Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Privacy”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Investigation of Strategies for Sensing Window Attachment Position: Survey of Candidate Technologies, Evaluation, and Deployment Recommendations

While windows are desirable feature in buildings for views, daylighting, and aesthetics, it is important to recognize that windows are a major contributor to a building’s energy load. Interior window coverings, such as shades and blinds, can help improve the energy performance of windows without sacrificing the benefits; however, while appropriate use of window coverings provide the potential for energy savings, there is little consensus regarding how occupants typically use these window attachments, which sometimes puts into question how much energy they actually save in an occupied home. Automated shading control strategies can reduce the uncertainty of energy savings; however, users can overwrite pre-defined schedules or write their own shade control programs to reach their personal comfort and privacy goals. Because it is difficult to identify how occupants interact with window shades, it is difficult to identify the energy savings potential of blinds. To address this problem, this study was developed to identify a candidate sensor that can be deployed to sense blind position. The intended application for this sensor is deployment in energy efficiency field studies so that shade usage can be measured to better understand results and identify the energy impact of specific window attachment technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Responsible Artificial Intelligence for Insider Threat Mitigation

This report examines the application of artificial intelligence (AI) technologies for insider threat mitigation (ITM) programs in nuclear security facilities. Insider threat detection presents unique challenges due to the subtle and adaptive nature of these threats, the complex signatures involved, and the scarcity of available data for analysis. Traditional human-centered approaches, while essential, face limitations in processing large amounts of data continuously and detecting subtle patterns across multiple systems. AI technologies can potentially address these limitations by providing 24/7 monitoring capabilities, identifying complex patterns that might escape human observation, and offering consistent application of security criteria. However, the deployment of AI in nuclear security contexts introduces significant new risks, including workflow disruption, expanded attack surfaces, potential for misuse, and ethical concerns regarding privacy, fairness, transparency, safety, and security. The high-consequence nature of nuclear security decisions demands careful consideration of these risks and systematic approaches to their mitigation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Summary of Responses to the Request for Information (RFI) on Partnerships for Transformational Artificial Intelligence Models

The Department of Energy (DOE) issued a Request for Information (RFI) in December 2025 inviting public comments regarding partnerships for transformational Artificial Intelligence (AI) models for the Genesis Mission Consortium, a public-private partnership platform. This RFI solicited feedback from industry, nonprofit organizations, universities, independent research organizations and other stakeholders. Specifically, the RFI asked three questions on (1) mobilizing DOE National Laboratories to curate the scientific data in a responsible and privacy-preserving manner, (2) the extent to which existing general-purpose AI models can be leveraged and which scientific disciplines are priorities for such model development, and (3) mechanisms by which these AI models can be provided to scientific communities. This document summarizes the input from 194 unique nonproprietary responses from businesses, universities, nonprofit organizations, research institutes and laboratories as well as a variety of other contributors, including individual contributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Genesis Mission-Enabled Secure AI to Fortify Energy Process Safety (Genesis-SAFE)

Argonne National Laboratory is supporting the U.S. Department of Transportation’s (USDOT’s) Bureau of Transportation Statistics (BTS) with collaborative research on development and application of privacy preserving AI frameworks that leverage unmatched AI expertise and secure computing resources made available through the U.S. Genesis Mission1 . This research advances U.S. energy security goals by supporting a safe offshore energy industry with secure, domain-specific AI tools to analyze confidential industry datasets collected by BTS to rapidly improve identification of hazards, precursors, and systemic safety risks in high-risk operational environments. The staged, security-first approach begins with development and testing of Argonne’s Genesis Mission-enabled Secure AI to Fortify Energy Process Safety (Genesis-SAFE) framework within Argonne’s accredited secure computing enclave (ABLE) leveraging Argonne’s AI scientific assistant substrate (AISAC). Methods to build synthetic datasets were developed together with BTS for use in preparing synthetic datasets that can be used to validate data containment, governance, and security controls in the ABLE environment. Future research directions would focus on applying the Genesis-SAFE framework to CIPSEA-protected datasets entirely within ABLE to support confidentiality-preserving analysis of safety risks, trends, and contributing factors.

Kim, Hyekyung [Argonne National Laboratory (ANL), ↗
DOE OSTI2020

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well as semantic reasoning and causality models is a potential solution to this problem. In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance. We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make.

adverse childhood experiences↗
DOE OSTI2020

Measuring Cities with Software-Defined Sensors

The Chicago Array of Things (AoT) project, funded by the US National Science Foundation, created an experimental, urban-scale measurement capability to support diverse scientific studies. Initially conceived as a traditional sensor network, collaborations with many science communities guided the project to design a system that is remotely programmable to implement Artificial Intelligence (AI) within the devices-at the “edge” of the network-as a means for measuring urban factors that heretofore had only been possible with human observers, such as human behavior including social interaction. The concept of “software-defined sensors” emerged from these design discussions, opening new possibilities, such as stronger privacy protections and autonomous, adaptive measurements triggered by events or conditions. We provide examples of current and planned social and behavioral science investigations uniquely enabled by software-defined sensors as part of the SAGE project, an expanded follow-on effort that includes AoT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Likeness: a toolkit for connecting the social fabric of place to human dynamics

The ability to produce richly-attributed synthetic populations is key for understanding human dynamics, responding to emergencies, and preparing for future events, all while protecting individual privacy. The Likeness toolkit accomplishes these goals with a suite of Python packages: pymedm/pymedm\_legacy, livelike, and actlike. This production process is initialized in pymedm (or pymedm\_legacy) that utilizes census microdata records as the foundation on which disaggregated spatial allocation matrices are built. The next step, performed by livelike, is the generation of a fully autonomous agent population attributed with hundreds of demographic census variables. The agent population synthesized in livelike is then attributed with residential coordinates in actlike based on block assignment and, finally, allocated to an optimal daytime activity location via the street network. We present a case study in Knox County, Tennessee, synthesizing 30 populations of public K–12 school students \& teachers and allocating them to schools. Validation of our results shows they are highly promising by replicating reported school enrollment and teacher capacity with a high degree of fidelity.

Tuccillo, Joe↗
DOE OSTI2025

Dependable classical-quantum computing systems engineering

Increasing evidence suggests quantum computing (QC) complements traditional High-Performance Computing (HPC) by leveraging its unique capabilities, leading to the emergence of a new, hybrid paradigm, QHPC. However, this integration introduces new challenges, with dependability–defined by reproducibility, resiliency, and security and privacy–emerging as a central concern for building trustworthy systems that provide an advantage to the users. This paper proposes a framework for dependable QHPC system design, organized around these three pillars. We identify integration challenges, anticipate roadblocks, and highlight productive synergies across QC, HPC, cloud platforms, and network security. Drawing from both classical computing principles and quantum-specific insights, we present a roadmap for co-design that supports robust hybrid architectures. Our approach offers concrete metrics for assessing dependability, provides design guidance for engineers working at the QC-HPC interface, and surfaces new engineering questions around complexity, scale, and fault tolerance. Ultimately, designing for dependability is key to realizing practical, scalable QHPC systems and accelerating the broader quantum ecosystem capable of translating quantum promises into actual application delivery.

HPC↗
DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗
DOE OSTI2024

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

A Comprehensive Analysis of Energy and Daylighting Impact of Window Shading Systems and Control Strategies on Commercial Buildings in the United States

Commercial buildings consume approximately 1.9 EJ of energy in the United States, 50% of which is for heating, cooling, and lighting applications. It is estimated that windows contribute up to 34% of the energy used for heating and cooling. However, window retrofits are not often undertaken to increase energy efficiency because of the high cost and disruptive nature of window installation. Highly efficient window technologies would also need shading devices for glare prevention and visual comfort. An automated window shading system with an appropriate control strategy is a technology that can reduce energy demand, maintain occupant comfort, and enhance the aesthetics and privacy of the built environment. However, the benefits of the automated shades currently used by the shading industry are not well studied. The topic merits an analysis that will help building owners, designers and engineers, and utilities make informed decisions using knowledge of the impact of this technology on energy consumption, peak demand, daylighting, and occupant comfort. This study uses integrated daylight and whole-building energy simulation to evaluate the performance of various control strategies that the shading industry uses in commercial office buildings. The analysis was performed for three different vintages of medium office buildings at six different locations in United States. The results obtained show the control strategies enabled cooling energy savings of up to 40% using exterior shading, and lighting energy savings of up to 25%. The control strategies described can help building engineers and researchers explore different control methods used to control shading in actual buildings but rarely discussed in the literature. This information will give researchers the opportunity to investigate potential improvements in current technologies and their performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids

An MILP-based distributed energy management for the coordination of networked microgrids is proposed in this paper. Multiple microgrids and the utility grid are coordinated through iteratively adjusted price signals. Based on the price signals received, the microgrid controllers (MCs) and distribution management system (DMS) update their schedules separately. Then, the price signals are updated according to the generation–load mismatch and distributed to MCs and DMS for the next iteration. The iteration continues until the generation–load mismatch is small enough, i.e., the generation and load are balanced under agreed price signals. Through the proposed distributed energy management, various microgrids and the utility grid with different economic, resilient, emission and socio-economic objectives are coordinated with generation–load balance guaranteed and the microgrid customers’ privacy preserved. In particular, a piecewise linearization technique is employed to approximate the augmented Lagrange term in the alternating direction method of multipliers (ADMM) algorithm. Thus, the subproblems are transformed into mixed integer linear programming (MILP) problems and efficiently solved by open-source MILP solvers, which would accelerate the adoption and deployment of microgrids and promote clean energy. The proposed MILP-based distributed energy management is demonstrated through various case studies on a networked microgrids test system with three microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Blockchain Research and Development Activities Sponsored by the U.S. Department of Energy and Utility Sector

This article provides an in-depth analysis of blockchain research in the energy sector, focusing on projects funded by the U.S. Department of Energy (DOE) and comparing them with industry-funded initiatives. A total of 110 funded activities within the U.S. power industry were successfully tracked and mapped into a newly developed categorization framework. This framework is designed to help research agencies to systematically understand their funded portfolio. Such characterization is expected to help them make effective investments, identify research gaps, measure impact, and advance technological progress to meet national goals. In line with this need, the proposed framework proposes a 2-D categorization matrix to systematically classify blockchain efforts within the energy sector.Under the proposed framework, the Energy System Domain serves as the primary classification dimension, categorizing use cases into 30 distinct applications. The second dimension, Blockchain Properties, captures the specific needs and functionalities provided by Blockchain technology. The aim was to capture blockchain’s applicability and functionality: where and why blockchain? Principles behind the selection of the viewpoint dimensions were carefully defined based on consensus obtained through the Blockchain for Optimized Security and Energy Management (BLOSEM) project. The mapped results show that activities within the Grid Automation, Coordination, and Control (31.8%), Marketplaces and Trading (25.5%), Foundational Blockchain Research (19.1%), and Supply Chain Management (17.3%) domains have been actively pursued to date. The three leading specific use case applications were identified as Transactive Energy Management for Marketplaces and Trading, Asset Management for Supply Chain Management, and Fundamental Blockchain for Foundational Blockchain Research. The Marketplaces and Trading and Retail Services Enablement domains stood out as being favored by industry by a factor greater than 2 (2.3 and 2.6, respectively), yet there seemed to be little to zero investment from DOE. Approximately 76% of the total projects prioritized Immutability, Identity Management, and Decentralization and/or Disintermediation compared to Asset Digitization and/or Tokenization, Automation, and Privacy and/or Anonymity. The greatest discrepancies between DOE and industry were in Asset Digitization and/or Tokenization and Automation. The industry efforts (36% in Asset Digitization/Tokenization and 22% in Automation) was 14 times and 2.4 times, respectively, more intensive than the DOE-sponsored efforts, indicating a significant discrepancy in industry versus government priorities. Overall, quantifying DOE-sponsored projects and industry activities through mapping provides clarity on portfolio investments and opportunities for future research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

The Influence of Demographic Variables on the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA)

Building on our prior research with a national survey sample of 5385 US participants, the Pooled Rideshare Acceptance Model (PRAM) was built upon two factor analyses. This exploratory study extends the PRAM framework using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) to examine how 16 demographic variables influence and interact with the acceptance of Pooled Rideshare (PR), filling a gap in understanding user segmentation and personalization. Using a national sample of 5385 US participants, this methodological approach allowed for the evaluation of how PRAM variables such as safety, privacy, service experience, and environmental impact vary across diverse groups, including gender, generation, driver’s license, rideshare experience, education level, employment status, household size, number of children, income, vehicle ownership, and typical commuting practices. Factors such as convenience, comfort, and passenger safety did not show significant differences across the moderators, suggesting their universal importance across all demographics. Furthermore, geographical differences did not significantly impact the relationships within the model, suggesting consistent relationships across different regions. The findings highlight the need to move beyond a “one size fits all” approach, demonstrating that tailored strategies may be crucial for enhancing the adoption and satisfaction of PR services among various demographic groups. The analyses provide valuable insight for policymakers and rideshare companies looking to optimize their services and increase user engagement in PR.

moderator↗
DOE OSTI2025

Pooled Rideshare in the U.S.: An Exploratory Study of User Preferences

Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims to evaluate potential service improvements and features that may increase users’ willingness to adopt the service. The study analyzed transportation behaviors, rideshare preferences, and willingness to adopt pooled rideshare services among 8296 U.S. participants in 2025, building on findings from a 2021 nationwide survey of 5385 U.S. participants. The study incorporated 77 actionable items developed from the results of the 2021 survey to assess whether addressing specific user-generated topics such as safety, reliability, convenience, and privacy can improve pooled rideshare use. A side-by-side comparison of the 2021 and 2025 data revealed shifts in transportation behavior, with personal rideshare usage increasing from 22% to 28%, public transportation from 21% to 27%, and pooled rideshare from 6% to 8%, while personal vehicle (79%) use remained dominant. Participants rated features such as driver verification (94%), vehicle information (93%), peak time reliability (93%), and saving time and money (92–93%) as most important for improving rideshare services. A pre-to-post analysis of willingness to use pooled rideshare utilizing the actionable items as per respondents’ preferences showed improvement: “definitely will” increased from 15.9% to 20.1% and “probably will” rose from 35.6% to 47.7%. These results suggest that well-targeted service improvements may meaningfully enhance pooled rideshare acceptance. This study offers practical guidance for Transportation Network Companies (TNCs) and policymakers aiming to improve pooled rideshare as well as potential future research opportunities.

Transportation Network Companies (TNCs)↗
DOE OSTI2025

Producing High-fidelity Synthetic Population Ensembles at Scale

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the US via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. Our initial task involves creating ensembles for 17 US metropolitan areas, each consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system comprised of a research cloud, virtual containerization, GPU-enhanced functionality, and a dual API/CLI to interact with UrbanPop’s maturing Likeness Python ecosystem. We observe a reduction in theoretical execution time while maintaining high-fidelity approximations of residential totals by metropolitan area and the demographic characteristics of neighborhoods. We discuss expansion of our approach to produce synthetic population ensembles for the entire US, particularly plans to establish automated workflows for job orchestration to increase computational efficiency, as well as provide outlook for broadening applications of the ensembles.

Gaboardi, James [ORNL] (ORCID:0000000247766826)↗
DOE OSTI2024

Enhancing Nuclear and Radiological Security in Polarized Times: Safeguarding Against Extremist Insider Threats

Domestic violent extremism has been on the rise in recent years, fueled by growing political polarization, the COVID-19 pandemic, and the spread of misinformation online. This concerning trend raises an important question: has this increase in extremist violence manifested in more incidents involving radioactive or nuclear (RN) materials and a corresponding security concern? To explore this question, we analyzed existing data on events involving chemical, biological, radiological, or nuclear materials over time. Specifically, we examined the rate of RN-related incidents, the targets of these events, and the ideologies motivating the perpetrators by using the Violent Non-State Actor Chemical, Biological, Radiological, and Nuclear Event Database from the National Consortium for the Study of Terrorism and Responses to Terrorism. Our analysis aimed to determine whether the broader rise of violent extremism has translated to an elevated risk of attacks involving RN materials. Results of the analysis revealed that there was no clear increase in events involving RN material in the dataset. However, given the rarity of these events, this conclusion does not indicate that such a trend might not appear in coming years if violent extremism continues to rise. To address the potential risk presented by violent extremism, in addition to quantifying the frequency of RN events, we also reviewed traits that could make organizations more susceptible to insider threats from radicalized employees. Focusing on the literature around counterproductive workplace behavior, we identified organizational characteristics that might increase the risk of malicious acts committed by radicalized insiders. These risk factors include abusive leadership, situational constraints, and organizational injustice. Finally, using these organizational risk characteristics, we discuss interventions that organizations with RN material can take to reduce their risk of attacks stemming from violent extremism while still protecting individual rights to privacy and civil liberties. Overall, this article provides an overview of the intersections between rising domestic extremism, insider threats, and risks posed by RN materials. We present current data on trends and patterns in this space as well as practical guidance for organizational resilience.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗