Hasil untuk "Instruments and machines"

Shenda Jiang, Israel Greenfeld, Lin Yang et al.

Abstract Two-dimensional materials (2DMs), possessing atomic-scale thickness, are prone to brittle fracture under loading conditions, which can lead to catastrophic failure. As their structural dimensions approach the nanoscale, conventional linear elastic fracture mechanics (LEFM) based on continuum assumptions is deficient in capturing the underlying failure mechanisms and accurately predicting potential crack instability. This limitation emphasizes the critical need for a new theoretical approach suited to the fracture behavior of 2DM systems. We propose a unified fracture mechanics (UFM) criterion that systematically incorporates two key physical mechanisms governing brittle fracture in 2DMs at the nanoscale, namely nonlinear elasticity and atomic-scale discreteness. By introducing two corrective parameters, for nonlinearity and quantization, the UFM model successfully resolves the limitations of LEFM in predicting failure. This is particularly important in the short crack regime, as small defects are frequent in 2DMs. The theoretical predictions show excellent agreement with molecular dynamics simulations of five different types of 2DMs and accurately capture the fracture strength of both cracked and defect-free structures. In addition, we present an empirical method that allows the fracture behavior of 2DMs to be estimated directly from their intrinsic structural and elastic properties. The unified theoretical framework is applicable not only to the materials simulated in this study but may also be applied to a broader class of atomically thin brittle systems.

Ruofeng Wei, Kai Chen, Yui Lun Ng et al.

Endoluminal surgery offers a minimally invasive option for early-stage gastrointestinal and urinary tract cancers but is limited by surgical tools and a steep learning curve. Robotic systems, particularly continuum robots, provide flexible instruments that enable precise tissue resection, potentially improving outcomes. This paper presents a visual perception platform for a continuum robotic system in endoluminal surgery. Our goal is to utilize monocular endoscopic image-based perception algorithms to identify position and orientation of flexible instruments and measure their distances from tissues. We introduce 2D and 3D learning-based perception algorithms and develop a physically-realistic simulator that models flexible instruments dynamics. This simulator generates realistic endoluminal scenes, enabling control of flexible robots and substantial data collection. Using a continuum robot prototype, we conducted module and system-level evaluations. Results show that our algorithms improve control of flexible instruments, reducing manipulation time by over 70% for trajectory-following tasks and enhancing understanding of surgical scenarios, leading to robust endoluminal surgeries.

Mohamed Rowaizak, Ahmad Farhat, Reem Khalil

Neuroscience education must convey 3D structure with clarity and accuracy. Traditional 2D renderings are limited as they lose depth information and hinder spatial understanding. High-resolution resources now exist, yet many are difficult to use in the class. Therefore, we developed an educational brain video that moves from gross to microanatomy using MRI-based models and the published literature. The pipeline used Fiji for preprocessing, MeshLab for mesh cleanup, Rhino 6 for target fixes, Houdini FX for materials, lighting, and renders, and Cinema4D for final refinement of the video. We had our brain models validated by two neuroscientists for educational fidelity. We tested the video in a class with 96 undergraduates randomized to video and lecture or lecture only. Students completed the same pretest and posttest questions. Student feedback revealed that comprehension and motivation to learn increased significantly in the group that watched the video, suggesting its potential as a useful supplement to traditional lectures. A short, well-produced 3D video can supplement lectures and improve learning in this setting. We share software versions and key parameters to support reuse.

Aikaterini Vriza, Michael H. Prince, Tao Zhou et al.

Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.

Kosuke Matsui, Jun-Yi Wu, Hayata Yamasaki et al.

Scaling up the number of qubits available on quantum processors remains technically demanding even in the long term; it is therefore crucial to clarify the number of qubits required to implement a given quantum operation. For the most general class of quantum operations, known as quantum instruments, the qubit requirements are not well understood, especially when mid-circuit measurements and delayed input preparation are permitted. In this work, we characterize lower and upper bounds on the number of qubits required to implement a given quantum instrument in terms of the causal structure of the instrument. We further apply our results to entanglement distillation protocols based on stabilizer codes and show that, in these cases, the lower and upper bounds coincide, so the optimal qubit requirement is determined. In particular, we compute that the optimal number of qubits is 3 for the $[[9,1,3]]$-code-based protocol and 4 for the $[[5,1,3]]$-code-based protocol.

Christopher S. Jackson

Many of the most fundamental observables | position, momentum, phase-point, and spin-direction | cannot be measured by an instrument that obeys the orthogonal projection postulate. Continuous-in-time measurements provide the missing theoretical framework to make physical sense of such observables. The elements of the time-dependent instrument define a group called the instrumental group (IG). Relative to the IG, all of the time-dependence is contained in a certain function called the Kraus-operator density (KOD), which evolves according to a classical Kolmogorov equation. Unlike the Lindblad master equation, the KOD Kolmogorov equation is a direct expression of how the elements of the instrument (not just the total quantum channel) evolve. For sequential measurements more generally, the structure of combining instruments in sequence is shown to correspond to the convolution of their KODs. This convolution promotes the IG to an involutive Banach algebra (a structure that goes all the way back to the origins of POVM and C*-algebra theory) which will be called the instrumental group algebra (IGA). The IGA is the true home of the KOD, similar to how the dual of a von Neumann algebra is the true home of the density operator. Operators on the IGA, which play the analogous role for KODs as superoperators play for density operators, are called "ultraoperators and various important examples are discussed. Certain ultraoperator-superoperator intertwining relations are also considered throughout, including the relation between the KOD Kolmogorov equation and the Lindblad master equation. The IGA is also shown to have actually two distinct involutions: one respected by the convolution ultraoperators and the other by the quantum channel superoperators. Finally, the KOD Kolmogorov generators are derived for jump processes and more general diffusive processes.

Yuan Alfinsyah Sihombing, Uperianti, Rizky Indah Sari et al.

In the development of biosensors, it is essential to have sensors that provide rapid responses, exhibit high sensitivity and selectivity, and are non-invasive, such as screen-printed carbon electrode-based biosensors. In this study, SPCE-based and fabric-based biosensors were fabricated by modifying the working electrode (WE) surface using functionalized Multi-walled Carbon Nanotube/AgNi nanocomposites (f-MWCNT/AgNi) to enhance the biosensor's performance in detecting uric acid (UA). The successful synthesis of the f-MWCNT/AgNi nanomaterial was confirmed through UV–Vis, Raman, SEM–EDX, and XRD analyses. The f-MWCNT/AgNi nanomaterials were deposited on the WE surface using drop-casting. Subsequently, electrochemical characteristic tests and UA detection performance were conducted using cyclic voltammetry (CV) and differential pulse voltammetry (DPV) methods. The DPV curves revealed sensitivities of 27.699 μA/mM and 4.638 μA/mM for SPCE-based and fabric-based electrodes, respectively. The limit of detection (LOD) values for UA detection were 0.024 and 0.017 mM, with linearity (R2) 0.997 and 0.999 observed within the linear ranges of 0.05–1.00 and 1.0–5.0 mM, respectively. Both biosensors exhibited strong selectivity for UA against other components, including ascorbic acid, glucose, lactic acid, and ethanol. Based on these parameter values, f-MWCNT/AgNi-modified SPCE and fabric-based electrodes can be promoted as biosensors for uric acid detection.

Jacques Matthee, Kenneth Uren, George van Schoor et al.

Simultaneous Localization and Mapping (SLAM) is a crucial component to the push towards full autonomy of robotic systems, yet it is computationally expensive and can rarely achieve real-time execution speeds on embedded platforms. Therefore, a need exists to  evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating  prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. The paper uses three embedded platforms: Nvidia Jetson TX2, Raspberry Pi 3B+ and the Raspberry Pi 4B, to generate a dataset that is used in training and  testing performance prediction models. The process of profiling ORB-SLAM3 aids in the selection of inputs to the prediction model as  well as benchmarking the embedded platforms’ performances by using PassMark. The EuRoC micro aerial vehicle (MAV) dataset is used to generate the average tracking time that the embedded platforms can achieve when executing ORB-SLAM3, which is the target  of the prediction model. The best-performing model has the following results 2.84%, 3.93%, and 0.95 for MAE, RMSE and R2 score  respectively. The results show the feasibility of predicting the performance that SLAM applications can achieve on embedded  platforms.

Khomdet Phapatanaburi, Longbiao Wang, Meng Liu et al.

Abstract Shouted and normal speech classification plays an important role in many speech-related applications. The existing works are often based on magnitude-based features and ignore phase-based features, which are directly related to magnitude information. In this paper, the importance of phase-based features is explored for the detection of shouted speech. The novel contributions of this work are as follows. (1) Three phase-based features, namely, relative phase (RP), linear prediction analysis estimated speech-based RP (LPAES-RP) and linear prediction residual-based RP (LPR-RP) features, are explored for shouted and normal speech classification. (2) We propose a new RP feature, called the glottal source-based RP (GRP) feature. The main idea of the proposed GRP feature is to exploit the difference between RP and LPAES-RP features to detect shouted speech. (3) A score combination of phase- and magnitude-based features is also employed to further improve the classification performance. The proposed feature and combination are evaluated using the shouted normal electroglottograph speech (SNE-Speech) corpus. The experimental findings show that the RP, LPAES-RP, and LPR-RP features provide promising results for the detection of shouted speech. We also find that the proposed GRP feature can provide better results than those of the standard mel-frequency cepstral coefficient (MFCC) feature. Moreover, compared to using individual features, the score combination of the MFCC and RP/LPAES-RP/LPR-RP/GRP features yields an improved detection performance. Performance analysis under noisy environments shows that the score combination of the MFCC and the RP/LPAES-RP/LPR-RP features gives more robust classification. These outcomes show the importance of RP features in distinguishing shouted speech from normal speech.

Davi M Fébba, Kingsley Egbo, William A. Callahan et al.

Large language models (LLMs) are transforming the landscape of chemistry and materials science. Recent examples of LLM-accelerated experimental research include virtual assistants for parsing synthesis recipes from the literature, or using the extracted knowledge to guide synthesis and characterization. Despite these advancements, their application is constrained to labs with automated instruments and control software, leaving much of materials science reliant on manual processes. Here, we demonstrate the rapid deployment of a Python-based control module for a Keithley 2400 electrical source measure unit using ChatGPT-4. Through iterative refinement, we achieved effective instrument management with minimal human intervention. Additionally, a user-friendly graphical user interface (GUI) was created, effectively linking all instrument controls to interactive screen elements. Finally, we integrated this AI-crafted instrument control software with a high-performance stochastic optimization algorithm to facilitate rapid and automated extraction of electronic device parameters related to semiconductor charge transport mechanisms from current-voltage (IV) measurement data. This integration resulted in a comprehensive open-source toolkit for semiconductor device characterization and analysis using IV curve measurements. We demonstrate the application of these tools by acquiring, analyzing, and parameterizing IV data from a Pt/Cr$_2$O$_3$:Mg/$β$-Ga$_2$O$_3$ heterojunction diode, a novel stack for high-power and high-temperature electronic devices. This approach underscores the powerful synergy between LLMs and the development of instruments for scientific inquiry, showcasing a path for further acceleration in materials science.

Rahul Thakur, S.C. Malik, Masum Raj

The Laplace distribution, also known as the double exponential distribution, is a continuous probability distribution that is often used for modelling the data having heavy tails. In this paper, we proposed the Neutrosophic Laplace distribution which is the extension of the classical Laplace Distribution. We derived various statistical properties of the Neutrosophic Laplace Distribution such as mean, variance, skewness, rth moment, quartiles, and moment-generating function. The expressions for the estimation of the parameters are also derived using the maximum likelihood function of the distribution. A simulation study has been done to evaluate the performance of estimates. An application of the Neutrosophic Laplace Distribution is discussed to study the daily return of the NIFTY50 from Indian Stock Market. The analysis shows that the neutrosophic Laplace Model is acceptable, effective, and adequate for dealing with uncertainty in an unpredictable context.

Zheng Wu

Based on the modern dilemma of the existence of the subject, information philosophy is transformed into ontological “subject information”, and the basic elements of the virtual dimension and the real dimension are abstracted from it. And then, with the help of the alternate transformation of the virtual dimension information and the real dimension information, the existence and evolution of subject information are explored.

Moe Huss, Daniel R. Herber, John M. Borky

This study compares the <i>reliability of estimation</i>, <i>productivity</i>, and <i>defect rate</i> metrics for sprints driven by a specific instance of the agile approach (i.e., scrum) and an agile model-Bbased software engineering (MBSE) approach called the integrated Scrum Model-Based System Architecture Process (sMBSAP) when developing a software system. The quasi-experimental study conducted ten sprints using each approach. The approaches were then evaluated based on their effectiveness in helping the <i>product development team</i> estimate the backlog items that they could build during a time-boxed sprint and deliver more product backlog items (PBI) with fewer defects. The <i>commitment reliability (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi></mrow></semantics></math></inline-formula>)</i> was calculated to compare the <i>reliability of estimation</i> with a measured average scrum-driven value of 0.81 versus a statistically different average sMBSAP-driven value of 0.94. Similarly, the average <i>sprint velocity</i> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>V</mi></mrow></semantics></math></inline-formula>) for the scrum-driven sprints was 26.8 versus 31.8 for the MBSAP-driven sprints. The average <i>defect density</i> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>D</mi></mrow></semantics></math></inline-formula>) for the scrum-driven sprints was 0.91, while that of the sMBSAP-driven sprints was 0.63. The average <i>defect leakage</i> (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>L</mi></mrow></semantics></math></inline-formula>) for the scrum-driven sprints was 0.20, while that of the sMBSAP-driven sprints was 0.15. The <i>t</i>-test analysis concluded that the sMBSAP-driven sprints were associated with a statistically significant larger mean <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>R</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>V</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>D</mi></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>L</mi></mrow></semantics></math></inline-formula> than that of the scrum-driven sprints. The overall results demonstrate formal quantitative benefits of an agile MBSE approach compared to an agile alone, thereby strengthening the case for considering agile MBSE methods within the software development community. Future work might include comparing agile and agile MBSE methods using alternative research designs and further software development objectives, techniques, and metrics.

Tobias Rueckert, Daniel Rueckert, Christoph Palm

In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking", resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.

Mansour Mededjel, Ghalem Belalem, Fatima Zohra Nesrine Benadda et al.

The emergence of the Internet of things (IoT), associated with the explosion in the number of connected objects, and the growth in user needs, makes the Internet network very complex. IoT objects are diverse and heterogeneous, which requires establishing interoperability and efficient identity management on the one hand. On the other hand, centralized architectures such as cloud-based ones can have overhead and high latency, with a potential risk of failure. Facing these challenges, Blockchain technology, with its decentralized architecture based on a distributed peer-to-peer network, offers a new infrastructure that allows IoT objects to interact reliably and securely. In this paper, a new approach is proposed with a three-layer architecture: layer of sensing and collection of data made up of the IoT network, layer of processing and saving of data exchanges at the Blockchain level, and access and visualization layer via a web interface. The prototype implemented in this study allows all transactions (data exchanges) generated by IoT devices to be recorded and stored on a dedicated Blockchain, assuring the security of IoT objects' communications. This prototype also enables access to and visualization of all data and information, thus enhancing the IoT network's transparency.

Ammar Elhassan, Saleh M. Abu-Soud, Firas Alghanim et al.

This article introduces ILA4: A new algorithm designed to handle datasets with missing values. ILA4 is inspired by a series of ILA algorithms which also handle missing data with further enhancements. ILA4 is applied to datasets with varying completeness and also compared to other, known approaches for handling datasets with missing values. In the majority of cases, ILA4 produced favorable performance that is on a par with many established approaches for treating missing values including algorithms that are based on the Most Common Value (MCV), the Most Common Value Restricted to a Concept (MCVRC), and those that utilize the Delete strategy. ILA4 was also compared with three known algorithms namely: Logistic Regression, Naïve Bayes, and Random Forest; the accuracy obtained by ILA4 is comparable or better than the best results obtained from these three algorithms.

Carlos Silva, Jorge Martins, Jonas Deuermeier et al.

In this article, characterization of fully patterned zinc-tin oxide (ZTO)-based memristive devices with feature sizes as small as 25 µm² is presented. The devices are patterned via lift-off with a platinum bottom contact and a gold-titanium top contact. An on/off ratio of more than two orders of magnitude is obtained without the need for electroforming processes. Set operation is a current controlled process, whereas the reset is voltage dependent. The temperature dependency of the electrical characteristics reveals a bulk-dominated conduction mechanism for high resistance states. However, the charge transport at low resistance state is consistent with Schottky emission. Synaptic properties such as potentiation and depression cycles, with progressive increases and decreases in the conductance value under 50 successive pulses, are shown. This validates the potential use of ZTO memristive devices for a sustainable and energy-efficient brain-inspired deep neural network computation.

S. A. Zolotoy, I. B. Strashko, Dz. S. Kotau et al.

O b j e c t i v e s. The task of improving the software package for detecting thermal anomalies based on meteorologicalsatellite data developed by the unitary enterprise "Geoinformation Systems" was solved.M e t h o d s. In the period from 2015 to the present, the work on practical testing and improvement of the software for natural fires detection has been carried out. For this purpose, satellite images of the territory of Belarus obtained from NOAA series spacecraft were used. Special attention was paid to the problem of improving the accuracy of determining the coordinates of fires and reducing the time required for initial data processing.Re s u l t s. A retrospective analysis of the main stages of improving the software for natural fires detection and obtained during practical tests generalized results are provided. The description of the web service developed on the basis of the software for detecting natural fires is presented.Co n c l u s i o n. The information can be useful for the specialists and researchers who are engaged in the detection of thermal anomalies (fires) using remote sensing data from meteorological satellites.

Rui Zhang, Krikamol Muandet, Bernhard Schölkopf et al.

Kernel maximum moment restriction (KMMR) recently emerges as a popular framework for instrumental variable (IV) based conditional moment restriction (CMR) models with important applications in conditional moment (CM) testing and parameter estimation for IV regression and proximal causal learning. The effectiveness of this framework, however, depends critically on the choice of a reproducing kernel Hilbert space (RKHS) chosen as a space of instruments. In this work, we presents a systematic way to select the instrument space for parameter estimation based on a principle of the least identifiable instrument space (LIIS) that identifies model parameters with the least space complexity. Our selection criterion combines two distinct objectives to determine such an optimal space: (i) a test criterion to check identifiability; (ii) an information criterion based on the effective dimension of RKHSs as a complexity measure. We analyze the consistency of our method in determining the LIIS, and demonstrate its effectiveness for parameter estimation via simulations.

Juan Carlos Angeles Ceron, Leonardo Chang, Gilberto Ochoa-Ruiz et al.

Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of robust models for such purposes, providing large, diverse, and annotated datasets. To date, most of the existing models for instance segmentation of medical instruments were based on two-stage detectors, which provide robust results but are nowhere near to the real-time (5 frames-per-second (fps)at most). However, in order for the method to be clinically applicable, real-time capability is utmost required along with high accuracy. In this paper, we propose the addition of attention mechanisms to the YOLACT architecture that allows real-time instance segmentation of instrument with improved accuracy on the ROBUST-MIS dataset. Our proposed approach achieves competitive performance compared to the winner ofthe 2019 ROBUST-MIS challenge in terms of robustness scores,obtaining 0.313 MI_DSC and 0.338 MI_NSD, while achieving real-time performance (37 fps)