Refinement Approach Research Articles

Conviction without confession: a case example of information seeking dialogue-based interviewing

Purpose Suspect interviewing in North America has evolved from coercive tactics to guilt-presumptive methods and, more recently, to information seeking dialogue-based (ISDB) approaches such as the PEACE model. Such approaches prioritize open dialogue and comprehensive suspect accounts over confession-driven strategies. These methods have been shown to reduce the risk of false confessions and enhance the quality of investigative information, though they are sometimes criticized for being “too soft” or insufficiently tested in real-world settings. This paper aims to explore the real-world application of an ISDB approach in the high-stakes interview of Adam Strong, who was ultimately convicted of first-degree murder and manslaughter. Design/methodology/approach Using PEACE as a framework, the authors detail how Detective Paul Mitton skillfully used rapport-building, strategic evidence presentation and open dialogue to elicit admissions without coercion or confrontation. Findings Although Strong did not confess to the homicides or discuss how the victims died, the admissions he provided during the 12-h interview were central to the court’s guilty rulings. Research limitations/implications Though a single-case analysis, this paper underscores the necessity for further empirical research on ISDB approaches across diverse real-world scenarios. Practical implications This case highlights how an ISDB approach can generate critical evidence while meeting both investigative and legal standards. The authors believe it underscores that the future of suspect interviewing lies in the continued adoption and refinement of approaches that prioritize rapport-building and open, free-flowing dialogue while incorporating safeguards to ensure the admissibility of the interview. Originality/value This paper presents a unique and practical application of an ISDB approach, contributing valuable insights for practitioners and researchers into advancing ethical and effective suspect interviewing practices.

Experiment-Guided Refinement of Milestoning Network.

Milestoning is an efficient method for calculating rare event kinetics by constructing a continuous-time kinetic network that connects the reactant and product states. Its accuracy depends on both the quality of the underlying force fields and the trajectory sampling. The sampling error can be effectively controlled through various methods. However, the force fields are often not accurate enough, leading to quantitative discrepancies between simulations and experimental data. To address this challenge, we present a refinement approach for Milestoning network based on the maximum caliber (MaxCal), a general variational principle for dynamical systems, to combine simulations and experimental data. The Kullback-Leibler divergence rate between two Milestoning networks is analytically evaluated and minimized as the loss function. Meanwhile, experimental thermodynamic (equilibrium constants) and kinetic (rate constants) data are incorporated as constraints. The use of MaxCal implies that the refined kinetic network is minimally perturbed from the original one while satisfying the experimental constraints. The refined network is expected to align better with available experimental data. The refinement approach is demonstrated using the binding and unbinding dynamics of a series of six small molecule ligands for the model host system, β-cyclodextrin.

Bearing capacity of strip footing subjected to eccentric loads found nearby prevailing footing by finite element analysis

ABSTRACT The utilisation of urban sites for the construction of buildings is undeniable due to population growth and suburbanisation. In these situations, it is compelled to build the structures in the vicinity of existing structures. The ultimate bearing capacity (UBC) of the new footing gets affected due to the interference. To study the effect of UBC on the new footing in the vicinity of an existing footing, the finite element method (FEM) analysis has been carried out to fact-finding the influence of UBC on the new footing found nearby the prevailing footing subjected to centric and eccentric loads resting on cohesionless soil. The current outcomes have been verified against the reviewed studies. The potency of UBC on the new footing has been expressed in terms of efficiency factor ξ q with regard to the spacing ratio, angle of internal friction of soil, eccentricity ratio and different width ratio. The schematic charts for practical implementation have been devised to assess the ξ q factors. The accuracy of the failure mechanism has been observed and improved by using an adaptive mesh refinement approach.

Weakly supervised land-cover classification of high-resolution images with low-resolution labels through optimized label refinement

ABSTRACT Land cover classification, or semantic segmentation, has been one of the most critical research areas in remote sensing (RS) and remains crucial for many downstream applications. Although deep learning (DL) based models have recently dominated this field, models trained using the dataset of one region generally cannot predict reliable classification results in other regions. Despite the abundance of high-resolution (HR) satellite images around the globe, not enough HR labels are available for building a one-for-all-purpose model. This study utilizes widely available low-resolution (LR) labels with weakly supervised methods to obtain land cover maps worldwide. Previous methods designed new learning components, such as loss functions, to fully use LR labels or trained DL models with LR labels to generate intermediate labels for further training and processing. Since this information is directly used or extracted from the original LR labels, methods without additional robustness are sensitive to noise in the labels, which causes error propagation in the results. On this basis, a novel label refinement approach is proposed that transforms noisy original LR labels into refined HR labels using two steps of noise filtering. First, based on spectral indices from the HR images, we select relatively confident labels from the LR labels through a Markov Random Field optimization framework. Second, a shallow classifier such as random forest (RF) is trained using the selected pixels to supplement previously unselected labels and refine low-confidence labels with new and high-confidence labels. The results showed that in the experiment on the Data Fusion Contest 2020 dataset, the semantic segmentation models trained using our refined HR labels had a 2–14% higher average accuracy than those trained using the original LR labels. They also outperformed other weakly supervised methods directly using original LR labels and had a 7.5% higher average accuracy than the winning method.

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.

Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularly with the development of AlphaFold2 (AF2), which can analyze sequences of amino acids and predict 3D structures with near experimental accuracy. Since the release of AF2, numerous studies have been conducted, either using AF2 directly for large-scale modeling or building upon the software for other use cases. Many reviews have been published discussing the impact of AF2 in the field of protein bioinformatics, particularly in relation to neural networks, which have highlighted what AF2 can and cannot do. It is evident that AF2 and similar approaches are open to further development and several new approaches have emerged, in addition to older refinement approaches, for improving the quality of predictions. Here we provide a brief overview, aimed at the general biologist, of how machine learning techniques have been used for improvement of 3D models of proteins following AF2, and we highlight the impacts of these approaches. In the most recent experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP15), the most successful groups all developed their own tools for protein structure modeling that were based at least in some part on AF2. This improvement involved employing techniques such as generative modeling, changing parameters such as dropout to generate more AF2 structures, and data-driven approaches including using alternative templates and MSAs.

Refinement Approach For Enhancing Artificial Intelligence's Spoken Pronunciation Precision In Online Reading

Refinement Approach For Enhancing Artificial Intelligence's Spoken Pronunciation Precision In Online Reading

An adaptive mesh refinement algorithm for crack propagation with an enhanced thermal–mechanical local damage model

An adaptive mesh refinement algorithm for crack propagation with an enhanced thermal–mechanical local damage model

Condensate Banking Analysis Optimizes Reservoir Development in Permian Basin

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4044947, “Numerical Analysis of Condensate Banking and Development Optimization in the Deep Formation of the Permian Basin,” by Jichao Han, SPE, Sunil Lakshminarayanan, and Jiasen Tan, SPE, Oxy, et al. The paper has not been peer reviewed. _ In the context of unconventional reservoir development, the issue of condensate banking—a factor contributing to reduced productivity in gas condensate reservoirs when the reservoir pressure falls below the dewpoint pressure—poses a constant concern. Before implementation of any mitigation strategies, it is prudent to assess the potential of condensate banking in the future, based on the limited early production data. A specialized workflow was devised for a deep formation in the Permian Basin. This workflow aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies. Introduction Well A represents a single appraisal well situated within deep formations of the Permian Basin. With only 5 months of production data available, pressure/volume/temperature analysis and modeling identified it as a gas condensate reservoir, with the dewpoint very close to the critical point. This prompts two critical business inquiries: Are there any concerns regarding condensate banking for this well? And what are the optimal drawdown strategies tailored to this specific format surrounding the appraisal well? Model Setup and Calibration Modeling Workflow. This study uses the comprehensive simulation workflow derived from Hydraulic Fracturing Test Site 2, which can effectively handle integrated models to provide valuable insight for condensate banking analysis and reservoir development optimization (Fig. 1). Hydraulic fracture and reservoir simulations were conducted using GOHFER 3D and Navigator software, respectively. The integration of fracture and reservoir models poses a significant challenge in accurately transferring fracture geometry and conductivity information from fracture simulators to reservoir simulators. To address this challenge, an in‑house preprocessor was developed, designed to project results from the fracture simulator into the reservoir simulator seamlessly, ensuring the fidelity of complex fracture geometries. This preprocessor enables the extension or contraction of fracture geometries in proportion to reduce uncertainties imposed by production data. Additionally, fracture conductivity is determined through regional correlations among proppant concentration, net closure stress, and fracture conductivity. Model Introduction. The geological model has been simplified to enhance modeling efficiency while still meeting the project requirements. The 3D geological model adopts a layer-cake structure with a flat geostructure. To ensure accuracy, the well trajectory is adjusted to align with the desired formation. The reservoir model measures 4,000×10,000×330 ft, with a grid resolution of 200×25×10 ft. Fracture geometry is imported from the fracture simulator, while fracture conductivity is primarily determined based on proppant concentration data exported from the fracture simulator. Fracture height remains unaltered in the reservoir simulator, reflecting available geochemical measurements. A compositional simulation approach is adopted instead of a black-oil model. To improve the accuracy of pressure drop and condensate dropout predictions, the local grid refinement (LGR) approach is selected, resulting in denser grids. This approach proves superior to the dual-porosity, dual-permeability (DPDP) method.

The performance of two urban flood regulation models using different input data

Increasing climate change has led to an increase in urban flood events. Events with a return period of twenty years become events with a period of two to three years. The primary objectives of this study are to evaluate the performance of flood regulation models using different input data and compare their performance for flood regulation supply (FRS) assessment. The assessment of the ecosystem services of urban FRS was performed using two models: Urban Flood Risk Mitigation (UFRM) by Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and IMECOGIP Waterflow Regulation (Retention Model) (WRRM). They rely on relatively accessible open-source data, require a short time to model each scenario and provide opportunities to interpret results in different spatial and measurement units. We performed several improvements to the input data, including updating the national ecosystem-type assessment for the case study area by connecting it with the Urban Atlas typology and proposing an approach for soil data refinement. The outputs of the two models show significant differences in FRS values for each land-use/land-cover (LULC) class. The UFRM assesses different impervious urban classes with different values, varying from very low to medium. In contrast, the WRRM assesses the FRS in densely sealed areas with one fixed value for low supply (where nearly all rainfall is transformed into runoff).

3DPSR: An innovative approach for pose and shape refinement in 3D human meshes from a single 2D image

3DPSR: An innovative approach for pose and shape refinement in 3D human meshes from a single 2D image

Analysis of crystallographic phase retrieval using iterative projection algorithms.

For protein crystals in which more than two thirds of the volume is occupied by solvent, the featureless nature of the solvent region often generates a constraint that is powerful enough to allow direct phasing of X-ray diffraction data. Practical implementation relies on the use of iterative projection algorithms with good global convergence properties to solve the difficult nonconvex phase-retrieval problem. In this paper, some aspects of phase retrieval using iterative projection algorithms are systematically explored, where the diffraction data and density-value distributions in the protein and solvent regions provide thesole constraints. The analysis is based on the addition of random error to thephases of previously determined protein crystal structures, followed by evaluation of the ability to recover the correct phase set as the distance from the solution increases. The properties of the difference-map (DM), relaxed-reflect-reflect (RRR) and relaxed averaged alternating reflectors (RAAR) algorithms are compared. All of these algorithms prove to be effective for crystallographic phase retrieval, and the useful ranges of the adjustable parameter which controls their behavior are established. When these algorithms converge to the solution, the algorithm trajectory becomes stationary; however, the density function continues to fluctuate significantly around its mean position. It is shown that averaging over the algorithm trajectory in the stationary region, following convergence, improves the density estimate, with this procedure outperforming previous approaches for phase or density refinement.

Subaerial landslide-induced waves investigated with an adaptively mesh refined multiphase granular flow model

Subaerial landslide-induced waves investigated with an adaptively mesh refined multiphase granular flow model

Aerodynamic and aeroacoustic characterization of an axial fan using Adaptive Mesh Refinement (AMR) based CFD and CAA

This work focuses on numerical analysis of a low-pressure axial fan using Computational Fluid Dynamics (CFD) and Computational Aeroacoustics (CAA) methods. These simulations are performed with the commercial software Hexagon's Cradle scFLOW. The investigation focuses on flow prediction using incompressible Large Eddy Simulation (LES), and acoustic propagation based on the Ffowcs Williams-Hawkings (FW-H) equation. We present a method to evaluate the mesh resolution for LES and a mesh refinement approach to resolve strong flow interactions, particularly near the tip-gap region. Both the aerodynamic and aeroacoustics performance of the fan are compared to experimental measurements for model validation. The pressure rise, velocity profiles and surface pressure fluctuations are captured well. Similarly, the sound propagation results are in good agreement with the experimental measurements. At low-mid frequencies, the blade passing frequencies and subharmonic peaks are captured, and at high frequencies, the broadband energy content is underpredicted, but still reasonably correlated. Additionally, a discussion regarding the mesh quality for LES is presented.

Deflagration-to-detonation transition and detonation propagation in supersonic flows with hydrogen injection and downstream ignition

This study investigates the mechanisms of flame acceleration and deflagration-to-detonation transition (DDT) in supersonic flows using transverse hydrogen injection and downstream ignition. Utilizing the graphics processing unit accelerated adaptive mesh refinement approach, we examine the influence of downstream ignition jet pressure on DDT through high-resolution computational simulations. Our results indicate that the transverse injection of hydrogen into the supersonic mainstream generates strong turbulence and numerous vortices due to Kelvin–Helmholtz instability, enhancing fuel mixing efficiency along the flow but deviating from the ideal premixed state. Following the injection of the downstream ignition jet into the supersonic main flow, initial flame acceleration is less effective than in the premixed state due to the non-uniformity of the incoming flow. However, within the boundary layer, the flame remains stable, and the intense turbulence fosters shock–flame interactions. The convergence of multiple compression waves into a shock wave facilitates energy deposition, coupling with the flame to trigger local detonation via the reactive gradient mechanism. The detonation wave exhibits complex wavefront structures, including vertical and oblique fronts induced by boundary layer interactions. Ignition jet pressure significantly impacts the DDT process and detonation wave characteristics, reducing ignition time and affecting the detonation temperature, pressure, and propagation speed. This study provides valuable insights into the dynamics of flame acceleration and DDT in supersonic flows with non-uniform fuel distribution and downstream jet ignition. The findings highlight the critical role of ignition jet pressure in optimizing ignition and detonation processes, offering new perspectives for achieving low-energy, rapid detonation initiation within the tube.

Maximizing reachability probabilities in rectangular automata with random events

Maximizing reachability probabilities in rectangular automata with random events

Design perspectives of a system identification approach of the NATO AVT-316 multi-swept wing aerodynamics

Design perspectives of a system identification approach of the NATO AVT-316 multi-swept wing aerodynamics

Microstructural refinement in a high elastic modulus Al-18Si-8Ni casting alloy

Microstructural refinement in a high elastic modulus Al-18Si-8Ni casting alloy

Combining Precision Boosting with LP Iterative Refinement for Exact Linear Optimization

This article studies a combination of the two state-of-the-art algorithms for the exact solution of linear programs (LPs) over the rational numbers in practice, that is, without any roundoff errors or numerical tolerances. By integrating the method of precision boosting inside an LP iterative refinement loop, the combined algorithm is able to leverage the strengths of both methods: the speed of LP iterative refinement, in particular, in the majority of cases when a double-precision floating-point solver is able to compute approximate solutions with small errors, and the robustness of precision boosting whenever extended levels of precision become necessary. We compare the practical performance of the resulting algorithm with both pure methods on a large set of LPs and mixed-integer programs (MIPs). The results show that the combined algorithm solves more instances than a pure LP iterative refinement approach while being faster than pure precision boosting. When embedded in an exact branch-and-cut framework for MIPs, the combined algorithm is able to reduce the number of failed calls to the exact LP solver to zero while maintaining the speed of the pure LP iterative refinement approach. History: Accepted by Antonio Frangioni, Area Editor for Design and Analysis of Algorithms: Continuous. Funding: The work for this article has been conducted within the Research Campus Modal funded by the German Federal Ministry of Education and Research (BMBF) [Grants 05M14ZAM and 05M20ZBM].

DendroTweaks: An interactive approach for unraveling dendritic dynamics.

Neurons rely on the interplay between dendritic morphology and ion channels to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental in exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks - a toolbox designed to illuminate how morpho-electric properties map to dendritic events and how these dendritic events shape neuronal output. DendroTweaks features a web-based graphical interface, where users can explore single-cell neuronal models and adjust their morphological and biophysical parameters with real-time visual feedback. In particular, DendroTweaks is tailored to interactive fine-tuning of subcellular properties, such as kinetics and distributions of ion channels, as well as the dynamics and allocation of synaptic inputs. It offers an automated approach for standardization and refinement of voltage-gated ion channel models to make them more comprehensible and reusable. The toolbox allows users to run various experimental protocols and record data from multiple dendritic and somatic locations, thereby enhancing model validation. Finally, it aims to deepen our understanding of which dendritic properties are essential for neuronal input-output transformation. Using this knowledge, one can simplify models through a built-in morphology reduction algorithm and export them for further use in faster, more interpretable networks. With DendroTweaks, users can gain better control and understanding of their models, advancing research on dendritic input-output transformations and their role in network computations.

OBSERVE: guidelines for the refinement of rodent cancer models.

Existing guidelines on the preparation (Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE)) and reporting (Animal Research: Reporting of In Vivo Experiments (ARRIVE)) of animal experiments do not provide a clear and standardized approach for refinement during in vivo cancer studies, resulting in the publication of generic methodological sections that poorly reflect the attempts made at accurately monitoring different pathologies. Compliance with the 3Rs guidelines has mainly focused on reduction and replacement; however, refinement has been harder to implement. The Oncology Best-practices: Signs, Endpoints and Refinements for in Vivo Experiments (OBSERVE) guidelines are the result of a European initiative supported by EurOPDX and INFRAFRONTIER, and aim to facilitate the refinement of studies using in vivo cancer models by offering robust and practical recommendations on approaches to research scientists and animal care staff. We listed cancer-specific clinical signs as a reference point and from there developed sets of guidelines for a wide variety of rodent models, including genetically engineered models and patient derived xenografts. In this Consensus Statement, we systematically and comprehensively address refinement and monitoring approaches during the design and execution of murine cancer studies. We elaborate on the appropriate preparation of tumor-initiating biologicals and the refinement of tumor-implantation methods. We describe the clinical signs to monitor associated with tumor growth, the appropriate follow-up of animals tailored to varying clinical signs and humane endpoints, and an overview of severity assessment in relation to clinical signs, implantation method and tumor characteristics. The guidelines provide oncology researchers clear and robust guidance for the refinement of in vivo cancer models.