User-friendly Graphical Interface Research Articles

Generic Graphical User Interface for CBIR Framework

Abstract Content-based image retrieval system (CBIR) is a well-known and widely used system for image retrieval. Most of the current CBIR systems are either command-based or specific to applications. However, due to the availability of a good computing facility, a graphical way of retrieving images may prove to be very useful for both industrial and research purposes. This paper proposes a generic and user-friendly graphical user interface (GUI) for CBIR framework. With the proposed GUI, any user with or without knowledge of CBIR can operate and retrieve images of their choice among a huge number of images. The GUI gives a vast range of facilities for selecting options. The proposed GUI is implemented and verified on a well-known image database.

Automation of Seating Plan for Examinations using Round-Robin Policy

Abstract: This paper introduces a Python-based Graphical User Interface (GUI) application designed to automate the seating arrangement process for examinations, specifically catering to the needs of examination departments. Leveraging the openpyxl and pandas libraries for Excel file manipulation and creating Dataframes the application aims to replace the laborious manual process traditionally employed by colleges. The main objective is to reduce the time and effort invested by examination departments in seating arrangement tasks. The user-friendly GUI simplifies data input, while the application's algorithm automates seating assignments, ensuring that no two students writing same exam can’t sit in same bench in optimal manner using excel and python . Results demonstrate a significant reduction in time spent on seating arrangement, with outputs including detailed room-wise seating sheets and comprehensive summaries listing roll numbers by regulation and branch. This innovative approach not only streamlines examination management but also enhances accuracy and efficiency.From this GUI we can add, edit and delete the halls in which the exams are being conducted.Further we input the excel sheet in which the roll numbers of students were there as per their branch and regulation as input and genrates formatted excel sheets that generate seating arrangement .Formatting the font and size of rows and columns and wrapping the columns when required are done automatically

DeepLOKI- a deep learning based approach to identify zooplankton taxa on high-resolution images from the optical plankton recorder LOKI

Zooplankton play a crucial role in the ocean’s ecology, as they form a foundational component in the food chain by consuming phytoplankton or other zooplankton, supporting various marine species and influencing nutrient cycling. The vertical distribution of zooplankton in the ocean is patchy, and its relation to hydrographical conditions cannot be fully deciphered using traditional net casts due to the large depth intervals sampled. The Lightframe On-sight Keyspecies Investigation (LOKI) concentrates zooplankton with a net that leads to a flow-through chamber with a camera taking images. These high-resolution images allow for the determination of zooplankton taxa, often even to genus or species level, and, in the case of copepods, developmental stages. Each cruise produces a substantial volume of images, ideally requiring onboard analysis, which presently consumes a significant amount of time and necessitates internet connectivity to access the EcoTaxa Web service. To enhance the analyses, we developed an AI-based software framework named DeepLOKI, utilizing Deep Transfer Learning with a Convolution Neural Network Backbone. Our DeepLOKI can be applied directly on board. We trained and validated the model on pre-labeled images from four cruises, while images from a fifth cruise were used for testing. The best-performing model, utilizing the self-supervised pre-trained ResNet18 Backbone, achieved a notable average classification accuracy of 83.9%, surpassing the regularly and frequently used method EcoTaxa (default) in this field by a factor of two. In summary, we developed a tool for pre-sorting high-resolution black and white zooplankton images with high accuracy, which will simplify and quicken the final annotation process. In addition, we provide a user-friendly graphical interface for the DeepLOKI framework for efficient and concise processes leading up to the classification stage. Moreover, performing latent space analysis on the self-supervised pre-trained ResNet18 Backbone could prove advantageous in identifying anomalies such as deviations in image parameter settings. This, in turn, enhances the quality control of the data. Our methodology remains agnostic to the specific imaging end system used, such as Loki, UVP, or ZooScan, as long as there is a sufficient amount of appropriately labeled data available to enable effective task performance by our algorithms.

Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data

BackgroundMicrobiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls.ResultsWe benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108–103 cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera.ConclusionsMicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

Joint 2D–3D cross-pseudo supervision for carotid vessel wall segmentation

IntroductionThe segmentation of the carotid vessel wall using black-blood magnetic resonance images was a crucial step in the diagnosis of atherosclerosis. The objective was to accurately isolate the region between the artery lumen and outer wall. Although supervised learning methods achieved remarkable accuracy in vessel segmentation, their effectiveness remained limited due to their reliance on extensive labeled data and human intervention. Furthermore, when confronted with three-dimensional datasets featuring insufficient and discontinuous label data, these learning-based approaches could lose their efficacy. In this paper, we proposed a novel Joint 2D–3D Cross-Pseudo Supervision (JCPS) method for accurate carotid vessel wall segmentation.MethodsIn this study, a vascular center-of-gravity positioning module was developed to automatically estimate the region of blood vessels. To achieve accurate segmentation, we proposed a joint 2D–3D semi-supervised network to model the three-dimensional continuity of vascular structure. In addition, a novel loss function tailored for vessel segmentation was introduced, consisting of four components: supervision loss, cross-pseudo supervision loss, pseudo label supervision loss, and continuous supervision loss, all aimed at ensuring the accuracy and continuity of the vessel structure. In what followed, we also built up a user-friendly Graphical User Interface based on our JCPS method for end-users.ResultsOur proposed JCPS method was evaluated using the Carotid Artery Vessel Wall Segmentation Challenge dataset to assess its performance. The experimental results clearly indicated that our approach surpassed the top 10 methods on the leaderboard, resulting in a significant enhancement in segmentation accuracy. Specifically, we achieved an average Dice similarity coefficient increase from 0.775 to 0.806 and an average quantitative score improvement from 0.837 to 0.850, demonstrating the effectiveness of our proposed JCPS method for carotid artery vessel wall segmentation.ConclusionThe experimental results suggested that the JCPS method had a high level of generalization performance by producing pseudo labels that were comparable with software annotations for data-imbalanced segmentation tasks.

Evaluation of Shear Stress in Soils Stabilized with Biofuel Co-Products via Regression Analysis Methods

In recent years, the employment of artificial neural networks (ANNs) has risen in various engineering fields. ANNs have been applied to a range of geotechnical engineering problems and have shown promising outcomes. The aim of this article is to enhance the effectiveness of estimating unfamiliar intermediate values from existing shear stress data by employing ANNs. Artificial neural network modelling was undertaken through the Regression Learner program that is integrated with the Matlab 2023a software package. This program offers a user-friendly graphical interface for developing AI models absent of the need for any coding. The validation and training of the ANNs were executed by relying on shear box test data which had been conducted at the Geotechnical Laboratory situated at Iowa State University. The objective of these experiments was to explore the potential of biofuel co-products (BCPs) in soil stabilization. The data should be structured with input and output parameters in columns and samples in rows. The dataset comprises a 216 × 6 matrix. The data columns provide information on soil type (pure soil—unadulterated; and 12% BCP-adulterated soil), time (1, 7, and 28 days), normal stress (0.069-DS10, 0.138-DS20, and 0.207-DS30 MPa), moisture content (OMC−4%, OMC%, and OMC+4%), and corresponding shear stress (σ, MPa) values. The AI predictions for the test data output provide an outstanding R2 score of 0.94. This indicates that employing ANN to teach shear test data facilitates gaining a large quantity of data more efficiently, with fewer experiments and in less time. Such an approach seems encouraging for geotechnical engineering.

Harmonising knowledge for safer materials via the “NanoCommons” Knowledge Base

In mediaeval Europe, the term “commons” described the way that communities managed land that was held “in common” and provided a clear set of rules for how this “common land” was used and developed by, and for, the community. Similarly, as we move towards an increasingly knowledge-based society where data is the new oil, new approaches to sharing and jointly owning publicly funded research data are needed to maximise its added value. Such common management approaches will extend the data’s useful life and facilitate its reuse for a range of additional purposes, from modelling, to meta-analysis to regulatory risk assessment as examples relevant to nanosafety data. This “commons” approach to nanosafety data and nanoinformatics infrastructure provision, co-development, and maintenance is at the heart of the “NanoCommons” project and underpins its post-funding transition to providing a basis on which other initiatives and projects can build. The present paper summarises part of the NanoCommons infrastructure called the NanoCommons Knowledge Base. It provides interoperability for nanosafety data sources and tools, on both semantic and technical levels. The NanoCommons Knowledge Base connects knowledge and provides both programmatic (via an Application Programming Interface) and a user-friendly graphical interface to enable (and democratise) access to state of the art tools for nanomaterials safety prediction, NMs design for safety and sustainability, and NMs risk assessment, as well. In addition, the standards and interfaces for interoperability, e.g., file templates to contribute data to the NanoCommons, are described, and a snapshot of the range and breadth of nanoinformatics tools and models that have already been integrated are presented Finally, we demonstrate how the NanoCommons Knowledge Base can support users in the FAIRification of their experimental workflows and how the NanoCommons Knowledge Base itself has progressed towards richer compliance with the FAIR principles.

SRplot: A free online platform for data visualization and graphing.

Graphics are widely used to provide summarization of complex data in scientific publications. Although there are many tools available for drawing graphics, their use is limited by programming skills, costs, and platform specificities. Here, we presented a freely accessible easy-to-use web server named SRplot that integrated more than a hundred of commonly used data visualization and graphing functions together. It can be run easily using all Web browsers and there are no strong requirements on the computing power of users' machines. With a user-friendly graphical interface, users can simply paste the contents of the input file into the text box according to the defined file format. Modification operations can be easily performed, and graphs can be generated in real-time. The resulting graphs can be easily downloaded in bitmap (PNG or TIFF) or vector (PDF or SVG) format in publication quality. The website is updated promptly and continuously. Functions in SRplot have been improved, optimized and updated depend on feedback and suggestions from users. The graphs prepared with SRplot have been featured in more than five hundred peer-reviewed publications. The SRplot web server is now freely available at http://www.bioinformatics.com.cn/SRplot.

306 Shinyanimalcv: Interactive Web Application for Object Detection and Three-Dimensional Visualization of Animals Using Computer Vision

Abstract Precision livestock farming applies integrated sensors and information technology to provide individualized animal care in a timely manner. A computer vision system, a non-intrusive and cost-efficient sensor technology, may be useful for monitoring animals to support farm management decisions. While the advancement in camera sensors provides a great opportunity for producers to improve animal health and welfare sustainably, the limited availability of user-friendly image data processing software tools substantially hinders the implementation of computer vision in livestock production systems. The objective of this study was to develop ShinyAnimalCV, which is a Shiny-based interactive animal computer vision web application. This software tool offers a user-friendly graphical user interface for object detection and three-dimensional visualization. The object detection module employs the Mask-RCNN to precisely segment the masks (regions) of the focal animals from two-dimensional images. A Python-based computer vision library OpenCV was used to draw minimum bounding boxes covering the segmented masks for object detection. The three-dimensional visualization module takes the depth map file captured from a top-view three-dimensional camera as an input, which contains the numerical distances between the camera and the objects (animals and background). The depth map file was first converted to a heatmap image, followed by identifying and segmenting the animal from the background using the Mask-RCNN model. The object detection module returns detection results, including the detected animal’s location, class/type, and morphological traits (e.g., body length and width). The visualization module generates a segmented mask to extract the height of the animal from the depth map file, which can be used to interactively visualize the three-dimensional surface of the animal and estimate its morphological traits, including length, width, height, and volume. By integrating these two modules into R Shiny, we deployed ShinyAnimalCV on a cloud server with pre-trained Mask-RCNN models using pig and cattle data to allow users to upload custom data and perform object detection and three-dimensional surface visualization. The features extracted by ShinyAnimalCV are expected to be useful for performing animal identification, feed intake monitoring, body weight predictions, and body condition score estimations. We conclude that the newly developed ShinyAnimalCV could facilitate the application of computer vision in the animal science community.

Unveiling the Veiled: Unmasking Fileless Malware through Memory Forensics and Machine Learning

In recent times, significant advancements within the realm of malware development have dramatically reshaped the entire landscape. The reasons for targeting a system have undergone a complete transformation, shifting from file-based to fileless malware.Fileless malware poses a significant cybersecurity threat, challenging traditional detection methods. This research introduces an innovative approach that combines memory forensics and machine learning to effectively detect and mitigate fileless malware. By analyzing volatile memory and leveraging machine learning algorithms, our system automates detection.We employ virtual machines to capture memory snapshots and conduct thorough analysis using the Volatility framework. Among various algorithms, we have determined that the Random Forest algorithm is the most effective, achieving an impressive overall accuracy rate of 93.33%. Specifically, it demonstrates a True Positive Rate (TPR) of 87.5% while maintaining a zero False Positive Rate (FPR) when applied to fileless malware obtained from HatchingTriage, AnyRun, VirusShare, PolySwarm, and JoESandbox datasets. To enhance user interaction, a user-friendly graphical interface is provided, and scalability and processing capabilities are optimized through Amazon Web Services.Experimental evaluations demonstrate high accuracy and efficiency in detecting fileless malware. This framework contributes to the advancement of cybersecurity, providing practical tools for detecting against evolving fileless malware threats.

ENVIRONMENTAL POLLUTION ANALYSIS AND PREDICTION OF INFLUENTIAL FACTORS: A DATA-DRIVEN INVESTIGATION

Environmental pollution is a pressing concern affecting humanity and other Earth species today. The escalation of air pollution due to heavy traffic density and industrial effluents in urban areas poses a significant threat to human life and the environment. The Air Quality Index (AQI) is employed as a crucial metric to gauge the extent of air pollution in a city. Apart from industrial and traffic emissions, meteorological factors, including wind speed, wind direction, temperature, humidity, and total precipitation, play a pivotal role in shaping a citys pollution levels. Mitigating the harm caused by air pollution is contingent on the ability to predict the factors influencing air pollutant levels. This predictive capability enables issuing advanced warnings to citizens or implementing precautionary measures for their protection. This paper focuses on forecasting temperature, humidity, wind speed, wind direction, and total precipitation in the town of Basel, Switzerland, on specific future dates, utilizing historical data maintained by local authorities. The methodology leverages Hive tools and MapReduce frameworks and employs machine learning techniques to predict future values of desired parameters. Various regression techniques, including Lasso, Linear Regression, Ridge Regression, and Polynomial Regression, were evaluated for their performance in predicting air quality factors. Polynomial Regression emerged as the preferred choice due to its superior performance. To enhance user accessibility, a user-friendly graphical interface (GUI) has been developed to input data and visualize predicted results. This study presents a comprehensive approach to address the critical issue of air pollution by providing accurate predictions of meteorological factors, thus enabling proactive measures to safeguard public health and the environment.

PACMan: A software package for automated single-cell chlorophyll fluorometry.

Microalgae, small photosynthetic unicells, are of great interest to ecology, ecotoxicology and biotechnology and there is a growing need to investigate the ability of cells to photosynthesize under variable conditions. Current strategies involve hand-operated pulse-amplitude-modulated (PAM) chlorophyll fluorimeters, which can provide detailed insights into the photophysiology of entire populations- or individual cells of microalgae but are typically limited in their throughput. To increase the throughput of a commercially available MICROSCOPY-PAM system, we present the PAM Automation Control Manager ('PACMan'), an open-source Python software package that automates image acquisition, microscopy stage control and the triggering of external hardware components. PACMan comes with a user-friendly graphical user interface and is released together with a stand-alone tool (PAMalysis) for the automated calculation of per-cell maximum quantum efficiencies (= Fv /Fm ). Using these two software packages, we successfully tracked the photophysiology of >1000 individual cells of green algae (Chlamydomonas reinhardtii) and dinoflagellates (genus Symbiodiniaceae) within custom-made microfluidic devices. Compared to the manual operation of MICROSCOPY-PAM systems, this represents a 10-fold increase in throughput. During experiments, PACMan coordinated the movement of the microscope stage and triggered the MICROSCOPY-PAM system to repeatedly capture high-quality image data across multiple positions. Finally, we analyzed single-cell Fv /Fm with the manufacturer-supplied software and PAMalysis, demonstrating a median difference <0.5% between both methods. We foresee that PACMan, and its auxiliary software package will help increase the experimental throughput in a range of microalgae studies currently relying on hand-operated MICROSCOPY-PAM technologies.

The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer

The traditional prognostic model may induce the possibility of incorrect assessment of mortality risk under the assumption of linearity. It is urgent to develop a non-linearity precise prognostic model for achieving personalized medicine in lung cancer. In our study, we develop and validate a prognostic model "Modified-DeepSurv" for patients with lung carcinoma based on deep learning and evaluate its value for prognosis, while Cox proportional hazard regression was used to develop another model "CPH." The C-index of the Modified-DeepSurv and CPH was 0.956 (95% confidence interval [CI]: 0.946-0.974) and 0.836 (95% CI: 0.774-0.896), respectively, in the training cohort, while the C-index of the Modified-DeepSurv and CPH was 0.932 (95%CI: 0.908-0.964) and 0.777 (95%CI: 0.633-0.919), respectively, in the test dataset. The Modified-DeepSurv model visualization was realized by a user-friendly graphic interface. Modified-DeepSurv can effectively predict the survival of lung cancer patients and is superior to the conventional CPH model.

VariantSurvival: a tool to identify genotype-treatment response.

Motivation: For a number of neurological diseases, such as Alzheimer's disease, amyotrophic lateral sclerosis, and many others, certain genes are known to be involved in the disease mechanism. A common question is whether a structural variant in any such gene may be related to drug response in clinical trials and how this relationship can contribute to the lifecycle of drug development. Results: To this end, we introduce VariantSurvival, a tool that identifies changes in survival relative to structural variants within target genes. VariantSurvival matches annotated structural variants with genes that are clinically relevant to neurological diseases. A Cox regression model determines the change in survival between the placebo and clinical trial groups with respect to the number of structural variants in the drug target genes. We demonstrate the functionality of our approach with the exemplary case of the SETX gene. VariantSurvival has a user-friendly and lightweight graphical user interface built on the shiny web application package.

An intelligent mushroom strain selection model based on their quality characteristics

The great versatility of mushroom production and the significant nutritional and medicinal properties of the crop make it a highly attractive product that is in continuous expansion around the world. However, its quality can be affected the combination of a large number of evaluable variables that are essential to take into account. Thus, the aim of this work was to build an intelligent model for the prediction of mushroom strains through the development of neural networks (ANNs) that takes into account the control of data processing times, with the use of the minimum possible number of parameters that define their quality control and subsequent selection. In addition, a user-friendly and intuitive graphical interface has been generated that shows the selection of the appropriate mushroom strain and may be useful for potential end-users in this field. For this purpose, 7 mushroom strains (Agaricus bisporus) defined by a total of 27 quality parameters were used (texture, colour, etc.). The results showed that, in the analysis of individual parameter combinations (Rt), the best overall accuracy achieved (OAA) was 52.43%, reaching 81.30% with the combination of four parameters (dry matter (%), crude protein (Nx4. 38. %), Fb and Wt) and 94.32% with 9 parameters (Cap diameter (mm), dry matter (%), crude protein (Nx4.38. %), ΔE, browning index (BI), Fb, Wb = Fb x Db/2, Wr and Rt). The development of this model allows for the identification of some of the most important commercial white hybrid strains of high-yielding mushrooms, while also being a useful tool for the selection of the most important parameters of interest as regards the quality and benefits of this product for the consumer.

An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water

Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.

Smart inhaler mask for portable medical gas cylinders utilizing IoT technologies

Over the past decade, there has been a significant increase in the development of wearable and smart health devices based on the Internet of Things (IoT), that enable real time remote control and monitoring of health parameters. In this context, the purpose of this article is to present the development and evaluation process of a smart inhaler mask designed for medical gas cylinders, utilizing IoT technologies. Handheld medical gas cylinders, although essential for a specific group of people, often come with a significant cost, and lack features such as dosage measurement and gas storage monitoring. Consequently, a smart inhaler mask was developed to address these limitations by enabling in-situ and real-time measurement of gas dosage, tracking the remaining gas within the cylinder, and assisting users in monitoring the overall usage of the medical gas cylinder. To facilitate the access and interpretation of this information, a newly developed and user-friendly Graphical User Interface (GUI) has been implemented in the form of a mobile application. This study outlines the evaluation process employed to verify the reliability and accuracy of the results obtained from the smart inhaler mask.

Identification and Prediction of Metastatic Tumor in Breast Using Deep Learning Techniques

Abstract: Our work aims to develop a system for breast cancer detection using Image Processing Techniques. It addresses the limitations of mammography, such as painful procedures, by introducing thermal image analysis. For mammogram analysis, morphology techniques are applied to enhance the images before feature extraction. The preprocessed image used to classify mammograms into threecategories: Normal Fatty Breast, Abnormal Fatty Breast, and Glandular Breast. In the case of thermal images, the system analyses the distribution of heat around the breast. Variations in heat levels are used to identify potential cancerous areas. Additionally, the project includes the development of a user-friendly Graphical User Interface (GUI) to enhance accessibility for users. The proposed system provides option on architectures namely CNN, VGG16 and Densenet121 using which the result is obtained.

Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase.

The μ phase is a type of hard and brittle constituent that exists in high-temperature alloys. The formation energy is a crucial thermochemical datum, and the accurate calculation of the formation energy of the μ phase contributes to the material design of high-temperature alloys. Traditional first-principles calculations demand significant computational time and resources. In this study, an innovative machine learning (ML)-based approach to accurately predict the formation energy of the μ phase is proposed. This approach involves the utilization of six algorithms and two model evaluation methods to construct the ML models. Leveraging a comprehensive data set containing 1036 binary configurations of the μ phase, the model trained using a 10-fold cross-validation technique, and the multilayer perceptron (MLP) algorithm achieves a mean absolute error (MAE) of 23.906 meV/atom. To validate its generalization performance, the trained model is further validated on 900 ternary configurations, resulting in an MAE of 32.754 meV/atom. Compared with solely using traditional first-principles calculations, our approach significantly reduces the computational time by at least 52%. Moreover, the ML model exhibits exceptional accuracy in predicting the lattice parameters of the μ phase. The MAE values for the a and c parameters are 0.024 and 0.214 Å, respectively, corresponding to low error rates of only 0.479 and 0.578%. Additionally, the ML model was utilized to accurately predict the formation energy of all of the possible ternary configurations. To enhance accessibility to the formation energy data of the μ phase, a user-friendly graphical user interface (GUI) was developed, ensuring convenient usability for researchers and practitioners.

PyClim-SDM: Service for generation of statistically downscaled climate change projections supporting national adaptation strategies

The climate change impact and adaptation communities need future scenarios with sufficient high resolution, which are frequently achieved by applying Statistical Downscaling Models (SDMs) over global climate models. A large variety of SDMs exists, and some can be more suitable than others for each specific purpose. For this reason, it is important to develop tools to facilitate the evaluation and generation of downscaled scenarios following different approaches. In this paper we present a service, ‘pyClim-SDM’, which allows users to generate and evaluate their own downscaled scenarios with a very simple and user-friendly graphical interface. This tool includes a large collection of state-of-the-art methods belonging to different families to downscale daily data of the following surface variables: temperature, precipitation, wind, relative humidity and cloud coverage. Additionally, the software is prepared to be applied over any other user-defined target variable. Thus, multivariable indexes can be tackled as target variables themselves, instead of being calculated from the downscaled primary variables. With this possibility, potential intervariableinconsistencies are avoided. An application example for a Fire Weather Index, dependent on temperature, wind, humidity and precipitation, is shown. The service here presented -mainly based on a new downscaling software and a user-friendly graphical interface- is an essential piece for evaluating and generating high-resolution projection data within the Spanish national climate change adaptation strategy which includes, among other elements, a common database for all sectors, viewer and data distribution portal, etc.