Fine Accuracy Research Articles

Static versus dynamic sitting modes when performing on-desk tasks amongst preschool children

Background Studying at school and engaging in social activities are primarily performed while sitting, contributing to a sedentary lifestyle, which poses a major health concern for adults and children. Sitting independently and being stable facilitates success in reaching and connecting with objects. Objective Recognizing the potential negative consequences of SB, this study explored the effect of using a fit ball as an alternative seating option in classrooms to reduce sedentary behavior. Methods We employed a repeated-measures design to compare the fine motor accuracy of 37 preschool children (18 boys and 19 girls, aged 4–6 years with a mean age of 5.48 ± 0.54 years) when seated on standard chairs versus fit balls. The children were assessed through six subtests designed to evaluate fine motor accuracy appropriate for their age group. Results Our findings revealed no significant difference in fine motor accuracy between the two seating modes (p > 0.05), indicating that using a fit ball as a seating alternative does not compromise the fine motor skills of preschool children. Furthermore, no clear preference for either seating mode among the children was detected (Z = −1.79, p = 0.074). Conclusion While using fit balls may not enhance fine motor accuracy, they could still be considered an alternative seating in classrooms to reduce sedentary behavior without negatively impacting fine motor development. Such seating arrangements, including dynamic chairs or fit balls, could foster the development of healthy habits without adversely affecting academic skills. Further research may explore other benefits of dynamic seating and its long-term effects on child development.

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the 'salt and pepper noise'. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Accurate, automated classification of radiographic knee osteoarthritis severity using a novel method of deep learning: Plug-in modules

BackgroundFine-grained classification deals with data with a large degree of similarity, such as cat or bird species, and similarly, knee osteoarthritis severity classification [Kellgren–Lawrence (KL) grading] is one such fine-grained classification task. Recently, a plug-in module (PIM) that can be integrated into convolutional neural-network-based or transformer-based networks has been shown to provide strong discriminative regions for fine-grained classification, with results that outperformed the previous deep learning models. PIM utilizes each pixel of an image as an independent feature and can subsequently better classify images with minor differences. It was hypothesized that, as a fine-grained classification task, knee osteoarthritis severity may be classified well using PIMs. The aim of the study was to develop this automated knee osteoarthritis classification model.MethodsA deep learning model that classifies knee osteoarthritis severity of a radiograph was developed utilizing PIMs. A retrospective analysis on prospectively collected data was performed. The model was trained and developed using the Osteoarthritis Initiative dataset and was subsequently tested on an independent dataset, the Multicenter Osteoarthritis Study (test set size: 17,040). The final deep learning model was designed through an ensemble of four different PIMs.ResultsThe accuracy of the model was 84%, 43%, 70%, 81%, and 96% for KL grade 0, 1, 2, 3, and 4, respectively, with an overall accuracy of 75.7%.ConclusionsThe ensemble of PIMs could classify knee osteoarthritis severity using simple radiographs with a fine accuracy. Although improvements will be needed in the future, the model has been proven to have the potential to be clinically useful.

Novel Hybrid Mexican Axolotl Optimization with Fuzzy Logic for Maximum Power Point Tracker of Partially Shaded Photovoltaic Systems

Due to the nonlinear relation between the generated power and voltage of photovoltaic (PV) arrays, there is a need to stimulate PV arrays to operate at maximum possible power. Maximum power can be tracked using the maximum power point tracker (MPPT). Due to the presence of several peaks on the power–voltage (P–V) characteristics of the shaded PV array, conventional MPPT such as hill climbing may show premature convergence, which can significantly reduce the generated power. Metaheuristic optimization algorithms (MOAs) have been used to avoid this problem. The main shortcomings of MOAs are the low convergence speed and the high ripples in the waveforms. Several strategies have been introduced to shorten the convergence time (CT) and improve the accuracy of convergence. The proposed technique sequentially uses a recent optimization algorithm called Mexican Axolotl Optimization (MAO) to capture the vicinity of the global peak of the P–V characteristics and move the control to a fuzzy logic controller (FLC) to accurately track the maximum power point. The proposed strategy extracts both the benefits of the MAO and FLC and avoids their limitations with the use of the high exploration involved in the MOA at the beginning of optimization and uses the fine accuracy of the FLC to fine-track the MPP. The results obtained from the proposed strategy show a substantial reduction in the CT and the highest accuracy of the global peak, which easily proves its superiority compared to other MPPT algorithms.

Triplet Epipolar Images for Spaceborne Three Linear Cameras

Abstract. Epipolar images can reduce conjugate points searching from a two-dimensional to a one-dimensional space, which significantly improves the robustness and efficiency of dense matching. However, two images matched the lack of redundancy and suffered from occlusions. The third image could introduce two more stereos and improve the accuracy and completeness of the three-dimension reconstruction, which were adopted by spaceborne three linear cameras (TLCs), the ALOS PRSIM and ZY-3. In this study, we propose a new triplet epipolar images generation method for spaceborne TLCs. Triplet epipolar images were defined as any two of the three epipolar image pairs. The triplet epipolar geometry requires the imaging rays of any conjugate point to be coplanar. For high-resolution satellite images, parallel projection can be used to approximate the imaging geometry of satellite images. Therefore, a coplanar principal optical axis is a fundamental requirement for spaceborne TLCs. We propose a general workflow to generate triplet epipolar images (TEIs), which includes free-net bundle adjustment of TLCs, building a triplet epipolar geometry, correcting the y-parallaxes, and generating a rational function model (RFM) for TEIs. The ZY3-02 satellite images were used to validate the proposed method. The root means square error (RMSEs) of the free-net bundle adjustment in the image space was 0.185 pixels, which proved the fine intrinsic accuracy. After compensation, the RMSEs of the y parallaxes of the three epipolar image pairs were 0.295, 0.310, and 0.370 pixels. Owing to the simple geometry of the TEIs, the RMSEs of the RFM replacements were within 0.001 pixels.

Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Self-Supervised Monocular Depth Estimation With Positional Shift Depth Variance and Adaptive Disparity Quantization.

Recently, attempts to learn the underlying 3D structures of a scene from monocular videos in a fully self-supervised fashion have drawn much attention. One of the most challenging aspects of this task is to handle independently moving objects as they break the rigid-scene assumption. In this paper, we show for the first time that pixel positional information can be exploited to learn SVDE (Single View Depth Estimation) from videos. The proposed moving object (MO) masks, which are induced by the depth variance to shifted positional information (SPI) and are referred to as 'SPIMO' masks, are highly robust and consistently remove independently moving objects from the scenes, allowing for robust and consistent learning of SVDE from videos. Additionally, we introduce a new adaptive quantization scheme that assigns the best per-pixel quantization curve for depth discretization, improving the fine granularity and accuracy of the final aggregated depth maps. Finally, we employ existing boosting techniques in a new way that self-supervises moving object depths further. With these features, our pipeline is robust against moving objects and generalizes well to high-resolution images, even when trained with small patches, yielding state-of-the-art (SOTA) results with four- to eight-fold fewer parameters than the previous SOTA techniques that learn from videos. We present extensive experiments on KITTI and CityScapes that show the effectiveness of our method.

Exploration of bio-convection for slippery two-phase Maxwell nanofluid past a vertical induced magnetic stretching regime associated for biotechnology and engineering

Research in the fields of thermal and process engineering has been growing daily due to the nanoparticles' broad relevance and dynamic applications. Nanomaterials are being researched because of their potential to make remarkable contributions in a number of different areas due to their exceptional thermal and physical characteristics. The thermal efficiency of heat transfer phenomena is considerably improved when nanoparticles are combined with microbes in a solution for collective transport, a process known as mix fluids. Light, gravity, density, and electric and magnetic forces are all physical stimuli that may be used to influence the particles in a fluid mixture. Several branches of biotechnology that rely on fluids and their properties have debated the viability of the bioconvection notion. Nanofluid flow has several potential uses, including but not limited to the storage of electronic devices, the cooling and heating of industrial frameworks, and the construction of accompanying information setups for pharmaceutical governance. The flow model is constructed using relevant variables and dimensionless equations.Under the effect of remarkable Maxwell nanofluid, which is two-dimensional and electrically conducting and includes gyrotactic microorganisms, the present work aims to give some novel uses of induced magnetic field for the bio-convection pattern nanoliquid. Interference between slip effects regulates the thermal phenomena.The BVP4C numerical determination of problem is worked out through fine accuracy. The physical dynamic of flow terms is visualized graphically. It is claimed that manage of thermal transportation phenomenon is produced when the slip effects are dominant. The physical outcomes of interesting terms are visualized.The motile density profile is goes up by increasing Pe. Nano fluid velocity with Momentum slip parameter decreases near the plate and increases as one moves away from it. Motile profile behavior of microorganisms is goes down with Lb.

Wireless sensor network for structural health monitoring using RFID based data mules

The wireless system did the huge cost-cutting in monitoring the structure so, now it can be used permanently as an integral part of the system as a smart infrastructure that will give them real-time information the structure. The wireless devices transmit the collected data about cracks, displacement, and excess vibration in slab-tracks. The train which will collect the data and train will be used as a data mule in this paper which will upload the information to a remote-control centre. The data which will be collected stored in the database and to know the status of the track a query will be fired from an application. In this paper, many design for communication systems are proposed which are efficient, with fine accuracy, and most importantly it is a low-cost system.

A multitiered haplotype strategy to enhance phased assembly and fine mapping of a disease resistance locus.

Fine mapping of quantitative trait loci (QTL) to dissect the genetic basis of traits of interest is essential to modern breeding practice. Here, we employed a multitiered haplotypic marker system to increase fine mapping accuracy by constructing a chromosome-level, haplotype-resolved parental genome, accurate detection of recombination sites, and allele-specific characterization of the transcriptome. In the first tier of this system, we applied the preexisting panel of 2,000 rhAmpSeq core genome markers that is transferable across the entire Vitis genus and provides a genomic resolution of 200 kb to 1 Mb. The second tier consisted of high-density haplotypic markers generated from Illumina skim sequencing data for samples enriched for relevant recombinations, increasing the potential resolution to hundreds of base pairs. We used this approach to dissect a novel Resistance to Plasmopara viticola-33 (RPV33) locus conferring resistance to grapevine downy mildew, narrowing the candidate region to only 0.46 Mb. In the third tier, we used allele-specific RNA-seq analysis to identify a cluster of 3 putative disease resistance RPP13-like protein 2 genes located tandemly in a nonsyntenic insertion as candidates for the disease resistance trait. In addition, combining the rhAmpSeq core genome haplotype markers and skim sequencing-derived high-density haplotype markers enabled chromosomal-level scaffolding and phasing of the grape Vitis × doaniana 'PI 588149' assembly, initially built solely from Pacific Biosciences (PacBio) high-fidelity (HiFi) reads, leading to the correction of 16 large-scale phasing errors. Our mapping strategy integrates high-density, phased genetic information with individual reference genomes to pinpoint the genetic basis of QTLs and will likely be widely adopted in highly heterozygous species.

Privacy-preserving healthcare monitoring for IoT devices under edge computing

With the rapid development of the Internet of Things (IoT) technology, e-healthcare has received extensive attention because it is able to provide real-time health status feedback for users. Due to the exponential growth of health data and the expensive cost of training the healthcare monitoring model, outsourcing the healthcare monitoring service to the edge servers becomes a popular way in e-healthcare. How to accomplish healthcare monitoring without leaking users' privacy to the edge servers is the key element of e-healthcare. In this paper, we propose a privacy-preserving healthcare monitoring framework based on long short-term memory (LSTM) neural network for IoT devices, which is performed on edge servers. In our framework, to reduce the computational burden, we outsource the complicated computations to the edge servers. To protect the privacy of health data and healthcare monitoring model, we design a series of secure interactive protocols based on replicated secret sharing and use three edge servers to collaboratively implement secure inference and model training for LSTM. We demonstrate the security of the proposed protocols and give the performance evaluation. Numerical experiments show that the proposed protocols are able to produce results with fine accuracy while requiring low computation and communication overheads.

High-precision moiré alignment method using centrosymmetric grating for 3D interlayer interconnect

The fabrication of integrated circuits in the future will rely heavily on high-precision three-dimensional interlayer interconnect alignment technology. In this study, we explore a controllable and clear-cut alignment method based on moiré centrosymmetric grating marks that can achieve fine alignment in the X-Y-θ direction simultaneously with only a single alignment mark without additional coarse alignment mark. Compared with the traditional peak-intensity-based detection method, circumferential alignment of the order of 10−4 deg and radial alignment of less than 10 nm can be achieved, and its fine alignment accuracy is improved by nearly two orders of magnitude by using the proposed local moiré phase alignment method. With further optimization of the parameters, sub-nanometer alignment is expected to be achieved. Moreover, our measurement system is simpler and more stable. Both simulation and experimental results verify the feasibility of the proposed method. The uncertainty analysis of the results is also reported.

A BCI system for imagined Bengali speech recognition

Non-invasive brain-computer interface (BCI) can be used in imagined speech recognition to aid people with speech disorders and has been investigated in different languages. However, this technology is in a very early stage for Bengali speech recognition. Therefore, the aim of this research is to develop a BCI system that can recognize imagined Bengali speech. In this study, a non-invasive 14-channel electroencephalography (EEG) headset was employed to record the EEG signals from 30 subjects. The subjects were instructed to imagine 11 Bengali vowels ▪ and 10 digits ▪. Then, we extracted four statistical features from the recorded EEG signals. A coarse-to-fine level classification was performed to categorize the imagined EEG data by exploiting these features. The data was classified at coarse level to distinguish between a vowel and a digit. Each vowel and digit were then classified at the fine level. In our classification, random forest achieved the highest recognition accuracy of 84.28% at the coarse level and 76.13% at the fine level. In order to comprehend the role played by each brain lobe in recognizing imagined speech, we additionally examined each of the brain lobes. The frontal lobe demonstrated the highest accuracy, with a coarse level accuracy of 80.65% and a fine level accuracy of 66.31%. Moreover, the proposed model outperformed the reported literature in this field.

High-frequency ultrasonic vibration-assisted sculpturing with a smoothed tool path for optical 3D micro-structured surfaces with sharp edges

The fabrication of complex 3D micro-structured surfaces on difficult-to-machine materials such as steel and tungsten carbide is the premise for mass production of glass components with functional surfaces by the glass moulding process. Ultrasonic vibration-assisted cutting (UVAC) is an effective method to machine 3D micro-structured surfaces on these difficult-to-machine materials. However, it is challenging and difficult for UVAC to machine microlens arrays with sharp edges, at which sudden acceleration of the z-axis slide is required to make the cutting tool follow the tool path. This could induce tool vibration and cause relative tool-work displacement, resulting in poor machining accuracy and surface finish of the machined 3D microstructure. In this paper, high-frequency ultrasonic vibration-assisted sculpturing (HFUVAS) with a smoothed tool trajectory is used to machine microlens array on steel mould for fine machining accuracy and surface quality. A comparison is made between the cutting performance of the circular microlens arrays machined by diamond turning and diamond sculpturing. The results found that relative tool-work displacement arising at sharp edge in diamond turning could be eliminated in HFUVAS with a smoothed tool path, demonstrating the effectiveness of a smoothed tool path in reducing relative tool-workpiece displacement in the fabrication of 3D micro-structured surfaces. Furthermore, a quadrilateral microlens array and hexagonal microlens array are also fabricated in order to extend the applications of HFUVAS, with a form error within 1 μm and surface roughness with an arithmetical mean height Sa of below 10 nm achieved.

Ultrasound-based radiomics analysis in the assessment of renal fibrosis in patients with chronic kidney disease.

Assessment of renal fibrosis non-invasively in chronic kidney disease (CKD) patients is still a clinical challenge. In this study, we aimed to establish a radiomics model integrating radiomics features derived from ultrasound (US) images with clinical characteristics for the assessment of renal fibrosis severity in CKD patients. A total of 160 patients with CKD who underwent kidney biopsy and renal US examination were prospectively enrolled. Patients were classified into the mild or moderate-severe fibrosis group based on pathology results. Radiomics features were extracted from the US images, and a radiomics signature was constructed using the maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithms. Multivariable logistic regression was employed to construct the radiomics model, which incorporated the radiomics signature and the selected clinical variables. The established model was evaluated for discrimination, calibration, and clinical utility in the derivation cohort and internal cross-validation (CV) analysis, respectively. The radiomics signature, consisting of nine identified fibrosis-related features, achieved moderate discriminatory ability with an area under the receiver operating characteristic curve (AUC) of 0.72 (95% confidence interval (CI) 0.64-0.79). By combining the radiomics signature with significant clinical risk factors, the radiomics model showed satisfactory discrimination performance, yielding an AUC of 0.85 (95% CI 0.79-0.91) in the derivation cohort and a mean AUC of 0.84 (95% CI 0.77-0.92) in the internal CV analysis. It also demonstrated fine accuracy via the calibration curve. Furthermore, the decision curve analysis indicated that the model was clinically useful. The proposed radiomics model showed favorable performance in determining the individualized risk of moderate-severe renal fibrosis in patients with CKD, which may facilitate more effective clinical decision-making.

Mathematical modeling and computational outcomes for the thermal oblique stagnation point investigation for non-uniform heat source and nonlinear chemical reactive flow of Maxwell nanofluid

Owing to fine thermal prospective of nanomaterials, different applications have been addressed in nanotechnology and thermal management systems. Multidisciplinary applications of such nanomaterials are commonly observed in nuclear reactors, thermal systems, transportation systems, industrial products cooling, power industry, renewable energy etc. The motivated work aims to predicts improved thermal consequences of Maxwell nanofluid with applications of non-uniform heat source and radiated phenomenon. The heat transfer analysis is assessed by using the Cattaneo-Christov theories. The thermal phenomenon is controlled with interference of slip effects. The flow pattern is based on the oblique stagnation point flow confined by stretched cylinder. The Keller Box numerical determination of problem is worked out with fine accuracy. The physical dynamic of flow parameters is visualized graphically. It is claimed that control of thermal transportation phenomenon is resulted when the slip effects are dominant. The exponential heat source with nonlinear radiated phenomenon is important when enhancement in thermal process needed.

A Fast and Robust Heterologous Image Matching Method for Visual Geo-Localization of Low-Altitude UAVs

Visual geo-localization can achieve UAVs (Unmanned Aerial Vehicles) position during GNSS (Global Navigation Satellite System) denial or restriction. However, The performance of visual geo-localization is seriously impaired by illumination variation, different scales, viewpoint difference, spare texture, and computer power of UAVs, etc. In this paper, a fast detector-free two-stage matching method is proposed to improve the visual geo-localization of low-altitude UAVs. A detector-free matching method and perspective transformation module are incorporated into the coarse and fine matching stages to improve the robustness of the weak texture and viewpoint data. The minimum Euclidean distance is used to accelerate the coarse matching, and the coordinate regression based on DSNT (Differentiable Spatial to Numerical) transform is used to improve the fine matching accuracy respectively. The experimental results show that the average localization precision of the proposed method is 2.24 m, which is 0.33 m higher than that of the current typical matching methods. In addition, this method has obvious advantages in localization robustness and inference efficiency on Jetson Xavier NX, which completed to match and localize all images in the dataset while the localization frequency reached the best.

Performance of Fine Motor Accuracy Skills and Preschool Children's Jawi Writing Skills

Preschool children in Malaysia have access to an early childhood education curriculum that includes components of fine motor proficiency through the acquisition of writing skills for a variety of characters. The purpose of this study is to determine the importance of fine motor accuracy abilities and Jawi character writing skills in preschool children, as well as to suggest the key components of Jawi writing skills test instruments. This study enlisted the participation of 22 youngsters (11 boys and 11 girls) aged 5 to 6 years old from a selected kindergarten. The Jawi Writing Skills Test and the Bruininks-Oseretsky Test of Motor Proficiency Instrument (BOTMP-2) were administered to each kid. Independent T Test analysis indicated a significant difference for Jawi Writing Skills Test between gender (t = 1.75, p <0.05) and BOTMP-2 Fine Motor Precision Test between 5 and 6 year old children (t = 4.59, p <0.05). The performance of the BOTMP -2 Fine Motor Precision Test items and the performance of each character on the Jawi Writing Skills Test had a moderate correlation (r value ranging from 0.318 to -.622). The findings suggest that fine motor precision skills can influence children's mastery of Jawi writing skills. Precision skills and fine motor integration, as well as observation towards motor skill movements, may be included in the construction of jawi writing skills testing instrument.

Decoupled Object-Independent Image Features for Fine Phasing of Segmented Mirrors Using Deep Learning

A segmented primary mirror is very important for extra-large astronomical telescopes, in order to detect the phase error between segmented mirrors. Traditional iterative algorithms are hard to detect co−phasing aberrations in real time due to the long-time iterative process. Deep learning has shown large potential in wavefront sensing, and it gradually focuses on detecting piston error. However, the current methods based on deep learning are mainly applied to coarse phase sensing, and only consider the detection of piston error with no tip/tilt errors, which is inconsistent with reality. In this paper, by innovatively designing the form of pupil mask, and further updating the OTF in the frequency domain, we obtain a new decoupled independent feature image that can simultaneously detect the piston error and tilt/tilt error of all sub-mirrors, which is effectively decoupled, and eliminates the dependence of the data set on the imaging object. Then, the Bi−GRU network is used to recover phase error information with high accuracy from the feature image proposed in this paper. The network’s detection accuracy ability is verified under single wavelength and broadband spectrum in simulation. This paper demonstrates that co−phasing errors can be accurately decoupled and extracted by the new feature image we proposed and will contribute to the fine phasing accuracy and practicability of the extended scenes for the segmented telescopes.

Mcera5: Driving microclimate models with ERA5 global gridded climate data

Abstract Microclimate models predict temperature and other meteorological variables at scales relevant to individual organisms. The broad application of microclimate models requires gridded macroclimatic variables as input. However, the spatial and temporal resolution of such inputs can be a limiting factor on the accuracy of microclimate predictions. Due to its fine resolution and accuracy, the ERA5 reanalysis dataset is emerging as the favoured resource for global historical weather and climate data and has great potential for aiding microclimate modelling. Here we describe mcera5, an R language package that provides convenient access to, and wrangling of, the ERA5 climate datasets for use in microclimate models. Through this package, we provide functions to query ERA5 data for desired spatial and temporal extents, to correct for spatial biases and process outputs for easy interpretation by ecologists, thereby allowing faster and more accurate microclimate predictions. By validating with empirical observations from multiple biomes globally, we demonstrate that the use of ERA5 climate forcing via mcera5 improves the prediction accuracy of soil moisture, air temperature and relative humidity as compared to forcing with other globally available data and offers comparable performance when predicting soil temperatures. Through the provision of fine‐resolution ERA5 data, the mcera5 package fits into an ecosystem of tools for modelling microclimate in a spatio‐temporally explicit fashion, advancing our ability to efficiently predict microclimate for any place on Earth for the past, present or future. The package also provides convenient access to ERA5 datasets for a range of other applications.