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This research explores the intricate dynamics of machining nickel-based single crystal superalloys, with a focused examination of the principal parameters influencing grinding forces and surface roughness. It marries micro-scale grinding simulations with sophisticated Support Vector Machine (SVM) modeling in Matlab, conducting an in-depth analysis of how variables such as grinding depth, abrasive grain size, spindle speed, and feed rate affect the surface integrity of these premium materials. The introduction of the SVM model represents a novel contribution to the field, undergoing extensive validation against traditional predictive methods and demonstrating superior predictive accuracy. Detailed investigation into grinding parameters uncovers a significant link between process variables and the microstructural alterations on the alloy's surface, leading to the determination of grinding conditions that substantially enhance surface quality. The accuracy of the SVM model in predicting surface roughness, evidenced by a Mean Squared Error (MSE) of 9.9662e-05 and an R-squared value of 99.9992%, positions it as a pivotal tool for process optimization. This, in turn, offers substantial advantages for precision engineering in aerospace component manufacturing. The findings underscore the transformative impact of SVM-based modeling in realizing exceptional performance in the machining of complex materials. [ABSTRACT FROM AUTHOR]

The essential source of errors in machining on five-axis milling centres are errors caused by the improper calibration of a machine tool, setup errors and a process plan which is not designed optimally. For the multilateral machining of the complex, precise parts of machines, a process plan should include, before and during the process, coordinate measurements by means of a touch probe in order to verify previously made surfaces or to determine accurately the position and orientation of a local coordinate system. The uncertainty of these measurements is connected with the estimate precision of performing the manufactured parts. This paper presents a tool devised for determining the uncertainty of the results of coordinate measurements by a simulation method, which is also useful at the stage of machine tool calibration. A basis of this method is a bipartite graph with a tree structure. Transitions in the graph constitute a set of elementary measurement activities and analytical activities which determine the uncertainty of the position and orientation of respective geometric features and abstract objects. Based on the results of the conducted simulation tests it is possible to build analytical models for the rapid determination of measurement uncertainty. The tool devised is aimed at vector dimensioning and therefore it enables simple extension, including integration with geometric dimensioning and tolerancing. This paper includes an example of applying the method devised, which confirms its practicability. [ABSTRACT FROM AUTHOR]

Chemical systems displaying directional motions are relevant to the operation of artificial molecular machines. Herein we present the functioning of a molecule capable of transporting a cyclic species in a preferential direction. Our system is based on a linear, non‐symmetric, positively charged molecule. This cation integrates into its structure two different reactive regions. On one side features a bulky ester group that can be exchanged by a smaller substituent; the other extreme contains an acid/base responsive moiety that plays a dual role, as part of the recognition motif and as a terminal group. In the acidic state, a dibenzo‐24‐crown‐8 ether slides into the linear component attracted by the positively charged recognition site. It does this selectively through the extreme that contains the azepanium group, since the other side is sterically hindered. After base addition, intermolecular interactions are lost; however, the macrocycle is unable to escape from the linear component since the energy barrier to slide over the neutral azepane is too large. Therefore, a metastable mechanically interlocked molecule is formed. A second reaction, now on the ester functionality, exchanges the bulky mesityl for a methyl group, small enough to allow macrocycle dissociation, completing the directional transit of the ring along the track. [ABSTRACT FROM AUTHOR]

Background: Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, feature selection methods using predictive Shapley values and p-values have been introduced, including powershap. Methods: We present a novel feature selection method, LLpowershap, that takes forward these recent advances by employing loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. We also enhance the calculation of p-values and power to identify informative features and to estimate number of iterations of model development and testing. Results: Our simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or comparable predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods. LLpowershap is also ranked the best in mean ranking among the seven feature selection methods tested on the benchmark datasets. Conclusion: Our results demonstrate that LLpowershap is a viable wrapper feature selection method that can be used for feature selection in large biomedical datasets and other settings. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the possibility of faking detection in a selection interview using a multimodal approach based on paraverbal, verbal/nonverbal cues, and facial expressions. In addition, we compared detection accuracies of simple linear and complex nonlinear machine learning algorithms. A sample of 102 participants were interviewed in two conditions—honest responding and simulated highly realistic selection. Results showed only several significant univariate effects of experimental condition for paraverbal, verbal, and facial expression cues. All the algorithms performed comparably and above chance levels, except for random forests, which overfitted on the training sets and underperformed on the testing sets. Still, considering the algorithms' accuracy was limited, usefulness of multimodal data for deception detection remains questionable. [ABSTRACT FROM AUTHOR]

To satisfy the performance and technological requirements of large-scale dynamic machines, the foundation is evolving toward considerable size and complex structure. This makes it vibrate asynchronously, and the existing relevant guidelines and specifications that consider the foundation as a rigid body are no longer applicable. Initially, by referencing the concept of the participating mass of soil (the mass of surrounding soil that vibrates synchronously), the concept of participating mass of large-scale complex dynamic machine foundations is proposed. Subsequently, the theoretical and corresponding numerical solutions are given based on the elastic half-space theory and finite element analysis. Furthermore, referring to the prototype of the foundation of the NFEES in China, a refined finite model encompassing the surrounding soil, multilayer complex machine foundations is established. After validating the numerical model, the vibration response and the participating mass of the foundation are analyzed. Additionally, parameterized analyses are conducted by varying factors such as the overall and local stiffness of the foundation, the presence of piles at the bottom of the foundation, the integration of a large shaking table foundation and an underwater shaking table foundation. Results indicate that the foundation of the large shaking table demonstrates vibration synchronization during operation, and the participating rates exceed 80%. The piles at the bottom and the separation of the large shaking table foundation from the underwater shaking table foundation perform effectively to reduce the vibration response and enhance the synchronization of the foundation vibration. The vibration response calculated by the numerical model can be directly applied to assess whether the foundation design satisfies the vibration limit requirements, and the parameterized analyses can provide suggestions for the dynamic design and analysis of similarly large-scale complex dynamic machine foundations. [ABSTRACT FROM AUTHOR]

Mildew infestation is a significant cause of loss during grain storage. The growth and metabolism of mildew leads to changes in gas composition and temperature within granaries. Recent advances in sensor technology and machine learning enable the prediction of grain mildew during storage. Current research primarily focuses on predicting mildew occurrence or grading using simple machine learning methods, without in-depth exploration of the time series characteristics of mildew process data. A monitoring device was designed and developed to capture high-quality microenvironment parameters and image data during a simulated mildew process experiment. Using the "Yongyou 15" rice varieties from Zhejiang Province, five simulation experiments were conducted under varying temperature and humidity conditions between January and May 2023. Mildew grades were defined through manual analysis to construct a multimodal dataset for the rice mildew process. This study proposes a combined model (CNN–LSTM–A) that integrates convolutional neural networks (CNN), long short-term memory (LSTM) networks, and attention mechanisms to predict the mildew grade of stored rice. The proposed model was compared with LSTM, CNN–LSTM, and LSTM–Attention models. The results indicate that the proposed model outperforms the others, achieving a prediction accuracy of 98%. The model demonstrates superior accuracy and more stable performance. The generalization performance of the prediction model was evaluated using four experimental datasets with varying storage temperature and humidity conditions. The results show that the model achieves optimal prediction stability when the training set contains similar storage temperatures, with prediction accuracy exceeding 99.8%. This indicates that the model can effectively predict the mildew grades in rice under varying environmental conditions, demonstrating significant potential for grain mildew prediction and early warning systems. [ABSTRACT FROM AUTHOR]

Objectives: The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. This research can contribute toward developing a more refined, accurate and effective therapy accounting for specific patient needs. Methods: To accomplish our research goal, we implement ensemble learning algorithms, bagging with regularized Cox regression models and nonparametric tree-based models via Random Survival Forests. A comprehensive meta-database was compiled from the NCBI Gene Expression Omnibus data repository for lung cancer patients to capture and utilize complex genomic patterns that can predict treatment outcomes more accurately. Results: The developed novel prediction algorithm demonstrates the ability to support complex clinical decision-making processes in the treatment of NSCLC. It effectively addresses patient heterogeneity, offering predictions that are both refined and personalized in improving the precision of chemotherapy regimens prescribed to the eligible patients. Conclusion: This research should contribute substantial advancement of cancer treatments by improving the accuracy and efficacy of chemotherapy treatments for a targeted group of patients who need the right treatment. The integration of complex machine learning techniques with genomic data holds substantial potential to transform current cancer treatment paradigms by providing robust support in clinical decision-making. [ABSTRACT FROM AUTHOR]

Trochoidal milling is an efficient machining technique that extends tool life and is widely used for slotting hard-to-cut materials. It offers the advantage of reducing cutting force, heat accumulation, and tool wear compared to conventional milling processes. However, due to the variable tool orientation in five-axis milling, trochoidal milling has been less commonly employed for machining 3D complex freeform slots. Existing 3D trochoidal milling methods primarily generate toolpaths by optimizing parameters based on fixed-curve paths, which do not fully leverage the efficient and stable characteristics of trochoidal milling. In this paper, a novel five-axis trochoidal milling method for machining complex 3D slots is proposed. This method generates the toolpath by determining suitable cutter location (CL) points by step, allowing flexible adjustment of the cutter-workpiece engagement (CWE) at each CL point. The CWE area must remain stable and maximized during the machining process to ensure stable cutting, reduce tool wear, and improve machining efficiency. An efficient and accurate CWE model for five-axis trochoidal milling slotting is established, and a multi-layer ruled surface fitting method to avoid interference is provided. Simulation and physical cutting experiments have been performed, and the results validate the effectiveness of the proposed method, which guarantees a stable cutting force and a 22.5% improvement in efficiency compared to the traditional method. [ABSTRACT FROM AUTHOR]

Double-arc milling cutter can effectively enhance the workpiece surface quality and is appropriate for finish machining complex surface. This article examines the effects of tool structure factor, two vertical milling orientations, workpiece inclination angle, cutting speed, and feed per tooth on the cutting force in order to thoroughly explore the cutting performance of double-arc milling cutter. Instantaneous effective cutting speed and radius models are established for various cutting orientations and conditions using the parameterized model of double-arc milling cutter. Additionally, a method for characterizing the tool structure factor of double-arc milling cutter is proposed. Three double-arc milling cutters with different structure factor and single factor experiments for milling concave and convex surfaces are designed. The impact of tool structure factor, workpiece inclination angle, cutting orientations, and cutting parameters on cutting force is thoroughly analyzed. Theoretical support is provided for the optimization of tool parameters, cutting orientations, and cutting parameters prior to surface machining. [ABSTRACT FROM AUTHOR]

Abnormal network traffic detection is a key cybersecurity technology that assists in identifying and preventing malicious network attacks. Existing methods for detecting abnormal network traffic typically rely on complex machine learning models and a large amount of labeled data. Consequently, these methods are challenging to apply to different scenarios without retraining the model and cannot effectively handle large-scale, ongoing network attacks in real-time. To address these issues, this paper proposes a classification method based on a network flow energy model. It utilizes a reverse statistical physics model to learn target traffic features in the network, allowing it to be based on macroscopic real observations or real data without the need for manual labeling. Subsequently, the paper combines the concept of the energy model to construct a network traffic recognition model. This model judges whether a sample conforms to the main statistical distribution. Specifically, the method describes individual behavior characteristics and interaction features between traffic packets through the local field and coupling field in the energy model. By combining these two features, the method calculates the sample' s energy. If the energy is below a threshold, the sample aligns with the main distribution, indicating normal data; otherwise, it is considered abnormal data. As this method does not rely on manual labeling, it can adapt to various network environments without the need for repetitive training. This addresses current issues in traffic abnormality detection methods, which struggle to adapt to different scenarios and require extensive labeling. To evaluate the effectiveness of this method, the paper validates it using the Kitsune-2018 and CTU-13 datasets. Experimental results demonstrate that the proposed method achieves good classification performance and overall effectiveness in network traffic classification tasks. This further indicates its accuracy in performing network flow classification tasks and its adaptability to changing scenarios. [ABSTRACT FROM AUTHOR]

RNA–protein complexes play a crucial role in cellular functions, providing insights into cellular mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming and resource-intensive, and it rarely yields high-resolution data. Many computational approaches have been developed to predict RNA–protein complex structures in recent years. Despite these advances, achieving accurate and high-resolution predictions remains a formidable challenge, primarily due to the limitations inherent in current RNA–protein scoring functions. These scoring functions are critical tools for evaluating and interpreting RNA–protein interactions. This review comprehensively explores the latest advancements in scoring functions for RNA–protein docking, delving into the fundamental principles underlying various approaches, including coarse-grained knowledge-based, all-atom knowledge-based, and machine-learning-based methods. We critically evaluate the strengths and limitations of existing scoring functions, providing a detailed performance assessment. Considering the significant progress demonstrated by machine learning techniques, we discuss emerging trends and propose future research directions to enhance the accuracy and efficiency of scoring functions in RNA–protein complex prediction. We aim to inspire the development of more sophisticated and reliable computational tools in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Fast and regulated potassium efflux by Slo1 channels is crucial in many tissues in animals including neurons, the kidney and smooth muscle. During the last decade, structures have revealed many details about the gating mechanism and regulation of these large and complex molecular machines. This review summarizes these findings and the current knowledge about the intricate regulation of these important channels. Slo1 integrates sensing of the membrane potential via a voltage-sensor domain that undergoes subtle but significant structural rearrangements with a calcium-induced expansion of parts of the intracellular gating ring. Together, these two signals synergistically lead to changes in the conformation and chemical nature of the pore domain, allowing potassium ions to be translocated. In many native tissues, Slo1 channels are assembled with at least three classes of auxiliary subunits that change the gating kinetics or allow the channel to open also in absence of one of the two signals. Finally, Slo1 is inhibited, activated or deregulated by natural toxins and synthetic compounds, underlining the importance of the channel for the organism and as a potential target for drugs and other molecules. [ABSTRACT FROM AUTHOR]

Five-axis hybrid robots are more extensively used in the machining of complex parts, owing to their high dynamic performance and flexible attitude adjustment. Ball-end milling is widely employed in sculptured surface machining. The tool orientation can be adjusted for enhanced machining efficiency and superior cutting performance. However, hybrid robots' complex kinematic structures raise challenges to tool orientation planning. To generate a smooth toolpath that has good cutting performance and satisfies all the geometric and mechanical constraints, a tool orientation optimization model is presented. The weighted sum of the joint kinematic performance index and the effective tool diameter indexes are taken as the objective function. By constructing the robot workspace in advance, the computation in the generation of feasible tool orientation regions is reduced. The feasible orientation regions are further simplified as linear inequality constraints which are updated during iteration. In each iteration, the initial nonconvex optimization problem is locally approximated by a convex problem and solved by quadratic programming (QP). Simulations and experiments are conducted for validation of the proposed method. [ABSTRACT FROM AUTHOR]

Trochoidal milling has become popular in high-speed milling due to its ability to reduce cutting force, enhance thermal dissipation, and prolong tool life. Among other toolpath patterns, the cubic Hermite spline offers significant advantages in generating trochoidal milling toolpath in machining complex and curved slots. However, determining those two different relation coefficients in the polynomial function of cubic Hermite spline remains complicated and empirical. This paper introduces a coefficient factor to simplify the determination process, which only requires the position and tangent vector parameters of each endpoint pair. The coefficient factor combines an instantaneous in-process slot width (IISW)-related width factor and a tangent vector direction-related direction factor. Two complex-curved slots are used to validate the performance of the proposed method. The results show that this method is straightforward and effective in generating trochoidal milling toolpaths for machining complex-curved slots while matching the slot geometry. Additionally, compared with a direct 5-axis trochoidal milling (one-step slotting strategy) in trochoidal milling of 3D slots on both serial and hybrid machining tools, a two-step slotting strategy (3-axis trochoidal milling and a successive 5-axis perpetual milling) is preferable in improving machining efficiency. [Display omitted] • A straightforward method to generate trochoidal milling toolpath is proposed. • The coefficient factor only facilitates position and tangent vector parameters. • The generated trochoidal milling toolpath matches complex-curved slots geometry. • The two-step slotting strategy is preferable in improving machining efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm for the automatic generation of toolpaths for machining complex geometric positions, such as molds used in orthosis production. The production of individualized orthoses often requires the use of multi-axis machining systems, such as five-axis machines or industrial robots. Typically, complex and expensive CAD/CAM systems are used to generate toolpaths for these machines, requiring the definition of a machining strategy for each surface. While this approach can achieve a reliable and high-quality machining process, it is very time-consuming and makes it challenging to meet the criteria for rapid production of orthopedic aids. Given that their production is a custom-made process using individual shapes as inputs, the toolpath generation process becomes even more demanding. To address these challenges, this paper proposes an algorithm suitable for the automatic generation of toolpaths for such complex positions. The proposed algorithm has been tested and has proven to be robust and applicable. [ABSTRACT FROM AUTHOR]

Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, small-batch, and high-precision parts processing requirements. To solve this problem, this paper proposes a machining quality prediction method based on the machining error transfer network and the grey neural network. Initially, by constructing a processing error transfer network, the error transfer law in part processing is described, and the PageRank algorithm and the influence degree of the nodes are used to determine the critical quality features. Additionally, the problem of low prediction accuracy due to small sample data and multiple coupling relationships is solved using the grey neural network algorithm, and a high accuracy prediction of critical quality features is achieved. Finally, the effectiveness and reliability of the method are verified by the case of medium-speed marine diesel engine fuselage processing. The results indicate that this method not only effectively identifies critical quality features in the machining process of complex parts, but it also maintains a high predictive accuracy for these features, even with small samples and limited data. [ABSTRACT FROM AUTHOR]

Because of changing consumer habits, manufacturing processes are shifting from mass production to small-batch production, which is making machining more complex and increasing demands for precision and stability. Machine tools, and thus machining accuracy, are affected by factors such as temperature and cutting load. Existing online estimation techniques often require the installation of additional sensors at specific locations, an approach that has cost and reliability issues, thus limiting industry's acceptance of these techniques. In practice, most manufacturers rely on offline detection methods, meaning that machining accuracy deviations can take some time to detect. In this study, we developed a technology for monitoring the status of machine tools; this technology, rather than requiring the installation of sensors, uses servo sensor signals to estimate accuracy, diagnose faults, and make recommendations regarding cutting depth parameters. The proposed method leverages a small number of experiments combined with extensive finite element analysis to construct a big data database, followed by the sensitivity and regression analyses of the generated database to produce an estimation model that evaluates machine tool conditions through servo feedback. The results showed that using linear regression to estimate the machine tool's status achieves good accuracy and that linear regression is easier to implement for real-time compensation. Ultimately, these results can enhance production efficiency and machining accuracy, as well as prevent unforeseen breakdowns. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Strawberry is a rich source of antioxidants, including ascorbic acid (ASA) and polyphenols, which have numerous health benefits. Antioxidant content and activity are often determined manually using laboratory equipment, which is destructive and time‐consuming. This study constructs a prediction model for antioxidant compounds utilizing machine learning (ML) and multiple linear regression based on environmental, plant growth and agronomic fruit quality‐related parameters as well as antioxidant levels. These were studied in three farms at two‐week intervals during two years of cultivation.During the ML model screening, artificial neural network (ANN)‐boosted models displayed a moderate coefficient of determination (R2) at 0.68–0.78 and relative root mean square error (RRMSE) at 3.8–4.8% in polyphenols and total ASA levels, as well as a high R2 of 0.96 and low RRMSE at <3.0% in antioxidant activity. Additionally, we developed variable selection models regarding the antioxidant activity, and variables two and five (environmental parameters and leaf length, respectively) with high accuracy were selected. The linear regression analysis between the actual and predicted data of antioxidants in the ANN‐boosted models revealed high fitness with all parameters in almost all training, validation and test sets. Furthermore, environmental parameters are essential in developing such reliable models.We conclude that ANN‐boosted, stepwise and double‐Lasso regression models can predict antioxidant compounds with enhanced accuracy, and the relevant parameters can be easily acquired on‐site without the need for any specific equipment. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A 5-DoF fully parallel machining robot (PMR) with attitude coupling motion property holds the potential to realize the high-efficiency and high-quality machining of complex parts with curved surfaces. Mechanistically, this type of 5-DoF fully PMR can achieve no singularity in the workspace after optimum design, allowing the mobile platform to be adjusted to an arbitrary orientation directly and flexibly. To full leverage these mechanical characteristics, the concept of planning orientation paths in a unit spherical coordinate system was proposed in our previous work, thereby enhancing orientation adjustment efficiency. However, challenges in toolpath continuity and computational simplicity still persist. In light of this concern, constraint equations were formulated to ensure continuity between two adjacent B-splines, and a configuration of control points was established to maintain controllable curvature. Building on this, a symmetrical arrangement strategy was introduced to set shape limits for fitted B-splines to achieve high-order continuity, and a corresponding toolpath planning method was developed. To decrease computational complexity during feedrate scheduling, a system of linear constraints was utilized to calculate the feedrate for each point, with the feedrate calculation and interpolation occurring concurrently to generate the motion trajectory in real time. Trajectory planning for S-shaped toolpaths was conducted using the proposed method and the commercial ISG CNC kernel, with comparison results demonstrating that our method significantly improves motion efficiency, reduces computational complexity, and mitigates feedrate and acceleration fluctuations. This study offers a more efficient and effective trajectory planning approach, significantly contributing to the advancement of 5-DoF PMR technology. [ABSTRACT FROM AUTHOR]

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]

Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithms. One unique advantage of this technology lies in the ability to reconfigure the geometry of the qubit register, from shot to shot. This unique feature makes possible the native embedding of graph-structured problems at the hardware level, with profound consequences for the resolution of complex optimization and machine learning tasks. By driving qubits, one can generate processed quantum states which retain graph complex properties. These states can then be leveraged to offer direct solutions to problems or as resources in hybrid quantum-classical schemes. In this paper, we review the advancements in quantum algorithms for graph problems running on neutral atom Quantum Processing Units (QPUs), and discuss recently introduced embedding and problem-solving techniques. In addition, we clarify ongoing advancements in hardware, with an emphasis on enhancing the scalability, controllability and computation repetition rate of neutral atom QPUs. [ABSTRACT FROM AUTHOR]

The compatibility between antioxidant compounds (ACs) and wall materials (WMs) is one of the most crucial aspects of the encapsulation process, as the encapsulated compounds' stability depends on the affinity between the compounds, which is influenced by their chemical properties. A compatibility model between the encapsulant and antioxidant chemicals was built using machine learning (ML) to discover optimal matches without costly and time-consuming trial-and-error experiments. The attributes of the required antioxidant and wall material components were recollected, and two datasets were constructed. As a result, a tying process was performed to connect both datasets and identify significant relationships between parameters of ACs and WMs to define the compatibility or incompatibility of the compounds, as this was necessary to enrich the dataset by incorporating decoys. As a result, a simple statistical analysis was conducted to examine the indicated correlations between variables, and a Principal Component Analysis (PCA) was performed to reduce the dimensionality of the dataset without sacrificing essential information. The K-nearest neighbor (KNN) algorithm was used and designed to handle the classification problems of the compatibility of the combinations to integrate ML in the model. In this way, the model accuracy was 0.92, with a sensitivity of 0.84 and a specificity of 1. These results indicate that the KNN model performs well, exhibiting high accuracy and correctly classifying positive and negative combinations as evidenced by the sensitivity and specificity scores. [ABSTRACT FROM AUTHOR]

The problem of machining complex surfaces with non-ball-end cutters by strip-width-maximization machining is formulated as a kind of surface fitting problem in which the tool surface envelope feature line approximates the design surface under the movement transform. The theory of surface envelope−approximation is proposed as a general method for optimizing tool movement in single-contact strip-width-maximization machining of sculptured surfaces with non-ball-end cutters. Based on the surface moving frame, the velocity equations and transformation matrices for the tool motion relative to the workpiece, described by the motion-invariant parameters of the tool surface and design surface, are derived. A functional extremum model for optimizing the tool position ensures continuous and symmetrical motion relative to the workpiece to achieve the highest machining efficiency and accuracy. Finally, a Matlab-based simulation example verifies the machining efficiency and accuracy of the envelope approximation theory. [ABSTRACT FROM AUTHOR]

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system. [ABSTRACT FROM AUTHOR]

Surface finishing applications are still cumbersome manual tasks, therefore, robotization of the process is of great interest as it allows for automation and increased versatility. However, finishing processes are difficult to automate, mainly because of the variation in material removal. In particular, the variables involved undergo changes that modify the material removal rate. This paper proposes a methodology for modeling material removal automatically based on experimental data. The procedure consists of monitoring the material removed from the parts under study that are in an automated precision measuring system during the finishing process. Based on the experimental models, a control algorithm for continuous material removal is presented. It guarantees a homogeneous surface finish by varying the robot feed rate. Finally, the results of several experimental material removal models under different process conditions and the validation of the proposed control algorithm are presented. The results show that the proposed method achieves a substantial improvement in the homogeneity of the finish. [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]

When individuals are paralyzed from injury or damage to the brain, upper body movement and function can be compromised. While the use of body motions to interface with machines has shown to be an effective noninvasive strategy to provide movement assistance and to promote physical rehabilitation, learning to use such interfaces to control complex machines is not well understood. In a five session study, we demonstrate that a subset of an uninjured population is able to learn and improve their ability to use a high-dimensional Body-Machine Interface (BoMI), to control a robotic arm. We use a sensor net of four inertial measurement units, placed bilaterally on the upper body, and a BoMI with the capacity to directly control a robot in six dimensions. We consider whether the way in which the robot control space is mapped from human inputs has any impact on learning. Our results suggest that the space of robot control does play a role in the evolution of human learning: specifically, though robot control in joint space appears to be more intuitive initially, control in task space is found to have a greater capacity for longer-term improvement and learning. Our results further suggest that there is an inverse relationship between control dimension couplings and task performance. [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]

Traditional approaches to safety event analysis in autonomous systems have relied on complex machine and deep learning models and extensive datasets for high accuracy and reliability. However, the emerge of multimodal large language models (MLLMs) offers a novel approach by integrating textual, visual, and audio modalities. Our framework leverages the logical and visual reasoning power of MLLMs, directing their output through object-level question–answer (QA) prompts to ensure accurate, reliable, and actionable insights for investigating safety-critical event detection and analysis. By incorporating models like Gemini-Pro-Vision 1.5, we aim to automate safety-critical event detection and analysis along with mitigating common issues such as hallucinations in MLLM outputs. The results demonstrate the framework's potential in different in-context learning (ICT) settings such as zero-shot and few-shot learning methods. Furthermore, we investigate other settings such as self-ensemble learning and a varying number of frames. The results show that a few-shot learning model consistently outperformed other learning models, achieving the highest overall accuracy of about 79%. The comparative analysis with previous studies on visual reasoning revealed that previous models showed moderate performance in driving safety tasks, while our proposed model significantly outperformed them. To the best of our knowledge, our proposed MLLM model stands out as the first of its kind, capable of handling multiple tasks for each safety-critical event. It can identify risky scenarios, classify diverse scenes, determine car directions, categorize agents, and recommend the appropriate actions, setting a new standard in safety-critical event management. This study shows the significance of MLLMs in advancing the analysis of naturalistic driving videos to improve safety-critical event detection and understanding the interactions in complex environments. [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]

Aiming at the difficulty in effectively identifying critical quality features in the complex machining process, this paper proposes a critical quality feature recognition method based on a machining process network. Firstly, the machining process network model is constructed based on the complex network theory. The LeaderRank algorithm is used to identify the critical processes in the machining process. Secondly, the Entropy-CRITIC method is used to calculate the weight of the quality features of the critical processes, and the critical quality features of the critical processes are determined according to weight ranking results. Finally, the feasibility and effectiveness of the method are verified by taking the medium-speed marine diesel engine coupling rod machining as an example. The results show that the method can still effectively identify the critical quality features in the case of small sample data and provide support for machining process optimization and quality control, thus improving product consistency, reliability, and machining efficiency. [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]

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]

The toolpath for complex surface machining mostly consists of massive micro-line segments with poor geometric continuity. The global smoothing method with curve fitting is commonly adopted in CNC systems for trajectory smoothing. However, for a large number of points of micro-line toolpath, it is generally difficult to fit one overall curve because of the issues related to error control and computational efficiency. The scheme of piecewise curve fitting is commonly used in engineering applications, but the problem of smooth transition of adjacent fitting curves has not been properly solved. Therefore, this paper proposes a global smoothing method which focuses on achieving smooth transition of adjacent fitting curves with optimization of fitting error and fairness. The proposed method firstly realizes segmentation of the micro-line toolpath and cubic B-spline curve fitting within the toolpath segments. Afterwards, a linear approximation model of geometric continuity conditions is established in the splicing area of two adjacent fitting curves. With the optimization of control points based on geometric continuity constraints, the shape of the curve is adjusted locally, which can achieve continuous splicing of adjacent fitting curves. Second-order geometric continuity is guaranteed at the junctions of adjacent fitting curves as well as the overall toolpath without introducing additional transition curves. The fitting error at micro-line points can be rigorously controlled while the fairness of the splicing area of fitting curves can be optimized. Simulation and experiment show that the method proposed can significantly reduce the fluctuation of the curvature and the feedrate of toolpath while satisfying the requirement of precision, to improve machining efficiency and workpiece surface quality. [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]

Since electrical insulation, mechanical performance and wear resistance properties are expected from some parts working in the electrical industry, PA6 (PA6-M) composites filled with 10, 20 and 30 wt% mica have been produced to provide the required performances. PA6-M composites were first produced in granule form in a compounding machine designed as twin screw. Afterwards, mechanical and tribological test specimens were molded using injection moulding method. The mechanical and tribological performances of the composite test specimens were investigated. Tensile, notched Izod impact and hardness tests were performed to determine mechanical properties. Tribological tests were carried out in a pin-disc wear test rig under dry conditions and at ambient temperature. Tribological tests were carried out at a speed of 0.6 m/s and three different loads (60 N, 120 N and 180 N). As a result of the experiments, the tensile strength, modulus of elasticity and hardness of the composite materials produced with the addition of mica filler to the PA6 polymer base matrix increased. In addition, impact strength, wear rate and coefficient of friction values decreased. Moreover, friction coefficient and wear rate values of all materials studied in tribological experiments decreased with increasing load. [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]

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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]