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DOE OSTI2026

Updating the BNB Flux Prediction at SBND

Precise, accurate neutrino flux predictions for neutrino beam experiments are crucial for physics results. Flux uncertainties contribute significantly to the total systematic uncertainties seen in modern accelerator neutrino measurements such as cross sections, oscillations, and BSM studies.. For over a decade, experiments utilizing Fermilab’s Booster Neutrino Beam (BNB) have relied on the 2009 MiniBooNE flux prediction. However, the high-statistics era of the Short-Baseline Neutrino (SBN) program, with both SBND and ICARUS now operating, demands a modernized flux model and framework. While SBND is using the MiniBooNE flux model for its Generation 1 analyses, including many upcoming cross section measurements, future work will be based on a new flux model. In this talk, the ongoing work towards this new model will be outlined, including a new simulation framework (G4BNB), a new evaluation of model parameters from hadron scattering data, and a new framework for evaluating systematic uncertainties (BNBFP). Additionally, expansions to the flux model to include BSM contributions from neutral mesons will be discussed, as well as the PRISM capabilities of SBND to observe a wide range of off axis angles of the neutrino beam.

Paton, Josephine [Fermilab]↗

DOE OSTI2026

Updating the BNB Flux Prediction at SBND

Paton, Josephine [Fermilab]↗

DOE OSTI2026

Evaluation of Howard A. Hanson Dam Juvenile Fish Passage and Survival Study Live Fish Injury Assessment, Sensor Fish, and BioPA Modeling Tasks

The live fish injury assessment, Sensor Fish, and BioPA modeling study tasks were conducted by researchers from Pacific Northwest National Laboratory (PNNL). The four tasks were part of the larger Evaluation of Howard A. Hanson Dam (HAHD) Juvenile Fish Passage and Survival study, which had six total tasks. To achieve study objectives for each of the four tasks, field work occurred at Green Peter Dam (GPR) to evaluate the highest elevation steep slope bypass pipe, at HAHD to evaluate baseline conditions of the horseshoe tunnel, and at PNNL’s Aquatic Research Laboratory (ARL) to evaluate simulated dam passage conditions (i.e., shear forces and collision). Each of these evaluations utilized live fish injury assessment, Sensor Fish, and BioPA modeling. Live fish injury assessment and survival (tagged with and without balloon or passive integrated transponder [PIT] tags) was correlated with Sensor Fish to determine thresholds. The CFD analyses were then performed, and the computed values were compared to the corresponding measured values of Sensor Fish data. The results of the overall injury and survival of fish was also used in the validation of the CFD modeling method. Collectively, the results will aid in future modeling of fish passage at HAHD. Results from these tasks can be used by biologists, engineers, resource managers, and regional decision-makers to inform baseline conditions under current operations and the engineering design of the new FPF at HAHD. This draft report contains initial data and results from the four tasks. Table 8 1, Table 8 2, and Table 8 3, and Figure 8 1, Figure 8 2, and Figure 8 3 depict the CFD modeling findings for the GPR steep slope bypass, HAHD horseshoe tunnel, and laboratory testing. Table 8 4, Table 8 5, and Table 8 6 depict the Sensor Fish findings for the GPR steep slope bypass and HAHD horseshoe tunnel testing. The Mv values observed in the HAHD were significantly lower compared to the laboratory experiments conducted at PNNL. Currently, investigations are underway to understand the reasons for this disparity and to establish an appropriate threshold value for Mv. Survival predictions presented in the tables below should be considered preliminary and should not be used until further analyses and adjustments are completed. The next steps for modeling will include the flow regime, (i.e., density of flow regimes due to water and air mixing ) to continue to improve on the threshold value for Mv.

13 HYDRO ENERGY↗

DOE OSTI2025

SpinQuest Polarized Target System

The SpinQuest experiment at Fermilab uses a solid-state polarized ammonia target held at a magnetic field of 5 T, immersed in liquid helium-4, which is held at approximately 1K by the evaporation refrigerator. The refrigerator provides the required cooling power during the dynamic nuclear polarization (DNP) process and the high intensity interaction with the 120 GeV proton beam from the Fermilab main injector. The refrigerator was designed in compliance with the American Society of Mechanical Engineers (ASME) to operate safely at Fermilab. The high pumping capacity ($17,000 \:m^3/h$) roots stack provides the required pumping speed during DNP production data taking and a custom-made radiation hard flow control valve regulates the refrigerator temperature during the thermal equilibrium calibration measurements. The frequency of the microwave generator, an Extended Interaction Oscillator (EIO), is automated to keep the maximum polarization while the (Nuclear Magnetic Resonance) NMR system continuously measures the polarization of the target material. In this talk, an overview of the SpinQuest polarized target system will be presented as well as a brief report of recent commissioning activities and target performance during the early production runs in 2024.

Bandara, Vibodha [Colombo U.]↗

DOE OSTI2023

Evaluation of BISON metallic fuel performance modeling against experimental measurements within FIPD and IMIS databases

Simulations were conducted using the BISON fuel performance code on an automated process to read initial and operating conditions from two databases—the Fuels Irradiation and Physics Database (FIPD) and Integral Fast Reactor Materials Information System (IMIS) database. These databases contain metallic fuel data from the Experimental Breeder Reactor-II (EBR-II) and the Fast Flux Test Facility (FFTF). The work demonstrates use of an integrated framework to access EBR-II fuel pin data for evaluating fuel performance models contained within BISON to predict fuel performance of next-generation metallic fuel systems. Between IMIS and FIPD, there is enough information to conduct 1,977 unique EBR-II metallic fuel pin histories from 29 different experiments, and 338 pins from FFTF MFF-3 and MFF-5 with varying levels of details between the two databases. Each of these fuel performance histories includes a high-resolution power history, flux history, coolant channel flow rates, and coolant channel temperatures, and new model developments in BISON since the initial demonstration of this integrated framework. Fission gas release (FGR), cumulative damage fraction, fuel axial swelling, FCCI wastage thickness, cladding profilometry, and burnup were all simulated in BISON and compared to post-irradiation examination (PIE) results to evaluate BISON fuel performance modeling. Implementation of new fuel performance models into a generic BISON input file coupled with IMIS and FIPD yielded results with a better representation of physics than the initial evaluation of the integrated framework. Cladding profilometry, FGR, and fuel axial swelling were found to be in good agreement with PIE measurements for most of the pins simulated. The chosen mechanical contact solver was found to significantly impact the axial fuel swelling and cladding strain predictions when used in conjunction with the U-Pu-Zr hot-pressing model since it bound the fuel to prevent further swelling and increased hydrostatic stresses. This work suggests that fuel performance modeling in BISON under steady-state conditions represents the PIE data well and should be reassessed when new PIE data become available in IMIS and FIPD databases and when improved physical models to better capture fuel performance are added to BISON.

Paaren, Kyle M.↗

DOE OSTI2021

Initial demonstration of automated fuel performance modeling with 1977 EBR-II metallic fuel pins using BISON code with FIPD and IMIS databases

Using the BISON fuel performance code, simulations were conducted using an automated process to read initial and operating conditions from the Fuels Irradiation and Physics Database (FIPD) and Integral Fast Reactor materials information system (IMIS) database, which contains metallic fuel data from the Experimental Breeder Reactor-II (EBR-II). This work demonstrates use of an integrated framework to access the vast majority of EBR-II experimental fuel pin data to support rapid development of fuel performance models for next-generation metallic fuel systems. With this capability, validation for fuel qualification can be performed rapidly. Between IMIS and FIPD, there is enough information to conduct 1977 unique EBR-II metallic fuel pin histories from 24 different experiments, at varying levels of detail between the two databases. Each of these histories includes a high-resolution power history, flux history, coolant channel flow rates, and coolant channel temperatures. Fission gas release (FGR), cumulative damage fraction (CDF), fuel axial swelling, cladding profilometry, and burnup were all simulated in BISON. The results were compared to post-irradiation examination (PIE) results for the initial demonstration of automated BISON modeling. BISON simulations conducted with IMIS and FIPD were in rough agreement with PIE measurements and calculations. Cladding profilometry, FGR, and fuel axial swelling were found to be in rough agreement with PIE measurements, depending on the physics used within the BISON input files. Here, the mechanical contact solver chosen was found to significantly impact axial fuel swelling and cladding strain predictions. CDF values were assessed to see whether pin failure may have been predicted (CDF ≥ 1). This work suggests that continued development of an automated tool for BISON should focus on inclusion of the Fast Flux Test Facility (FFTF) experimental data for a larger database for metallic fuel, improved physical models to better capture fuel performance, such as fuel-cladding interactions, and a more detailed comparison with available PIE data to further the BISON model development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Progress and innovations in the TCV tokamak research programme

Research on the Tokamak à Configuration Variable addresses a wide range of key questions relevant to ITER and future fusion power plants. Over the past two years, highly productive experimental campaigns have led to major advances across several areas: the ITER baseline scenario; pedestal properties in low-collisionality, peeling-limited conditions; and the development of high- β N , non-inductive regimes. Alternative high-confinement scenarios have likewise received significant attention, with remarkable progress in quasi-continuous exhaust operation, X-point radiator plasmas, and negative triangularity configurations. Substantial achievements were also made in the mitigation or benign termination of runaway electron beams, in elucidating fast-ion loss mechanisms, and in improving exhaust behaviour in both conventional and alternative divertor geometries. These experimental results have been strongly supported by advances in modelling and their direct application to the experiment, ranging from gyrokinetic simulations of core and pedestal turbulence to fluid-based studies of scrape-off layer and divertor physics in diverse geometries. Plasma control has taken on an increasingly important role, with model-based and data-driven approaches now closely intertwined with physics studies. This article provides a overview of these recent activities, together with a brief outlook on forthcoming upgrades and next steps.

EPFL↗

DOE OSTI2025

One- and two-body current contributions to lepton-nucleus scattering

Modeling lepton-nucleus scattering with the accuracy required to extract neutrino-oscillation parameters from long- and short-baseline experiments necessitates retaining most quantum-mechanical effects. One such effect is the interference between one- and two-body current operators in the transition currents, which has been known to enhance the cross sections, especially in transverse kinematics. Here, we incorporate such interference in the spectral function formalism, which combines relativistic currents and kinematics with an accurate description of the initial target state. Our analysis of lepton-scattering off 12 C demonstrates that interference effects appreciably enhance the transverse electromagnetic response functions and the flux-folded neutrino-nucleus cross section, in both cases, improving the agreement with experimental data. We discuss the impact on the neutrino-oscillation program and the determination of nucleon axial form factors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

MicroBooNE's First Search for the MiniBooNE Anomalous Excess Under a Photon-Like Hypothesis with High-Sensitivity Search for Neutrino-Induced Neutral Current Delta Production and Radiative Decay

MicroBooNE is a liquid argon time projection chamber that collected neutrino data at Fermilab's Booster Neutrino Beam from 2015 to 2020. One of its primary goals is to investigate the “Low Energy Excess” of neutrino events observed by the MiniBooNE experiment, for which candidate photon-like interpretations include an underestimation of neutrino neutral current (NC) resonant $\Delta$ production with subsequent radiative decay or another anomalous source of single photon production in neutrino interactions. In particular, NC $\Delta$ radiative decay is poorly constrained background process to electron neutrino measurements and could be a sizable contribution to the “Low Energy Excess.” This thesis will present the analysis developed to search for NC $\Delta \rightarrow N\gamma$ events in MicroBooNE, consisting of a boosted decision tree based event selection with an NC neutral pion background constraint, using data from the first three years of operations corresponding to $6.9\times10^{20}$ POT.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Experimental system for studying temperature gradient-driven fracture of oxide nuclear fuel out of reactor

Temperature gradients in ceramic light water reactor (LWR) uranium dioxide (UO 2 ) nuclear fuel pellets generate thermal stresses that cause fractures in the fuel beginning early in the life of fresh fuel. The combination of heating due to fission and forced convective cooling on the exterior of LWR fuel rods generates a temperature profile that is difficult to replicate outside the reactor environment. In the present study, a state-of-the-art experimental set-up using electrical heating to replicate fission heating was built and surrogate fuel materials such as ceria (CeO 2 ) were used to validate the system. Cracking experiments were conducted on these surrogates by inducing reactivity-initiated-accident (RIA) like temperature gradients in the pellets via induction and direct resistance heating. Induction heating was done using copper coils and molybdenum susceptors which heated the surrogates to a threshold temperature that is sufficiently high for the fuel material to conduct current. Thereafter, direct resistance heating was used by a D.C. power supply to introduce volumetric heating to replicate LWR operating conditions analogous to fission heating. The pellets were held against nickel electrodes and mounted on a boron nitride test-stand. All the tests were carried out in a stainless-steel vacuum chamber. Simultaneous real-time dual imaging of the surrogate pellet surface has been implemented using an optical and infrared camera system which will be mounted along axial and perpendicular directions to the pellet surface respectively. A beam-splitter was used to split the incoming radiation from the sample into two halves. While one of the beams is transmitted from the splitter through a bandpass filter to obtain optical images, the other beam is reflected from the splitter to the thermal camera to capture full field temperature gradients of the as fabricated pellet surface during crack initiation and propagation. In the current series of tests, a 2-color pyrometer was used for recording and comparing the surface and centerline temperatures of the surrogate pellets in lieu of the thermal camera. A LabVIEW data acquisition system has been set up for collecting useful data during experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Development of experimental and computational frameworks to predict subcooled flow boiling in the LANL Isotope Production Facility

Cooling is crucial to maintain the integrity of target systems in isotope production facilities. At Los Alamos National Laboratory (LANL)’s Isotope Production Facility (IPF), multiple encapsulated targets are stacked and irradiated in tandem with a 100 MeV, ~250μA proton beam. To facilitate effective heat removal, these stacked targets are separated and cooled via a series of water channels. At these beam currents, this high-energy proton beam heats the target system, likely initiating subcooled flow boiling in the cooling channels. However, in-beam monitoring of the IPF target system is not possible due to the extreme radiation environment, and the necessarily significant shielding. To better understand high-power target performance, we developed ex-situ experimental and computational frameworks to predict the behavior of subcooled flow boiling at IPF. Subcooled flow boiling experiments on Inconel 625 samples under IPF conditions (2 bar pressure, 10 GPM flow rate (i.e., 2249 kg/m 2 /s), 85 K subcooling) revealed that IPF's average operating power is at the early stage of boiling with a heat transfer coefficient of 48,000 W/m 2 /s. The proposed modeling framework enables us to predict a complete boiling curve, i.e., single-phase heat transfer, onset of nucleate boiling, two-phase heat transfer, and critical heat flux (CHF), with specification of input boiling parameters up to intermediate heat flux levels. The estimated CHF under IPF conditions is 5.2 MW/m 2 . Experimental data under reduced conditions (2 bar pressure, 1.5 GPM flow rate (i.e., 337 kg/m 2 /s), 45 K subcooling) served as validation cases for the computational modeling. This computational model can be further extended to more complicated systems replicating the real IPF configuration, for instance, to study void distribution as a function of the incident proton beam profile and coolant velocity profile of multiple cooling channels. Finally, the proposed experimental and computational frameworks provide a means to better understand cooling systems in the isotope production facilities at different accelerators, where in-beam monitoring of the cooling process is not available.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

Stimulated Raman Scattering Microscopy: Real-Time In-Situ Physical and Chemical Characterization of Reverse Osmosis Desalination Membrane Scaling

We introduce a stimulated Raman scattering (SRS) methodology designed for rapid, real-time, and in situ monitoring of RO membrane scaling adapted for bench-scale desalination flow cells. The methodology can provide new insights into membrane scaling dynamics by offering time-resolved reflection imaging of inorganic crystal growth, coupled with chemical identification from Raman spectral data. These capabilities allow for direct local measurement of the membrane surface area covered by different scalants as well as an approximation of the scalant volume using three-dimensional, integrated Raman intensity. The 2D and 3D SRS results obtained from CaSO 4 scaling experiments are compared to and are in reasonable agreement with those provided by confocal microscopy. The real-time physical and chemical characterization capabilities presented here could be extended to study combinations of inorganic, organic, and biological fouling. Overall, the SRS methodology represents an advancement in real-time sensing of membrane fouling that offers the potential for improved operation, lower cost, and more resilient RO membrane systems for sustainable water management.

42 ENGINEERING↗

DOE OSTI2026

Operating advanced scientific instruments with AI agents that learn on the job

Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.

Large Language Models↗

DOE OSTI2021

Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

BigPanDA monitoring system evolution in the ATLAS Experiment

Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗

DOE OSTI2020

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

Machine Learning↗

DOE OSTI2023

Safer Foundation Solar Demands Skill Collaborative

The milestones and accomplishments achieved in Safer’s Solar Demands Skill Training program required a collective effort of stakeholders. The ability to achieve the desired results and impact lives of the population we serve works best when collaborating with community stakeholders such as community-based organizations, faith-based organizations, community activists, local law enforcement, community residents, employers, and elected and appointed officials. We are working harder than ever to place clients in-demand careers and high-growth industry sectors. Our employer engagement with high growth sectors continues to increase, we have been able to deepen our relationships in this space by providing in demand stackable credential training. Launched our initiative in the green job space by partnering with organizations like ComEd, the Department of Energy, community-based organizations, faith-based organizations, and manufacturers that helped to train clients in the green jobs and renewable energy space. By the end of the year, we will have completed our tenth cohort of photovoltaic solar installation training. The training has been used as a foundation for solar panel installers to acquire new skills in in the green jobs career pathways, such as sales and customer service. It has also served as a pipeline to union-level trade positions through the skills participants obtained through partnership engagement. Safer Foundation's policy and advocacy team plays a major role in expanding opportunities for people with records to over one hundred occupations, including the trades. This reform has allowed many to secure living wage employment, reducing the high recidivism rate within Illinois. Safer continues to build our social enterprise with Reconstructive Technology Partners (RTP). RTP is introducing people with records to the construction trades such as solar, carpentry and electrical through residential remodeling of homes on smaller construction projects. Understanding the need to have a greater community presence, outreach was extended to a boots on the ground model, including, but not limited to door-to-door engagement, DE-EE0008571 Safer Foundation Page 4 of 29 local radio broadcasting and print ads in local newspapers. We continue to seek innovative ways to increase Safer Foundations' presence in the renewable energy space. Outreach efforts gave us an opportunity to share critical reentry information and opportunities about our PV installer program. Giving us reach with local and national audiences alike. We launched a systematic approach to improving client data and client tracking through an evidence-based practices initiative. That implemented an agency wide cross-functional data management system. The system supported our goal of reviewing, evaluating, and making recommendations to improve operational processes, program delivery, information sharing, and more. Played a significant part in the success of the solar program. Finally, economists around the nation agree that there is a significant labor shortage. The shortage directly threatens our ability to sustain our economic growth; if employers do not have access to the workers they need, it can lead to a shutdown in the economic recovery. The demand for Safer Foundation services is more important and impactful than ever. We will continue to build upon our 50 years of experience to address the challenges ahead and serve more people in a better way. We are confident we will accomplish beautiful things that benefit everyone involved because together, we are powerful.

14 SOLAR ENERGY↗