Comprehensive Dataset Research Articles

The impact of genetic variants related to the fatty acid metabolic process pathway on milk production traits in Jersey cows

The synthesis of fatty acids plays a critical role in shaping milk production characteristics in dairy cattle. Thus, identifying effective haplotypes within the fatty acid metabolism pathway will provide novel and robust insights into the genetics of dairy cattle. This study aimed to comprehensively examine the individual and combined impacts of fundamental genes within the fatty acid metabolic process pathway in Jersey cows. A comprehensive phenotypic dataset was compiled, considering milk production traits, to summarize a cow’s productivity across three lactations. Genotyping was conducted through PCR-RFLP and Sanger sequencing, while the association between genotype and phenotype was quantified using linear mixed models. Moderate biodiversity and abundant variation suitable for haplotype analysis were observed across all examined markers. The individual effects of the FABP3, LTF and ANXA9 genes significantly influenced both milk yield and milk fat production. Additionally, this study reveals novel two-way interactions between genes in the fatty acid metabolism pathway that directly affect milk fat properties. Notably, we identified that the GGAAGG haplotype in FABP3×LTF×ANXA9 interaction may be a robust genetic marker concerning both milk fat yield and percentage. Consequently, the genotype combinations highlighted in this study serve as novel and efficient markers for assessing the fat content in cow’s milk.

CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany

Abstract. Comprehensive large-sample hydrological datasets, particularly the CAMELS datasets (Catchment Attributes and MEteorology for Large-sample Studies), have advanced hydrological research and education in recent years. These datasets integrate extensive hydro-meteorological observations with landscape features, such as geology and land use, across numerous catchments within a national framework. They provide harmonised large-sample data for various purposes, such as assessing the impacts of climate change or testing hydrological models on a large number of catchments. Furthermore, these datasets are essential for the rapid progress of data-driven models in hydrology in recent years. Despite Germany's extensive hydro-meteorological measurement infrastructure, it has lacked a consistent, nationwide hydrological dataset, largely due to its decentralised management across different federal states. This fragmentation has hindered cross-state studies and made the preparation of hydrological data labour-intensive. The introduction of CAMELS-DE represents a step forward in bridging this gap. CAMELS-DE includes 1582 streamflow gauges with hydro-meteorological time series data covering up to 70 years (median length of 46 years and a minimum length of 10 years), from January 1951 to December 2020. It includes consistent catchment boundaries with areas ranging from 5 to 15 000 km2 along with detailed catchment attributes covering soil, land cover, hydrogeologic properties, and data on human influences. Furthermore, it includes a regionally trained long short-term memory (LSTM) network and a locally trained HBV (Hydrologiska Byråns Vattenbalansavdelning) model that were used as quality control and that can be used to fill gaps in discharge data or act as baseline models for the development and testing of new hydrological models. Given the large number of catchments, including numerous relatively small ones (636 catchments < 100 km2), and the time series length of up to 70 years (166 catchments with 70 years of discharge data), CAMELS-DE is one of the most comprehensive national CAMELS datasets available and offers new opportunities for research, particularly in studying long-term trends and runoff formation in small catchments and in analysing catchments with strong human influences. This article describes CAMELS-DE version 1.0, which is available at https://doi.org/10.5281/zenodo.13837553 (Dolich et al., 2024).

Topology Bench: systematic graph-based benchmarking for core optical networks

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterized by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource, and represents a 61.5% increase in spatially referenced real-world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyze both real and synthetic networks using structural, spatial, and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalizability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups based on nine optimal graph metrics using K-means. It employs a two-step optimization process: optimal features are selected by maximizing feature uniqueness through principal component analysis, and the optimal number of clusters is determined by maximizing decision boundary distances via support vector machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Dataset 1 (https://zenodo.org/records/13921775) and Code 1 (https://github.com/TopologyBench), promotes accessibility, consistency, and reproducibility.

Growth vs. green: unpacking the economic–environmental dilemma in major carbon emitters with panel ARDL analysis

Today's world is facing a criticalglobal challenge of rising temperatures and disrupted ecosystems, primarily driven by increased carbon emissions. The study aims to determine the major factors leading to carbon emissions in five major carbon-emitting countries: China, the United States, India, Russia, and Japan. Specifically, it explores the interplay between economic growth, natural resource rent (Natural Resource Rent: The economic benefit a country gains from its natural resources, such as oil or minerals, after subtracting the cost of extracting and using them), technological change, renewable energy use, and carbon emissions. The study postulates that economic growth and natural resource extraction bear a substantial interaction that contributes to carbon emissions. At the same time, technological change and the usage of renewable energy could probably become imperative to alleviate such emissions. This research, therefore, provides a good insight into the determinants of CO2 emissions in these key economies and the probable pathways toward emission reduction through innovation and harnessing of renewable sources of energy. Utilizing the comprehensive dataset between 1990 and 2021 and employing the ARDL [Panel ARDL (Autoregressive Distributed Lag): A statistical method used to analyze how different factors affect each other over time in a dataset that includes multiple subjects (e.g., countries)] model on the panel data, this study examines both the short- and long-term effects of these variables on carbon emissions, considering cross-country heterogeneity. The results underscore the significance related to guidelines for sustainable development practices and the necessity for these leading economies to lead the shift towards sustainability. By identifying factors that can mitigate emissions and enhance resilience, this research provides useful inputs to the policymakers who intend to promote renewable sources of energy usage along with technological upgrades. This study’s results contribute to the existing literature by making it a significant resource for future policy formulation and academic research in the field of mitigating climate change.

In Vitro-to-In Vivo Extrapolation on Lung Toxicity Induced by Metal Oxide Nanoparticles via Data-Mining.

While in silico analyses are commonly employed for chemical risk assessments, predicting chronic lung toxicity induced by engineered nanoparticles (ENMs) in vivo still faces many challenges due to complex interactions at multiple nanobio interfaces. In this study, we developed a rigorous method to compile published evidence on the in vivo lung toxicity of metal oxide nanoparticles (MeONPs) and revealed previously overlooked in vitro-to-in vivo extrapolation (IVIVE) relationships. A comprehensive multidimensional data set containing 1102 in vivo data points, 75 pulmonary toxicological biomarkers, and 20 features (covering in vitro effects, physicochemical properties, and exposure conditions) was constructed. An IVIVE approach that related effects at the cellular level to in vivo lung toxicity in rodent model was established with prediction accuracy reaching 89 and 80% in training and test sets. Experimental validation was conducted by testing chronic lung fibrosis of 8 new MeONPs in 32 independent mice, with prediction accuracy reaching 88%. The IVIVE model indicated that the proinflammatory cytokine IL-1β in THP-1 cells could serve as an in vitro marker to predict lung toxicity. The IVIVE model showed great promise for minimizing unnecessary animal tests and understanding toxicological mechanisms.

A Machine Learning-Based Approach for Predicting Aerodynamic Coefficients Using Deep Neural Networks and CFD Data

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted across various fields, including aerodynamics, exhibiting impressive results in complex computational processes and improving prediction accuracy. This study introduces a novel method for airfoil performance assessment through the development and training of a deep Artificial Neural Network (ANN), used for predicting aerodynamic coefficients and pressure distributions, leveraging comprehensive data obtained by using a Computational Fluid Dynamics (CFD) solver. First, an automated CFD solver was developed for obtaining the extensive dataset needed for the effective training of the ANN. The automation process consisted in the generation of a geometry and a mesh, along with the successful integration of the open-source SU2 solver for conducting the aerodynamic simulations, chosen for its versatility and straightforward integration. Once various airfoil analyses were performed and a comprehensive dataset was obtained, data was normalized and the model was trained. Throughout the training process, several model configurations were tested, varying different architectures, hyperparameters and layer settings, until the best-performing layout was chosen. After broad testing and validation, the optimal configuration was identified as being the one to demonstrate the lowest error rates and the most accurate predictions on both training and unseen data, highlighting the model’s generalization capabilities. This Machine Learning-based approach, used as a substitute for traditional methods, provides remarkable accuracy and robustness, capturing complex behaviors and significantly reducing the computational costs associated with CFD simulations.

Optimising Venturi flume oxygen transfer efficiency using uncertainty-aware decision trees

ABSTRACT This study optimizes standard oxygen transfer efficiency (SOTE) in Venturi flumes investigating the impact of key parameters such as discharge per unit width (q), throat width (W), throat length (F), upstream entrance width (E), and gauge readings (Ha and Hb). To achieve this, a comprehensive experimental dataset was analyzed using multiple linear regression (MLR), multiple nonlinear regression (MNLR), gradient boosting machine (GBM), extreme gradient boosting (XRT), random forest (RF), M5 (Pruned and Unpruned), random tree (RT), and reduced error pruning (REP). Model performance was evaluated based on key metrics: correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). Among the proposed models, M5_Unprun emerged as the top performer, exhibiting the highest CC (0.9455), the lowest RMSE (0.1918), and the lowest MAE (0.0030). GBM followed closely with a CC value of 0.9372, an RMSE value of 0.2067, and an MAE value of 0.0006. Uncertainty analysis further solidified the superior performance of M5_Unpruned (0.7522) and GBM (0.8055), with narrower prediction bands compared to other models, including MLR, which exhibited the widest band (1.4320). One-way analysis of variance confirmed the reliability and robustness of the proposed models. Sensitivity, correlation, and SHapley Additive exPlanations analyses identified W and Hb as the most influencing factors.

Ambient vibration dataset of a short-span cable-stayed bridge

A full-scale short-span cable-stayed bridge on a wind-exposed hill in New South Wales in Australia, was instrumented to assess its dynamic response to ambient vibration. The primary goal was to generate extensive datasets for Operational Modal Analysis (OMA) and develop new Structural Health Monitoring (SHM) techniques. Wind, vehicular, and pedestrian traffic on the bridge, and highway traffic beneath it, provided sufficient ambient vibration excitation. Uni-axial accelerometers were installed on the deck and cables, and a shear strain sensor was placed at one end of the bridge to measure traffic volume. Continuous ambient temperature recording was included due to its known impact on structural modal features. This data note presents four comprehensive datasets: ‘A-Single-Day,’ ‘Seasonal,’ ‘Constant-Temperature,’ and ‘Damaged-Structure’ which correspond to different environmental, operational, and structural conditions. These datasets are intended to support researchers by providing high-quality, real-world data that can be used for validating and developing OMA frameworks and SHM techniques. This paper fills a critical gap in the literature by making these detailed datasets available for broader research applications. Detailed descriptions of the instrumentation and data collection procedures are provided, along with discussions on the validity and plausibility of the datasets.

Historical Bird Atlas and Contemporary Citizen Science Data Reveal Long‐Term Changes in Geographic Range of Kenyan Birds

ABSTRACTAimHistorical bird atlases provide comprehensive datasets for investigating long‐term changes in species' distribution. In the context of accelerating biodiversity loss, these datasets can lend critical insights into the state of bird distributions across broad spatio‐temporal scales and provide much‐needed information for impactful conservation. In Africa, the potential of atlas data to understand changes in avian populations remains largely untapped.LocationThis study mapped changes in national distribution patterns of 1088 bird species found in Kenya.MethodsTapping into one of the earliest atlas databases, this study compared Kenyan bird atlas data collected between 1970 and 1984 with recent citizen science data sourced from the Kenya Bird Map project and eBird to determine changes in ranges across 50 years. We produced maps displaying, for every 27 × 27 km square of the country, whether a species appeared, was present throughout both periods, or disappeared. We account for the change in data collection effort between the two periods by quantifying the confidence of the change for each square.ResultsThe maps produced for each species are publicly accessible through an interactive website: https://kenyabirdtrends.co.ke/. We found that related species tended to experience similar changes in their distribution ranges. The ranges of Palearctic migrants and scavengers declined drastically, while introduced birds experienced a significant range increase over the past 50 years.Main ConclusionsThis study demonstrates the potential of integrating recent citizen science data with historical atlas data to draw out the changes in range for all species at national level. The range contraction of Palearctic migrants and scavengers echoed corresponding drops in abundance at local, regional and global scales. These findings lend additional weight to the need for an increased conservation focus on migratory and scavenging birds in Kenya.

How Good are Current Pocket-Based 3D Generative Models?: The Benchmark Set and Evaluation of Protein Pocket-Based 3D Molecular Generative Models.

The development of a three-dimensional (3D) molecular generative model based on protein pockets has recently attracted a lot of attention. This type of model aims to achieve the simultaneous generation of molecular graphs and 3D binding conformation under the constraint of protein binding. Various pocket-based generative models have been proposed; however, currently, there is a lack of systematic and objective evaluation metrics for these models. To address this issue, a comprehensive benchmark data set, named POKMOL-3D, is proposed to evaluate protein pocket-based 3D molecular generative models. It includes 32 protein targets together with their known active compounds as a test set to evaluate the versatility of generation models to mimic the real-world scenario. Additionally, a series of two-dimensional (2D) and 3D evaluation metrics with some newly created ones was integrated to assess the quality of generated molecular structures and their binding conformations. It is expected that this work can enhance our comprehension of the effectiveness and weakness of current 3D generative models and stimulate the discussion on challenges and useful guidance for developing the next wave of molecular generative models.

Effects of land cover and protected areas on flying insect diversity.

Widespread insect losses are a critical global problem. Mitigating this problem requires identifying the principal drivers across different taxa and determining which insects are covered by protected areas. However, doing so is hindered by missing information on most species owing to extremely high insect diversity and difficulties in morphological identification. To address this knowledge gap, we used one of the most comprehensive insect DNA metabarcoding data sets assembled (encompassing 31,846 flying insect species) in which data were collected from a network of 75 Malaise traps distributed across Germany. Collection sites encompass gradients of land cover, weather, and climate, along with differences in site protection status, which allowed us to gain broader insights into how insects respond to these factors. We examined changes in total insect biomass, species richness, temporal turnover, and shifts in the composition of taxa, key functional groups (pollinators, threatened species, and invasive species), and feeding traits. Lower insect biomass generally equated to lower richness of all insects and higher temporal turnover, suggesting that biomass loss translates to biodiversity loss and less stable communities. Spatial variability in insect biomass and composition was primarily driven by land cover, rather than weather or climate change. As vegetation and land-cover heterogeneity increased, insect biomass increased by 50% in 2019 and 56% in 2020 and total species richness by 58% and 33%, respectively. Similarly, areas with low-vegetation habitats exhibited the highest richness of key taxa, including pollinators and threatened species, and the widest variety of feeding traits. However, these habitats tended to be less protected despite their higher diversity. Our results highlight the value of heterogeneous low vegetation for promoting overall insect biomass and diversity and that better protection of insects requires improved protection and management of unforested areas, where many biodiversity hotspots and key taxa occur.

Navigating sociopolitical waters: exploring the influence of key factors on global supply base concentration

PurposeVarious economic, strategic and sociopolitical factors drive the decisions related to the location of global supply chain partners. Yet, research on how sociopolitical factors affect these decisions is not that prevalent. We contribute to this line of research by evaluating the changes in global supply base concentration accounted for by three key sociopolitical factors – populism, state fragility and political constraints – that magnify or attenuate the effects of institutional risks, uncertainties and volatility.Design/methodology/approachApart from hypothesizing the negative effects of populism and state fragility, political constraints are conjectured to significantly attenuate these negative effects. The proposed hypotheses were motivated using the tenets of institutional economics theory and tested using a comprehensive dataset compiled from six widely used secondary data sources. The panel data spanning 2003–2018 focused on a sample of global US-listed companies with worldwide suppliers.FindingsOur results supported three of the four hypotheses. Specifically, the direct effects of populism and state fragility were found to be significant, suggesting that they reduced the number of suppliers in the country. However, political constraints did not moderate the effect of populism on global supply base concentration, while they negatively moderated the effect of the state fragility index. This result showcases the overpowering effect of populism on a country’s market environment.Originality/valueSupply chain scholars resoundingly acknowledge the need for more research on the effect of broader sociopolitical factors on global supply chain management. Responding to this call, we evaluate the direct and contingent effects of populism, state fragility and political constraints on global supply base concentration. Our choice of these factors was based on their representation of most of the critical institutional environments prevalent in various countries. One surprising result we found was that populism was found to be pervasive even in countries with strong checks and balances, suggesting its paramount importance to practicing management. Overall, by unraveling the interesting effects of these multidimensional sociopolitical factors, our study makes a valuable contribution to not only theory but also practice.

Exploring research trends and patterns in leadership research: a machine learning, co-word, and network analysis

AbstractLeadership is recognized as playing a crucial role in the organization’s performance and success. As a result, the scientific literature on leadership has become quite extensive, making it difficult to identify and understand the current state of research. Most literature studies focus on a specific aspect of the field or a limited time frame, providing a fragmented view of the overall landscape. Therefore, this research aims to provide new insights into the current state of research through two studies. Using advanced Natural Language Processing (NLP) techniques, the first study focuses on identifying emerging research trends in the field through a Latent Dirichlet Allocation (LDA) model, providing insights into future areas of interest and investigation. The second study centers on analyzing consolidated research patterns through co-word and network analysis, shedding light on the connections and interrelationships between leadership research topics. By applying these techniques to a comprehensive dataset of 56,547 research papers gathered from Web of Science and Scopus, this study provides a detailed understanding of the current state of leadership research and identifies potential areas for future exploration. Five research trends were identified: (1) Leadership and Digital Transformation Research (LDTR); (2) Leadership and Organizational Performance Research (LOPR); (3) Educational Leadership Research (ELR); (4) Leadership Practices and Development Research (LPDR); and (5) Gender and Diversity Leadership Research (GDLR). Combining these five research trends with the consolidated research patterns identified, we propose several research directions identified for advancing leadership studies.

Deep Learning Enhanced Wind Speed and Direction Forecasting for Airport Regions

Abstract This work introduces a deep-learning architecture tailored for accurate wind speed and direction forecasting for airports using a grid-based input. Moving beyond the limitations of conventional forecasting methods, which struggle with rapid and localized atmospheric changes and demand substantial computational power, this study positions a machine-learning approach as a superior alternative for wind nowcasting. By employing a comprehensive dataset covering 75 years, the proposed model distinguishes itself by achieving a mean absolute error of 1.26 m/s for wind speed and 16.18° for wind direction in the examined location, Madeira International Airport area. The model's robustness was further validated using transfer learning across ten global airport areas, each with diverse atmospheric conditions, resulting in consistent accuracy. Additionally, a precision forecast focused specifically on the runway was conducted, and its performance was found to be consistent with the broader spatial area forecasts. These results underscore the potential of machine learning in wind prediction for aviation, highlighting a promising pathway for future advancements in weather forecasting technology.

Enhanced Diagnosis of Lung Cancer through an Ensemble Learning Model leveraging an Adaptive Optimization Algorithm

Early and accurate diagnosis of lung cancer is crucial to improving patient outcomes and survival rates. Machine and deep learning models have emerged as promising tools to improve the accuracy and efficiency of disease diagnosis. However, achieving optimal diagnostic performance remains a challenging task in medical research. This study integrates ensemble learning techniques with an adaptive optimization algorithm to enhance the accuracy of lung cancer diagnosis. By combining the predictive potential of multiple base classifiers, the ensemble-learning model improves overall performance and mitigates the weaknesses of individual classifiers. Additionally, the adaptive optimization algorithm dynamically adjusts the model parameters to optimize the classification performance. The effectiveness of the approach was evaluated using a comprehensive dataset that includes lung cancer images. Rigorous evaluation and comparison with state-of-the-art models showed that the proposed method achieved superior diagnostic performance, reaching an overall accuracy of 99%.

The odyssey of women in university education: stochastic growth models for studying access and graduation process

This research aims to explore the evolution of statistics related to the proportion of women in university student populations (focusing on bachelor’s or equivalent degree programs) over an extensive period, spanning from the founding of the first university until recent times. This is done by utilizing empirical insights derived from comprehensive data series, presented here for the first time and primarily encompassing Greece, UK, Canada and USA. Motivated by the distinctive properties of these series and their sigmoid pattern, we delve into a general family of stochastic growth models for data analysis. A notable contribution of this paper lies in offering a novel interpretation of this family within a competing cause scenario, similar to those used in the theory of event history analysis. We emphasize on key common elements within the two statistical domains of research, thereby highlighting potential future research directions; besides, the interpretability of model parameters stands out as an advantage compared to other approaches such as those based on regression model theory. For assessing the proposed parameter estimation methodology under realistic conditions a simulation study is also conducted. The application and results are aimed at making a significant contribution to research in higher and university education by presenting comprehensive sets of consistent data and statistical tools to capture their fundamental characteristics and trends. Additionally, they can serve as valuable resources for education policy-makers and human resources planners alike.

Salinity Effects on Soil Structure and Hydraulic Properties: Implications for Pedotransfer Functions in Coastal Areas

Understanding the effects of salinity on soil structure and hydraulic properties is critical for addressing environmental challenges in coastal saline and sodic areas. In this study, soil samples were collected from a coastal region in eastern China to investigate how salinity affected the soil structure and hydraulic properties based on lab experiments. A comprehensive soil dataset was also compiled from the experimental results to develop a salinity-based pedotransfer function (PTF-S) tailored to the coastal environment. The results showed that salinity significantly altered the soil aggregate size distribution and hydraulic properties. Higher salinity promoted the formation of larger aggregates (0.25–2 mm), particularly in silty clay soil. Salinity positively correlated with the saturated hydraulic conductivity (Ks) in sandy loam soil, regardless of the cation type (Na⁺ or Ca2⁺). By comparison, Na+ increased the Ks of silty clay soil up to a certain threshold, while Ca2+ enhanced the Ks regardless of the soil texture. Increased salinity also reduced the soil water retention of sandy loam soil; however, Na+ increased the soil water retention of silty clay soil and Ca2+ had different effects depending on the suction levels. The newly developed PTF-S model, which included the electrical conductivity (EC) and cation exchange capacity (CEC), showed better predictions for the volumetric water content (R = 0.886 and RMSE = 0.057 cm3/cm3) and log Ks (R = 0.991 and RMSE = 0.073 mm/h) than the traditional model that excludes the salinity variables EC and CEC (PTF-N) (R = 0.839 and RMSE = 0.066 cm3/cm3 for the volumetric water content, and R = 0.966 and RMSE = 0.140 mm/h for the log Ks). This study highlights the importance of developing salinity-based PTFs for addressing soil salinization challenges.

Kinase-Bench: Comprehensive Benchmarking Tools and Guidance for Achieving Selectivity in Kinase Drug Discovery.

Developing selective kinase inhibitors remains a formidable challenge in drug discovery because of the highly conserved structural information on adenosine triphosphate (ATP) binding sites across the kinase family. Tailoring docking protocols to identify promising kinase inhibitor candidates for optimization has long been a substantial obstacle to drug discovery. Therefore, we introduced "Kinase-Bench," a pioneering benchmark suite designed for an advanced virtual screen, to improve the selectivity and efficacy of kinase inhibitors. Our comprehensive data set includes 6875 selective ligands and 422,799 decoys for 75 kinases, using extensive bioactivity and structural data from the ChEMBL database and decoys generated by the Directory of Useful Decoys-Enhanced version. Our benchmarking sets and retrospective case studies were designed to provide useful guidance in discovering selective kinase inhibitors. We employed a Glide High-Throughput Virtual Screen and Standard Precision complemented by three scoring functions and customized protein-ligand interaction filters that target specific kinase residue interactions. These innovations were successfully implemented in our virtual screen efforts targeting JAK1 inhibitors, achieving selectivity against its family member, TYK2. Consequently, we identified novel potential hits: Compound 2 (JAK1 IC50: 980.5 nM, TYK2 IC50: 4.5 μM) and the approved pan-AKT inhibitor Capivasertib (JAK1 IC50: 275.9 nM, TYK2 IC50: 10.9 μM). Using the Kinase-Bench protocol, both compounds demonstrated substantial JAK1 selectivity, making them strong candidates for further investigation. Our pharmaceutical results underscore the utility of tailored virtual screen protocols in identifying selective kinase inhibitors with substantial implications for rational drug design. Kinase-Bench offers a robust toolset for selective kinase drug discovery with the potential to effectively guide future therapeutic strategies effectively.

Computational Intelligence for Modeling the Rheological Properties of the Developed Hydrated Lime-Based Alkali-Activated Materials

The development of alkali-activated materials (AAMs) has gained attention as a sustainable alternative to Portland cement-based concrete. A key factor in AAM performance is its rheological behavior, which influences workability and pumpability. Accurate prediction of these properties is vital for optimizing mix design. This study introduces a computational framework using ensemble and non-ensemble machine learning (ML) techniques to model the yield stress (YS) and plastic viscosity (PV) of AAMs. A comprehensive dataset from previous experiments was used to train models, including artificial neural networks (ANN), gene expression programming (GEP), decision tree, random forest, adaptive boosting, and gradient boosting. The models achieved R² values exceeding 0.95 and correlation coefficients above 0.97. Error metrics, such as MAPE, MAE, MSE, and RMSE, further validated the models’ generalizability. A parametric analysis showed that a 20% increase in hydrated lime (HL) content raised YS by 350Pa, which is similar to the effect of varying fly ash (FA) from 60% to 100%. A 40% increase in FA and HL content increased PV by 4Pa.s and 25Pa.s, respectively. A 3% rise in sodium silicate led to a 400Pa increase in YS, while a similar increase in sodium hydroxide raised YS by 170Pa and PV by 15Pa·s. Overall, this study provides an effective tool for optimizing AAM mix designs by accurately predicting rheological properties and quantifying the impact of key parameters, contributing to the advancement of sustainable cementitious materials in construction.

Numerical analysis and data-driven optimization of energy harvesting efficiency from wake-induced vibration in a multi-cylinder configuration

Wake-induced vibrations (WIV) in multi-cylinder configurations have demonstrated greater energy harvesting efficiency in hydrokinetic applications compared to conventional vortex-induced vibrations (VIV) of a single cylinder. However, the complex fluid-structure interactions make it challenging to identify optimal configurations for maximum power output, as extensive simulations across numerous parameter combinations lead to substantial computational costs with traditional computational fluid dynamics (CFD) methods. To address this challenge, we developed a data-driven model using automated machine learning (AutoML) techniques, focusing on four key parameters: spacing, diameter, damping, and reduced velocity. Trained on comprehensive datasets from validated CFD simulations, this model integrates multiple algorithms to predict the power efficiency of WIV systems with high accuracy. Our approach enables rapid and precise evaluations of power efficiency across a broad range of configurations, significantly reducing the computational burden compared to traditional CFD approaches. The results indicate that optimal configurations, characterized by larger upstream cylinder diameters, higher damping ratios, and ideal spacing ratios, can achieve power generation efficiencies of up to 59.15%. Further analysis of vorticity contours reveals that synchronized interactions between upstream vortex shedding and downstream structure motions enhance WIV, thereby improving energy harvesting efficiency.