Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “evaluation framework”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasi-Experimental Analytical Approach

This study implements a data-intensive supervised ML approach through a quasi-experimental framework with the objective of quantifying the impact of oil and gas operator-specific proprietary data on ML-based predictive model performance relative to using oil and gas datasets that may be more commonly publicly available. The models are designed to jointly predict daily oil, gas, and water production for horizontal wells as a function of bottom-hole pressure drawdown, spatial placement across the study domain, and well completion attributes. Model performance is quantified on holdout test data to evaluate how each dataset affects resulting model variant performance.

02 PETROLEUM↗
DOE OSTI2021

Linking habitat suitability with a longleaf pine-hardwood model: Building a species-predictive fire-land management framework

Active management of fire-dependent ecosystems for specific species leads to complex tradeoffs, which affect conservation outcomes to other species. Therefore a multi-species evaluation of management actions is required. Habitat Suitability Models (HSMs) can help in predicting the likelihood of species occurrence using corresponding environmental variables and empirical relationships that link occurrence with specific environmental conditions. Incorporating multiple species into HSMs and relating them to habitat dynamics is crucial for ecosystems that require active management with prescribed fire. To address this issue, we developed multi-species HSM driven within an existing population model of the longleaf pine-hardwood ecosystem to assess the suitability of an ecosystem given different fire management strategies and environmental conditions. The population model used in this study provides spatial and temporal changes of longleaf pine-hardwood habitat structure in response to fire. These habitat values are used by the HSM to calculate habitat suitability for three threatened and endangered faunal species of this ecosystem, which all thrive with frequent fire, but have unique habitat requirements. Transient habitat conditions are traced to predict longleaf pine ecosystem trajectories under various management strategies, thereby evaluating current land management actions, such as thinning or prescribed fire frequencies. We tested a suite of environmental conditions to emphasize the sensitivity of the species to different fire management actions. The results of our modeling suggest that maximum suitable habitat for all three species can be achieved with fire frequency occurring at approximately once every three years. The modeling results support current management actions and provide a new habitat assessment tool that incorporates ecological factors for multiple species, thus providing for habitat optimization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

LDRD23-0184: Resilience and Hazard Risk Assessment to Prioritize Security Operations for Decisions and Impacts (RHAPSODI)

Recent examples provide a significant concern for the resilience of the U.S. electric grid and represent a need for enhanced decision-making to address an increasingly wide range of complex system interactions and potential consequences. In response, this LDRD project produced a proof-of-concept evaluation called the Resilience and Hazard Assessment to Prioritize Security Operations for Decisions and Impacts (RHAPSODI) methodology as an agile and flexible analytic framework capable of addressing multiple, diverse threats to desired electric grid performance. After empirically grounding needs for the future of U.S. electric grid resilience, this project employed the systems-theoretic process analysis (STPA) to develop a systems engineering risk model. The results of a completed feasibility study of a notional high voltage transmission system demonstrate an improved ability to incorporate both spatial (e.g., geographically distributed) and temporal (e.g., dynamic and time-dependent) elements of security risk to the gird. The success of this LDRD project provides the foundation for further evolution of the systems engineering risk model for the grid; derivation of quantitative approaches to evaluate risk and resilience performance; facilitation of agile experimenting and grid sensitivity to a range of vulnerabilities; and development of tools to assist decision-makers in enhancing U.S. electrical grid resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

dCache – Efficient Message Encoding For Inter-Service Communication in dCache: Evaluation of Existing Serialization Protocols as a Replacement for Java Object Serialization

As a well established, large-scale distributed storage system, dCache is used to manage and serve huge amounts of data collected by high energy physics, astrophysics and photon science experiments. Based on a microservices-like architecture, dCache is built as a modular distributed system, where each component provides a different core functionality. These services communicate by passing serialized messages to each other, a core behavior whose performance properties can consequently affect the entire system.This paper compares and evaluates different data serialization protocols in computer science with the objective of replacing and improving upon Java Object Serialization (JOS), which has increasingly presented itself as no longer being sufficiently performant for encoding messages. The criteria for choosing a new framework are collected, analyzed and formalized. The primary motivation for replacing Java serialization for encoding dCache messages is increasing the general speed of message-passing and thereby reducing the round-trip time for user requests. Emphasis is also placed on schema evolution capabilities and framework usability. Approaches for generalizing (de)serialization speed and size measurements based on data structure complexity are introduced, criteria for measuring documentation, learning curve, maintainability and introduction effort are defined. Finally, several selected serialization protocols are evaluated and compared accordingly, concluding with a recommendation for a suitable JOS replacement.

Morschel, Lea↗
DOE OSTI2026

Demonstration of Grid Services Using Mixed Grid-Forming and Grid-Following Technologies at the Wheatridge Renewable Energy Facility (Final Technical Report)

This Final Technical Report summarizes the analytical, modeling, and engineering work performed to evaluate grid-forming (GFM) inverter capabilities within a large hybrid renewable energy facility. The project focused on assessing the ability of advanced inverter-based resources to provide essential reliability services through coordinated operation of grid-forming (GFM) and grid-following (GFL) technologies. During Budget Period 1, the project developed a comprehensive framework of GFM performance metrics, including angle support, voltage regulation, frequency response, damping behavior, and current-limiting performance. Extensive electromagnetic transient (EMT) studies and hardware-in-the-loop (HIL) testing were conducted to validate GFM behavior under a range of operating conditions, including weak-grid scenarios, voltage disturbances, and multi-resource interactions. The project also established validated modeling approaches, plant-level control integration strategies, commissioning frameworks, and high-speed measurement infrastructure to support future field demonstration. Although the project concluded prior to field demonstration, the results provide a utility-scale foundation for evaluating, modeling, and deploying grid-forming technologies. The methodologies and tools developed contribute to industry understanding of inverter-based resource behavior and support future power system reliability under increasing renewable penetration.

14 SOLAR ENERGY↗
DOE OSTI2025

Effectiveness of nature-based solutions to reduce flooding in Quad Cities Metro Area (QCMA) using SWMM-HEC based flood model

Nature-based solutions (NbS) have gained significant attention as strategies for addressing urban environmental challenges, particularly since the establishment of the UN Sustainable Development Goals (SDGs) for 2030. However, the current research on NbS for urban flood management lacks comprehensive methodological approaches for identifying suitable areas and evaluating their effectiveness across different urban settings. Here, this study attempts to fill this gap by proposing a methodological framework integrating multi-criteria analysis with a SWMM-HEC-based hydrologic and hydraulic (HH) model to assess the suitability of NbS for the Quad Cities Metro Area (QCMA), consisting of Davenport, Bettendorf, Moline, and Rock Island. Eight NbS options-green roofs, rain gardens, infiltration trenches, permeable pavements, vegetative swales, dry detention basins, retention ponds, and rain barrels/cisterns - were considered based on volumetric efficiency and runoff reduction efficiency. The study reveals that implementing the proposed NbS could have substantially reduced flood depths in key historical flood events by 21% in 1993, 15% in 2008, 16% in 2011, 23% in 2014, 40% in 2019, and 10% in 2023. The findings highlight a critical trade-off between peak runoff and NbS implementation: while NbS effectively reduce flood impacts, they also enhance volumetric efficiency by approximately 43%. In high-density areas of the QCMA, flood depth reductions of around 20% suggest that NbS are a viable solution for dense urban environments with limited space. This shows the potential for integrating NbS into existing infrastructure, offering a promising approach for cities facing increasing flooding risks. The proposed methodology provides a practical framework for incorporating NbS into urban stormwater management, addressing gaps in optimizing NbS performance, and offering a pathway to scale their application in other urban areas with various environmental and social contexts.

CMIP6↗
DOE OSTI2020

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. Here, we present TERACHEM CLOUD, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TERACHEM CLOUD to distribute ab initio calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TERACHEM CLOUD framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Different flavors of exact-factorization-based mixed quantum-classical methods for multistate dynamics

Here, the exact factorization approach has led to the development of new mixed quantum-classical methods for simulating coupled electron–ion dynamics. We compare their performance for dynamics when more than two electronic states are occupied at a given time, and analyze: (1) the use of coupled versus auxiliary trajectories in evaluating the electron–nuclear correlation terms, (2) the approximation of using these terms within surface-hopping and Ehrenfest frameworks, and (3) the relevance of the exact conditions of zero population transfer away from nonadiabatic coupling regions and total energy conservation. Dynamics through the three-state conical intersection in the uracil radical cation as well as polaritonic models in one dimension are studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

PFPO plasma scenarios for exploration of long pulse operation in ITER

Long Pulse Scenarios (LPS) in ITER foreseen during the Pre-Fusion Power Operation (PFPO) phase of the ITER Research Plan (IRP) are assessed using 1.5D transport simulations within the ASTRA framework. Such assessment is required to predict the operational space for LPS operation in PFPO, as well as to evaluate which physics processes for LPS operation during Fusion Power Operation (FPO) could be studied during PFPO. An important aspect in the development of LPSs in PFPO is to minimize lifetime consumption of the Central Solenoid (CS) for these scenarios. The maximum pulse length achievable for LPSs in PFPO with no consumption of CS lifetime (currents in CS coils $⩽$30 kA per turn) has been assessed for a range of heating schemes and heating mixes, confinement regimes (L-mode and H-mode) and for helium and hydrogen plasmas. The operational space of LPS and pulse length has been explored through density scans with the Heating and Current Drive mix required for the FPO Q $⩾$ 5 steady-state plasma scenario (namely Neutral Beam Injection and Electron Cyclotron Heating) including acceptable shine through losses on the first wall for both helium and hydrogen plasmas. Fast particle physics aspects that are common between FPO plasmas and LPS PFPO H-mode plasmas at low densities are studied including MHD stability analysis with the KINX code and non-perturbative critical gradient model based on high-n Toroidal Alfven Eigenmodes (TAE) stability kinetic ballooning code HINST calculations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Variational Monte Carlo calculations of n + H 3 scattering

A paramount goal in nuclear physics is to unify ab initio treatments of bound and unbound states. The position-space quantum Monte Carlo (QMC) methods have a long history of successful bound-state calculations in light systems but have seen minimal implementation in unbound systems. Here, we introduce a numerical method to improve the efficiency and accuracy of unbound-state calculations in QMC. As an initial application, we compute scattering observables for the smallest system available to probe three-body forces, the neutron-triton system, using variational Monte Carlo (VMC) wave functions. The method involves inferring long-range amplitudes in the wave function from integrals over the short-range region where all the particles interact. This approach using integral relations is well established in the literature; here, we develop it for the QMC framework. We validate our approach with a consistency check between short-range spectroscopic overlap functions computed from direct evaluation and from the integral relations; scattering amplitudes are long-range asymptotics of those overlaps. Comparison against published benchmark calculations using the same potential demonstrates that when applied to the current VMC wave functions, the integral method produces more accurate scattering observables than direct evaluation from the same variational wave function. However, it still differs noticeably from the exact results. Using additional interactions, we then present phase shifts and mixing parameters for the n + 3 H system. In particular, we present one of the first applications of the Norfolk family of local coordinate-space chiral potentials in unbound systems of A > 2. The Norfolk results accurately describe s-wave scattering but predict p-wave cross sections too large. Compared with previous QMC scattering calculations, the integral method avoids difficulties associated with the precise computation of energy differences and with convergence outside the interaction region, which is particularly severe in the variational calculation. Application of the integral method here paves the way for its use in Green's function Monte Carlo (GFMC) calculations. In GFMC, the wave functions are more accurate, but the high-precision convergence of their tails is slow, and there are additional difficulties in reading out amplitudes. Here, the integral methods will address both of those remaining problems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Error mitigation in variational quantum eigensolvers using tailored probabilistic machine learning

Quantum computing technology has the potential to revolutionize the simulation of materials and molecules in the near future. A primary challenge in achieving near-term quantum advantage is effectively mitigating the noise effects inherent in current quantum processing units (QPUs). This challenge is also decisive in the context of quantum-classical hybrid schemes employing variational quantum eigensolvers (VQEs) that have attracted significant interest in recent years. In this paper, we present a method that employs parametric Gaussian process regression (GPR) within an active learning framework to mitigate noise in quantum computations, focusing on VQEs. Our approach, grounded in probabilistic machine learning, exploits a custom prior based on the VQE ansatz to capture the underlying correlations between VQE outputs for different variational parameters, thereby enhancing both accuracy and efficiency. We demonstrate the effectiveness of our method on a two-site Anderson impurity model and a eight-site Heisenberg model, using the IBM open-source quantum computing framework, Qiskit, showcasing substantial improvements in the accuracy of VQE outputs while reducing the number of direct QPU energy evaluations. This paper contributes to the ongoing efforts in quantum-error mitigation and optimization, bringing us a step closer to realizing the potential of quantum computing in quantum matter simulations. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Mobile Edge Computing Framework for Traffic Optimization At Urban Intersections Through Cyber-Physical Integration

The stop-and-go traffic pattern on urban roads often results in excessive energy consumption because of unnecessary vehicle braking, idling, and accelerations. With the widespread and increased use of automobiles, this traffic pattern creates many negative impacts (e.g., delayed travel time, air pollution, and additional carbon emission) on the sustainability of our cities. Taking advantage of the recent emerging Internet of Things (IoT) and edge computing paradigms, we propose a mobile edge computing framework that integrates the capability of real-time vehicle-to-infrastructure communication and intelligent speed optimization algorithms into a mobile app to optimize individual vehicles' driving speed at signalized intersections. The optimization aims to mitigate the stop-and-go traffic pattern and its undesirable consequences in urban transportation systems. The framework consists of (1) a cyberinfrastructure-enabled dynamic messaging system for retrieving and delivering real-time traffic and signal phase and timing information from IoT-connected signal controllers and sensors, (2) a real-time speed optimization algorithm for generating intelligent speed advisory using vehicle's information (e.g., GPS and driving directions from mobile sensing) and corresponding signal and traffic information, and (3) an ad-hoc mobile computing environment that converts drivers' smartphones into edge devices to host the speed optimization algorithms for enabling intelligent advisory on the vehicle's driving speed within signalized corridors. The paper presents the design and implementation of the proposed framework. Finally, we demonstrate the feasibility, usefulness, and energy-saving benefits of our proposed framework and its prototyping mobile app on urban transportation systems through traffic simulation, real-vehicle laboratory experiments, an evaluative survey, and field communication tests. The simulation-based energy evaluation results show that the 100% usage of the mobile app can achieve 24% energy savings in the transportation system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Enhancing trap and fault seal analyses by integrating observations from HR3D seismic data with well logs and conventional 3D seismic data, Texas inner shelf

An understanding of trap and fault seal quality is critical for assessing hydrocarbon prospectivity. To achieve this, modern analytical techniques leverage well data and conventional industry-standard 3D seismic data to evaluate the trap, and any faults displacing the reservoir and top seal intervals. Above all, geological interpretation provides the framework of trap and fault seal analyses, but can be hindered by the data resolution, quality and acquisition style of the conventional seismic data. Furthermore, limiting the analysis to only the petroleum system at depth may lead to erroneous perceptions because interpreting overburden features, such as shallow faults or gas chimneys, can provide valuable observations with respect to container performance, and can to help validate trap and fault seal predictions. Here, a supplement to conventional 3D data are high-resolution 3D seismic (HR3D) data, which provide detailed images of the overburden geology. This study utilizes an HR3D seismic volume in the San Luis Pass area of the Texas inner shelf, where shallow fault tips and a sizeable gas chimney are interpreted over an unsuccessful hydrocarbon prospect. Static post-drill fault seal and trap analyses suggest that the primary fault displacing the structural closure could have withheld columns of gas c. 100 m high, but disagree with our HR3D seismic interpretations and dry-well analyses. From our results, we hypothesize that tertiary gas migration through fault conduits reduced the hydrocarbon column in the prospective Early Miocene reservoir, and may have resulted from continued movement along the intersecting faults. Overall, this study reinforces the importance of understanding the overburden geology and geohistory of faulted prospects, and demonstrates the utility of pre-drill HR3D acquisition when conducting trap and fault seal analyses.

3D seismic↗
DOE OSTI2025

HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs

Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. However, most existing progressive methods are designed for CPUs, leaving a gap for them to unleash the power of today’s heterogeneous computing systems with GPUs.In this work, we propose HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs. Our contributions are four-fold: (1) We carefully optimize the bitplane encoding and lossless encoding, two key stages in progressive methods, to achieve high performance on GPUs; (2) We propose pipeline optimization and incorporate it with data refactoring and progressive retrieval workflows to further enhance the performance for large data process; (3) We leverage our framework to enable high-performance data retrieval with guaranteed error control for common Quantities of Interest; (4) We evaluate HP-MDR and compare it with state of the arts using five real-world datasets. Experimental results demonstrate that HP-MDR delivers an average 13.68 × and 6.31 × throughput in data refactoring and progressive retrieval tasks, respectively. It also leads to 11.22 × throughput for recomposing required data representations under Quantity-of-Interest error control and 6.04 × performance for the corresponding end-to-end data retrieval, when compared with state-of-the-art solutions.

Li, Yanliang [University of Oregon]↗
DOE OSTI2026

Modeling and Calibration of Supplier Selection Problem in Freight Agent-Based Simulations

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for international shipments. Our approach integrates trade relationships between industry sectors, transportation costs, and a supplier-rating model adapted from existing literature. The model’s core objective is to minimize the discrepancy between modeled and observed commodity flows while ensuring a close match to regional shipping distance distributions. Implemented and tested across four major U.S. metropolitan areas—Atlanta, Chicago, Dallas–Fort Worth, and Los Angeles—the model demonstrates high fidelity in replicating observed freight patterns. Key findings reveal consistent alignment with national shipping distance trends and highlight significant spatial variations in commodity trade assignments and demand across the study regions. This behaviorally informed and transport-sensitive framework is designed to approximate real-world decision making, providing a robust tool for policymakers and planners to evaluate targeted interventions, assess infrastructure investments, and enhance supply chain resilience in the face of disruptions.

Ismael, Abdelrahman (ORCID:0000000303712110)↗
DOE OSTI2026

Tribological behavior of nuclear graphite in high-temperature inert environment

This report formally documents the completion of the Advanced Reactor Technologies Level 2 Milestone (M2AT-26OR0605058), “Complete initial wear/abrasion studies,” due June 1, 2026. The work presented in this report expend upon our previously published manuscript titled Sliding friction and wear behavior of nuclear graphite in high temperature inert environment: Influence of contact load, speed and temperature. The sliding friction and wear of self-mated ET-10 nuclear graphite were characterized across a range of elevated temperatures (650°C and 750°C), contact loads (20 N and 40 N), and sliding speeds (1 mm/s and 10 mm/s) within a controlled argon environment. Tribological data combined with advanced morphological characterization were used to provide a detailed mechanistic framework for the frictional and wear behavior of nuclear graphite in high-temperature inert conditions. Furthermore, the report evaluates the inherent limitations of bench-scale characterization and addresses the critical disparities between laboratory findings and the complex, in-service friction and wear behavior of fuel pebbles within a reactor environment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Utility of Macroecological Rules for Microbial Biogeography

Macroecological rules have been developed for plants and animals that describe large-scale distributional patterns and attempt to explain the underlying physiological and ecological processes behind them. Similarly, microorganisms exhibit patterns in relative abundance, distribution, diversity, and traits across space and time, yet it remains unclear the extent to which microorganisms follow macroecological rules initially developed for macroorganisms. Additionally, the usefulness of these rules as a null hypothesis when surveying microorganisms has yet to be fully evaluated. With rapid advancements in sequencing technology, we have seen a recent increase in microbial studies that utilize macroecological frameworks. Here, we review and synthesize these macroecological microbial studies with two main objectives: (1) to determine to what extent macroecological rules explain the distribution of host-associated and free-living microorganisms, and (2) to understand which environmental factors and stochastic processes may explain these patterns among microbial clades (archaea, bacteria, fungi, and protists) and habitats (host-associated and free living; terrestrial and aquatic). Overall, 78% of microbial macroecology studies focused on free living, aquatic organisms. In addition, most studies examined macroecological rules at the community level with only 35% of studies surveying organismal patterns across space. At the community level microorganisms often tracked patterns of macroorganisms for island biogeography (74% confirm) but rarely followed Latitudinal Diversity Gradients (LDGs) of macroorganisms (only 32% confirm). However, when microorganisms and macroorganisms shared the same macroecological patterns, underlying environmental drivers (e.g., temperature) were the same. Because we found a lack of studies for many microbial groups and habitats, we conclude our review by outlining several outstanding questions and creating recommendations for future studies in microbial ecology.

59 BASIC BIOLOGICAL SCIENCES↗