with one coil acting as a transmitter and the other as a receiver. The coil is interfaced with a custom-built data acquisition system, which provides current to the transmit coil, records signals from the receive coil, and is controlled by a graphical user interface which allows the user to select multiple excitation frequencies. The equipment has a useable frequency range of approximately 1–100 kHz and outputs phase and magnitude readings at a rate of approximately 50 samples per second. Simultaneous DSC-EM measurements were performed on a nickel sample up to a temperature of 600 °C, with the reversable ferromagnetic to paramagnetic transition in the nickel sample invoking a clear EM response. The results show that the combined DSC-EM apparatus has the potential to provide a powerful tool for the analysis of thermally induced microstructural changes in metals, feeding into research on steel production, development of magnetic and conductive materials, and many more areas. [ABSTRACT FROM AUTHOR]

The integration of automation equipment with intelligence has undoubtedly become the trend in the automation industry. In this regard, automated optical inspection (AOI) application, with advantages such as not being limited by working hours, rapid inspection, and low labor costs, are applied in the automation field. For mass production scenarios, AOI can address the allocation of operators, learning costs, and deficiencies in production efficiency. In this study, we adopted the You Only Look Once version 7 (YOLOv7) architecture for the AOI system to conduct sample inspections. At the same time, generative adversarial networks (GANs) were used to expand the sample database. The Ethernet communication architecture was also employed to integrate equipment such as a six-axis robotic arm, programmable logic controller (PLC), and an industrial computer. This integration gave the system intelligent functions such as automatic scheduling, production report statistics, and real-time monitoring. A graphical user interface was also designed to reduce personnel learning costs, simplify operations, and enhance equipment uptime. Ultimately, through the training of the YOLOv7 detection model, we achieved excellent detection results. The detection precision reached 91%, and the mean average precision (mAP@0.5) reached 83%. This confirms the value of using GAN technology to expand the AOI sample database, especially when there is a shortage of samples in the early stages of production. This high accuracy not only helps improve the accuracy of AOI detection but also enhances the production efficiency of the intelligent processing system. [ABSTRACT FROM AUTHOR]

Krillscan software was developed to automatically process echosounder data and achieve an accelerated and transparent analysis of backscatter data that allows calculation of target biomass. Herein, the fishery for Antarctic krill (Euphausia superba, Henceforth Krill) was used as a case study to develop the approach. Implementation of a sustainable management strategy for the krill fishery is complicated by a lack of regularly updated krill abundance data on spatiotemporal scales of the fishery. To increase krill biomass data availability, automatic echosounder data processing and swarm detection software was tested against traditional manual scrutinization with LSSS software and agreed with only minor offsets in estimated nautical area scattering coefficients. In addition to automatic processing and data transfer, Krillscan also has a graphical user interface to supervise automatic krill swarm detection. Echogram size can be compressed up to 100 times and raw data are processed faster than generated, thereby enabling near‐real time analysis and data transfer. Compressed data can be transmitted online to allow fishing vessels to conduct surveys without having scientific personnel with special expertise on board. [ABSTRACT FROM AUTHOR]

Molecular docking is by far the most preferred approach in structure‐based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI‐based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co‐crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK. [ABSTRACT FROM AUTHOR]

StreamSAXS is a Python‐based small‐ and wide‐angle X‐ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user‐friendly tool for analysis of both batch data files and real‐time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug‐in framework, which enables developers to extend the built‐in workflow tasks. Another feature is the support for both already acquired and real‐time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large‐scale acquisition systems for end‐to‐end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios. [ABSTRACT FROM AUTHOR]

Considering the assembly as a multi-span, continuous beam-column, a system of "three moment equations" can be derived to compute side force components as well as all reactive forces, deflection, bending moment, and slope. Solving high-order nonlinear equations involves programming and debugging, which can be time-consuming. Moreover, many methods for solving such equations may not necessarily yield correct results. However, the Python open-source library "sympy" simplifies this process by eliminating the need for complex programming and debugging. It only requires the necessary equation information to quickly solve the high-order nonlinear equations. Additionally, C# provides the capability to rapidly create graphical user interfaces, enhancing the solution's usability. By utilizing Python.NET to call Python code, it reduces the programming workload and effectively addresses the problem of solving a system of three moment equations. [ABSTRACT FROM AUTHOR]

Enzymes are biological catalysts that accelerate chemical reactions by reducing their activation energy. Enzymes specific environmental conditions to function optimally. Additive molecules and compounds, such as organic solvents, ionic liquids, and deep eutectic solvents, have crucial effects on enzyme behavior by changing activity and stability. However, finding and testing different additives is an expensive process that requires specialists, laboratory equipment, and chemical compounds. Machine learning, which has been present in all fields of science and technology in recent years, is one of the ways to find a suitable additive for our desired enzyme without doing a time-consuming experimental process. In this manuscript, we introduce ADDZYME, a machine learning-based software, to predict the effect of additives on enzyme activity. ADDZYME utilizes an ensemble of extremely randomized trees models alongside physicochemical descriptors to make a prediction. To ease usage, ADDZYME is accompanied by a graphical user interface. ADDZYME is freely available on www.github.com/miladrayka/addzyme for further experiments. [ABSTRACT FROM AUTHOR]

Pupil size change is a widely adopted, sensitive indicator for sensory and cognitive processes. However, the interpretation of these changes is complicated by the influence of multiple low-level effects, such as brightness or contrast changes, posing challenges to applying pupillometry outside of extremely controlled settings. Building on and extending previous models, we here introduce Open Dynamic Pupil Size Modeling (Open-DPSM), an open-source toolkit to model pupil size changes to dynamically changing visual inputs using a convolution approach. Open-DPSM incorporates three key steps: (1) Modeling pupillary responses to both luminance and contrast changes; (2) Weighing of the distinct contributions of visual events across the visual field on pupil size change; and (3) Incorporating gaze-contingent visual event extraction and modeling. These steps improve the prediction of pupil size changes beyond the here-evaluated benchmarks. Open-DPSM provides Python functions, as well as a graphical user interface (GUI), enabling the extension of its applications to versatile scenarios and adaptations to individualized needs. By obtaining a predicted pupil trace using video and eye-tracking data, users can mitigate the effects of low-level features by subtracting the predicted trace or assess the efficacy of the low-level feature manipulations a priori by comparing estimated traces across conditions. [ABSTRACT FROM AUTHOR]

Modeling the response of gamma detectors has long been a challenge within the nuclear community. Significant research has been conducted to digitally replicate instruments that can cost over USD 100,000 and are difficult to operate outside of a laboratory setting. The large cost and availability prevent some from making use of such equipment. Subsequently, there have been multiple attempts to create cost-effective codes that replicate the response of sodium-iodide and high-purity germanium detectors for data derivation related to gamma-ray interaction with matter. While robust programs do exist, they are often subject to export controls and/or they are not intuitive to use. Through the use of hybrid Monte Carlo methods, MATLAB can be used to produce a fast first-order response of various gamma-ray detectors. The combination of a graphical user interface with a numerical-based script allows for open-source and intuitive code. When benchmarked with experimental data from Co-60, Cs-137, and Na-22, the code can numerically calculate a response comparable to experimental and industry-standard response codes. Evidence supports both savings in computational requirements and the inclusion of an intuitive user experience that does not heavily compromise data when compared to other standard codes, such as MCNP and GADRAS, or experimental results. When the application is installed on a Dell Intel i7 computer with 16 cores, the average time to simulate the benchmarked isotopes is 0.26 s. Installation on an HP Intel i7 four-core machine runs the same isotopes in 1.63 s. The results indicate that simple gamma detectors can be modeled in an open-source format. The anticipation for the MATLAB application is to be a tool that can be easily accessible and provide datasets for use in an academic setting requiring gamma-ray detectors. Ultimately, this article provides evidence that hybrid Monte Carlo codes in an open-source format can benefit the nuclear community in both computational time and up-front cost for access. [ABSTRACT FROM AUTHOR]

Stroke is the main cause of disability among adults. Decision-making in stroke rehabilitation is increasingly complex; therefore, the use of decision support systems by healthcare providers is becoming a necessity. However, there is a significant lack of software for the management of post-stroke telerehabilitation (TR). This paper presents the results of the developed software "TeleRehab" to support the decision-making of clinicians and healthcare providers in post-stroke TR. We designed a Python-based software with a graphical user interface to manage post-stroke TR. We searched Scopus, ScienceDirect, and PubMed databases to obtain research papers with results of clinical trials for post-stroke TR and to form the knowledge base of the software. The findings show that TeleRehab suggests recommendations for TR to provide practitioners with optimal and real-time support. We observed feasible outcomes of the software based on synthetic data of patients with balance problems, spatial neglect, and upper and lower extremities dysfunctions. Also, the software demonstrated excellent usability and acceptability scores among healthcare professionals. [ABSTRACT FROM AUTHOR]

The double sampling plan (DSP) is a generalized version of the single sampling plan (SSP) that provides several advantages, such as reduced sample size, increased discriminatory power, and better communication between producers and consumers. This study proposes a DSP for truncated life tests (TLT-DSP) using a two-parameter Lindley distribution. The proposed TLT-DSP's parameters are determined by a mathematical model designed to minimize the average sample number while fulfilling two constraints related to predefined quality levels and tolerated risks. Performance measures of the sampling plans are investigated to evaluate and compare their efficiency and effectiveness. Our results demonstrate that the proposed DSP is more efficient than the traditional SSP, especially in cases where the lot's quality is excellent or poor, and provides necessary protection to both parties involved. Additionally, two examples are presented, discussed and illustrated through a graphical user interface designed to validate the practicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Two novel web apps for W7-X are introduced: Profile Cooker and Power House. They are designed to streamline the workflow of profile fitting and power balance analysis while offering a graphical user interface that works in any common browser. This allows us to compile a comprehensive database of experimental power balance results. All fitting functions available in Profile Cooker are presented and compared on the basis of example profiles. The power balance equation assumed in Power House is established and its individual terms are discussed. The main focus of the power balance analysis is on the turbulent transport coefficients. A model for quick calculation of neutral beam power deposition based on experimental profiles is presented. Neoclassical root transition poses an issue for power balance analysis due to the uncertainty of the radial electric field. A global, neoclassical simulation with the code EUTERPE is performed for a set of experimental profiles to gain an understanding of the neoclassical root transition. [ABSTRACT FROM AUTHOR]

Haxe is a general purpose, object-oriented programming language supporting syntactic macros. The Haxe compiler is well known for its ability to translate the source code of Haxe programs into the source code of a variety of other programming languages including Java, C++, JavaScript, and Python. Although Haxe is more and more used for a variety of purposes, including games, it has not yet attracted much attention from bioinformaticians. This is surprising, as Haxe allows generating different versions of the same program (e.g. a graphical user interface version in JavaScript running in a web browser for beginners and a command-line version in C++ or Python for increased performance) while maintaining a single code, a feature that should be of interest for many bioinformatic applications. To demonstrate the usefulness of Haxe in bioinformatics, we present here the case story of the program SeqPHASE, written originally in Perl (with a CGI version running on a server) and published in 2010. As Perl+CGI is not desirable anymore for security purposes, we decided to rewrite the SeqPHASE program in Haxe and to host it at Github Pages (https://eeg-ebe.github.io/SeqPHASE), thereby alleviating the need to configure and maintain a dedicated server. Using SeqPHASE as an example, we discuss the advantages and disadvantages of Haxe's source code conversion functionality when it comes to implementing bioinformatic software. [ABSTRACT FROM AUTHOR]

A multiscale feature fusion of Efficient‐Residual Net is proposed for classifying tumors on brain Magnetic resonance images with solid or cystic masses, inadequate borders, unpredictable cystic and necrotic regions, and variable heterogeneity. Therefore, in this research, Efficient‐Residual Net is proposed by efficaciously amalgamating features of two Deep Convolution Neural Networks—ResNet50 and EffficientNetB0. The skip connections in ResNet50 have reduced the chances of vanishing gradient and overfitting problems considerably thus learning of a higher number of features from input MR images. In addition, EffficientNetB0 uses a compound scaling coefficient for uniformly scaling the dimensions of the network such as depth, width, and resolution. The hybrid model has improved classification results on brain tumors with similar morphology and is tested on three internet repository datasets, namely, Kaggle, BraTS 2018, BraTS 2021, and real‐time dataset from Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh. It is observed that the proposed system delivers an overall accuracy of 96.40%, 97.59%, 97.75%, and 97.99% on the four datasets, respectively. The proposed hybrid methodology has given assuring results of 98%–99% of other statistical such parameters as precision, negatively predicted values, and F1 score. The cloud‐based web page is also created using the Django framework in Python programming language for accurate prediction and classification of different types of brain tumors. [ABSTRACT FROM AUTHOR]

The production of municipal solid waste (MSW) has led to an unprecedented level of environmental pollution, worsening the global challenges posed by climate change. Researchers and policymakers have recently made significant strides in the field of sustainable and renewable energy sources, which are viable from technological, environmental, and economic perspectives. Consequently, the waste-to-energy programs enhance nations' socioeconomic status while positively impacting the environment. To predict the higher heating value (HHV) of MSW fuel based on carbon, hydrogen, oxygen, nitrogen, and sulfur content, the current study introduces a Gradient Boosting Regression Tree (GBRT) model optimized with the Slime Mold Algorithm (SMA). This model was evaluated using an additional 50 data points after being trained with 202 MSW biomass data points. The performance of the model was assessed using three metrics: root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The results indicated that our model outperformed previously developed models in terms of accuracy and reliability. Additionally, a graphical user interface (GUI) was developed to facilitate the practical application of the model, allowing users to easily input data and receive predictions on the enthalpy of the combustion of MSW fuel. [ABSTRACT FROM AUTHOR]

Overbreak is a prevalent and detrimental phenomenon in hard rock tunnel excavation that escalates construction costs and compromises tunnel structural stability. Therefore, accurate prediction of overbreak during excavation is significant for cost reduction and risk mitigation. This study introduces an efficient metaheuristic method, namely the honey badger algorithm (HBA), for optimizing the light gradient boosting machine (LGBM) model, and proposes an explicit equation for the prediction of overbreak based on gene expression programming (GEP) technology. Utilizing a dataset comprising 523 overbreak cases collected from the Huxitai (HXT) tunnel in China, this study conducts the modeling of overbreak prediction and assesses the model's performance through various metrics and non-parametric statistical tests. The results indicate that the HBA-LGBM hybrid model developed herein achieves the highest coefficient of determination (R2) of 0.9472 among the tested models, while the GEP model reaches a R2 of 0.9275. Clearly, the overbreak prediction model constructed in this paper shows superior overall performance, and the comparative analysis of multiple models also highlights HBA's significant advantage in mitigating overfitting. Lastly, various interpretive techniques were applied to analyze the impact of input variables on overbreak, providing insights into the decision-making principles of the predictive model from both global and individual case levels. The analysis revealed that the total charge (TC) and powder factor (PF) are the most influential blasting parameters on overbreak occurrence. Furthermore, a graphical user interface for testing purposes was developed and showcased. In summary, the overbreak models established in this study effectively predict tunnel overbreak caused by blasting, demonstrating superior predictive performance and interpretability compared to previous efforts. Highlights: An efficient hybrid ensemble model based on HBA is proposed for predicting tunnel overbreak caused by blasting. An explicit high-accuracy prediction equation for tunnel overbreak is constructed using GEP technology. The model performance is comprehensively evaluated through various metrics and non-parametric statistical tests. The constructed model demonstrates excellent generalization capabilities and interpretability. [ABSTRACT FROM AUTHOR]

The chloride migration coefficient (CMC) of concrete is crucial for evaluating its durability. This study develops ensemble models to predict the CMC of concrete, addressing the limitations of traditional, labor-intensive laboratory tests. We developed three ensemble models: an inverse variance-based model, an Artificial Neural Network (ANN)-based model, and a tree-based model using the random forest regression algorithm. These models were trained on a dataset comprising 843 concrete mix proportions from existing literature. Results indicate that ensemble models outperform single models such as ANN and Support Vector Regression (SVR) in predicting CMC, with the combined random forest and ANN model showing the highest accuracy. Sensitivity analysis using Shapley Additive Explanations (SHAP) reveals that the CMC is most influenced by the water-to-cement ratio and curing age. Additionally, we designed a graphical user interface (GUI) to facilitate the practical application of our models. This research offers a robust methodology for evaluating concrete durability and potential for extending the prediction to other concrete properties. [ABSTRACT FROM AUTHOR]

Segmenting autophagic bodies in yeast TEM images is a key technique for measuring changes in autophagosome size and number in order to better understand macroautophagy/autophagy. Manual segmentation of these images can be very time consuming, particularly because hundreds of images are needed for accurate measurements. Here we describe a validated Cellpose 2.0 model that can segment these images with accuracy comparable to that of human experts. This model can be used for fully automated segmentation, eliminating the need for manual body outlining, or for model-assisted segmentation, which allows human oversight but is still five times as fast as the current manual method. The model is specific to segmentation of autophagic bodies in yeast TEM images, but researchers working in other systems can use a similar process to generate their own Cellpose 2.0 models to attempt automated segmentations. Our model and instructions for its use are presented here for the autophagy community. Abbreviations: AB, autophagic body; AvP, average precision; GUI, graphical user interface; IoU, intersection over union; MVB, multivesicular body; ROI, region of interest; TEM, transmission electron microscopy; WT,wild type. [ABSTRACT FROM AUTHOR]

Coulomb's law and Gauss's law are two fundamental laws in electrostatics that involve electric charges and electric fields. These concepts that are not visible physically in real life can be challenging for students to understand, especially when it involves complex charge distributions. This paper presents a MATLAB‐based graphical user interface (GUI) to visualise the concept of Coulomb's law and Gauss's law as well as a serious snake game to reinforce users' knowledge. This MATLAB GUI features a diverse range of 3‐dimensional (3D) visualisations illustrating electric field attributes such as electric force, charge distribution and electric flux. These visualisations dynamically adapt to user‐defined parameters, offering a rich and varied exploration of the electric field's characteristics without confining the scope to a specific count of models. In Coulomb's law section, the GUI plots the electric forces in 3‐D for dipole, three‐point charges, and unlimited charge. The Gauss's law section consists of windows for illustration of the fundamentals of Gauss's law for the electric field, and the illustration for the application of Gauss's law in finding the electric field for a point charge, infinite line charge and infinite plane charge. The serious snake game developed in this work allows students to engage in quizzes related to Coulomb's law and Gauss's law to serve as a tool for reinforcing their learning outcomes. The MATLAB‐based GUI was chosen as the platform due to its excellent visualisation capabilities, ease of programming and capability to act as a powerful tool to enhance the learning of Coulomb's law and Gauss's law for students. [ABSTRACT FROM AUTHOR]

What drives recessions and expansions? Since it was introduced in 2007, there have been hundreds of business cycle accounting (BCA) exercises, a procedure aimed at identifying classes of models that hold quantitative promise to explain economic fluctuations. This paper contributes with a software—a graphical user interface that allows practitioners to perform BCA exercises with minimal effort—and exemplifies the procedure by studying the U.S. recessions in 1973 and 1990 and reflecting upon the critiques BCA has been subject to. We look into the many equivalence theorems that the literature has produced and that allow BCA practitioners to identify the theories that are quantitatively relevant for the economic period under study. The methodological extensions that have been brought forth since BCA's original inception are addressed as well as conclusions regarding the relative contribution of each wedge: GDP and investment are usually driven by an efficiency wedge, hours worked are closely related to the labor wedge and, in an open economy extension, the investment wedge helps to explain country risk spreads on international bonds. Finally, larger changes in interest rates and currency crises are usually associated with the investment and/or the labor wedge. [ABSTRACT FROM AUTHOR]

The process of deep‐frying brings about significant physicochemical changes in the color and moisture content of foods, influenced by heat and mass transfer phenomena. Achieving consistent quality in fried foods, such as nuggets, fries, and chicken pies, is challenging, highlighting the importance of predicting these changes. This study delves into the evolving dynamics of color and moisture content, specifically in French fries during deep frying. To model these changes, the study employs a first‐order kinetic equation for color and moisture content, utilizing numerical solutions like the Runge–Kutta fourth‐order method as well as the Nelder–Mead algorithm in MATLAB. The Arrhenius equation plays a key role in this model. The modified model is compared against an existing model using the root mean square error (RMSE) and Akaike information criterion (AIC). Note that the investigation evaluates how oil temperature (at 150, 170, and 190°C) as well as sample thickness (at 5, 10, and 15 mm) impact the French fries' moisture and color content during frying. Results indicate that the modified model, with its improved accuracy and lower RMSE and AIC values compared to the existing model, provides a more reliable tool for understanding the frying process. Consequently, the inclusion of a user‐friendly graphical interface (GUI) makes this modeling approach accessible even to those with limited mathematical or programming expertise, benefiting professionals seeking more profound insights into the frying process. Practical applications: The graphical user interface (GUI) for predicting deep‐fried food quality presented in this study holds broad industrial applications. It serves as a pivotal tool in the food industry for ensuring consistent quality across products like fries and nuggets, enabling manufacturers to optimize frying processes. Fast‐food chains can use the GUI to minimize costs and energy consumption while maintaining product quality. In addition, it aids in equipment calibration, becoming an invaluable asset for operators seeking optimal performance and longevity in deep‐frying equipment. Culinary schools benefit from this GUI as an educational tool, offering aspiring chefs a practical understanding of deep‐frying science. Researchers and food scientists can accelerate R&D cycles by efficiently assessing the impact of variables. SMEs, regulatory bodies, and food retailers find utility in the GUI for quality control, compliance, and consumer education, collectively contributing to industry transparency, sustainability, and improved global health outcomes. [ABSTRACT FROM AUTHOR]

In today's digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m3). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m3. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R2, i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model's capabilities within the domain of civil engineering. [ABSTRACT FROM AUTHOR]

Data plots of daughter against parent concentration (D–P plots) are a potential tool for analyzing low-temperature thermochronology, similar to isochron plots in radioisotopic geochronology. Their purposes are to visualize the main term of the radiometric age equation – the daughter–parent ratio – and to inspect the daughter–parent relationship for anomalies indicating influences of geological processes or analytical bias. The main advantages of the D–P plot over other data analysis tools are (1) its ability to detect systematic offsets in D and P concentrations, (2) its unambiguous representation of radiation-damage-dependent daughter retention, and (3) the possibility to analyze potential age outliers. Despite these benefits, the D–P plot is currently not used for analyzing low-temperature thermochronology data, e.g., from fission-track, (U–Th) / He, or zircon Raman dating. We present a simple, decision-tree-based classification for daughter–parent relationships based on the D–P plot that places a dataset into one of seven classes: linear relationship with zero intercept, cluster, linear relationship with systematic offset, nonlinear relationship, several age populations, scattered data, and inverse relationship. Assigning a class to a dataset enables choosing further data analysis steps and how to report a sample age, e.g., as a pooled, central, or isochron age or a range of ages. This classification scheme aims at facilitating thermochronological data analysis and making decisions more transparent. We demonstrate the proposed procedure by analyzing published datasets from a variety of geological settings and thermochronometers and introduce Incaplot, which is graphical user interface software that we developed to facilitate D–P plotting of thermochronology data. [ABSTRACT FROM AUTHOR]

MuscleX is an integrated, open‐source computer software suite for data reduction of X‐ray fiber diffraction patterns from striated muscle and other fibrous systems. It is written in Python and runs on Linux, Microsoft Windows or macOS. Most modules can be run either from a graphical user interface or in a 'headless mode' from the command line, suitable for incorporation into beamline control systems. Here, we provide an overview of the general structure of the MuscleX software package and describe the specific features of the individual modules as well as examples of applications. [ABSTRACT FROM AUTHOR]

This paper presents the design of an artificial neural network (ANN) and proportional integral derivative (PID) controller using particle swarm optimization (PSO) for Boeing 747‐400 aircraft pitch control (APC). The combinations of disturbance, open loop unstable and nonlinear dynamics are major problems in a Boeing 747‐400 commercial aircraft. This paper investigates the control mechanism of pitch angle control of Boeing 747‐400 with small disturbance theory linearization methods and ANN based non‐linear controllers. A PID controller is tuned by PSO, whereas the PID is tuned by graphical user interface (GUI) when compared with an ANN controller. The controller for this system was designed using an ANN controller and PID tuned using a recent optimization technique such as the PSO method with integral square error (ISE) as an objective function. A comparative study of the time domain performances of the pitch control of the Boeing 747‐400 commercial aircraft was presented. The ANN controller outperformed the PID‐PSO and PID‐GUI controllers in terms of system performance, including rising time (tr), settling time (ts), percentage overshoot (percent OS), and steady state error, across various elevator deflection angles. Basically, the percentage overshoot and steady state error were 0% and 0 respectively, indicating that the ANN controller achieved an improvement of 100%. Various parameters were compared with the PID‐GUI, PID‐PSO, and ANN controllers for pitch control of the Boeing 747‐400 air craft. The ANN controller architecture comprises of two input neurons, two hidden layer neurons, and one output layer neuron. The simulation was performed using Matlab/Simulink. The results show that the PID‐PSO controller was improved by the ANN controller and the performance specifications of the aircraft obtained by the ANN controller were satisfactory. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new algorithm to model and characterize an autonomous high‐power rocket using an indigenously developed graphical user interface (GUI) platform. This platform features a newly devised app, termed as THIEC Rocketry App which embeds the simulation based analysis to determine the design parameters of the rocket, required for a vertical flight. A solid propellant using potassium nitrate and sucrose, also known as rocket‐candy, is considered for the GUI development. The GUI facilitates the designer to specify the desired flight parameters for the rocket propulsion system. Various characteristic plots for visualization and analysis are made available in GUI. The obtained parameters from the GUI are then utilized in computer‐aided designing (CAD) for further identification of geometrical parameters like inertia tensor, center of gravity (CG) and center of pressure (CP). The mathematical control amenable model of the rocket is then developed using first principles so as to achieve an altitude up to 3 km. The overall system represents a complex nonlinear multi‐input multi‐output (MIMO) dynamics, having six degrees of freedom. The Newton‐Euler formulation is employed to develop the equations of motion. The attitude control using canards is analyzed via simulations for the complete flight path ‐ the boost and coast flights. Finally, the developed GUI based model is validated by practically manufacturing the components of the propulsion system for the small‐scale high‐power rocket. The proposed model will create the pathway for the development of some robust model‐based control schemes for such autonomous rockets in future. [ABSTRACT FROM AUTHOR]