Level Of Precision Research Articles

Tunable 30 GHz laser frequency comb for astronomical spectrograph characterization and calibration

The search for Earth-like exoplanets with the Doppler radial velocity (RV) technique is an extremely challenging and multifaceted precision spectroscopy problem. Currently, one of the limiting instrumental factors in reaching the required long-term 10−10 level of radial velocity precision is the defect-driven subpixel quantum efficiency (QE) variations in the large-format detector arrays used by precision echelle spectrographs. Tunable frequency comb calibration sources that can fully map the point spread function (PSF) across a spectrograph’s entire bandwidth are necessary for quantifying and correcting these detector artifacts. In this work, we demonstrate a combination of laser frequency and mode spacing control that allows full and deterministic tunability of a 30 GHz electro-optic comb together with its filter cavity. After supercontinuum generation, this gives access to any optical frequency across 700–1300 nm. Our specific implementation is intended for the comb deployed at the Habitable-Zone Planet Finder (HPF) spectrograph and its near-infrared Hawaii-2RG array, but the techniques apply to all laser frequency combs (LFCs) used for precision astronomical spectrograph calibration and other applications that require broadband tuning.

Cost-Effective Multispectral Sensor and Artificial Neural Networks for the Detection of Starch Adulteration in Raw Milk

This study aims to detect starch adulteration in dairy products utilizing an artificial neural network (ANN) model. Globally, milk fraud represents a significant challenge to food safety, posing substantial health risks to consumers. In this context, spectral data derived from milk samples with varying starch concentrations were processed using feature scaling and normalization techniques. The ANN model was rigorously trained and validated employing the stratified k-fold cross-validation method, demonstrating exceptional proficiency in detecting starch-adulterated milk samples and effectively differentiating among various starch concentrations. The principal findings indicate that the model achieved 100% accuracy, coupled with high levels of precision, sensitivity, and F1-scores. Future research should explore the application of this model to different types of adulteration and extend its validation on larger datasets. Furthermore, the potential adaptability of this method for other food products and field applications warrants investigation. This study offers valuable insights for milk producers, food safety professionals, and consumers, particularly highlighting the implications for small-scale rural farms, thereby enriching the discourse on food safety within short food supply chains.

Superposition of standing waves induced ultra high-resolution atom localization

Abstract In this study, we have theoretically demonstrated ultra-high resolution two-dimensional atom localization within a three-level $\lambda$-type atomic medium via superposition of asymmetric and symmetric standing wave fields. Our analysis provides understanding into the precise spatial localization of atomic positions at the atomic level, utilizing advanced theoretical approaches and principles of quantum mechanics. The dynamical behavior of a three-level atomic system is thoroughly analyzed using the density matrix formalism within the realm of quantum mechanics. A theoretical approach is constructed to describe the interaction between the system and external fields, specifically a control field and a probe field. The absorption spectrum of the probe field is thoroughly examined to clarify the spatial
localization of the atom within the proposed configuration. We have theoretically investigated that symmetric and asymmetric superposition phenomena significantly influence the localized peaks within a two-dimensional spatial domain. Specifically, the emergence of one and two sharp localized peaks has been observed within a region of one wavelength domain. We observed notable influences of the intensity of the control field, probe field detuning, and decay rates on atom localization. Ultimately, we have achieved an unprecedented level of ultra-high resolution and precision in localizing an atom within an area smaller than λ/35×λ/35. These findings hold promise for potential applications in domains such as Bose-Einstein condensation, nanolithography, laser cooling, trapping of neutral atoms, and the measurement of center-of-mass wave-functions.

High precision accelerator for our hybrid model of the redshift space power spectrum

Abstract Upcoming Large Scale Structure surveys aim to achieve an unprecedented level of precision in measuring galaxy clustering. However, accurately modelling these statistics may require theoretical templates that go beyond two-loop order perturbation theory, especially for achieving precision at smaller scales. In our previous work, we introduced a hybrid model for the redshift space power spectrum of galaxies. This model combines two-loop order templates with N-body simulations to capture the influence of scale-independent parameters on the galaxy power spectrum. However, the impact of scale-dependent parameters was addressed by precomputing a set of input statistics derived from computationally expensive N-body simulations. As a result, exploring the scale-dependent parameter space was not feasible in this approach. To address this challenge, we present an accelerated methodology that utilizes Gaussian processes, a machine learning technique, to emulate these input statistics. Our emulators exhibit remarkable accuracy, achieving reliable results with just 13 N-body simulations for training. Our emulators can reproduce the set of statistics we are interested in with less than 0.1% error in the parameter space within 5σ of the Planck ΛCDM predictions, specifically for scales around k > 0.1 h Mpc−1. Following the training of our emulators, we can predict all inputs for our hybrid model in approximately 0.2 seconds at a specified redshift. Given that performing 13 N-body simulations is a manageable task, our present methodology enables us to construct efficient and highly accurate models of the galaxy power spectra within a manageable time frame.

Up, Up, and Away: Winds and Dynamical Structure as a Function of Altitude in the Ultrahot Jupiter WASP-76b

Due to the unprecedented signal strengths offered by the newest high-resolution spectrographs on 10 m class telescopes, exploring the 3D nature of exoplanets is possible with an unprecedented level of precision. In this paper, we present a new technique to probe the vertical structure of exoplanetary winds and dynamics using ensembles of planet absorption lines of varying opacity, and apply it to the well-studied ultrahot Jupiter WASP-76b. We then compare these results to state-of-the-art global circulation models (GCMs) with varying magnetic drag prescriptions. We find that the known asymmetric velocity shift in Fe i absorption during transit persists at all altitudes, and observe tentative trends for stronger blueshifts and more narrow line profiles deeper in the atmosphere. By comparing three different model prescriptions (a hydrodynamical model with no drag, a magnetic drag model, and a uniform drag model) we are able to rule out the uniform drag model due to inconsistencies with observed trends in the data. We find that the magnetic model is slightly favored over the the hydrodynamic model, and note that this 3-Gauss kinematic magnetohydrodynamical GCM is also favored when compared to low-resolution data. Future generation high-resolution spectrographs on extremely large telescopes will greatly increase signals and make methods like these possible with higher precision and for a wider range of objects.

Effect of Laser Power and Diamond Tool Parameters for Micro Laser-Assisted Ductile Mode Material Removal on Fused Silica

Abstract Fused silica is an essential material used in various applications due to its excellent optical and mechanical properties. However, processing it can be challenging due to its high hardness and brittleness, leading to sub-surface damage during grinding and polishing operations. Micro laser-assisted ductile mode material removal is a promising technique for achieving high levels of precision and accuracy in material removal. This technique leads to controlled and precise removal of material without inducing cracks or fractures. Despite its advantages, there are several challenges associated with the process, such as selecting the appropriate laser power and diamond tool geometry. In this study, we employed a Universal Mechanical Tester equipped with modified OPTIMUS, a laser-assisted machining technology to investigate the impact of laser and diamond tool geometry on scratch cut quality. Introducing and increasing the laser power demonstrated an improvement of around 251.7% in cut ductility, leading to a decrease in the severity of sub-surface damage (SSD). Altering the rake angle from -25° to -45° resulted in a 45.3% reduction in the critical depth of cuts (DOCs). However, when laser was employed, the critical DOCs increased around 34.4% in ductile mode material removal on fused silica samples, which underscores the criticality of the laser in this process.

Emotion Classification from Speech Waveform Using Machine Learning and Deep Learning Techniques

Emotions play a key role in determining the human mental state and indirectly express an individual’s well- being. A speech emotion recognition system can extract a person’s emotions from his/her speech inputs. There are some universal emotions such as anger, disgust, fear, happiness, pleasantness, sadness and neutral. These emotions are of significance especially in a situation like the Covid pandemic, when the aged or sick are vulnerable to depression. In the current paper, we examined various classification models with finite computational strength and resources in order to determine the emotion of a person from his/her speech. Speech prosodic features like pitch, loudness, and tone of speech, and work spectral features such as Mel Frequency Capstral Coefficients (MFCCs) of the voice were used to analyze the emotions of a person. Although sequence to sequence state of the art models for speech detection that offer high levels of accuracy and precision are currently in use, the computational needs of such approaches are high and inefficient. Therefore, in this work, we emphasised analysis and comparison of different classification algorithms such as multi layer perceptron, decision tree, support vector machine, and deep neural networks such as convolutional neural network and long short term memory. Given an audio file, the emotions that were exhibited by the speaker were recognized using machine learning and deep learning techniques. A comparative study was performed to identify the most appropriate algorithms that could be used to recognize emotions. Based on the experiment results, the MLP classifier and convolutional neural network model offered better accuracy with smaller variations when compared with other models used for the study.

Computational approaches on integrating vaccination and treatment strategies in the SIR model using Galerkin time discretization scheme

ABSTRACT In this manuscript, we carried out a thorough analysis of the general SIR model for epidemics. We broadened the model to include vaccination, treatment, and incidence rate. The vaccination rate is a testament to the alternatives made by individuals when it comes to receiving vaccinations and merging with the community of the recovered. The treatment rate measures how often people who have contracted a disease are able to transition into the recovered category. The continuous Galerkin-Petrov method, specifically the cGP(2)-method, is employed to calculate the numerical solutions of the models. The cGP(2)-method imperative to analyse two unknowns over every time interval. The unknowns can be determined by solving a block system of size (2 × 2). This approach demonstrates a strong level of precision throughout the entire time interval, with an impressive rate of convergence at the discrete time points. In addition, the article investigates the concept of the basic reproduction number ( R 0 ) and explores the intricacies of conducting sensitivity analysis of the developed system.

Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT

BackgroundThis study investigates the integration of Artificial Intelligence (AI) in compensating the lack of time-of-flight (TOF) of the GE Omni Legend PET/CT, which utilizes BGO scintillation crystals.MethodsThe current study evaluates the image quality of the GE Omni Legend PET/CT using a NEMA IQ phantom. It investigates the impact on imaging performance of various deep learning precision levels (low, medium, high) across different data acquisition durations. Quantitative analysis was performed using metrics such as contrast recovery coefficient (CRC), background variability (BV), and contrast to noise Ratio (CNR). Additionally, patient images reconstructed with various deep learning precision levels are presented to illustrate the impact on image quality.ResultsThe deep learning approach significantly reduced background variability, particularly for the smallest region of interest. We observed improvements in background variability of 11.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, 17.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, and 14.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} for low, medium, and high precision deep learning, respectively. The results also indicate a significant improvement in larger spheres when considering both background variability and contrast recovery coefficient. The high precision deep learning approach proved advantageous for short scans and exhibited potential in improving detectability of small lesions. The exemplary patient study shows that the noise was suppressed for all deep learning cases, but low precision deep learning also reduced the lesion contrast (about −30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}), while high precision deep learning increased the contrast (about 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}).ConclusionThis study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.

Micro Reference Electrode with an Ultrathin Ionic Path.

Reference electrode (RE) plays the core role in accurate potential control in electrochemistry. However, nanoresolved electrochemical characterization techniques still suffer from unstable potential control of pseudo-REs, because the commercial RE is too large to be used in the tiny electrochemical cell, and thus only pseudo-RE can be used. Therefore, microsized RE with a stable potential is urgently required to push the nanoresolved electrochemical measurements to a new level of accuracy and precision, but it is quite challenging to reproducibly fabricate such a micro RE until now. Here, we revisited the working mechanism of the metal-junction RE and clearly revealed the role of the ionic path between the metal wire and the borosilicate glass capillary to maintain a stable potential of RE. Based on this understanding, we developed a method to fabricate micro ultrastable-RE, where a reproducible ultrathin ionic path can form by dissolving a sandwiched sacrificial layer between the Pt wire and the capillary for the ion transfer. The potential of this new micro RE was almost the same as that of the commercial Ag/AgCl electrode, while the size is much smaller. Different from commercial REs that must be stored in the inner electrolyte, the new RE could be directly stored in air for more than one year without potential drift. Eventually, we successfully applied the micro RE in the electrochemical tip-enhanced Raman spectroscopy (EC-TERS) measurement to precisely control the potential of the working electrode, which makes it possible to compare the results from different laboratories and techniques to better understand the electrochemical interface at the nanoscale.

Application of integrated geodetic and UAV technologies for monitoring environmental changes due to the mining activities in Solotvyno salt mine, Ukraine

This paper covers the spatio-temporal evolution of land surface deformation in the Tisza River basin within the Transcarpathian region, analyzing geodetic observations obtained over the last decade. In the town of Solotvyno, in the western part of Ukraine, near the Romanian border, there is an abandoned salt deposit where salt was extracted in several mining operations. In 2010, the mine was closed, and mine chambers 7 and 8 were flooded with water, causing the collapse of this region. Since then, the extent of the damage has been increasing, with sinkholes steadily growing larger and larger, threatening not only the entire surface area around the quarry but especially the groundwater that connects the deposit to the Tisza River. Several research institutions have been involved in monitoring the shifts over time. We have monitored the karst by identifying the most dangerous areas of the earth's surface subject to vertical shifts - trench sinkholes by UAV survey and terrestrial measurements using precise levelling in 4 stages. The situation on the site is an emergency with a major ecological impact of salt contamination in the whole Upper Tisza Basin region and requires not only continuous geodetic, geological, and chemical monitoring but especially an urgent solution to reverse the state of damage to the region.

Quantitative Evaluations of Pumping-Induced Land Subsidence and Mitigation Strategies by Integrated Remote Sensing and Site-Specific Hydrogeological Observations

Land subsidence is an environmental hazard occurring gradually over time, potentially posing significant threats to the structural stability of civilian buildings and essential infrastructures. This study presented a workflow using the SBAS-PSInSAR approach to analyze surface deformation in the Choushui River Fluvial Plain (CRFP) based on Sentinel-1 SAR images and validated against precise leveling. Integrating the InSAR results with hydrogeological data, such as groundwater levels (GWLS), multilayer compactions, and borehole loggings, a straightforward model was proposed to estimate appropriate groundwater level drops to minimize further subsidence. The results showed a huge subsidence bowl centered in Yunlin, with maximal sinking at an average 60 mm/year rate. High-resolution subsidence maps enable the quantitative analyses of safety issues for Taiwan High-Speed Rail (THSR) across the areas with considerable subsidence. In addition, the analysis of hydrogeological data revealed that half of the major compaction in the study area occurred at shallow depths that mainly included the first and second aquifers. Based on a maximal subsidence control rate of 40 mm/year specified in the CRFP, the model results indicated that the groundwater level drops from wet to dry seasons needed to be maintained from 3 to 5 m for the shallowest aquifer and 4–6 m for Aquifers 3 and 4. The workflow demonstrated the compatibility of InSAR with traditional geodetic methods and the effectiveness of integrating multiple data sources to assess the complex nature of land subsidence in the CRFP.

Estimating Grassland Carrying Capacity in the Source Area of Nujiang River and Selinco Lake, Tibetan Plateau (2001–2020) Based on Multisource Remote Sensing

Estimating the spatiotemporal variations in natural grassland carrying capacity is crucial for maintaining the balance between grasslands and livestock. However, accurately assessing this capacity presents significant challenges due to the high costs of biomass measurement and the impact of human activities. In this study, we propose a novel method to estimate grassland carrying capacity based on potential net primary productivity (NPP), applied to the source area of the Nujiang River and Selinco Lake on the Tibetan Plateau. Initially, we utilize multisource remote sensing data—including soil, topography, and climate information—and employ the random forest regression algorithm to model potential NPP in areas where grazing is banned. The construction of the random forest model involves rigorous feature selection and hyperparameter optimization, enhancing the model’s accuracy. Next, we apply this trained model to areas with grazing, ensuring a more accurate estimation of grassland carrying capacity. Finally, we analyze the spatiotemporal variations in grassland carrying capacity. The main results showed that the model achieved a high level of precision, with a root mean square error (RMSE) of 4.89, indicating reliable predictions of grassland carrying capacity. From 2001 to 2020, the average carrying capacity was estimated at 9.44 SU/km2, demonstrating a spatial distribution that decreases from southeast to northwest. A slight overall increase in carrying capacity was observed, with 65.7% of the area exhibiting an increasing trend, suggesting that climate change has a modest positive effect on the recovery of grassland carrying capacity. Most of the grassland carrying capacity is found in areas below 5000 m in altitude, with alpine meadows and alpine meadow steppes below 4750 m being particularly suitable for grazing. Given that the overall grassland carrying capacity remains low, it is crucial to strictly control local grazing intensity to mitigate the adverse impacts of human activities. This study provides a solid scientific foundation for developing targeted grassland management and protection policies.

A Comprehensive Framework for Machine Learning-Based Threat Intelligence in Health Information Systems

This research work provides a comprehensive architecture of Machine Learning based threat intelligence particularly for Health Information System (HIS). The number of cybersecurity threats executed by healthcare companies is even higher since healthcare organizations continue to introduce digitized data into medical data. This work employs complex machine learning techniques from the MIMIC-III Critical Care Database to develop a practical threat identification and mitigation system. In this case, the strategy of analysis involves selection of data, data processing, modeling and real time dangers identification considering both supervised and unsupervised learning. The results reveal that the proposed framework covers high performance indicators such as: accuracy that equals 97.92%, and the level of precision and recall which also equal 90% ROC AUC has reached 0.94. These results demonstrate that the framework can identify and categorise cybersecurity risks in systems of health information on a regular basis. It not only increases threat perception but also makes the system internally valuable for healthcare IT professionals since it contains real-time monitoring and anomaly detection functionality. Therefore, this study stands in support of the ongoing efforts to enhance the security of the healthcare bodies on the use of policies on cybersecurity so as to ensure the protection of individual patient’s information against new forms of threats.

SMARCD1 is an essential expression-restricted metastasis modifier

Breast cancer is the most frequently diagnosed cancer worldwide, constituting 15% of cases in 2023. The predominant cause of breast cancer-related mortality is metastasis, and a lack of metastasis-targeted therapies perpetuates dismal outcomes for late-stage patients. By using meiotic genetics to study inherited transcriptional network regulation, we have identified, to the best of our knowledge, a new class of “essential expression-restricted” genes as potential candidates for metastasis-targeted therapeutics. Building upon previous work implicating the CCR4-NOT RNA deadenylase complex in metastasis, we demonstrate that RNA-binding proteins NANOS1, PUM2, and CPSF4 also regulate metastatic potential. Using various models and clinical data, we pinpoint Smarcd1 mRNA as a target of all three RNA-BPs. Strikingly, both high and low expression of Smarcd1 correlate with positive clinical outcomes, while intermediate expression significantly reduces the probability of survival. Applying the theory of “essential genes” from evolution, we identify 50 additional genes that require precise expression levels for metastasis to occur. Specifically, small perturbations in Smarcd1 expression significantly reduce metastasis in mouse models and alter splicing programs relevant to the ER+/HER2-enriched breast cancer. Identification subtype-specific essential expression-restricted metastasis modifiers introduces a novel class of genes that, when therapeutically “nudged” in either direction, may significantly improve late-stage breast cancer patients.

Seed traits inheritance in Fagopyrum esculentum Moench. based on image analysis method.

Common buckwheat (Fagopyrum esculentum Moench.) is one of the most important orphan crops worldwide. Various research efforts have been done to improve cultivation methods to enhance important agronomic traits such as productivity and biotic/abiotic resistance. One important aspect is the seed trait, which has not been extensively studied due to the time-consuming and tedious nature of its examination. Despite this, understanding seed traits is crucial for meeting consumer needs and optimizing crop yields. Therefore, the aim of the study is to investigate the inheritance of common buckwheat seed traits-such as shape, size, and coat color-using an image-based approach. This method allows for the analysis of a large number of seeds with a level of accuracy and precision that was previously unattainable. The results indicate that seed coat color is inherited maternally. Notably, the parameters in size had substantial increases acting like overdominance. The number of seeds that were harvested from F1s of each cross differed a lot depending on the cross combinations and pin/thrum type. In addition, seed size had large reduction in F1s from the different seed-sized parents, especially in thrum type. These may show that there could be cross barriers. The results revealed trends of maternal inheritance for seed shape and coat color in buckwheat, an area that has not been extensively studied. These findings could support buckwheat breeding efforts, helping to address market needs and food demands in the face of significant climate change.

Enhancing Unconfined Compressive Strength prediction in geotechnical engineering: a novel approach using LSZO and LSWG optimization

ABSTRACT The Unconfined Compressive Strength (UCS) measures a rock’s ability to withstand axial loads without lateral support, crucial for engineering applications. Accurate prediction of UCS is critical for the stability and safety of various civil engineering projects, such as foundation design, mining, and tunneling. The ability to predict UCS reliably ensures better project planning and execution, ultimately enhancing the safety and efficiency of engineering applications. The paper aims to develop an innovative method for predicting the UCS of rocks using a combination of the Zebra Optimisation Algorithm (ZOA) and the Wild Geese Algorithm (WGA) integrated with the Least Square Support Vector Regression (LSSVR) predictive model. The novelty of the paper lies in the integration of ZOA and WGA optimizers with the LSSVR model to address the limitations of conventional methods, which often face challenges like slow achieving convergence and being stuck in local minima. The combination of these advanced algorithms results in significantly improved accuracy and convergence speed in UCS predictions, demonstrating a robust and reliable approach that advances the field of geotechnical engineering. The results show that combining ZOA and WGA optimizers with LSSVR enhances UCS prediction accuracy and speed convergence. The LSZO models achieve a high level of precision, with a low RMSE value of 2.206 and an R2 value of 0.993. The safety and effectiveness of civil engineering projects are improved by this model, which provides a strong and trustworthy method for UCS prediction.

The secondary classification of unequilibrated chondrites

AbstractThe multiplication of decimal petrologic schemes for different or the same chondrite groups evinces a lack of unified guiding principle in the secondary classification of type 1–3 chondrites. We show that the current OC, R and CO classifications can be a posteriori unified, with only minor reclassifications, if the decimal part of the subtype is defined as the ratio m = FaI/FaII of the mean fayalite contents of type I and type II chondrules, rounded to the nearest tenth (with adaptations from Cr systematics for the lowest subtypes following the past literature). This parameter is more efficiently evaluable than the oft‐used relative standard deviation of fayalite contents and defines a general metamorphic scale from M0.0 to M1, where the suffixed number is the rounded m. Type 3 chondrites thus span the range M0.0–M0.9 (i.e. subtypes 3.0–3.9) and M1 designates type 4. Corresponding applications are then proposed for other chondrite groups (with, e.g., CV secondary classification reduced to essentially three grades from M0.0 to M0.2, that is, subtypes 3.0–3.2). Known type 1 and 2 chondrites are at M0.0 (i.e. the metamorphic grade of type 3.0 chondrites), even so‐called “CY” chondrites, since our metamorphic scale is insensitive to brief heating. Independently, we define an aqueous alteration scale from A0.0 to A1.0, where the suffixed number is the (rounded) phyllosilicate fraction (PSF). For CM and CR chondrites, the alteration degrees can be characterized in terms of the thin‐section‐based criteria of previous schemes which are thus incorporated in the present framework, if in a coarser, but hereby more robust form. We propose their corresponding petrologic subtype to be 3‐PSF, rounded to the nearest tenth (so that type 1 would correspond to subtypes 2.0 and 2.1). Since nonzero alteration and metamorphic degrees remain mutually exclusive at the level of precision chosen, a single petrologic subtype ≈3+m‐PSF indeed remains a good descriptor of secondary processes for all unequilibrated chondrites, obviating the explicit mention of our separate scales unless finer subdivisions are adopted for the most primitive chondrites.

Duration versus accuracy—what matters for computerised adaptive testing in schools?

AbstractBackgroundComputerised adaptive tests (CATs) are tests that provide personalised, efficient and accurate measurement while reducing testing time, depending on the desired level of precision. Schools have different types of assessments that can benefit from a significant reduction in testing time to varying degrees, depending on the area of application, but for which the loss of measurement accuracy has a different impact. The implementation of CAT can take several approaches, each of which can potentially affect the resulting test length and accuracy.ObjectivesWe compare the methods of estimation‐based CAT and binary‐search‐based CAT to determine the extent to which they are suitable for school assessment in terms of their length and accuracy.MethodsThis study uses simulations based on empirical data from a cohort of pupils with and without special needs (n = 400) to examine the effects of probabilistic estimation‐based CAT and deterministic binary‐search‐based CAT on the length and accuracy of an adaptive reading test for pupils with different ability levels.Results and ConclusionsEstimation‐based CAT leads to a 40% test reduction with an average accuracy of r = 0.96, while binary‐search‐based CAT leads to a test reduction of up to 88% with an average accuracy of r = 0.83. Both methods demonstrate the applicability of CAT in educational environments. Practical advantages and disadvantages of both methods for learning environments are discussed, as well as which method is best suited for specific assessment needs.

An integrated systematic method for interlaced unmanned spatial systems (IUSS) design process

Unmanned Aerial Systems (UAS) have garnered significant interest in recent years. These systems, commonly consisting of one or more Unmanned Aerial Vehicles (UAV) and satellite systems, have been extensively used to enhance the effectiveness of various SOSs, such as disaster management and relief efforts. In addition, the inaugural Mars unmanned helicopter, Ingenuity (Ginny), successfully took flight in April 2021, marking the beginning of the utilization of these systems on Mars. This research aims to build an integrated approach for developing Interlaced Unmanned Spatial Systems, considering the high level of precision required for space systems. This study aims to set up and optimize all parts of the proposed architecture for the design of IUSS, using Model-Based Systems Engineering theories and Dependency Structural Matrix foundations. This research introduces a comprehensive coherence architecture that considers all design domains, including the design process, design office, products, and requirements. Additionally, a design workflow model is provided.