Search

The potential of micro electrochemical slotting is significant due to its adaptability and usefulness across diverse manufacturing applications. This technique holds potential, particularly for handling advanced engineering materials like superalloys, with a focus on applications requiring high strength, i.e., micro-cutting for gas-turbine parts, biomedical applications, etc. With this view, this article has been attempted to explore the influence of various considered machining factors, namely as; applied potential difference, tool feed rate (TFR), pulse supply, and modes of tool insulation on the performance measures, i.e. material removal rate (MRR) and surface roughness (Ra) while attempting micro-slotting in Nimonic-263 superalloy work samples. The entire experimentation was conducted on the in-house fabricated 3-axis micro-electrochemical setup designed for performing various complex machining operations at a miniature scale. Furthermore, this study has also been focused toward investigating the impact of varying the electrical conductivity by employing different tool materials (i.e. copper and SS304) on the process characteristics measured. The observed effects of the tool electrode's conductivity have also been expressed and supported by performing the simulation for electric field distribution using COMSOL Multiphysics software. The experimental results have revealed that the process input variables remarkably influence MRR and Ra, mainly applied voltage and TFR. The best values of MRR and Ra attained with SS304 and copper tool electrodes were 1.521 mg/min and 0.721 mg/min, and 0.038 µm and 0.029 µm, correspondingly. The microstructure analysis has further been conducted on machined micro-slotted work samples that provided detailed insights into various surface integrity aspects, which are essential for microdevice applications. [ABSTRACT FROM AUTHOR]

Glass is a widely used material in key fields such as Micro-Electro-Mechanical Systems (MEMS) due to its excellent properties. The existing non-traditional glass machining methods have problems such as high pollution, difficult operation, and poor sustainability, this article utilizes the effective combination of electrochemical discharge machining and grinding (named electrochemical discharge grinding, ECDG), by using NaHCO 3 solution as electrolyte to achieve green machining. Utilizing ultrasonic vibration and multi-hole tube electrode to achieve precise and stable machining. Modeling and simulation analysis were conducted on the material removal rate and grinding force during the machining process, which profoundly revealed the joint improvement mechanism of spark discharge and ultrasonic vibration on grinding quality. First, a single factor experiment was used to preliminarily determine the machining threshold. Second, the Plackett-Burman experiment was used to screen key machining parameters. Then, Box-Behnken experiment was conducted on key machining parameters, and multi-objective and multi-factor optimization was performed to obtain the optimal combination of machining parameters. Compared with normal ECDG with cylindrical grinding electrode, the overcut is reduced by 8.3 %, the edge damage is reduced by 17.5 % and the surface roughness value is reduced by 70.6 %. Finally, by using the optimized combination of machining parameters, high-quality and stable machining of typical microchannel structures was achieved. The milling depth of the microchannel is 400 µm. The machining width is 1175 ± 5 µm. The surface roughness of the measurement area is 0.375 µm. The green, high-quality and stable machining of micro glass micro components further demonstrates the potential application of this compound technology. [Display omitted] [ABSTRACT FROM AUTHOR]

The rapid development of new materials and tools demonstrates the need for efficient and feasible machining processes. Modern production systems must guarantee sustainable, flexible, productive and high-quality production at low cost. Therefore, the combination of technological data with advanced software solutions is very important, especially when using complex CNC machining systems, where the reliability of the technological information is crucial, as simple measures can make a very positive contribution to the productivity achieved. When optimising the machining parameters, costs and machining time must be taken into account. The article deals with the optimisation of the turning process with a large number of influencing variables (machine, workpiece material, tool, cutting parameters, costs, etc.) and two objectives: the fastest possible machining (minimisation of machining time) and the lowest possible machining costs. The model is designed in an Excel spreadsheet and the multi-criteria optimisation is carried out using the approximation method. Step by step, we can find the optimal processing regime for each selected case. [ABSTRACT FROM AUTHOR]

The field of 'explainable' artificial intelligence (XAI) has produced highly acclaimed methods that seek to make the decisions of complex machine learning (ML) methods 'understandable' to humans, for example by attributing 'importance' scores to input features. Yet, a lack of formal underpinning leaves it unclear as to what conclusions can safely be drawn from the results of a given XAI method and has also so far hindered the theoretical verification and empirical validation of XAI methods. This means that challenging non-linear problems, typically solved by deep neural networks, presently lack appropriate remedies. Here, we craft benchmark datasets for one linear and three different non-linear classification scenarios, in which the important class-conditional features are known by design, serving as ground truth explanations. Using novel quantitative metrics, we benchmark the explanation performance of a wide set of XAI methods across three deep learning model architectures. We show that popular XAI methods are often unable to significantly outperform random performance baselines and edge detection methods, attributing false-positive importance to features with no statistical relationship to the prediction target rather than truly important features. Moreover, we demonstrate that explanations derived from different model architectures can be vastly different; thus, prone to misinterpretation even under controlled conditions. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to study the effectiveness of various simple machine learning methods in the prediction of bank failures. From a raw dataset of 10,938 US banks during the period of 2000–2020, we find that machine learning approaches do not really outperform the benchmark of conventional statistical method, logistic regression. However, using PCA to retain relevant variance in variables significantly improve the performance of machine learning methods and raise the out-of-sample accuracy of those method to over 70% to over 80%. Of all the machine learning methods used in this paper, the simple KNN seems to be the best model in forecasting bank failure in the United States. [ABSTRACT FROM AUTHOR]

Smartphones are highly complex machines serving multiple purposes. Although access to different applications may be a source of inspiration and may lead to increased well‐being, excessive screen time usage can lead to increased stress, reduced physical activity, and neck and shoulder discomfort. The purpose of the present study was to evaluate the effects of (a) goal setting, (b) turning off notifications, and (c) sharing information about screen time with peers on smartphone usage in a sample of young adults who reported interest in reducing their smartphone usage. Goal setting and turning off notifications for social media applications effectively reduced smartphone usage for five out of seven participants. Sharing information with peers did not lead to decreased smartphone usage. Follow‐up measures showed an increase in smartphone usage compared to the intervention phases. With only a few studies on strategies to reduce smartphone usage within behavior analysis, the current study expands the literature and provides directions for future studies. [ABSTRACT FROM AUTHOR]

Highlights The primary factors affecting the geometric accuracy of abrasive water jet (AWJ) machining of ultra‐thick carbon fiber‐reinforced polymer (CFRP) laminates are the cutting front drag and kerf width deviation caused by energy dissipation. Firstly, this study comprehensively analyzes the influence of the coupling relationship between cutting front drag and kerf width deviation on the geometric accuracy of hole machining. Secondly, a gradient distribution theory for jet traverse speed with cutting depth is proposed for taper hole machining characteristics. Finally, a general geometric error model applicable to both straight and taper hole machining of ultra‐thick CFRP laminates using AWJ is established, along with a geometric error compensation method. A series of experiments validate the accuracy of the proposed geometric error model and compensation method. The novelty of this research lies in unifying the geometric error models for straight and taper hole machining of ultra‐thick CFRP laminates by considering the gradient distribution of jet traverse speed. The proposed error compensation method reduces the high degree of freedom required for machine tools. This study provides a general geometric error model and compensation strategy for AWJ hole machining of ultra‐thick CFRP laminates, which has significant engineering value for achieving 3D controllable AWJ machining of complex ultra‐thick CFRP components. The high geometric precision taper holes machining technique is a necessary prerequisite for achieving precise machining of complex trajectories in ultra‐thick CFRP with abrasive water jet. The influence of the coupling relationship between cutting front drag and kerf taper on the geometric accuracy of hole processing is comprehensively analyzed. A model considering the gradient distribution of traverse speed during taper holes machining was established, and a geometric error model considering this gradient distribution of traverse speed was developed. A taper holes machining geometric error compensation method with high engineering applicability was proposed. Geometric error prediction experiments and error compensation experiments were conducted, and the results proved the accuracy of the geometric error model and error compensation method. [ABSTRACT FROM AUTHOR]

In Computer-Controlled Optical Surfacing technology, the precision of the removal function directly affects the accuracy of computer-aided processing software predictions, which in turn influences subsequent polishing machine processing. A key parameter for constructing the removal function is the material removal rate, which is often challenging to obtain accurately. Currently, the Preston equation is widely used to describe the principles of material removal. However, as a linear equation that omits many factors, it struggles to accurately model the removal function in complex machining scenarios. Therefore, this paper proposes a hybrid neural network model combining Convolutional Neural Networks and Bidirectional Long Short-Term Memory to predict the material removal rate. The model's parameters are optimized using an improved Grey Wolf Optimization algorithm, ultimately establishing a removal function closely consistent with an actual removal function. We first tested our method on the PHM2016 Data Challenge dataset, achieving a mean squared error of 6.19 and an R2 of 0.9949, outperforming other mainstream neural network prediction models developed in recent years. Additionally, we further validated the performance of the neural network using a small grinding head polishing dataset, achieving MSE and R2 values of 1.9035 and 0.99902, respectively. Finally, we applied this method to construct the removal function on an actual small grinding head production line. Compared to the traditional Preston equation-based removal function, the predicted residual surface's PV and RMS errors were reduced from 28.24 % to 35.58 %–4.563 % and 4.86 %, respectively. These validation results demonstrate that the proposed method not only facilitates easier acquisition of the removal function model but also significantly enhances the accuracy of computer-aided processing software predictions, thereby better guiding ultra-precision machining processes. • Proposed a CNN-BiLSTM hybrid neural network to predict material removal rates. • First to construct a small grinding head removal function using a BiLSTM Predictor. • Optimized the neural network parameter sets using an improved Grey Wolf algorithm. • Achieved MSE of 6.19 and R2 of 0.9949 on the PHM2016 Data Challenge dataset. • Reduced polishing residual surface PV and RMS prediction errors to 4.563 % and 4.86 %. [ABSTRACT FROM AUTHOR]

Polarimetric Thomson scattering (PTS) is a technique that allows for accurate measurements of electron temperature (Te) in very hot plasmas (Te > 10 keV, a condition expected to be regularly achieved in ITER). Under such conditions, the spectral region spanned by the TS spectrum is large and extends to low wavelengths, where the transmission of the collection optics decreases, available detectors are less efficient, and the high level of plasma background light perturbs the measurements. This work presents the recent developments in the design of a PTS system for ITER, along with the challenges posed by the complex machine design. The system performance is assessed for an updated geometry (with respect to previous publication), showing that, with a scattering angle θscat = 167°, the expected signal is strongly reduced. Potential alternatives are analyzed: (1) a system employing a different laser injection position, allowing for a more favorable scattering angle and (2) a recently proposed dual-polarization laser pulse technique. The latter is evaluated for the possible ITER geometry, again showing that a more favorable scattering angle is needed for a robust performance. [ABSTRACT FROM AUTHOR]

There is a growing interest in the early prediction of outcomes in ongoing business processes. Predictive process monitoring distills knowledge from the sequence of event data generated and stored during the execution of processes and trains models on this knowledge to predict outcomes of ongoing processes. However, most state-of-the-art methods require the training of complex and inefficient machine learning models and hyper-parameter optimization as well as numerous input data to achieve high performance. In this paper, we present a novel approach based on Hyperdimensional Computing (HDC) for predicting the outcome of ongoing processes before their completion. We highlight its simplicity, efficiency, and high performance while utilizing only a subset of the input data, which helps in achieving a lower memory demand and faster and more effective corrective measures. We evaluate our proposed method on four publicly available datasets with a total of 12 binary prediction tasks. Our proposed method achieves an average 6% higher area under the ROC curve (AUC) and up to a 14% higher F1-score, while yielding a 20× earlier prediction than state-of-the-art conventional machine learning- and neural network-based models. [ABSTRACT FROM AUTHOR]

Political partisanship constitutes a pivotal group identity that significantly influences individuals' voting behaviors and shapes their ideological and cultural perspectives. While traditional surveys and experimental studies can directly capture political identity by asking the participants, this task has become intricate when employing digital trace data sourced from social media. Previous classification methods, attempting to infer political identity from users' networks or textual content, suffered from limited efficiency or generalizability. In response, this study introduces a two-step method that utilizes deep learning models to enhance classification efficiency, generalizability, and interpretability. In the first step, two deep learning models, trained on 2.5 million tweets from 825 Congressional politicians in the U.S., achieved accuracy rates of 87.71% and 89.54%, respectively, in detecting politicians' partisanships based on their individual tweets. Subsequently, in the second step, by employing a simple machine learning model that leverages the aggregated predicted values derived from the first-step models, accuracy rates of 94.92% and 96.61% were attained for identifying non-politician users' political identities based off their 50 and 200 tweets, respectively. In addition, an attention mechanism was integrated into the deep learning model to assess the contribution of each word in the classification process. [ABSTRACT FROM AUTHOR]

Counterfactual explanations modify the feature values of an instance in order to alter its prediction from an undesired to a desired label. As such, they are highly useful for providing trustworthy interpretations of decision-making in domains where complex and opaque machine learning algorithms are utilized. To guarantee their quality and promote user trust, they need to satisfy the faithfulness desideratum, when supported by the data distribution. We hereby propose a counterfactual generation algorithm for mixed-feature spaces that prioritizes faithfulness through k-justification, a novel counterfactual property introduced in this paper. The proposed algorithm employs a graph representation of the search space and provides counterfactuals by solving an integer program. In addition, the algorithm is classifier-agnostic and is not dependent on the order in which the feature space is explored. In our empirical evaluation, we demonstrate that it guarantees k-justification while showing comparable performance to state-of-the-art methods in feasibility, sparsity, and proximity. [ABSTRACT FROM AUTHOR]

The grouping of nodes into subsets that are relatively densely interconnected and separable from the rest of the network is a property often displayed in many complex real-world networks; this feature is known as a community structure. There is a growing demand for algorithms that can find partitions that resemble the community structure of a given network as closely as possible. However, most popular algorithms for community detection in graphs have one serious drawback, namely, they are heuristic-based and in many cases are unable to find a near-optimal solution. Moreover, their results are volatile, impacting the replicability of their results. In this paper, we investigate if the performance of greedy algorithms might be improved by initialising such algorithms with some carefully chosen partition of nodes, namely a partition obtained by embedding the nodes into real numbers space and then running a clustering algorithm on this latent representation. We believe that embedding will filter unwanted noise while retaining the proximity of nodes belonging to the same community or will learn more complex and elusive relations between nodes. Then, clustering algorithms run on this embedding will create a stable partitioning that will reduce the uncertainty in the initial phases of the community detection algorithms. The experiments show that the proposed procedure significantly improves the results over baseline community detection algorithms, namely Louvain and Leiden. It also reduces the inherent volatility of such algorithms. The impact depends on the given graph's properties, especially the strength of the community structure and degree distribution. The largest boost in performance is given in the cases when networks are 'noisier', that is, when the community structure is less pronounced and there are many connections between communities. Furthermore, the design and parametrization of the procedure depend on the network's topology, not on the size of the network itself. [ABSTRACT FROM AUTHOR]

Deep generative models, especially Generative Adversarial Networks (GANs), have recently demonstrated outstanding performance in real-world applications such as image generation and content production for large-scale public datasets and social media platforms, enabling high-resolution compelling fake content generation. Deepfakes have raised global concerns and disbelief due to the potential implications of misleading multimedia. Several algorithms to detect deepfakes have emerged; however, most rely on deep learning and video datasets. So, an automated approach is needed to detect the generated deepfakes. This paper presents a deepfake detection technique employing simple machine learning to identify low-resolution deepfakes and introduces a dataset that contains high-resolution, low-resolution, and mixed images. The method utilizes classification algorithms and an ensemble model in the frequency domain. K-fold cross-validation determines an average detection accuracy of 99.97% for high-resolution datasets using random forest, 98.27% for low-resolution datasets using SVM polynomial, and 98.72% for mixed datasets using voting classifier. [ABSTRACT FROM AUTHOR]

Many odors, like perfumes, are complex mixtures of chiral and achiral molecules where the cost‐efficient (enantio−)selective sensing represents a major technical challenge. Here, we present a colorimetric sensor array of surface‐mounted metal‐organic‐framework (SURMOF) films in Fabry‐Pérot (FP) cavities. The optical properties of the FP‐SURMOF films with different chiral and achiral structures are affected by the (enantio−)selective adsorption of the analytes in the SURMOF pores, resulting in different responses to the analyte molecules. The read‐out of the sensor array is performed by the digital camera of a common smartphone, where the RGB values are determined. By analyzing the sensor array data with simple machine learning algorithms, the analytes are discriminated. After demonstrating the enantioselective response for a pair of pure chiral odor molecules, we apply the sensor array to detect and discriminate a large number (16) of common commercial perfumes and eau de toilettes. While our untrained human nose is not able to discriminate all perfumes, the presented colorimetric sensor array can classify all perfumes with great classification accuracy. Moreover, the sensor array was used to identify unlabeled samples correctly. We foresee such an FP‐chiral‐SURMOF‐based sensor array as a powerful approach toward inexpensive selective odors sensing applications. [ABSTRACT FROM AUTHOR]

Interactions between eukaryotic hosts and their bacterial symbionts drive key ecological and evolutionary processes, from regulating ecosystems to the evolution of complex molecular machines and processes. Over time, endosymbionts generally evolve reduced genomes, and their relationship with their host tends to stabilize. However, host–bacteria relationships may be heavily influenced by environmental changes. Here, we review these effects on one of the most ancient and diverse endosymbiotic groups, formed by—among others— Legionellales, Francisellaceae , and Piscirickettsiaceae. This group is referred to as Deep-branching Intracellular Gammaproteobacteria (DIG), whose last common ancestor presumably emerged about 2 Ga ago. We show that DIGs are globally distributed, but generally at very low abundance, and are mainly identified in aquatic biomes. Most DIGs harbour a type IVB secretion system, critical for host-adaptation, but its structure and composition vary. Finally, we review the different types of microbial interactions that can occur in diverse environments, with direct or indirect effects on DIG populations. The increased use of omics technologies on environmental samples will allow a better understanding of host–bacterial interactions and help unravel the definition of DIGs as a group from an ecological, molecular, and evolutionary perspective. [ABSTRACT FROM AUTHOR]

Background: It is vital to accurately and promptly distinguish unstable from stable intracranial aneurysms (IAs) to facilitate treatment optimization and avoid unnecessary treatment. The aim of this study is to develop a simple and effective predictive model for the clinical evaluation of the stability of IAs. Methods: In total, 1,053 patients with 1,239 IAs were randomly divided the dataset into training (70%) and internal validation (30%) datasets. One hundred and ninety seven patients with 229 IAs from another hospital were evaluated as an external validation dataset. The prediction models were developed using machine learning based on clinical information, manual parameters, and radiomic features. In addition, a simple model for predicting the stability of IAs was developed, and a nomogram was drawn for clinical use. Results: Fourteen machine learning models exhibited excellent classification performance. Logistic regression Model E (clinical information, manual parameters, and radiomic shape features) had the highest AUC of 0.963 (95% CI 0.943-0.980). Compared to manual parameters, radiomic features did not significantly improve the identification of unstable IAs. In the external validation dataset, the simplified model demonstrated excellent performance (AUC = 0.950) using only five manual parameters. Conclusion: Machine learning models have excellent potential in the classification of unstable IAs. The manual parameters from CTA images are sufficient for developing a simple and effective model for identifying unstable IAs. [ABSTRACT FROM AUTHOR]

In order to improve the performance of any machine learning model, it is important to focus more on the data itself instead of continuously developing new algorithms. This is exactly the aim of feature engineering. It can be defined as the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to our benefit, ideally both in terms of accuracy and interpretability at the same time. Often times it will be applied in combination with simple machine learning techniques such as regression models or decision trees to boost their performance (whilst maintaining the interpretability property which is so often needed in analytical modeling) but it may also improve complex techniques such as XGBoost and neural networks. Feature engineering aims at designing smart features in one of two possible ways: either by adjusting existing features using various transformations or by extracting or creating new meaningful features (a process often called "featurization") from different sources (e.g., transactional data, network data, time series data, text data, etc.). [ABSTRACT FROM AUTHOR]

Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learning models in the industry and academia. There are several algorithmic approaches for computing different versions of Shapley value explanations. Here, we consider Shapley values incorporating feature dependencies, referred to as conditional Shapley values, for predictive models fitted to tabular data. Estimating precise conditional Shapley values is difficult as they require the estimation of non-trivial conditional expectations. In this article, we develop new methods, extend earlier proposed approaches, and systematize the new refined and existing methods into different method classes for comparison and evaluation. The method classes use either Monte Carlo integration or regression to model the conditional expectations. We conduct extensive simulation studies to evaluate how precisely the different method classes estimate the conditional expectations, and thereby the conditional Shapley values, for different setups. We also apply the methods to several real-world data experiments and provide recommendations for when to use the different method classes and approaches. Roughly speaking, we recommend using parametric methods when we can specify the data distribution almost correctly, as they generally produce the most accurate Shapley value explanations. When the distribution is unknown, both generative methods and regression models with a similar form as the underlying predictive model are good and stable options. Regression-based methods are often slow to train but quickly produce the Shapley value explanations once trained. The vice versa is true for Monte Carlo-based methods, making the different methods appropriate in different practical situations. [ABSTRACT FROM AUTHOR]

Hand gesture recognition using a camera provides an intuitive and promising means of human-computer interaction and allows operators to execute commands and control machines with simple gestures. Research in hand gesture recognition-based control systems has garnered significant attention, yet the deploying of microcontrollers into this domain remains relatively insignificant. In this study, we propose a novel approach utilizing micro-hand gesture recognition built on micro-bottleneck Residual and micro-bottleneck Conv blocks. Our proposed model, comprises only 42K parameters, is optimized for size to facilitate seamless operation on resource-constrained hardware. Benchmarking conducted on STM32 microcontrollers showcases remarkable efficiency, with the model achieving an average prediction time of just 269ms, marking a 7× faster over the state-of-art model. Notably, despite its compact size and enhanced speed, our model maintains competitive performance result, achieving an accuracy of 99.6% on the ASL dataset and 92% on OUHANDS dataset. These findings underscore the potential for deploying advanced control methods on compact, costeffective devices, presenting promising avenues for future research and industrial applications. [ABSTRACT FROM AUTHOR]

The use of loose parts can be an effective teaching method to improve and enhance student understanding of Next Generation Science Standards (NGSS). Loose parts can inspire children to use their creativity and critical thinking skills in the classroom, which encourages them to embrace science curriculum and connections to standards at any age. Students learn most readily and positively in a laboratory type environment, where they can experiment, create, invent, appreciate, and find out things for themselves. Children should be encouraged to be part of the experiment, not just listening at their desk, or watching on the sidelines. This article outlines the theory, associated research, principles, benefits, and developing a loose parts mindset, with examples of light exploration, magnetic games, forces loose parts learning lab, and nest building using natural materials. Loose parts can be applied to most educational topics and content, making it a universal learning tool, putting students at the center of their education. [ABSTRACT FROM AUTHOR]

The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era. [ABSTRACT FROM AUTHOR]

This work presents the application of an Encoder-Decoder convolutional neural network (ED-CNN) model to automatically segment COVID-19 computerised tomography (CT) data. By doing so we are producing an alternative model to current literature, which is easy to follow and reproduce, making it more accessible for real-world applications as little training would be required to use this. Our simple approach achieves results comparable to those of previously published studies, which use more complex deep-learning networks. We demonstrate a high-quality automated segmentation prediction of thoracic CT scans that correctly delineates the infected regions of the lungs. This segmentation automation can be used as a tool to speed up the contouring process, either to check manual contouring in place of a peer checking, when not possible or to give a rapid indication of infection to be referred for further treatment, thus saving time and resources. In contrast, manual contouring is a time-consuming process in which a professional would contour each patient one by one to be later checked by another professional. The proposed model uses approximately 49 k parameters while others average over 1,000 times more parameters. As our approach relies on a very compact model, shorter training times are observed, which make it possible to easily retrain the model using other data and potentially afford "personalised medicine" workflows. The model achieves similarity scores of Specificity (Sp) = 0.996 ± 0.001, Accuracy (Acc) = 0.994 ± 0.002 and Mean absolute error (MAE) = 0.0075 ± 0.0005. [ABSTRACT FROM AUTHOR]

The application of Machine Learning (ML) algorithms in chemical sciences, particularly computational chemistry, is a vastly emerging area of modern research. While many applications of ML techniques have already been in place to use ML based potential energies in various dynamical simulation studies, specific applications are also being successfully tested. In this work, the ML algorithms are tested to calculate the unimolecular dissociation time of benzene–hexachlorobenzene, benzene–trichlorobenzene, and benzene–monochlorobenzene complexes. Three ML algorithms, namely, Decision-Tree-Regression (DTR), Multi-Layer Perceptron, and Support Vector Regression are considered. The algorithms are trained with simulated dissociation times as functions (attributes) of complexes' intramolecular and intermolecular vibrational energies. The simulation data are used for an excitation temperature of 1500 K. Considering that the converged result is obtained with 1500 trajectories, an ML algorithm trained with 700 simulation points provides the same dissociation rate constant within statistical uncertainty as obtained from the converged 1500 trajectory result. The DTR algorithm is also used to predict 1000 K simulation results using 1500 K simulation data. [ABSTRACT FROM AUTHOR]

Nanofluidics, the field interested in flows at the smallest scales, has grown at a fast pace, reaching an ever finer control of fluidic and ionic transport at the molecular level. Until now, artificial pores are far from reaching the wealth of functionalities of biological channels that regulate sensory detection, biological transport, and neurostransmission—all while operating at energies comparable to thermal noise. Here, we argue that artificial ionic machines can be designed by harnessing the entire wealth of phenomena available at the nanoscales and exploiting techniques developed in various fields of physics. As they are generally based on solid-state nanopores, rather than soft membranes and proteins, they should, in particular, aim at taking advantage of their specific properties, such as their electronic structure or their ability to interact with light. These observations call for the design of new ways of probing nanofluidic systems. Nanofluidics is now at the crossroads, there are new avenues to build complex ionic machines, and this may allow to develop new functionalities inspired by nature. [ABSTRACT FROM AUTHOR]

Background: Surgery in geriatric patients often poses risk of major postoperative complications. Acute kidney injury (AKI) is a common complication following noncardiac surgery and is associated with increased mortality. Early identification of geriatric patients at high risk of AKI could facilitate preventive measures and improve patient prognosis. This study used machine learning methods to identify important features and predict AKI following noncardiac surgery in geriatric patients. Methods: The data for this study were obtained from a prospective cohort. Patients aged ≥ 65 years who received noncardiac surgery from June 2019 to December 2021 were enrolled. Data were split into training set (from June 2019 to March 2021) and internal validation set (from April 2021 to December 2021) by time. The least absolute shrinkage and selection operator (LASSO) regularization algorithm and the random forest recursive feature elimination algorithm (RF-RFE) were used to screen important predictors. Models were trained through extreme gradient boosting (XGBoost), random forest, and LASSO. The SHapley Additive exPlanations (SHAP) package was used to interpret the machine learning model. Results: The training set included 6753 geriatric patients. Of these, 250 (3.70%) patients developed AKI. The XGBoost model with RF-RFE selected features outperformed other models with an area under the precision-recall curve (AUPRC) of 0.505 (95% confidence interval [CI]: 0.369–0.626) and an area under the receiver operating characteristic curve (AUROC) of 0.806 (95%CI: 0.733–0.875). The model incorporated ten predictors, including operation site and hypertension. The internal validation set included 3808 geriatric patients, and 96 (2.52%) patients developed AKI. The model maintained good predictive performance with an AUPRC of 0.431 (95%CI: 0.331–0.524) and an AUROC of 0.845 (95%CI: 0.796–0.888) in the internal validation. Conclusions: This study developed a simple machine learning model and a web calculator for predicting AKI following noncardiac surgery in geriatric patients. This model may be a valuable tool for guiding preventive measures and improving patient prognosis. Trial registration: The protocol of this study was approved by the Committee of Ethics from West China Hospital of Sichuan University (2019–473) with a waiver of informed consent and registered at www.chictr.org.cn (ChiCTR1900025160, 15/08/2019). [ABSTRACT FROM AUTHOR]

The characterization of the detection capability assumes significance when the reliable monitoring of the region of interest by a non-contact sensor is a safety-relevant function. This paper introduces new validation scores that evaluate the detection capability of non-contact sensors intended to be applied to outdoor machines. The scores quantify, in terms of safety, the suitability of the sensor for the intended implementation in an environmental perception system of (highly) automated machines. This was achieved by developing an extension to the new Real Environment Detection Area (REDA) method and linking the methodology with the sensor standard IEC/TS 62998-1. The extension includes point-by-point and statistic-based error evaluation which leads to the Usability-Score, Availability-Score, and Reliability-Score. By applying the principle in the agricultural sector using ISO 18497 and linking this with data from a real outdoor test stand, it was possible to show that the validation scores offer a generic approach to quantify the detection capability and express this in a machine manufacturer-oriented manner. The findings of this study have significant implications for the advancement of safety-related sensor systems integrated into machines operating in complex environments. In order to achieve full implementation, it is necessary to define in the standards which score is required for each Performance Level (PL). [ABSTRACT FROM AUTHOR]

Robotic control is a fundamental part of autonomous robots. Modular legged and climbing robots are complex machines made up of a variety of subsystems, ranging from a single robot with simple legs to a complex system composed of multiple legs (or modules) with computing power and sensitivity. Their complexity, which is increased by the fact of needing elements for climbing, makes a correct structure crucial to achieve a complete, robust, and versatile system during its operation. Control architectures for legged robots are distinguished from other software architectures because of the special needs of these systems. In this paper, we present an original classification of modular legged and climbing robots, a comprehensive review of the most important control architectures in robotics, focusing on the control of modular legged and climbing robots, and a comparison of their features. The control architecture comparison aims to provide the analytical tools necessary to make informed decisions tailored to the specific needs of your robotic applications. This article includes a review and classification of modular legged and climbing robots, breaking down each category separately. [ABSTRACT FROM AUTHOR]

Molecular robotics is challenging, so it seems best to keep it simple. We consider an abstract molecular robotics model based on simple folding instructions that execute asynchronously. Turning Machines are a simple 1D to 2D folding model, also easily generalisable to 2D to 3D folding. A Turning Machine starts out as a line of connected monomers in the discrete plane, each with an associated turning number. A monomer turns relative to its neighbours, executing a unit-distance translation that drags other monomers along with it, and through collective motion the initial set of monomers eventually folds into a programmed shape. We provide a suite of tools for reasoning about Turning Machines by fully characterising their ability to execute line rotations: executing an almost-full line rotation of 5 π / 3 radians is possible, yet a full 2 π rotation is impossible. Furthermore, line rotations up to 5 π / 3 are executed efficiently, in O (log n) expected time in our continuous time Markov chain time model. We then show that such line-rotations represent a fundamental primitive in the model, by using them to efficiently and asynchronously fold shapes. In particular, arbitrarily large zig-zag-rastered squares and zig-zag paths are foldable, as are y-monotone shapes albeit with error (bounded by perimeter length). Finally, we give shapes that despite having paths that traverse all their points, are in fact impossible to fold, as well as techniques for folding certain classes of (scaled) shapes without error. Our approach relies on careful geometric-based analyses of the feats possible and impossible by a very simple robotic system, and pushes conceptional hardness towards mathematical analysis and away from molecular implementation. [ABSTRACT FROM AUTHOR]

Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. Neural correlates of elicited perceptions could be used for fast automatic optimization, with electroencephalography as a natural choice as it can be acquired non-invasively. Nonetheless, its low signal-to-noise ratio may hinder discrimination of similar visual patterns, preventing its use in the optimization of electrical stimulation. Our work investigates for the first time the discriminability of the electroencephalographic responses to visual stimuli compatible with electrical stimulation, employing a newly acquired dataset whose stimuli encompass the concurrent variation of several features, while neuroscience research tends to study the neural correlates of single visual features. We then performed above-chance single-trial decoding of multiple features of our newly crafted visual stimuli using relatively simple machine learning algorithms. A decoding scheme employing the information from multiple stimulus presentations was implemented, substantially improving our decoding performance, suggesting that such methods should be used systematically in future applications. The significance of the present work relies in the determination of which visual features can be decoded from electroencephalographic responses to electrical stimulation-compatible stimuli and at which granularity they can be discriminated. Our methods pave the way to using electroencephalographic correlates to optimize electrical stimulation parameters, thus increasing the effectiveness of current visual neuroprostheses. [ABSTRACT FROM AUTHOR]

Glass fiber–reinforced polymer (GFRP) composites are widely used materials in construction. Their use in structural applications, especially in civil infrastructure, requires structural analysis and design of their connections with proper safety and reliability levels. This work implements complex machine learning tools and schemes on a large amount of pin-bearing strength data on pultruded GFRP materials from the literature to extract correlations between various input parameters that affect the resulting bearing strength. This is performed to bridge the gaps in the findings that are associated with these data and to verify the recommended formulas in the corresponding design manuals and codes that are used for these materials in construction. The results of the derived gradient boosting model (GBM) could be used to predict the bearing strength of any pultruded composite without implementing experimental testing, therefore reducing operational costs and time. The derived model was exploited to investigate the effect of the hole diameter, the angle between the applied load and the pultrusion direction, and the effect of service temperature on the bearing strength of pultruded composites. The results imply the need to modify the used formulas in design standards; therefore, the proposed modifications are highlighted to properly predict the bearing strength for composite materials to be used in construction. [ABSTRACT FROM AUTHOR]

This study explores the ways in which young second language learners in an intact fourth‐grade content and language integrated (CLIL) science class drew on the affordances of multiple semiotic resources including language, images, sound, movement, etc., to construct disciplinary knowledge in the context of a multimodal project on machines. After an instructional period in which attention to literacy was embedded in their science classes, the learners designed and illustrated their own complex machine, filmed a digital video and produced a written explanation. By integrating a systemic functional linguistics‐informed analysis of the linguistic features of the children's written texts with a semiotic analysis of the intermodal relationships in their projects, we offer insights into the interrelation of different modes (linguistic, visual and aural) in facilitating children's meaning‐making in the L2. The results revealed considerable within‐group variability in the learners' written texts and intermodal relations of concurrence and complementarity in their multimodal projects. The role of multimodal tasks in providing all children with effective tools for communicating knowledge is discussed. [ABSTRACT FROM AUTHOR]

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning. This task is very complex, as the behaviour of road agents depends on many factors and the number of possible future trajectories can be considerable (multi‐modal). Most prior approaches proposed to address multi‐modal motion prediction are based on complex machine learning systems that have limited interpretability. Moreover, the metrics used in current benchmarks do not evaluate all aspects of the problem, such as the diversity and admissibility of the output. The authors aim to advance towards the design of trustworthy motion prediction systems, based on some of the requirements for the design of Trustworthy Artificial Intelligence. The focus is on evaluation criteria, robustness, and interpretability of outputs. First, the evaluation metrics are comprehensively analysed, the main gaps of current benchmarks are identified, and a new holistic evaluation framework is proposed. Then, a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system. To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework, an intent prediction layer that can be attached to multi‐modal motion prediction models is proposed. The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi‐modal trajectories and intentions. The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autonomous vehicles, advancing the field towards greater safety and reliability. [ABSTRACT FROM AUTHOR]

In South Korea, the configuration of land parcels within apartment complexes plays a pivotal role in optimizing land use and facility placement. Given the significant impact of land shape on architectural and urban planning outcomes, its analysis is essential. However, studies on land shape have been limited due to the lack of definitive survey criteria. To address these challenges, this study utilized a map application programming interface (API) to gather raw data on apartment complex layouts in South Korea and processed these images using a Python-based image library. An initial analysis involved categorizing the data through K-means clustering. Each cluster's average image was classified into four distinct groups for comparison with the existing literature. Shape indices were employed to analyze land configurations and assess consistency across classes. These classes were annotated on a parcel level using the Roboflow API, and YOLOv8s-cls was developed to classify the parcels effectively. The evaluation of this model involved calculating accuracy, precision, recall, and F1-score from a confusion matrix. The results show a strong correlation between the identified and established classes, with the YOLO model achieving an accuracy of 86% and demonstrating robust prediction capabilities across classes. This confirms the effective typification of land shapes in the studied apartment complexes. This study introduces a methodology for analyzing parcel shapes through machine learning and deep learning. It asserts that this approach transcends the confines of South Korean apartment complexes, extending its applicability to architectural and urban design planning on a global scale. Analyzing land shapes earmarked for construction enables the formulation of diverse design strategies for building placement and external space arrangement. This highlights the potential for innovative design approaches in architectural and urban planning worldwide. [ABSTRACT FROM AUTHOR]

In the present work, four machine learning algorithms back propagation neural network, multilayer perceptron, random forest and support vector regression were employed to predict both the impact sensitivity and electrostatic spark sensitivity of energetic compounds. Based on the density functional theory calculations mainly, there are nine and ten different kinds of descriptors which contain the important parameters related to the molecular composition and structure, the electronic structure, the structural stability and strength, and the energy performance have been selected out to construct the ML models for predicting the impact sensitivity and electrostatic spark sensitivity, respectively. It could be found that selected descriptors are well correlated with the experimental results which have high Pearson's correlation coefficients generally. For the impact sensitivity, the oxygen balance, charge of nitro group, heat of release and self-multiplying coefficient are key descriptors which should be with larger weightiness in the construction of the ML model, while those are the detonation velocity and detonation pressure in the ML of electrostatic spark sensitivity. Based on the analysis and comparing of various parameters for the assessment of ML models, for both the impact sensitivity and electrostatic spark sensitivity, the back propagation neural network model was found to be possess high accuracy and better than other three used models. This work may provide new methods for predicting the sensitivity of energetic compounds and promote the application of machine learning in the field of energetic compounds. [ABSTRACT FROM AUTHOR]

The capacity of zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complex (Zn(NH2-TBA)2) and modified Zn(NH-TBA)2COMe complex for removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions was investigated through adsorption modeling and artificial intelligence tools. Analyzing the adsorption characteristics of pesticides helps in studying the groundwater pollution by pesticides in agriculture area. In this study, Zn(NH2-TBA)2 was synthesized using Schiff base and its surface was modified using acetic anhydride group and their physical characteristics were identified using proton NMR, FTIR, and XRD. NMR results showed maximum modification yield obtained was 65% after 5 days. The porous structure and surface area monitored using nitrogen isotherm and BET surface area analysis presented relatively less surface area and porosity after modification. Adsorption modelling indicated that Toth model with a maximum adsorption capacity of 150.8 mg/g and 100.7 mg/g represents the homogenous adsorption systems which satisfy both low- and high-end boundary of adsorbate concentration in all settings according to the optimum point, while the kinetics and rate of 2,4-D adsorption follow the pseudo-first-order kinetic model in all situations. Artificial neural network (ANN), support vector regression, and particle swarm optimized least squares-support vector regression (PSO-LSSVR) were used for the optimization and modelling of adsorbent mass, adsorbate concentration, contact time, and temperature to develop predictive equations for the simulation of the adsorption efficiency of 2,4-D pesticide. The obtained results exhibited the better performance of ANN and PSO-LSSVR for prediction of adsorption results. The mean square error values of ANN (0.001, 0.012) and PSO-LSSVR (0.121, 0.105) were obtained for Zn(NH2-TBA)2 and Zn(NH-TBA)2COMe, respectively, while their respective coefficient of determination (R2) obtained were 0.999 and 0.988 for ANN and 0.980 and 0.825 for PSO-LSSVR. The study specified that machine learning predictive behavior performed better for Zn(NH2-TBA)2 compared to Zn(NH-TBA)2COMe that is also supported by theoretical kinetics and isotherm models. The research concludes that artificial intelligence models are the most efficient tools for studying the predictive behavior of adsorption data. [ABSTRACT FROM AUTHOR]

The use of CNC equipment that integrates several machining operations eliminates downtime due to changes in setup and clamping of workpieces in more than one machining device. A review of CNC equipment and tools known from the literature and from manufacturers' offerings indicates that new technical solutions are being developed to integrate two or more technological operations. However, these examples have numerous limitations and are mostly not suitable for machining surfaces with complex shapes. An example of such solutions is the use of a dual-tool grinding head, which integrates the process of rough grinding with a ceramic grinding wheel and finish grinding with a flexible grinding wheel. Unfortunately, it has the disadvantage of being limited by the angular shape of the ceramic grinding wheel, making it unable to adapt to the complex geometries of the shaped surfaces being ground. The need to overcome this limitation became the motivation for the research work described in this article. By means of experimental research, it was verified what effect the radial outline on the periphery of a ceramic grinding wheel realized by rough grinding would have on the surface roughness parameters obtained in the process of grinding shaped surfaces. For this purpose, grinding processes using a ceramic wheel with a conical and radial outline were compared. The result of the study was a summary of the surface roughness parameters Sa, St, Sq, Spk, Str, and Sds obtained after two-stage machining (rough and finish grinding). The obtained analysis results showed that changing the axial outline of the ceramic grinding wheel makes it possible to significantly expand the range of applications of the dual-tool head without negatively affecting the quality of the machined surface. Thus, such an improvement will make it possible to increase the applicability of the head by grinding shaped surfaces with a radial profile of curvature. [ABSTRACT FROM AUTHOR]

Accurate prediction of survival days (SD) is vital for planning treatments in glioma patients, as type‐IV tumors typically have a poor prognosis and meager survival rates. SD prediction is challenging and heavily dependent on the extracted feature sets. Additionally, comprehending the behavior of complex machine learning models is a vital yet challenging aspect, particularly to integrate such models into the medical domain responsibly. Therefore, this study develops a robust feature set and an ensemble‐based regressor model to predict patients' SD accurately. We aim to understand how these features behave and contribute to predicting SD. To accomplish this, we employed post‐hoc interpretable techniques, precisely Shapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), and Accumulated Local Effects (ALE) plots. Furthermore, we introduced an investigation to establish a direct connection between radiomic features and their biological significance to enhance the interpretability of radiomic features. The best SD scores on the BraTS2020 training set are 0.504 for accuracy, 59927.38 mean squared error (MSE), 20101.86 median squared error (medianSE), and 0.725 Spearman ranking coefficient (SRC). The validation set's accuracy is 0.586, MSE is 76529.43, medianSE is 41402.78, and SRC is 0.52. The proposed predictor model exhibited superior performance compared with leading contemporary approaches across multiple performance metrics. [ABSTRACT FROM AUTHOR]

In response to the poor fertilization performance of the deep application mechanism of the deep application liquid fertilizer applicator under multiple parameters, the fertilization variation of the crank rocker deep application mechanism under multiple working parameters is explored. To obtain the fertilization variation of the crank rocker deep application mechanism, a fertilization performance test bench for the crank rocker deep application mechanism is developed. On this test bench, the crank speed, liquid pump pressure, and spray hole diameter are used as experimental factors, and fertilizer application rate is used as experimental indicators. A composite design scheme of rotation center is adopted to establish a relationship model and response surface diagram between experimental influencing factors and influencing indicators. Design Expert 8.0.10 software is used to analyze and optimize the experimental data. The optimal results are a crank speed of 145.80 r/min, a liquid pump pressure of 0.25 MPa, a spray hole diameter of 3.02 mm, a fertilizer application rate of 28.6 mL, and a fertilizer loss rate of 1.95%. At this time, the fertilization performance of the mechanism is optimal. This parameter combination is applied for testing and verification to verify its rationality. The results can ensure that the crank rocker deep application mechanism has good working performance when working under multiple parameters, providing theoretical reference for designing deep application liquid fertilizer machines with simple structure and optimal fertilization performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a daily living situation where objects in a kitchen can be grasped and stored in specific containers using a virtual robot arm operated by different myoelectric control modes. The main goal of this study is to prove the feasibility of providing virtual environments controlled through surface electromyography that can be used for the future training of people using prosthetics or with upper limb motor impairments. We propose that simple control algorithms can be a more natural and robust way to interact with prostheses and assistive robotics in general than complex multipurpose machine learning approaches. Additionally, we discuss the advantages and disadvantages of adding intelligence to the setup to automatically assist grasping activities. The results show very good performance across all participants who share similar opinions regarding the execution of each of the proposed control modes. [ABSTRACT FROM AUTHOR]

Exploring the Moon and Mars are crucial steps in advancing space exploration. Numerous missions aim to land and research in various lunar locations, some of which possess challenging surfaces with unchanging features. Some of these areas are cataloged as lunar light plains. Their main characteristics are that they are almost featureless and reflect more light than other lunar surfaces. This poses a challenge during navigation and landing. This paper compares traditional feature matching techniques, specifically scale-invariant feature transform and the oriented FAST and rotated BRIEF, and novel machine learning approaches for dense feature matching in challenging, unstructured scenarios, focusing on lunar light plains. Traditional feature detection methods often need help in environments characterized by uniform terrain and unique lighting conditions, where unique, distinguishable features are rare. Our study addresses these challenges and underscores the robustness of machine learning. The methodology involves an experimental analysis using images that mimic lunar-like landscapes, representing these light plains, to generate and compare feature maps derived from traditional and learning-based methods. These maps are evaluated based on their density and accuracy, which are critical for effective structure-from-motion reconstruction commonly utilized in navigation for landing. The results demonstrate that machine learning techniques enhance feature detection and matching, providing more intricate representations of environments with sparse features. This improvement indicates a significant potential for machine learning to boost hazard detection and avoidance in space exploration and other complex applications. [ABSTRACT FROM AUTHOR]

Smoothing curves have been used in computer aided design software for years. This is the reason why machinists very early wondered about the possibilities of creating toolpaths directly generated from this type of mathematical curves. In addition, in recent research work, we have shown that the non-uniform rational Bspline interpolation is the most favorable towards high-speed machining of complex shapes. As a result, we are thinking of simulating by this type of interpolation the pocket recess machining strategies proposed by a computer aided manufacturing software, currently developed in linear and circular interpolations only. In this article we propose a study of the influence of the parameters of Bspline curves on the hollowing out of pockets by smoothing initial cutting methods. To do this, we chose the smoothing of the zigzag method by Bspline and NURBS while varying the order and the discretization parameters to know the impact on the dynamics of the high-speed machining mechanism and on the non-conventional machining precision. This work has shown that parametric curves are favored for this type of machining provided that this tool is mastered. [ABSTRACT FROM AUTHOR]

Damping is one of the critical factors in evaluating the performance of a structure under loads resulting from winds, waves, and earthquakes. Due to significant uncertainties associated with the damping mechanism and methods of its evaluation, its accurate estimation remains a challenging task. Therefore, many studies have focused on the development of damping estimation schemes. In this study, a simple yet effective scheme based on "Dynamics 101" for estimating damping in structures that draws upon attributes of the cumulative power spectral density (CPSD) function is proposed. The underlying principle is that the damping ratio can be estimated by comparing the computed CPSD of the modal response derived from measured data with the corresponding theoretical CPSD that is, defined by natural frequency and damping ratio. Unlike the jaggedness often observed in the power spectral density (PSD) that leads to uncertainty in damping estimates, the CPSD is characterized by its smoothness as it stems from the integration of the PSD. Theoretical characteristics of CPSD were utilized for extracting damping, and it was also extended to include non-classically damped systems. Using numerically simulated data and full-scale measurements, the validity and efficacy of the proposed scheme have been demonstrated. It is also demonstrated that this simplistic approach, requiring elementary knowledge of dynamics, yields results of the quality that match those of more advanced techniques. This offers an attractive scheme for practical applications with a promise of automation through a simple algorithm or a machine learning-based environment. [ABSTRACT FROM AUTHOR]

Meta-few-shot learning algorithms, such as Model-Agnostic Meta-Learning (MAML) and Almost No Inner Loop (ANIL), enable machines to learn complex tasks quickly with limited data and based on previous experience. By maintaining the inner loop head of the neural network, ANIL leads to simpler computations and reduces the complexity of MAML. Despite its benefits, ANIL suffers from issues like accuracy variance, slow initial learning, and overfitting, hardening its adaptation and generalization. This work proposes "Look-Ahead ANIL" (LaANIL), an enhancement to ANIL for better learning. LaANIL reorganizes ANIL's internal architecture, integrating parallel computing techniques (to process multiple training examples simultaneously across computing units) and incorporating Nesterov momentum (which accelerates convergence by adjusting the learning rate based on past gradient information and extracting informative features for look-ahead gradient computation). These additional features make our model more state-of-the-art capable and better edge-compatible and thus improve few-short learning by enabling models to quickly adapt to new information and tasks. LaANIL's effectiveness is validated on established meta-few-shot learning datasets, including FC100, CIFAR-FS, Mini-ImageNet, CUBirds-200-2011, and Tiered-ImageNet. The proposed model achieved an increased validation accuracy by 7 ± 0.7% and a variance reduction by 44 ± 4% in two-way two-shot classification as well as increased validation by 5 ± 0.4% and a variance reduction by 18 ± 2% in five-way five-shot classification on the FC100 dataset and similarly performed well on other datasets. [ABSTRACT FROM AUTHOR]