Version Of Model Research Articles

Effects of Temperature and Random Forces in Phase Transformation of Multi-Stable Systems

Multi-stable behavior at the microscopic length-scale is fundamental for phase transformation phenomena observed in many materials. These phenomena can be driven not only by external mechanical forces but are also crucially influenced by disorder and thermal fluctuations. Disorder, arising from structural defects or fluctuations in external stimuli, disrupts the homogeneity of the material and can significantly alter the system’s response, often leading to the suppression of cooperativity in the phase transition. Temperature can further introduce novel effects, modifying energy barriers and transition rates. The study of the effects of fluctuations requires the use of a framework that naturally incorporates the interaction of the system with the environment, such as Statistical Mechanics to account for the role of temperature. In the case of complex phenomena induced by disorder, advanced methods such as the replica method (to derive analytical formulas) or refined numerical methods based, for instance, on Monte Carlo techniques, may be needed. In particular, employing models that incorporate the main features of the physical system under investigation and allow for analytical results that can be compared with experimental data is of paramount importance for describing many realistic physical phenomena, which are often studied while neglecting the critical effect of randomness or by utilizing numerical techniques. Additionally, it is fundamental to efficiently derive the macroscopic material behavior from microscale properties, rather than relying solely on phenomenological approaches. In this perspective, we focus on a paradigmatic model that includes both nearest-neighbor interactions with multi-stable (elastic) energy terms and linear long-range interactions, capable of ensuring the presence of an ordered phase. Specifically, to study the effect of environmental noise on the control of the system, we include random fluctuation in external forces. We numerically analyze, on a small-size system, how the interplay of temperature and disorder can significantly alter the system’s phase transition behavior. Moreover, by mapping the model onto a modified version of the Random Field Ising Model, we utilize the replica method approach in the thermodynamic limit to justify the numerical results through analytical insights.

Navigating Gynecological Oncology with Different Versions of ChatGPT: A Transformative Breakthrough or the Next Black Box Challenge?

The study evaluates the performance of large language model (LLM) versions of ChatGPT-ChatGPT-3.5, ChatGPT-4, and ChatGPT-Omni-in addressing inquiries related to the diagnosis and treatment of gynecological cancers, including ovarian, endometrial, and cervical cancers. A total of 804 questions were equally distributed across four categories: True/False, Multiple-Choice, Open-Ended, and Case-scenario, with each question type representing varying levels of complexity. Performance was assessed using a six-point Likert scale, focusing on accuracy, completeness, and alignment with established clinical guidelines. For True/False queries, ChatGPT-Omni achieved accuracy rates of 100% for easy, 98% for medium, and 97% for complicated questions, higher than ChatGPT-4 (94%, 90%, 85%) and ChatGPT-3.5 (90%, 85%, 80%) (p=0.041, 0.023, 0.014, respectively). In Multiple-Choice, ChatGPT-Omni maintained superior accuracy with 100% for easy, 98% for medium, and 93% for complicated queries, compared to ChatGPT-4 (92%, 88%, 80%) and ChatGPT-3.5 (85%, 80%, 70%) (p=0.035, 0.028, 0.011). For Open-Ended questions, ChatGPT-Omni had mean Likert scores of 5.8 for easy, 5.5 for medium, and 5.2 for complex levels, outperforming ChatGPT-4 (5.4, 5.0, 4.5) and ChatGPT-3.5 (5.0, 4.5, 4.0) (p=0.037, 0.026, 0.015). Similar trends were observed in Case-Scenario questions, where ChatGPT-Omni achieved scores of 5.6, 5.3, and 4.9 for easy, medium, and hard levels, respectively (p=0.017, 0.008, 0.012). ChatGPT-Omni exhibited superior performance in responding to clinical queries related to gynecological cancers, underscoring its potential utility as a decision-support tool and an educational resource in clinical practice.

Deep learning models for MRI-based clinical decision support in cervical spine degenerative diseases

PurposeThe purpose of our study is to develop a deep learning (DL) model based on MRI and analyze its consistency with the treatment recommendations for degenerative cervical spine disorders provided by the spine surgeons at our hospital.MethodsIn this study, MRI of patients who were hospitalized for cervical spine degenerative disorders at our hospital from July 2023 to July 2024 were primarily collected. The dataset was divided into a training set, a validation set, and an external validation set. Four versions of the DL model were constructed. The external validation set was used to assess the consistency between the DL model and spine surgeons’ recommendations about indication of cervical spine surgery regarding the dataset.ResultsThis study collected a total of 756 MR images from 189 patients. The external validation set included 30 patients and a total of 120 MR images, consisting of 43 images for grade 0, 20 images for grade 1, and 57 images for grade 2. The region of interest (ROI) detection model completed the ROI detection task perfectly. For the binary classification (grades 0 and 1, 2), DL version 1 showed the best consistency with the spine surgeons, achieving a Cohen’s Kappa value of 0.874. DL version 4 also achieved nearly perfect consistency, with a Cohen’s Kappa value of 0.811. For the three-class classification, DL version 1 demonstrated the best consistency with the spine surgeons, achieving a Cohen’s Kappa value of 0.743, while DL version 2 and DL version 4 also showed substantial consistency, with Cohen’s Kappa values of 0.615 and 0.664, respectively.ConclusionWe initially developed deep learning algorithms that can provide clinical recommendations based on cervical spine MRI. The algorithm shows substantial consistency with experienced spine surgeons.

Glacier Surges and Seasonal Speedups Integrated Into a Single, Enthalpy‐Based Model Framework

AbstractGlacier speedups occur on daily to centennial timescales. While basal water and subglacial drainage configuration are thought to drive glacier speedups across these timescales, it remains unclear whether this forcing always occurs through the same mechanisms. Here, we explore whether the enthalpy model of glacier surging can explain speedups over a broader range of timescales if modified to account for seasonality in surface melt and continuous water supply to the glacier bed. We simulate velocity oscillations that range from seasonal to years. Our model results more closely resemble observations of surges than previous model versions because ice flow variability at seasonal and multi‐year timescales is reproduced simultaneously through hydrological forcing. Under favorable conditions, seasonal water delivery to the bed gradually accumulates in a poorly‐connected basal drainage system, priming the glacier to surge. Surges themselves are marked by high water fluxes and enthalpy drainage from the glacier base.

Responsibility fictionalism

ABSTRACT This paper develops and defends a revisionary fictionalist version of the ‘social connection model’ of responsibility. The social connection model has become a prominent account of responsibility in recent years but, as many critics have noted, has difficulty in providing an account of the connection required to generate responsibility which is sufficiently determinate and generates acceptable outcomes. In light of this problem, I first consider whether a fictionalist interpretation of Iris Marion Young’s articulation of the social connection model is tenable. I show that no such re-interpretation is possible, as it runs against Young’s explicit aims. I then develop a more thoroughly revisionary fictionalist version of the social connection model. I argue that this form of responsibility fictionalism is able to account for the central aims of the social connection model without succumbing to the issues standard versions of the model face in grounding the attributions of responsibility it generates. Furthermore, I argue that attributions of responsibility can retain motivational force on this view.

The impact of quasi-biennial oscillation (QBO) disruptions on diurnal tides over the low- and mid-latitude mesosphere and lower thermosphere (MLT) region observed by a meteor radar chain

Abstract. A quasi-biennial oscillation (QBO) disruption is a very rare phenomenon in which QBO westward wind is temporarily interrupted by the occurrence of a band of westward wind in the tropical stratosphere. This phenomenon is important as it could greatly affect the global atmospheric circulation, especially in the mesosphere. Past observational and modelling studies have shown the QBO varying mesospheric diurnal tide, but the mechanism is still not fully understood. In this study, we report on the strong response of mesospheric diurnal tides to the two QBO disruptions that occurred in 2015–2016 and 2019–2020 and their possible mechanisms. The diurnal tidal winds are observed by a meteor radar chain, consisting of meteor radars located at Kunming (25.6° N, 103.8° E), Wuhan (30.5° N, 114.2° E), Mengcheng (33.4° N, 116.5° E), Beijing (40.3° N, 116.2° E), and Mohe (53.5° N, 122.3° E) in China. These observations provide clear evidence that mesospheric diurnal tides are unusually weakened (by ∼ −6 m s−1) during these QBO disruptions, over Kunming, Wuhan, Mengcheng, and Beijing. Using the Specific Dynamics version of the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (SD-WACCM-X) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset, the analysis indicates that the QBO wind affects mid-latitude mesospheric diurnal tides by modulating both the solar radiative absorption by subtropical stratospheric ozone (∼ 5 to 0.5 hPa) and the tidal–gravity wave interaction in the mesosphere (∼ 60 to 100 km). Thus, these unexpected QBO disruptions provide an opportunity to better understand the coupling between climate change and middle-atmospheric dynamics.

Diagnosing Excessive Subsidence Across the Southeast Atlantic in the Goddard Earth Observing System (GEOS) During ORACLES‐1

AbstractComplicated by seasonal transport of biomass burning aerosol plumes primarily overlaying the marine stratocumulus‐to‐cumulus transition, the complexity of the atmosphere over the southeast Atlantic Ocean is difficult to represent in models. Biases with respect to the height of the aerosol plume and excessive subsidence have previously been documented in MERRA‐2. This paper further diagnoses these biases, and addresses the unreasonable subsidence over the southeast Atlantic region currently simulated by the Goddard Earth Observing System (GEOS) model during the first deployment of the ORACLES campaign in September 2016. Our study is based on model simulations where the meteorological state is constrained by the GEOS‐based MERRA‐2 reanalysis, as well as by ERA5, using the so‐called replay technique. Free running simulations with the GEOS model show excessive subsidence that begins over land such that aerosol transported by the easterly jet reaches a strip of complex terrain and is forced downward; a similar behavior can be found in the MERRA‐2 reanalysis. This excessive subsidence has been somewhat reduced by improved parameterizations found in recent versions of the GEOS model. However, any replay constrained by MERRA‐2 meteorology, even those based on recent versions of GEOS, suffer from the same excessive subsidence. An additional set of simulations in which the GEOS model was free‐running was used to quantify the role of aerosol itself in adjusting the profile of vertical motion. While self‐lofting of biomass burning aerosol reduces the subsidence over the ocean, the magnitude of its impact is only a fraction of the bias in MERRA‐2.

Asymmetries in the Simulated Ozone Distribution on TRAPPIST-1e due to Orography

TRAPPIST-1e is a tidally locked rocky exoplanet orbiting the habitable zone of an M dwarf star. Upcoming observations are expected to reveal new rocky exoplanets and their atmospheres around M dwarf stars. To interpret these future observations we need to model the atmospheres of such exoplanets. We configured Community Earth System Model version 2–Whole Atmosphere Community Climate Model version 6, a chemistry climate model, for the orbit and stellar irradiance of TRAPPIST-1e assuming an initial Earth-like atmospheric composition. Our aim is to characterize the possible ozone (O3) distribution and explore how this is influenced by the atmospheric circulation shaped by orography, using the Helmholtz wind decomposition and meridional mass streamfunction. The model included Earth-like orography, and the substellar point was located over the Pacific Ocean. For such a scenario, our analysis reveals a north–south asymmetry in the simulated O3 distribution. The O3 concentration is highest at pressures >10 hPa (below ∼30 km) near the south pole. This asymmetry arises from the higher landmass fraction in the northern hemisphere, which causes drag in near-surface flows and leads to an asymmetric meridional overturning circulation. Catalytic species were roughly symmetrically distributed and were not found to be primary driver for the O3 asymmetry. The total O3 column density was higher for TRAPPIST-1e compared to Earth, with 8000 Dobson units (DUs) near the south pole and 2000 DU near the north pole. The results emphasize the sensitivity of O3 to model parameters, illustrating how incorporating Earth-like orography can affect atmospheric dynamics and O3 distribution. This link between surface features and atmospheric dynamics underlines the importance of how changing model parameters used to study exoplanet atmospheres can influence the interpretation of observations.

Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model

The sample covariance matrix of a random vector is a good estimate of the true covariance matrix if the sample size is much larger than the length of the vector. In high-dimensional problems, this condition is never met. As a result, in high dimensions the Ensemble Kalman Filter’s (EnKF) ensemble does not contain enough information to specify the prior covariance matrix accurately. This necessitates the need for regularization of the analysis (observation update) problem. We propose a regularization technique based on a new spatial model. The model is a constrained version of the general Gaussian process convolution model. The constraints include local stationarity and smoothness of local spectra. We regularize EnKF by postulating that its prior covariances obey the spatial model. Placing a hyperprior distribution on the model parameters and using the likelihood of the prior ensemble data allows for an optimized use of both the ensemble and the hyperprior. A linear version of the respective estimator is shown to be consistent. A more accurate nonlinear neural-Bayes implementation of the estimator is developed. In simulation experiments, the new technique led to substantially better EnKF performance than several existing techniques.

Novelle approach to simulating spinal cord stimulation during tSCS using CT images and FEM

Abstract Transcutaneous spinal cord stimulation (tSCS) offers non-invasive relief for chronic pain and improves motor function in spinal cord injured (SCI) patients. However, its mechanisms are not currently fully understood, and patientspecific factors, such as Body mass index (BMI) and age, complicate treatments. This paper aims to understand tSCS better by developing a novel Finite Element Model (FEM) of the human body using CT scans. Three subjects (sex, male, female, male. Age: 26, 27, 64. BMI: 38.9, 24.1, 28.4) underwent a CT scan, performed on a Cannon Aquilion Prime (Slice thickness [mm]: 0.8, Voxel size [mm3]: 0.564, 0.328, 0.527), which imaged the trunk of the body, from top of the abdomen to the bottom of the pelvis. The images were then used in Materialize Mimics Research 21.0 to create 3D images of individual organs, skin, fat, muscles, skeleton, and spinal cord. After pre-processing in Autodesk Meshmixer, the models were converted into solid CAD objects in Ansys SpaceClaim R2021 and combined into a single abdominal model. Ansys Maxwell R2021 was then used for simulations. Five different two-electrode configurations were tested in the prototype phase with a simplified model setup. The positive electrodes were placed over the (Thoracic) T10, T12, (Lumbar) L2 and L4 vertebrae sequentially, with the negative electrode over (Sacral) S2. The simulations took on average 47.4 hours. A marked decline in electrical current penetration depth was observed as the electrodes were placed closer together which was consistent with known current distribution patterns. A preliminary validation test was also performed using a lamb’s thigh. Two electrodes were placed a known distance apart and a stimulation was given, needle electrodes were then inserted in a grid-like pattern to obtain voltage values. The setup was recreated and simulated in Ansys Maxwell. The resulting average percent difference was 44.93 ± 33.3 % (5Vpp Square wave) and 35.02 ± 23.38 % (5V DC). In both instances, the highest difference was at the edges of the electrodes and the lowest difference in the midpoint between electrodes. Later versions of FEM models incorporated more organs and had improved on previous mesh generation flaws, but encountered new mesh generation errors which could not be rectified before the conclusion of the master’s project. Despite complications, this project has provided a pipeline for creating similar models and shown their usability. Future work will involve overcoming the current mesh generation errors, reducing calculation time, and performing a thorough validation test.

Elevation-Dependent Snow Cover Dynamics and Associated Topo-Climate Impacts in Upper Indus River Basin

In the present study, Improved Moderate Resolution Imaging Spectro-radiometer (MODIS) snow cover product (MOYDGL06*) has been used to evaluate the snow cover area (SCA) in Kabul, Jhelum, and Indus river basins for the time period of 2003 – 2020 with available MODIS land surface temperature (LST), and CHIRPS (precipitation) with objectives to evaluate the spatio-temporal SCA, and climate variables with respect to different elevations analyzed from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM v3) and also to correlate the climatic variables with SCA. The results presented average annual SCA is around 50.7% – 64.7% in sub-basins of UIB, further it has been observed that SCA is decreasing on annual and seasonal timescale in all three basins. Elevation-dependent SCA, temperature, and precipitation presented a mix of trend on annual, seasonal, and monthly timescale at lower and higher altitude in all selected basins. Moreover, it was noticed that topography (slope, & aspect) also influences the SCA in the region. Furthermore, it has been examined that temperature has significant inverse relationship with SCA at middle and higher altitude in Indus, while in Kabul, and Jhelum no significant relationship observed at extreme lower and higher altitudes. It is also evident from relationship between SCA and climate variable that temperature is significantly responsible for decreasing trend of SCA rather than intense precipitation in all three river basins. Thus, all these elevation-dependent changes can improve our hydrological understanding which can have a considerable implication for hydrology, climate science, water resource management and socio-economic activities.

Effects of the Species Number of Hydrometeors on the Rapid Intensification of Super Typhoon Mujigae (2015)

Super Typhoon Mujigae (2015) was simulated using the WRF-ARW model version 4.1 with the WSM3, WSM5, WSM6, and WSM7 microphysics schemes, which include 3, 5, 6, and 7 hydrometeor classes, respectively. This study investigated the species number of hydrometeors (SNHs) from simple to complex on the rapid intensification (RI) of a tropical cyclone (TC). SNHs significantly affected the distribution of hydrometeors, microphysical conversion processes (MCPs), latent heat budget, and the interaction between thermal and dynamic processes, thereby influencing the RI. Different SNHs resulted in varied MCPs and a latent heat budget. The WSM3 and WSM5 schemes share the same top three dominating MCPs: condensation of cloud water (COND), accretion of cloud water by rain (RACW), and evaporation of rain (REVP). COND, accretion of cloud water by graupel (GACR), and RACW contributed to the WSM6 scheme. The WSM7 scheme included hail, with contributions from the instantaneous melting of snow, graupel, and COND, respectively. The dominating latent cooling processes were identical, while in different orders, which were evaporation of rain (REVP), sublimation of snow (SSUB), and evaporation of cloud water (CEVP) in the WSM3 and WSM5 schemes; while CEVP, REVP, and SSUB were in the WSM6 and WSM7. The interaction between thermal and dynamic processes was ultimately responsible for the RI. The WSM6 scheme presented an excellent latent heating rate, warm-core structure, and secondary circulation, which enhanced convection and absolute angular momentum transportation, and further indicating RI. The results highlighted the importance of an adequate complexity microphysics scheme to better reproduce the RI.

Gravity model: Assessing the strength of economic ties between regions in the Ural Federal District

Subject. The article deals with the use of gravity models to analyze economic interactions between regions. Objectives. The study aims to develop and test a modified version of the gravity model to assess the strength of economic ties between regions in the Ural Federal District, using Data Science methods and data analysis tools of the R language of the modified gravity model. Methods. The study offers a modified gravity model enabling to more accurately assess the strength of economic ties between regions based on the aggregation of a variety of economic indicators under the principal components method, and obtain indicators of economic potential of the region in the economic system. The Haversine formula was applied to calculate distances between the capitals of regions of the Ural Federal District. Results. The model was applied to the Ural Federal District regions. This showed that the Sverdlovsk and Chelyabinsk Oblasts are the leading economic centers of the region, while the Khanty-Mansi and Yamalo-Nenets Autonomous Districts demonstrate less close ties with industrial regions due to their remoteness from the centers. The findings can be used to work out regional development strategies aimed at strengthening inter-regional ties, improving infrastructure, and diversifying the economy. Conclusions. The proposed model can be useful for analyzing and evaluating economic ties and the potential of regions in various macroeconomic contexts, which make it applicable for regional policy planning.

Enhancements in Artificial Intelligence for Medical Examinations: A Leap from ChatGPT 3.5 to ChatGPT 4.0 in the FRCS Trauma & Orthopaedics Examination

IntroductionChatGPT is a sophisticated AI model capable of generating human-like text based on the input it receives. ChatGPT 3.5 showed an inability to pass the FRCS (Tr&Orth) examination due to a lack of higher-order judgement in previous studies. Enhancements in ChatGPT 4.0 warrant an evaluation of its performance. MethodologyQuestions from the UK-based December 2022 In-Training examination were input into ChatGPT 3.5 and 4.0. Methodology from a prior study was replicated to maintain consistency, allowing for a direct comparison between the two model versions. The performance threshold remained at 65.8%, aligning with the November 2022 sitting of Section 1 of the FRCS (Tr&Orth). ResultsChatGPT 4.0 achieved a passing score (73.9%), indicating an improvement in its ability to analyse clinical information and make decisions reflective of a competent trauma and orthopaedic consultant. Compared to ChatGPT 4.0, version 3.5 scored 38.1% lower, which represents a significant difference (p<0.0001; Chi-square). The breakdown by subspecialty further demonstrated version 4.0’s enhanced understanding and application in complex clinical scenarios. ChatGPT 4.0 had a significantly significant improvement in answering image-based questions (p=0.0069) compared to its predecessor. ConclusionChatGPT 4.0's success in passing Section 1 of the FRCS (Tr&Orth) examination highlights the rapid evolution of AI technologies and their potential applications in healthcare and education.

Validation of Light–Ion Quantum Molecular Dynamics (LIQMD) model for hadron therapy

Purpose:This study aims to validate the Light-Ion Quantum Molecular Dynamics (LIQMD) model, an advanced version of the QMD model for more accurate simulations in hadron therapy, incorporated into Geant4 (release 11.2). Methods:Two sets of experiments are employed. The first includes positron-emitter distributions along the beam path for 350 MeV/u 12C ions incident on a PMMA target, obtained from in–vivo Positron Emission Tomography (PET) experiments at QST (Chiba, Japan). The second comprises cross-sections for 95 MeV/u 12C ions incident on thin targets (H, C, O, Al, and Ti), obtained from experiments at GANIL (Caen, France). The LIQMD model’s performance is compared with the experimental data and the default QMD model results. Results:The LIQMD model can predict the profile shape of positron-emitting radionuclide yields with better accuracy than the default QMD model, although some discrepancies remains. The consistency observed in the production of positron-emitting radionuclides aligns with the thin target cross-section analysis. The LIQMD model significantly improves the differential and double-differential cross-sections of fragments produced in thin targets, especially in the forward direction. The overestimation of 10C production in the in–vivo PET benchmark is consistent with the 95 MeV/u 12C cross-section test. Overall, the LIQMD model demonstrates better agreement with experimental measurements for nearly all fragment species compared to the QMD model. Conclusions:The LIQMD model offers an improved description of the fragmentation process in hadron therapy. Future work should involve further validation against additional experimental measurements to confirm these findings.

Planetary boundary layer scheme in the INMCM Earth system model

Abstract The paper reviews the planetary boundary layer parameterizations in the current generation of the INMCM Earth system model. We discuss some of the challenges and improvements necessary to correctly reproduce the essential non-linear interactions of physical processes common to the boundary-layer physics. Overview of some of the improvements implemented in the PBL single-column version of the INMCM model is presented. These include the hierarchy of turbulence closures of different computational complexity suited for modelling a thin stable boundary layer. The closures are based on a consistent definition of the first-order, single- and two-equation approaches and inclusion of stability functions in the surface layer parameterizations tailored for strong static stability of the atmosphere.

Siftr as a Socio-affective Tool in the Spanish Heritage Language Classroom: Exploring the Effects of Landscape Identity

ABSTRACT Siftr (https://siftr.org) is a geolocation platform developed by the Wisconsin Institute for Discovery’s Field Day Lab. Among its affordances, this application allows users to post images, geolocate them, and comment and share posts with a public or a private group. Considering the versatility of this platform, it has been utilised in different fields such as education (e.g. Bell et al., 2019), folklore (e.g. DuBois et al., 2018), or social sciences and humanities (e.g. Guerrero-Rodriguez et al., 2022). However, despite the high potential this platform offers for language education, its implementation in heritage language courses remains unexplored. To address this gap in the literature, following an adapted version of the LESCANT model (Victor, 1992), the present study seeks to observe the effects of using Siftr in two intermediate courses for Spanish heritage speakers at a public university in Florida. During a 16-week semester, participants used this geolocation application to record their experiences using Spanish and/or observing the presence of their Hispanic heritage in their day-to-day lives. Based on the qualitative data collected, results show that this project encouraged learners to actively notice their and their peers’ surroundings while reflecting on how the landscape identity helped them construct their collective and individual identities (Ramos et al., 2016) as Hispanic American individuals.

Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students

This study evaluates the effectiveness of generative artificial intelligence (GAI) in identifying and reconstructing legal arguments from judges’ reasons in court cases, focusing on the practical implications for law students and legal educators. By examining the performance of two versions of popular Large Language Models – ChatGPT and Claude – across five recent High Court of Australia decisions, the study makes a preliminary assessment of the accuracy of LLM systems in replicating a skill essential for lawyers: identification of arguments and argument chains in judges’ reasons. The methodology involves marking LLM-generated outputs with reference to both a sample answer and a detailed rubric. Key findings reveal a significant variance in the accuracy of different LLMs, with Claude 3.5 markedly outperforming all others, achieving average grades up to 90 per cent. In contrast, ChatGPT versions demonstrated lower accuracy, with average marks not exceeding 50 per cent. These results highlight the critical importance of selecting the right GAI system for legal applications, as well as the necessity for users to critically engage with AI outputs rather than relying solely on automated tools. The study concludes that while LLMs hold potential benefits for the legal profession, including increased efficiency and enhanced access to justice, for GAI use that may be carried out by a law student, the technology cannot yet replace the nuanced human skill of legal argument analysis.

Contributions to the Development of Tetrahedral Mobile Robots with Omnidirectional Locomotion Units

In this paper, the authors present the process of modeling, building, and testing two prototypes of tetrahedral robots with omnidirectional locomotion units. The paper begins with a detailed description of the first tetrahedral robot prototype, highlighting its strengths as well as the limitations that led to the need for improvements. The robot’s omnidirectional movement allowed it to move in all directions, but certain challenges related to stability and adaptability were identified. The second prototype is presented as an advanced and improved version of the first model, integrating significant modifications in both the structural design and the robot’s functionality. The authors emphasize how these optimizations were achieved, detailing the solutions adopted and their impact on the robot’s overall performance. This paper includes an in-depth comparative analysis between the two prototypes. The analysis highlights the considerable advantages of the second prototype, demonstrating its superiority. The conclusions of the paper summarize the main findings of the research and emphasize the significant progress made from the first to the second prototype. Finally, future research directions are discussed, which include refining control algorithms, miniaturizing the robot, improving structural performance by integrating shock-absorbing dampers, and integrating lighting systems and video cameras.

Tuning Earth System Models Without Integrating to Statistical Equilibrium.

This paper proposes algorithms for estimating parameters in Earth System Models (ESMs), specifically focusing on simulations that have not yet achieved statistical equilibrium and display climate drift. The basic idea is to treat ESM time series as outputs of an autoregressive process, with parameters that depend on those of the ESM. The maximum likelihood estimate of the parameters and the associated uncertainties are derived. This method requires solving a nonlinear system of equations and often results in unsatisfactory parameter estimates, especially in short simulations. This paper explores a strategy for overcoming this limitation by dividing the estimation process into two linear phases. This algorithm is applied to estimate parameters in the convection scheme of the Community Earth System Model version 2 (CESM2). The modified algorithm can produce accurate estimates from perturbation runs as short as 2years, including those exhibiting climate drift. Despite accounting for climate drift, the accuracy of these estimates is comparable to that of algorithms that do not. While these initial results are not optimal, the autoregressive approach presented here remains a promising strategy for model tuning since it explicitly accounts for climate drift in a rigorous statistical framework. The current performance issues are believed to be technical in nature and potentially solvable through further investigation.