Level Of Precision Research Articles

Streamlining Radiology Reporting: A Hands-Free Approach With Voice to Text and Generative Artificial Intelligence

ObjectiveIn radiology, report generation is critical for patient management, though can be time-consuming and labor-intensive. This study aims to investigate the feasibility of a hands-free radiology reporting system, leveraging voice-to-text technology and generative AI to improve time efficiency, report standardization, and reduce radiologist workload. Methods100 chest and musculoskeletal radiographs reports were generated using both traditional methods and an automated process using generative AI. To use generative AI, a radiologist dictated their findings into a voice-to-text software (Dragon), creating a raw text transcript. GPT-4 was then used to develop a report based on our institution’s template. Completion time for each traditional report and voice-to-text transcript was recorded. All reports were then evaluated for comprehensiveness, clarity, factual accuracy, and conciseness on a 5-point Likert scale. ResultsThe AI-driven system demonstrated considerable proficiency in transcribing and generating radiology reports, albeit with occasional inaccuracies. While it achieved a high level of precision in standard scenarios, its performance varied in more complex cases. Notably, the system significantly reduced the time taken to generate reports by 38% (p < 0.0001), signifying a potential increase in overall radiological workflow efficiency. ConclusionThis study highlights the potential of voice-to-text and generative AI integration in radiology reporting. Increased time efficiency, even in standard scenarios, can allow radiologists to focus more on complex cases and patient care. However, further development and refinement are required to address comprehensiveness and occasional inaccuracies. Ongoing advancements in AI and speech recognition technologies suggest a positive outlook for future implementations in medical reporting.

Critical Thinking Evaluation Scale: Design and Validation in a Colombian Population

Critical thinking stands out as one of the most important cognitive abilities needed for effective adaptation to a knowledge-based society in the 21st century. Despite its significance, there remains a lack of consensus regarding the conceptual and methodological frameworks for measuring it. This study aimed to design and validate a comprehensive assessment scale for critical thinking. A specification table was constructed based on the critical thinking components agreed upon in the current literature, from which corresponding items were formulated and subsequently validated by expert judges. Following adjustments to the test, 258 Colombian participants completed it. Sample adequacy (KMO), Bartlett’s sphericity, and collinearity were confirmed, and the results underwent exploratory factor analysis. Reliability analysis was conducted using McDonald’s ω, Cronbach’s α, Guttman’s λ6, and Greatest Lower Bound (GLB) statistics. The final test comprised 17 items organized into 2 constituent factors, demonstrating robust content and internal structure validity, as well as high levels of precision and internal consistency (overall GLB of .93). The Critical Thinking Evaluation Scale (CTES) exhibits validity and reliability for use within the Colombian population. Its adaptation for other contexts and countries, both Spanish and English-speaking, is recommended. The Spanish version, along with the validated English version for potential adaptations, and scoring norms are provided in the attached documents.

Solid particle-induced flow in a cavity with slip-spin sphere surfaces

A quasi-steady flow due to a solid sphere moving in a micropolar fluid inside a concentric cavity is analyzed, where on the particle's external surface and the cavity's internal surface, the fluid can slip-spin. The solid particle translates upon the diameter connecting their centers when the Reynolds numbers are low. A general solution, to resolve the Stokesian equations for the fluid velocity field, is obtained according to the spherical coordinates, depending on the concentric position between the particle and cavity. Boundary conditions are satisfied, on the particle's external surface and the cavity's internal surface. Moreover, a tangential couple stress is used on both surfaces. The normalized drag force affecting a translating particle is determined to be a function that increases monotonically for the ratio of the solid-to-cavity radii, becoming infinite when the particle surface touches the cavity surface. Numerical outcomes for the normalized force affecting the particle's surface are gained at different values for the ratio of the particle-to-cavity radii and also the slip-spin surface coefficients of the particle and cavity. Our outcomes are in a high level of precision with the solutions available in the literature. The current study is significant for the domains of industrial, natural, biological, and medical processes, like the production of raindrops, the flow of blood cells in arteries and veins, sedimentation, coagulation, suspension rheology, and liquid–liquid extraction.

Reducing CRISPR-Cas9 off-target effects by optically controlled chemical modifications of guide RNA

A photocatalytic click chemistry approach, offering a significant advancement over conventional methods inRNA function modulation is described. This innovative method, utilizing light-activated small molecules, provides a high level of precision and control in RNA regulation, particularly effective in intricate cellular processes. By applying this strategy to CRISPR-Cas9 gene editing, we demonstrate its effectiveness in enhancing gene editing specificity and markedly reducing off-target effects. Our approach employs a vinyl ether modification in RNA, which activated under visible light with a phenanthrenequinone derivative, creating a CRISPR-OFF switch that precisely regulates CRISPR system activity. This method not only represents an advancement in genomic interventions but also offers broad applications in gene regulation, paving the way for safer and more reliable gene editing in therapeutic genomics.

Application of a BIM model for demolition work planning

The article explores the use of BIM tools in the planning of demolition works for a structure. The analysis includes the utilization of BIM to create a three-dimensional model of the object, with a level of detail that allows for accurate cost estimation of the demolition. Additionally, it enables the precise determination of the demolition sequence, identification of potential issues, and optimization of the process. BIM tools also facilitate the analysis of the environmental impact of demolition and the safety of workers. Furthermore, through virtual simulation, demolition planning can take into account ecological and social effects. The authors highlight that the application of a BIM model simplifies risk analysis and allows for the creation of scenarios for the duration of demolition work, enabling the selection of the fastest solution while simultaneously reducing costs and optimizing resources. Conclusions drawn from the case study demonstrate how advanced BIM tools can revolutionize the planning of demolition processes, introducing a new level of precision and a sustainable approach to such projects.

Deploying large language models on diverse computing architectures: A performance evaluation framework

Deploying large language models (LLMs) across diverse computing architectures is a critical challenge in the field of artificial intelligence, particularly as these models become increasingly complex and resource-intensive. This review presents a performance evaluation framework designed to systematically assess the deployment of LLMs on various computing architectures, including CPUs, GPUs, TPUs, and specialized accelerators. The framework is structured around key performance metrics such as computational efficiency, latency, throughput, energy consumption, and scalability. It considers the trade-offs associated with different hardware configurations, optimizing the deployment to meet specific application requirements. The evaluation framework employs a multi-faceted approach, integrating both theoretical and empirical analyses to offer comprehensive insights into the performance dynamics of LLMs. This includes benchmarking LLMs under varying workloads, data batch sizes, and precision levels, enabling a nuanced understanding of how these factors influence model performance across different hardware environments. Additionally, the framework emphasizes the importance of model parallelism and distribution strategies, which are critical for efficiently scaling LLMs on high-performance computing clusters. A significant contribution of this framework is its ability to guide practitioners in selecting the optimal computing architecture for LLM deployment based on application-specific needs, such as low-latency inference for real-time applications or energy-efficient processing for large-scale deployments. The framework also provides insights into cost-performance trade-offs, offering guidance for balancing the financial implications of different deployment strategies with their performance benefits. Overall, this performance evaluation framework is a valuable tool for researchers and engineers, facilitating the efficient deployment of LLMs on diverse computing architectures. By offering a systematic approach to evaluating and optimizing LLM performance, the framework supports the ongoing development and application of these models across various domains. This paper will evaluate the deployment of large language models (LLMs) on diverse computing architectures, including x86, ARM, and RISC-V platforms. It will discuss strategies for optimizing LLM performance, such as dynamic frequency scaling, core scaling, and memory optimization. The research will contribute to understanding the best practices for deploying AI applications on different architectures, supporting technological innovation and competitiveness.

Multi Graded Brain Tumor Classification using YOLOv7

In recent years, significant progress has been made in the field of diagnosis and classification of brain tumors, mainly attributed to advancements in artificial intelligence and medical imaging. The main objective of this study is to improve the detection and classification of brain tumors by applying and utilizing of artificial intelligence (AI) and recent advancements in medical imaging techniques. Automation of the process of tumor identification and then classification, in addition to tumor grading, will definitely improve all procedures of brain tumor treatment and enhance patient care. The proposed system combines convolutional neural networks (CNNs), which act as extract features, and the You Only Look Once Algorithm (YOLOv7) for effective object identification and accurate classification. The methodology described in this study involves employing a technique of multilayer classification, which integrates three distinct datasets. This comprehensive approach shows an exceptional levels of accuracy and precision. At the initial level, the model attains a 99.78% accuracy in distinguishing between tumor and nontumor cases. At the next level the system accurately sorts types of brain tumors (such, as glioma, meningioma and pituitary tumors) with an average accuracy of 99.35%. Moving on to the final stage it successfully distinguishes between low grade and high grade glioma tumors with a precision of 93.07%. Moreover the model shows accuracies ranging from 99.41%, to 99.61% when classifying types of brain tumors and nontumor cases. The proposed system has the ability to determine the boundaries of the tumor, and thus this has helped in calculating the sizes of tumors with high accuracy. Index Terms- Brain tumor, MRI, Convolutional Neural Network, YOLO, Tumor grading.

Data driven approach for state-of-charge estimation of lithium-ion cell using stochastic variational Gaussian process

Modern electric vehicles rely on lithium-ion batteries. Electric vehicles (EVs) utilize intricate battery packs that require the oversight of a battery management system (BMS) to ensure safe, reliable, and efficient operation. The state estimation of the battery pack is an important responsibility carried out by the BMS. Accurately estimating the State-of-Charge (SOC) poses a considerable engineering challenge since it cannot be directly measured at the battery terminals. This study introduces a novel data-driven methodology for accurately estimating the SOC in Lithium-ion batteries, with a particular focus on its relevance in EV contexts. The framework is built upon the Stochastic Variational Gaussian Process (SVGP)—an improved version of the conventional Gaussian Process (GP). Unlike GP, It can scale up to very large datasets. Furthermore, SVGP uses variational inference to estimate the posterior instead of calculating, making it computationally efficient. The model training process involves using laboratory test data from an 18650 Lithium-ion Nickel Manganese Cobalt (NMC) cell that has gone through eight dynamic drive cycles. The findings showcase a remarkable level of precision in estimation, as indicated by an average R2 value of 0.99 and a Mean Square Error (MSE) as low as 0.02.

Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model

This paper introduces an advanced methodology for the calibration of Finite Element Models (FEM) utilizing a surrogate model-based Bayesian updating framework. The approach is exemplified through a case study on motor design, where precise FEM calibration is essential for predicting and optimizing motor performance. Traditional calibration techniques are often computationally expensive due to the iterative nature of the simulation process. To mitigate this, the proposed method integrates surrogate models to approximate FEM simulations, significantly reducing the computational burden without sacrificing accuracy. Bayesian updating is then employed to iteratively refine the surrogate model by incorporating new data, thereby enhancing prediction accuracy. This dual approach not only accelerates the calibration process but also ensures a high level of precision, making it highly suitable for complex engineering applications requiring both efficiency and reliability. The case study underscores the effectiveness of this methodology, demonstrating its potential to streamline the design process in motor development and other FEM-dependent engineering fields. The findings suggest that the surrogate model-based Bayesian updating approach achieves robust calibration with significantly fewer simulations, thereby optimizing both time and computational resources.

Mass scaling relations for dark halos from an analytic universal outer density profile

Context. The average matter density within the turnaround scale, which demarcates where galaxies shift from clustering around a structure to joining the expansion of the Universe, is an important cosmological probe. However, a measurement of the mass enclosed by the turnaround radius is difficult. Analyses of the turnaround scale in simulated galaxy clusters place the turnaround radius at about three times the virial radius in a ΛCDM universe and at a (present-day) density contrast with the background matter density of the Universe of δ ~ 11. Assessing the mass at such extended distances from a cluster’s center is a challenge for current mass measurement techniques. Consequently, there is a need to develop and validate new mass-scaling relations, to connect observable masses at cluster interiors with masses at greater distances. Aims. Our research aims to establish an analytical framework for the most probable mass profile of galaxy clusters, leading to novel mass scaling relations, allowing us to estimate masses at larger scales. We derive such analytical mass profiles and compare them with those from cosmological simulations. Methods. We used excursion set theory, which provides a statistical framework for the density and local environment of dark matter halos, and complement it with the spherical collapse model to follow the non-linear growth of these halos. Results. The profile we developed analytically showed good agreement (better than 30%, and dependent on halo mass) with the mass profiles of simulated galaxy clusters. Mass scaling relations were obtained from the analytical profile with offset better than 15% from the simulated ones. This level of precision highlights the potential of our model for probing structure formation dynamics at the outskirts of galaxy clusters.

Integrating LGBTI Inclusivity and Innovative Capacity in India: Analyzing the Effects of Globalization

Globalization can have enormous effects if LGBTI people are well blended into the pool of resources and tapped efficiently. This research investigates LGBTI inclusivity and the effects of globalization on economic development since the 2009 legalization verdict favoring homosexuality. The theoretical prospects are trifold: trade liberalization and labor market de-regulation; Becker’s theory of discrimination; and Heckscher-Ohlin-Samuelson postulate, along with a diagrammatic representation showcasing the impact of LGBTI inclusivity in the labor market. An OLS approach in conjunction to the ECI_Trade and ECI_Technology has been employed to examine the integration of LGBTI individuals into the Indian economy considering capital stock, domestic credit, employment, population and human capital as default controls to the model and year dummies have been incorporated at a later stage of modelling to highlight the precision level. The study found that LGBTI inclusivity is positively correlated with India's ECI_Trade and ECI_Technology value, increasing it approximately by 6.3% and 15.4%. Further, a number of robustness checks show that even after accounting for macroeconomic variables as default controls, the positive association continues to persist. The LGBTI inclusivity increases the ECI_Trade value by 13.2% and ECI_Technology value by 24.4%, respectively. Consequently, the authors emphasized on the economic ramifications of inclusivity in relation to wage gaps, discrimination, and the development of human capital and have tried to exemplify the integration of LGBTI individuals in EPZs, trade and technological advancement, and skill development to justify the default conceptualized postulates in support of inclusivity.

Gabor wavelet and multiclass support vector machine for braille image classification

Braille is a letter designed for the visually impaired. As a family with normal vision who have a visually impaired child find it difficult to Teach their child how to learn and understand the process of learning from home. Learning braille requires good finger sensitivity and memory to memorize each letterform, making it difficult to learn. With this study, braille letters can be detected from the image using the Gabor Wavelet method to extract braille images and combined with the Multiclass Support Vector Machine (Multiclass SVM) algorithm as a classification method for extracted braille images. Data testing was performed using a confusion matrix to determine the level of precision, accuracy, and recall. According to the results of tests performed on 910 braille data using confusion matrix, the highest recognition accuracy was 98,02%. The accuracy of these results is impacted by the parameters of the training process, the training data, and the test data used. This research has the opportunity to be developed in voice-based card recognition to help the visually impaired in the future research.

‘Watkins’ & ‘Watkins2.0’: Smart phone applications (Apps) for gait-assessment in normal pressure hydrocephalus and decompensated long-standing overt ventriculomegaly

ObjectiveGait disturbance is one of the features of normal pressure hydrocephalus (NPH) and decompensated long-standing overt ventriculomegaly (LOVA). The timed-up-and-go (TUG) test and the timed-10-m-walking test (10MWT) are frequently used assessments tools for gait and balance disturbances in NPH and LOVA, as well as several other disorders. We aimed to make smart-phone apps which perform both the 10MWT and the TUG-test and record the results for individual patients, thus making it possible for patients to have an objective assessment of their progress. Patients with a suitable smart phone can perform repeat assessments in their home environment, providing a measure of progress for them and for their clinical team.Methods10MWT and TUG-test were performed by 50 healthy adults, 67 NPH and 10 LOVA patients, as well as 5 elderly patients as part of falls risk assessment using the Watkins2.0 app. The 10MWT was assessed with timed slow-pace and fast-pace. Statistical analysis used SPSS (version 25.0, IBM) by paired t-test, comparing the healthy and the NPH cohorts. Level of precision of the app as compared to a clinical observer using a stopwatch was evaluated using receiver operating characteristics curve.ResultsAs compared to a clinical observer using a stopwatch, in 10MWT the app showed 100% accuracy in the measure of time taken to cover distance in whole seconds, 95% accuracy in the number of steps taken with an error ± 1–3 steps, and 97% accuracy in the measure of total distance covered with error of ± 0.25–0.50 m. The TUG test has 100% accuracy in time taken to complete the test in whole seconds, 97% accuracy in the number of steps with an error of ± 1–2 steps and 87.5% accuracy in the distance covered with error of ± 0.50 m. In the measure of time, the app was found to have equal sensitivity as an observer. In measure of number of steps and distance, the app demonstrated high sensitivity and precision (AUC > 0.9). The app also showed significant level of discrimination between healthy and gait-impaired individuals.Conclusion‘Watkins’ and ‘Watkins2.0’ are efficient apps for objective performance of 10MWT and the TUG-test in NPH and LOVA patients and has application in several other pathologies characterised by gait and balance disturbance.

40Ar/39Ar geochronologic and paleoenvironmental constraints to glacial termination III and MIS 7e, 7c, and 7a sea level fluctuations on the Tyrrhenian Sea coast of Italy

We provide detailed sedimentological, paleontological, and tephrochronological data on a complex sedimentary succession cropping out in the Tyrrhenian coastal area of central Italy, which was deposited in response to sea-level rise during MIS 7, coeval with the Latera phase of activity in the Vulsini Volcanic District. Diffuse intercalations of primary volcanic layers erupted during this phase and their geochronologic and chemostratigraphical characterization based on 40Ar/39Ar dating and EMP analyses, allowed for the identification of three stacked aggradational successions separated by erosive phases and their correlation with the Oxygen isotope record and phases in the relative sea-level curve. The ages of the tephra layers strictly frame the sedimentation in the interval of 253–206 ka, providing independent dating to glacial Termination III and to the three sea-level oscillations during MIS 7e, 7c, and 7a.Moreover, micro- and macrofaunal-based analyses provide information on the paleoenvironments and bathymetry during the highstands, which complement the geomorphological analysis reconstructing the inner edges of the corresponding marine terraces, allowing us to assess precise maximum sea level reached during MIS 7e and MIS 7a.The results of this multidisciplinary study enable us to establish in great detail the chronology, dynamics, relative amplitude, and effects of the sea-level fluctuations in the Tyrrhenian Sea during the whole MIS 7, providing independent, precise geochronological constraints for this period.

Spatial impulse response analysis and ensemble learning for efficient precision level sensing

In this paper, we propose an innovative method for determining the fill level of containers, such as trash cans, addressing a critical aspect of waste management. The method combines spatial impulse response analysis with machine learning (ML) techniques, offering a unique and effective approach for sound-based classification that can be extended to various domains beyond waste management. By employing a buzzer-generated sine sweep signal, we create a distinctive signature specific to the fill level of the waste container. This signature, once accurately decoded, is then interpreted by a specially developed ensemble learning algorithm. Our approach achieves a classification accuracy of over 90% when implemented locally on a development board, optimizing operational efficiencies and eliminating the need to delegate complex classification tasks to external entities. Using low-cost and energy-efficient hardware components, our method offers a cost-effective approach that contributes to sustainable and efficient waste management practices, providing a reliable and locally deployable solution.

Forecasting glucose values for patients with type 1 diabetes using heart rate data

Background:Type 1 Diabetes Mellitus (T1DM) is a chronic metabolic disease affecting millions of people worldwide. T1DM requires patients to continuously monitor their blood glucose levels. Due to pancreatic dysfunctions, patients use insulin injections to correct glucose values by synthetic insulin. Continuous Glucose Monitoring (CGM) is a system which includes an algorithm allowing to measure (and in some cases to predict) glucose levels at a frequent sampling time. This enable implementing advanced devices, including automated insulin pump delivery. Nevertheless, CGM still presents some limitations, including (i) the delay (time lag) in detecting change in glucose levels compared to the traditional blood glucose measurement, and (ii) the lack of a sufficient and acceptable time to accurately predict glucose values. Methods:We propose a framework based on a Gated Recurrent Unit (GRU) model to forecast both short- and long-term glucose values using heart rate (HR) and interstitial glucose (IG) values. The framework acquires HR and IG data and predicts glucose values with higher precision compared to state-of-the-art models. For training and testing the proposed framework, we used the OhioT1DM Dataset, which includes physiological data such as HR and IG values collected over an 8-week observation period. Additionally, we validated our framework using two other glucose datasets to ensure its generalizability across different HR and IG sampling frequencies. The proposed framework can be used to optimize the CGM system by incorporating patient HR measurements, thereby improving the prediction of short- and long-term glucose levels and reducing risks associated with conditions like hypoglycemia. Results:Experimental tests were conducted using HR and IG data from the OhioT1DM Dataset, as well as from two additional T1DM patient datasets. We analyzed 6 patients from Ohio dataset while we validated the algorithm on 23 patients coming from two different university hospitals (6 from the University of Catanzaro medical hospital and 17 gathered from a validated study at IRCCS San Matteo Hospital in Pavia) for a total number of 29 patients. Our framework demonstrates an improvement in forecasting IG values in terms of RMSE and MAE for different choice of prediction horizons (PH). In the case of a PH of 5, 10, 20, 30, and 60 min, we reach an RMSE of 5.0, 9.38, 15.27, 20.48, and 34.16 respectively. The framework is freely available as an open-source, with an example dataset on a GitHub repository (see https://github.com/rafgia/attention_to_glycemia). Conclusion:Our framework offers a promising solution for improving glucose level prediction and management in T1DM patients. By leveraging a GRU model and incorporating HR and IG values, we achieve more precise glucose level forecasting compared to state-of-the-art models. This approach not only enhances the accuracy of glucose predictions but also mitigates the risks associated with hypoglycemia.

An optimal goose lithium-ion batteries accurate and rapid RUL prediction method with automatic initial hyperparameters settings

Abstract Machine learning has emerged as a highly effective tool for addressing complex data problems, garnering significant attention in the field of equipment degradation and remaining service life prediction. Existing prediction models typically exhibit two primary shortcomings: on the one hand, the accuracy of life prediction reaches the desired level of precision while failing to achieve a sufficiently fast prediction speed, and on the other hand, generalization is not guaranteed while requiring the model to be robust. These two aspects present a significant challenge to the field of machine learning. In light of the aforementioned issues, we propose a prediction model based on the goose algorithm. Initially, we set the goose algorithm using adaptive initialization of the goose population to guarantee that the goose population is set at the appropriate interval, and we incorporate it into the extreme learning machine model through the improved goose algorithm. goose algorithm is used to predict the service life. Finally, we utilize different types of lithium batteries with varying operational conditions to conduct pertinent case studies to validate the proposed prediction model. The results demonstrated that the average accuracy was above 98% in all validated datasets. The shortest computation time was 0.19 s.

High–precision determination of carbon stable isotope in silicate glasses by secondary ion mass spectrometry: Evaluation of international reference materials

Secondary ion mass spectrometry (SIMS) has been used for isotope analysis of volatile components dissolved in silicate melts for decades. However, carbon in situ stable isotope analysis in natural silicate glasses has remained particularly challenging, with the few published attempts yielding high uncertainties. In this context, we characterized 31 reference silicate glasses of basaltic and basanitic compositions, which we then used as reference materials to calibrate δ13C − value analyses in silicate glasses by SIMS. This set of reference materials covers a wide range of CO2 concentrations (380 ppm – 12,000 ppm) and δ13C values (−28.1 ± 0.2 to −1.1 ± 0.2 ‰, ±1σ). The sets of reference materials were analyzed using large−geometry SIMS at two ion microprobe facilities to test reproducibility across different instrumental setups. The instrumental mass fractionation (IMF) varied widely with two different large−geometry SIMS instruments as well as with different analytical parameters such as field aperture size and primary beam intensity. We found that a precision better than ±1.1 ‰ (both average internal and external precision, ±1σ) for CO2 content higher than 1800 ppm could be achieved using a primary beam intensity of less than 5 nA, resulting in a final spot size of 10–20 μm, allowing precise analysis of δ13C in mineral−hosted melt inclusions. This level of precision was achieved at CO2 concentrations as low as 1800 ppm. This advance opens a wide range of new possibilities for the study of δ13C − value in mafic melts and their mantle sources. The reference materials are now available at the CNRS–CRPG ion microprobe facility in Nancy, France and will be deposited at the Smithsonian National Museum of Natural History, Washington, USA where they will be freely available on loan to any researcher.

A peridynamic formulation for planar arbitrarily curved beams with Timoshenko–Ehrenfest beam model

In this study, a peridynamic formulation is proposed for analysis of planar arbitrarily curved beams. Assumptions of Timoshenko–Ehrenfest beam model are considered such that linear kinematic relations are described by displacements of the beam axis and rotation of the cross-section. Equilibrium conditions and boundary conditions are derived by means of the principle of virtual work. Then, peridynamic functions are constructed, and incorporated with equilibrium conditions to develop a peridynamic formulation. Several examples are exhibited to examine the performance of the proposed formulation in some regards, i.e., numerical accuracy, convergence properties, and locking effects. It is delineated that the proposed formulation provides a good level of precision for the analysis of relatively thick beams. However, membrane and shear locking effects are present in the analysis of slender beams, and peridynamic functions of higher polynomial degree can be used to mitigate these unfavorable effects.

The AllWISE Catalog as an Infrared Celestial Reference Frame in the Gaia Era

The launch of Gaia in 2013 December ushered in a new era of space astrometry, allowing for fundamental reference research at an unprecedented level of precision. The international celestial reference frames in the radio band and the Gaia celestial reference frame in the optical band have been established and are consistent within several microarcseconds for axes orientation. To bridge the gap between the visual and radio bands, an infrared reference frame should be investigated. We present a study aimed at constructing an infrared reference frame using the observations from the AllWISE catalog. It is compared with Gaia DR3 for approximately 0.57 million extragalactic sources and for a full set of 273 million sources. Systematic differences in positions and proper motions, such as magnitude or color equations and vector spherical harmonics, are derived. These systematic differences are comparable to the random errors of AllWISE measurements and can be used to improve the AllWISE source positions and proper motions, making the AllWISE catalog a valuable all-sky reference frame in the infrared band. Our investigation of the AllWISE catalog reveals that extragalactic sources and stars exhibit different astrometric properties. The global difference between the extragalactic source reference frame and the stellar reference frames is found to be 8.6 mas and 13.7 mas for global rotation and glide amplitudes, respectively. Such internal inconsistency should be considered when using AllWISE as an infrared reference frame. Finally, we determine the orientation of the mean Galactic plane using the calibrated source distribution of the AllWISE catalog.