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DOE OSTI2024

Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments

The project titled “Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments” addresses energy-related challenges associated with adaptive traffic control systems by integrating connected vehicles (CV) and connected infrastructure (CI). The system developed in this project, a CV-based adaptive traffic control system, aims to improve fuel consumption in mixed traffic environments by capitalizing on emerging CV and CI communication technologies, as well as leveraging recent advances in Artificial Intelligence (AI), optimization, and edge computing. The system was tested at the MLK Smart Corridor, an urban testbed managed by the University of Tennessee at Chattanooga (UTC) and the City of Chattanooga. The system was validated through extensive simulations, both Software-in-the-Loop (SILS) and Hardware-in-the-Loop (HILS), and was further implemented and tested in real-world conditions at several intersections along the corridor. The Fuel Consumption Performance Index (FC-PI) and the Ecological Performance Index (Eco-PI) were developed as the key components for evaluating the system’s impact on fuel consumption and emissions. These metrics provided a comprehensive means of understanding the impact of traffic signal control optimization in mixed traffic environments. The report presents an in-depth analysis of the Eco-PI, FC-PI, adaptive traffic control system integration, and the testing and field implementation of the system. The results demonstrate significant reductions in fuel consumption and emissions, showcasing the system’s capability to contribute to more sustainable urban traffic management. The report also documents the challenges encountered and recommendations for scaling and further improving the system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Cross-Vendor and Cross-State Analysis of GPS Probe Data Latency

Crowd sourced GPS probe data have become a major source of real-time traffic information applications. In addition to traditional traveler advisory systems such as dynamic message signs (DMS) and 511 systems, probe data are being used for automatic incident detection, integrated corridor management (ICM), end of queue warning systems, and mobility-related smartphone applications. Several private sector vendors offer minute by minute network-wide travel time and speed probe data. The quality of such data in terms of deviation of the reported travel time and speeds from ground-truth has been extensively studied in recent years, and as a result concerns over the accuracy of probe data have mostly faded away. However, the latency of probe data - defined as the lag between the time at which disturbance in traffic speed is reported in the outsourced data feed, and the time at which the traffic is perturbed - has become a subject of interest. The extent of latency of probe data for real-time applications is critical, so it is important to have a good understanding of the amount of latency and its influencing factors. This paper uses high-quality independent Bluetooth/Wi-Fi re-identification data collected on multiple freeway segments in three different states, to measure the latency of the vehicle probe data provided by three major vendors. The statistical distribution of the latency and its sensitivity to speed slowdown and recovery periods are discussed.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Lessons Learned on the Interface Between Quantum and Conventional Networking

The future Quantum Internet is expected to be based on a hybrid architecture with core quantum transport capabilities complemented by conventional networking. Practical and foundational considerations indicate the need for conventional control and data planes that (i) utilize extensive existing telecommunications fiber infrastructure, and (ii) provide parallel conventional data channels needed for quantum networking protocols. We propose a quantum-conventional network (QCN) harness to implement a new architecture to meet these requirements. The QCN control plane carries the control and management traffic, whereas its data plane handles the conventional and quantum data communications. We established a local area QCN connecting three quantum laboratories over dedicated fiber and conventional network connections. We describe considerations and tradeoffs for layering QCN functionalities, informed by our recent quantum entanglement distribution experiments conducted over this network.

Alshowkan, Muneer↗

DOE OSTI2021

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. Here, the framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗

DOE OSTI2024

Air quality impacts from the development of unconventional oil and gas well pads: Air toxics and other volatile organic compounds

Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production. These operations offer an especially useful case to study as several management practices were implemented to reduce emissions (e.g., electrified, grid-powered drill rigs and closed loop fluid handling systems to reduce truck traffic and limit fluid handling on the pad). With simultaneous measurements of methane and 50 VOCs from October 2018 to December 2022 at as many as 19 sites near well pads, in adjacent neighborhoods, and at a more distant reference location, we identify impacts from each phase of well development and production. Use of weekly, time-integrated canisters, a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous photoionization detectors (PID) to trigger canister collection upon detection of VOC-rich plumes, and an instrumented vehicle, provided a powerful suite of measurements to characterize both transient plumes and longer-term changes in air quality. Prior to the start of well development, VOC gradients were small across Broomfield. Once drilling commenced, concentrations of oil and gas (O&G) related VOCs, including alkanes and aromatics, increased around active well pads. Concentration increases were clearly apparent during certain operations, including drilling, coil tubing/millout operations, and production tubing installation. Emissions of C 8 –C 10 n-alkanes during drilling operations highlighted the importance of VOC emissions from synthetic drilling mud chosen to reduce odor impacts. More than 90 samples were collected of transient plumes. Using composition measurements, meteorological data, and information about well pad activities, these plumes were connected with specific UOGD operations including drilling, flowback, and production equipment maintenance. The chemical signatures of these plumes differed by operation type (e.g., C 8 –C 10 n-alkanes constituted a larger fraction of measured VOCs in drilling-related plumes). Concentrations of individual, oil and gas-related VOCs in these plumes were often several orders of magnitude higher than in background air, with maximum ethane and benzene concentrations of 79,600 and 819 ppbv, respectively. Because these plumes typically impact a monitoring site for just several minutes, they are easily missed by slower-responding instruments. Study measurements highlight future emission mitigation opportunities during UOGD operations, including better control of emissions from shakers that separate drill cuttings from drilling mud, production separator maintenance operations, and periodic emptying of sand cans during flowback operations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Device Access Abstractions for Resilient Information Architecture Platform for Smart Grid

An open application platform distributes the intelligence and control capability to local endpoints (or nodes) reducing total network traffic, improving speed of local actions by avoiding latency, and improving reliability by reducing dependencies on numerous devices and communication interfaces. The platform must be multi-tasking and able to host multiple applications running simultaneously. Given such a system, the core functions of power grid control systems include grid state determination, low level control, fault intelligence and reconfiguration, outage intelligence, power quality measurement, remote asset monitoring, configuration management, power and energy management (including local distributed energy resources, such as wind, solar and energy storage) can be eventually distributed. However, making this move requires extensive regression testing of systems to prove out new technologies, such as phasor measurement units (PMU). Additionally, as the complexity of the systems increase with the inclusion of new functionality (especially at the distribution and consumer levels), hidden coupling issues becomes a challenge with possible N-way interactions known and not known by device and application developers. Therefore, it is very important to provide core abstractions that ensure uniform operational semantics across such interactions. Here in this paper, we describe the pattern for abstracting device interactions we have developed for the RIAPS platform in the context of a microgrid control application we have developed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Network Anomaly Detection in Distributed Edge Computing Infrastructure

As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. We show that by leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by MannWhitney U and Kolmogorov-Smirnov tests (p<0.05).

Marfo, William [University of Texas at El Paso,Dep↗

DOE OSTI2018

Multivariate network traffic analysis using clustered patterns

Traffic analysis is a core element in network operations and management for various purposes including change detection, traffic prediction, and anomaly detection. In this paper, we introduce a new approach to online traffic analysis based on a pattern-based representation for high-level summarization of the traffic measurement data. Unlike the past online analysis techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the observed patterns from previous time windows enabling intuitive analysis. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility. Our extensive experimental results with public traces and collected monitoring measurements from ESnet traffic traces show that our pattern-based approach is effective for multivariate analysis of online network traffic with visual and quantitative tools.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Assessing variation in faecal glucocorticoid concentrations in gray whales exposed to anthropogenic stressors

Understanding how individual animals respond to stressors behaviourally and physiologically is a critical step towards quantifying long-term population consequences and informing management efforts. Glucocorticoid (GC) metabolite accumulation in various matrices provides an integrated measure of adrenal activation in baleen whales and could thus be used to investigate physiological changes following exposure to stressors. In this study, we measured GC concentrations in faecal samples of Pacific Coast Feeding Group (PCFG) gray whales (Eschrichtius robustus) collected over seven consecutive years to assess the association between GC content and metrics of exposure to sound levels and vessel traffic at different temporal scales, while controlling for contextual variables such as sex, reproductive status, age, body condition, year, time of year and location. We develop a Bayesian Generalized Additive Modelling approach that accommodates the many complexities of these data, including non-linear variation in hormone concentrations, missing covariate values, repeated samples, sampling variability and some hormone concentrations below the limit of detection. Estimated relationships showed large variability, but emerging patterns indicate a strong context-dependency of physiological variation, depending on sex, body condition and proximity to a port. Our results highlight the need to control for baseline hormone variation related to context, which otherwise can obscure the functional relationship between faecal GCs and stressor exposure. Therefore, extensive data collection to determine sources of baseline variation in well-studied populations, such as PCFG gray whales, could shed light on cetacean stress physiology and be used to extend applicability to less-well-studied taxa. GC analyses may offer greatest utility when employed as part of a suite of markers that, in aggregate, provide a multivariate measure of physiological status, better informing estimates of individuals’ health and ultimately the consequences of anthropogenic stressors on populations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season

There is a growing need for operational oceanographic predictions in both the Arctic and Antarctic polar regions. In the former, this is driven by a declining ice cover accompanied by an increase in maritime traffic and exploitation of marine resources. Oceanographic predictions in the Antarctic are also important, both to support Antarctic operations and also to help elucidate processes governing sea ice and ice shelf stability. However, a significant gap exists in the ocean observing system in polar regions, compared to most areas of the global ocean, hindering the reliability of ocean and sea ice forecasts. This gap can also be seen from the spread in ocean and sea ice reanalyses for polar regions which provide an estimate of their uncertainty. The reduced reliability of polar predictions may affect the quality of various applications including search and rescue, coupling with numerical weather and seasonal predictions, historical reconstructions (reanalysis), aquaculture and environmental management including environmental emergency response. Here, we outline the status of existing near-real time ocean observational efforts in polar regions, discuss gaps, and explore perspectives for the future. Specific recommendations include a renewed call for open access to data, especially real-time data, as a critical capability for improved sea ice and weather forecasting and other environmental prediction needs. Dedicated efforts are also needed to make use of additional observations made as part of the Year of Polar Prediction (YOPP; 2017– 2019) to inform optimal observing system design. To provide a polar extension to the Argo network, it is recommended that a network of ice-borne sea ice and upper-ocean observing buoys be deployed and supported operationally in ice-covered areas together with autonomous profiling floats and gliders (potentially with ice detection capability) in seasonally ice covered seas. Finally, additional efforts to better measure and parameterize surface exchanges in polar regions are much needed to improve coupled environmental prediction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

Abstract There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.

Qin, Rong↗

DOE OSTI2017

One network metric datastore to track them all: the OSG network metric service

The Open Science Grid (OSG) relies upon the network as a critical part of the distributed infrastructures it enables. In 2012, OSG added a new focus area in networking with a goal of becoming the primary source of network information for its members and collaborators. This includes gathering, organizing, and providing network metrics to guarantee effective network usage and prompt detection and resolution of any network issues, including connection failures, congestion, and traffic routing. In September of 2015, this service was deployed into the OSG production environment. We will report on the creation, implementation, testing, and deployment of the OSG Networking Service. Starting from organizing the deployment of perfSONAR toolkits within OSG and its partners, to the challenges of orchestrating regular testing between sites, to reliably gathering the resulting network metrics and making them available for users, virtual organizations, and higher level services, all aspects of implementation will be reviewed. In particular, several higher-level services were developed to bring the OSG network service to its full potential. These include a web-based mesh configuration system, which allows central scheduling and management of all the network tests performed by the instances; a set of probes to continually gather metrics from the remote instances and publish it to different sources; a central network datastore (esmond), which provides interfaces to access the network monitoring information in close to real time and historically (up to a year) giving the state of the tests; and a perfSONAR infrastructure monitor system, ensuring the current perfSONAR instances are correctly configured and operating as intended. We will also describe the challenges we encountered in ongoing operations of the network service and how we have evolved our procedures to address those challenges. Finally we will describe our plans for future extensions and improvements to the service.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Safe Operations at Roadway Junctions: Intelligent Roadway Infrastructure as Functional Interlocking

Automated vehicle (AV) technology is quickly maturing, and the corresponding infrastructure systems that evaluate traffic and communicate to vehicles requires sophisticated sensing and perception technologies, referred to as intelligent roadway infrastructure (IRI), to complement emerging AV capabilities. IRI provides signals to vehicles, indicating right-of-way for vehicles and communicating to approaching AVs that no other vehicle is failing to yield. This capability, denoted as safety-affirmative signaling, provides a green light or a green arrow as appropriate and affirms through communication links to connected vehicles when it is safe to proceed. About 36% of collisions occur at intersections, with most occurring upon left turns (22.2%) or crossing over (12.6%), and only a small percentage (1.2%) while turning right at an intersection. Of all intersection crashes about half (52.5%) of those vehicles were traveling through a signalized intersection 2. Safety-affirmative signaling would guarantee safety of AV fleet vehicles, by providing the interlocking principle, a term from automated train control that only allows progression through a railway intersection after affirming no opportunity for a crash exists. IRI through safety-affirmative signaling would bring performance and safety to complex roadway intersections where AV transit fleet service is most needed, as well as safety benefits to traditional, non-automated vehicles and vulnerable road users. The implementation of IRI has functional, programmatic, and technical challenges. Research work performed at the National Renewable Energy Laboratory (NREL) in an integrative approach encapsulating these themes, and termed infrastructure perception and control (IPC) is motivated by improved performance (travel time), improved safety (reduced collisions), and improved energy efficiency (less fuel burned and minimized production of greenhouse gases). IPC is intended not only for roadway and intersection applications but also in extension to inform complementary buildings and grid systems to enable better co-management, as vehicles and their charging needs become increasingly integrated into the built environment. The NREL IPC project presents an open-source framework, architecture, and supporting technology to implement IRI, addressing critical issues such as fusion of data, reliability, standardization of data interfaces, and confidence of detection. The framework is informed by previous experience in U.S. Department of Defense research technology, specifically in the use of radar to detect, identify, and track aerial threats. These principles combined with multi-sensor fusion provides for a complete digital twin with known and measurable confidence and accuracy from which safety-affirmative signaling can be developed and deployed.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

DOE OSTI2019

Comparing Ecological Risk between D and D Facilities and Operating Facilities at the Hanford Site - 19531

Many countries are faced with a large remediation task resulting from World War II, the Cold War, and subsequent industrial development. In the U.S. the Department of Energy (DOE) faces the largest hazardous waste remediation tasks, with a commitment to protect human health and the environment during and following remediation. DoE's biggest challenge is remediation of the many remaining cleanup areas at the Hanford Site, Washington. Some Hanford sites have extensive ecological resources and ecosystems that require protection or restoration. We briefly 1) describe a rapid evaluation method for ecological resources on units slated for cleanup that allows comparison among the different areas at the Hanford Site, 2) examine some D4 (deactivation, decommissioning, decontamination and demolition) facilities and Operating facilities that require remediation, 3) describe potential ecological harm to resources on these sites, and 4) compare how risks from potential harm vary at the Hanford Site. The ecological evaluation includes three phases: 1) general steps (identify resource categories, identify cleanup areas), 2) ecological descriptions (describe resources on the site to be cleaned up), and 3) ecological ratings (rate the value of the resources on a scale that can be applied across remediation sites). After completion of these steps for the sites requiring cleanup, a comparison of potential risks can be made among types of facilities (e.g. D4 facilities, legacy waste sites, operating facilities). D4 facilities are those slated for deactivation, decommissioning, decontamination and demolition, while operating facilities at Hanford include waste treatment plants, sludge basins, waste trenches, Central Waste Complex, storage facilities, and disposal facilities, among others required to support the D4 and other remediation activities, before they too are remediated. Currently the potential harm to ecological (and eco-cultural) resources on the Hanford Site for remediation at D4 facilities is very low, but increases to very high (for reactors) during remediation when there is physical disruption, increased traffic and personnel, and possible increased contamination. Following remediation, the potential harm to ecological resources is low, and the value may increase due to restoration of native vegetation on sites that were largely industrial prior to remediation. In contrast, the potential harm to ecological resources at Operating Facilities was higher currently than for D4 facilities, but was low to medium following cleanup. The higher harm (risk) at D4 facilities partly derives from the location of the reactors near the Columbia River, a resource extremely important to Tribal Nations and to other peoples in the Northwestern United States, as well as for the iconic and threatened salmon populations. Methods described in this paper provide Congress, DOE managers, regulators, Tribes, and the general public with assurance that ecological and eco-cultural resources and the environment are being protected during and following remediation. The analysis of specific sites provides managers, regulators and the public with information to make cost-effective decisions about remediation that continues to enhance ecological value while protecting human health. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗