cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27701734
title: Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics

description: Major depressive disorder (MDD) is a serious and widespread mental health condition that remains challenging to diagnose accurately. Traditional psychological assessments, which can be subjective and sometimes unreliable, emphasize the need for more objective diagnostic tools. In this study, we present a machine learning (ML) model designed to diagnose depression by analysing statistical time-domain features extracted from Electroencephalography (EEG) data. The model is built using a stacked ensemble ML approach, incorporating nine-base estimators with various meta-classifiers. Through multiple trials, the model achieved an accuracy of 98.01%, with precision and recall rates of 97.78% and 96.61%, respectively with Adaptive Boosting (AdaBoost) as the meta-classifer. We also investigated the effects of data sampling and the number of base classifiers on the model’s performance. The findings demonstrate that the stacked ensemble approach significantly enhances the accuracy of diagnosing MDD and that the proposed model outperforms the methods used in previous studies. This model offers a promising tool for psychologists and medical professionals to diagnose depression more reliably, potentially leading to better treatment outcomes for those affected by the disorder.
DOI: 10.6084/m9.figshare.27701734.v1
title: Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics

description: Major depressive disorder (MDD) is a serious and widespread mental health condition that remains challenging to diagnose accurately. Traditional psychological assessments, which can be subjective and sometimes unreliable, emphasize the need for more objective diagnostic tools. In this study, we present a machine learning (ML) model designed to diagnose depression by analysing statistical time-domain features extracted from Electroencephalography (EEG) data. The model is built using a stacked ensemble ML approach, incorporating nine-base estimators with various meta-classifiers. Through multiple trials, the model achieved an accuracy of 98.01%, with precision and recall rates of 97.78% and 96.61%, respectively with Adaptive Boosting (AdaBoost) as the meta-classifer. We also investigated the effects of data sampling and the number of base classifiers on the model’s performance. The findings demonstrate that the stacked ensemble approach significantly enhances the accuracy of diagnosing MDD and that the proposed model outperforms the methods used in previous studies. This model offers a promising tool for psychologists and medical professionals to diagnose depression more reliably, potentially leading to better treatment outcomes for those affected by the disorder.
DOI: 10.6084/m9.figshare.26861067
title: Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

description: Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.
DOI: 10.6084/m9.figshare.26861067.v1
title: Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

description: Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.
DOI: 10.6084/m9.figshare.26424244
title: An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning

description: Knowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. The drawbar pull prediction models from ANN and six ML algorithms were developed, and the data analysis with hyperparameter tuning concluded that the Extreme Gradient Boosting (XGB) ML model outperformed the other ML models. A reasonable accuracy with R<sup>2 </sup>= 0.93 and MAPE = 6.77% was achieved using the XGB ML model for a separate validation dataset, which was not used for training. Furthermore, a cloud-based serverless Android App integrated with the XGB ML-based drawbar pull prediction model was developed for real-time tractor drawbar pull prediction and monitoring during tillage operations. The field validation demonstrated the XGB ML model's generalisation ability and effectiveness, with R<sup>2</sup> = 0.90 and maximum MAPE of 9.86%. It can be used to simulate and optimize tractor performance, guiding manufacturers in selecting geometric parameters for tractor design.
DOI: 10.6084/m9.figshare.26424244.v1
title: An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning

description: Knowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. The drawbar pull prediction models from ANN and six ML algorithms were developed, and the data analysis with hyperparameter tuning concluded that the Extreme Gradient Boosting (XGB) ML model outperformed the other ML models. A reasonable accuracy with R<sup>2 </sup>= 0.93 and MAPE = 6.77% was achieved using the XGB ML model for a separate validation dataset, which was not used for training. Furthermore, a cloud-based serverless Android App integrated with the XGB ML-based drawbar pull prediction model was developed for real-time tractor drawbar pull prediction and monitoring during tillage operations. The field validation demonstrated the XGB ML model's generalisation ability and effectiveness, with R<sup>2</sup> = 0.90 and maximum MAPE of 9.86%. It can be used to simulate and optimize tractor performance, guiding manufacturers in selecting geometric parameters for tractor design.
DOI: 10.6084/m9.figshare.27325503.v1
title: An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers

description: Anaemia is a disorder marked by low blood levels of haemoglobin (HGB), affecting people of all ages and ethnicities and is a major global public health concern. Anaemia must be diagnosed as soon as possible to enable prompt treatment and intervention, which can reduce complications and enhance patient outcomes. With the ability to improve diagnostic precision and expedite patient care procedures, machine learning (ML) has become a potent instrument in the healthcare industry. Hence, we examine the use of ML approaches to predict haemoglobin-like anaemia in this research article. Based on a heterogeneous dataset of blood markars, we investigate the performance of many machine learning techniques such as Logistic Regression, CatBoost, XgBoost Decision Trees, KNN and others. The algorithms are further ensembled using a customized stacking approach. The ML models' judgments are interpreted using explainable artificial intelligence (XAI) methods. The xgboost and the stacking classifier obtained an accuracy, precision and recall of 99% each. Our research shows how ML models can help with the early diagnosis and treatment of anaemia, which will ultimately lead to better patient outcomes and healthcare results. Overall, the research shows how ML emphasizes the value of interdisciplinary cooperation in solving challenging medical problems.
DOI: 10.6084/m9.figshare.c.3606131_d1.v1
title: Additional file 1: of How size matters: exploring the association between quality of mental health services and catchment area size

description: Catchment areas background information. (XLSX 41 kb)
DOI: 10.6084/m9.figshare.27325503
title: An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers

description: Anaemia is a disorder marked by low blood levels of haemoglobin (HGB), affecting people of all ages and ethnicities and is a major global public health concern. Anaemia must be diagnosed as soon as possible to enable prompt treatment and intervention, which can reduce complications and enhance patient outcomes. With the ability to improve diagnostic precision and expedite patient care procedures, machine learning (ML) has become a potent instrument in the healthcare industry. Hence, we examine the use of ML approaches to predict haemoglobin-like anaemia in this research article. Based on a heterogeneous dataset of blood markars, we investigate the performance of many machine learning techniques such as Logistic Regression, CatBoost, XgBoost Decision Trees, KNN and others. The algorithms are further ensembled using a customized stacking approach. The ML models' judgments are interpreted using explainable artificial intelligence (XAI) methods. The xgboost and the stacking classifier obtained an accuracy, precision and recall of 99% each. Our research shows how ML models can help with the early diagnosis and treatment of anaemia, which will ultimately lead to better patient outcomes and healthcare results. Overall, the research shows how ML emphasizes the value of interdisciplinary cooperation in solving challenging medical problems.
DOI: 10.6084/m9.figshare.27854513
title: Beyond Markovian queue: a heuristic optimization of a fluid queue with a delay-aware retrial strategy for unprecedented energy efficiency in 5G-based ad hoc networks

description: An energy crisis poses a significant challenge in wireless communication networks and looms large due to power-constrained supply and unforeseeable data traffic. As a solution, optimizing energy usage is crucial for enhancing data transmission efficiency and alleviating data overload. Additionally, addressing delays is vital for maintaining energy stability. Our model is dedicated to optimizing energy levels to significantly prolong the system's performance during data transmission. The proposed work thoroughly analyzes the impact of implementing a delay-aware mechanism using a fluid retrial queueing model to enhance energy optimization. Moreover, pioneered a heuristic approach for fluid queueing analysis to effectively reduce energy usage. Our numerical visualizations validate the analytical results, demonstrating an impressive 83.7% accuracy compared to traditional queueing methods.
DOI: 10.6084/m9.figshare.27854513.v1
title: Beyond Markovian queue: a heuristic optimization of a fluid queue with a delay-aware retrial strategy for unprecedented energy efficiency in 5G-based ad hoc networks

description: An energy crisis poses a significant challenge in wireless communication networks and looms large due to power-constrained supply and unforeseeable data traffic. As a solution, optimizing energy usage is crucial for enhancing data transmission efficiency and alleviating data overload. Additionally, addressing delays is vital for maintaining energy stability. Our model is dedicated to optimizing energy levels to significantly prolong the system's performance during data transmission. The proposed work thoroughly analyzes the impact of implementing a delay-aware mechanism using a fluid retrial queueing model to enhance energy optimization. Moreover, pioneered a heuristic approach for fluid queueing analysis to effectively reduce energy usage. Our numerical visualizations validate the analytical results, demonstrating an impressive 83.7% accuracy compared to traditional queueing methods.
DOI: 10.6084/m9.figshare.26519277
title: Optimal scheduling model of microgrid based on improved dung beetle optimization algorithm

description: In view of the strong uncertainty and intermittency of distributed power sources in microgrids and the shortcomings of the traditional dung beetle optimizer (DBO) algorithm with slow convergence, poor robustness and ease of falling into a local optimum, an optimal scheduling model for microgrids based on the improved dung beetle optimization algorithm is proposed. First, a multiobjective optimal scheduling model of the microgrid is constructed and a typical daily output scenario generation method for wind power generation and photovoltaic power generation is constructed based on the Gaussian kernel density estimation, Frank-copula and K-means clustering algorithms. Second, to address the shortcomings of the DBO algorithm, the spiral position update strategy, adaptive weight factor, levy flight strategy and <i>t</i>-distribution variation strategy are introduced on the basis of the DBO algorithm, which effectively solves the problem of premature convergence of particles owing to falling into a local optimum. Finally, five benchmark test functions were selected for simulation experiments. Finally, the simulation results show that the computational performance of the IDBO algorithm is significantly better than the other five intelligent algorithms. The algorithm proposed in this paper also achieves more satisfactory results in microgrid optimal scheduling based on the scenario generation method.
DOI: 10.6084/m9.figshare.26519277.v1
title: Optimal scheduling model of microgrid based on improved dung beetle optimization algorithm

description: In view of the strong uncertainty and intermittency of distributed power sources in microgrids and the shortcomings of the traditional dung beetle optimizer (DBO) algorithm with slow convergence, poor robustness and ease of falling into a local optimum, an optimal scheduling model for microgrids based on the improved dung beetle optimization algorithm is proposed. First, a multiobjective optimal scheduling model of the microgrid is constructed and a typical daily output scenario generation method for wind power generation and photovoltaic power generation is constructed based on the Gaussian kernel density estimation, Frank-copula and K-means clustering algorithms. Second, to address the shortcomings of the DBO algorithm, the spiral position update strategy, adaptive weight factor, levy flight strategy and <i>t</i>-distribution variation strategy are introduced on the basis of the DBO algorithm, which effectively solves the problem of premature convergence of particles owing to falling into a local optimum. Finally, five benchmark test functions were selected for simulation experiments. Finally, the simulation results show that the computational performance of the IDBO algorithm is significantly better than the other five intelligent algorithms. The algorithm proposed in this paper also achieves more satisfactory results in microgrid optimal scheduling based on the scenario generation method.
DOI: 10.6084/m9.figshare.c.3606122_d5.v1
title: Additional file 4: of 
                     In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

description: List of 181 essential proteins. The amino acid sequence of hubs proteins was compared against bacterial proteins sequence from Database of Essential Genes (DEG). (TXT 62 kb)
DOI: 10.6084/m9.figshare.c.3606122_d6.v1
title: Additional file 6: of 
                     In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

description: Essential protein alignment against host. Blast alignment summary of 181 essential proteins of Corynebacteria pseudotuberculosis against the host. (TXT 65 kb)
DOI: 10.6084/m9.figshare.c.3606128_d11.v1
title: Additional file 7: of CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types

description: CellMapper predictions for each of the 30 cell types. (XLSX 2677 kb)
DOI: 10.6084/m9.figshare.c.3606173_d2.v1
title: Additional file 3: of Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series

description: Supplementary Tables 4-6. (XLSX 28452 kb)
DOI: 10.15468/dl.57mzpv
title: Occurrence Download

description: A dataset containing 554 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-7.5039 35.93749,-0.60745 35.93749,-0.60745 39.91063,-7.5039 39.91063,-7.5039 35.93749))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alburnus alburnus (Linnaeus, 1758)"
 ]
}

 The dataset includes 554 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037530-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.c.3606191_d2.v1
title: Additional file 2: of Identification of combinatorial host-specific signatures with a potential to affect host adaptation in influenza A H1N1 and H3N2 subtypes

description: This file contains the rule-based models for all the proteins of both subtypes. (XLSX 41 kb)
DOI: 10.6084/m9.figshare.c.3606197_d1.v1
title: MOESM1 of Three new species of Begonia endemic to the Puerto Princesa Subterranean River National Park, Palawan

description: Additional file 1. Voucher specimen details and genbank accession numbers for the DNA sequences used in the phylogenetic analysis.
DOI: 10.6084/m9.figshare.c.3606152_d10.v1
title: Additional file 10: Table S7. of Prepartum body condition score and plane of nutrition affect the hepatic transcriptome during the transition period in grazing dairy cows

description: Complete result datasets generated by the DIA analysis. The KEGG pathways are sorted by category and subcategory. Blue bars represent the Impact along the transition period, while red and green bars depict the Direction of the Impact (red = upregulation, green = downregulation). (XLSX 77 kb)
DOI: 10.6084/m9.figshare.28042997
title: <b>Digitalization and Employee Health and Wellbeing during COVID-19</b>

description: COVID-19 resulted organizations adopt digitalization at a greater scale than before. As a result, employees were required to adopt new working methods within a very short time frame. This has had an impact on employee health and well-being. This paper uses the 2020 data from the Australian Public Service Commission (APSC) State of the Service Employee Census (n=108,085) to identify the impact of digitalization of the workplace on employee health and well-being as well as any mediating effects of health management and leadership on these relationships. Using the ordinal as well as multi-nominal GSEM method with a mediator, this study finds that digital meeting (DM) and the digital training (DT) decreased employee’s health and well-being during the COVID-19 pandemic period, but digital clearance (DC), which is the streamlining of existing work processes, increased employee’s health and wellbeing. Furthermore, health management and leadership showed partial or full mediation role on their relationships. One of policy implications is that an employee-first approach will help leadership to deliver the best possible outcomes for the organization without negatively affecting employee health and wellbeing
DOI: 10.6084/m9.figshare.28042997.v1
title: <b>Digitalization and Employee Health and Wellbeing during COVID-19</b>

description: COVID-19 resulted organizations adopt digitalization at a greater scale than before. As a result, employees were required to adopt new working methods within a very short time frame. This has had an impact on employee health and well-being. This paper uses the 2020 data from the Australian Public Service Commission (APSC) State of the Service Employee Census (n=108,085) to identify the impact of digitalization of the workplace on employee health and well-being as well as any mediating effects of health management and leadership on these relationships. Using the ordinal as well as multi-nominal GSEM method with a mediator, this study finds that digital meeting (DM) and the digital training (DT) decreased employee’s health and well-being during the COVID-19 pandemic period, but digital clearance (DC), which is the streamlining of existing work processes, increased employee’s health and wellbeing. Furthermore, health management and leadership showed partial or full mediation role on their relationships. One of policy implications is that an employee-first approach will help leadership to deliver the best possible outcomes for the organization without negatively affecting employee health and wellbeing
DOI: 10.6084/m9.figshare.c.3606278_d3.v1
title: Additional file 2: Table S2. of Development of a gene synthesis platform for the efficient large scale production of small genes encoding animal toxins

description: Primer sequences used to assembly of gene A using different approaches for gene synthesis. (XLSX 12 kb)