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At least 19 records

Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

This three-year project supported by the U.S. Department of Energy Vehicle Technologies Office has the potential to radically improve the urban freight system in ways that help both the public and private sectors. Working from 2018-2021, project researchers at the University of Washington’s Urban Freight Lab and collaborators at the Pacific Northwest National Laboratory have produced key data, tested technologies in complex urban settings, developed a prototype parking availability app, and helped close major knowledge gaps. All the fruits of this project can be harnessed to help cities better understand, support and actively manage truck load/unload operations and their urban freight transport infrastructure. Project learnings and tools can be used to help make goods delivery firms more efficient by reducing miles traveled and the time it takes to complete deliveries, benefitting businesses and residents who rely on the urban freight system for supplies of goods. And, ultimately, these project learnings and tools can be used to make cities more livable by minimizing wasted travel, which, in turn, contributes to reductions in fuel consumption and emissions. Cities today are challenged to effectively and efficiently manage their infrastructure to absorb the impacts of ever-increasing e-commerce-fueled delivery demand. All delivery trucks need to park somewhere to unload and load. Yet today’s delivery drivers have no visibility on available parking until they arrive at a site, which may be full. That means they can wind up cruising for parking, which wastes time and fuel and contributes to congestion. Once drivers do find parking, the faster they can unload at the spot, the faster they free up space for other drivers, helping others avoid circling for parking. This makes the parking space—and thus the greater load/unload network—more productive. To this end, the research team successfully met the project’s three goals, developing and piloting strategies and technologies to: 1. Reduce parking-seeking behavior in the study area by 20%; 2. Reduce parcel truck dwell time (the time a truck spends in a spot to load/unload) in the study area by 30%; 3. Increase curb space, alley space and private loading bay occupancy rates in the study area. The research team met these goals by creating and piloting on Seattle streets OpenPark, a first-of-its-kind real-time and forecasting curb parking app customized for commercial delivery drivers—giving drivers the “missing link” in their commonly used routing tools that tell them how best to get to delivery locations, but not what parking is available to use when they get there. Installing in-ground sensors on commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) in the 10-block study area in Seattle’s downtown neighbourhood of Belltown let researchers glean real-time curb parking data. The research team also met project goals by piloting three parcel lockers in public and private spaces open to any delivery carrier, creating a consolidated delivery hub that lets drivers complete deliveries faster and spend less time parked. Researchers collected and analysed data to produce the first empirical, robust, statistically significant results as to the impact of the lockers, and app, on on-the-ground operations. In addition to collecting and analyzing sensor and other real-time and historical data, researchers rode along with delivery drivers to confirm real-world routing and parking behavior. Researchers also surveyed building managers on their private loading bay operations to understand how to boost usage.

97 MATHEMATICS AND COMPUTING↗

Towards Digital and Performance-Based Supervisory HVAC Control Delivery

Upgrading supervisory HVAC control in commercial buildings is one of the most attractive decarbonization tools at our disposal. Modern controls are software programs and can in theory be deployed at scale and with a low up-front carbon "pulse". In practice, however, control delivery is a disjointed and inefficient process, dominated by manual handoffs of imprecise English language documents. A particularly high barrier exists between control implementation and building energy modeling (BEM) which results in control sequences typically not being tested for correctness or performance before implementation. Together with industry partners, DOE and the national labs are developing an ecosystem of tools and standards that can support fully digital performance-based control delivery workflows. This paper describes this ecosystem, which consists of three mutually supportive efforts. Semantic models of buildings and their systems enable automatic configuration and installation of control software. Platform-neutral control descriptions separate control algorithms from control platforms and enable the creation of libraries of reference control implementations. Dynamic whole-building energy-control simulation that can execute physically realistic control sequences makes it possible to test and evaluate the performance of control sequences and then directly compile them for installation and execution in control systems. In addition to digitizing and streamlining project-level control delivery, these standards and related software support benchmarking of control algorithms, both rule-based and optimization-based, and help to both advance the state of the art and to implement ratings and programs that encourage the adoption of high-performance control.

building controls↗

Surrogate model for third-integer resonance extraction at the Fermilab Delivery Ring

We present an ongoing work in which a surrogate model is being developed to reproduce the response dynamics of the third-integer resonant extraction process in the Delivery Ring (DR) at Fermilab. This is in pursuit of smoothly extracting circulating beam to the Mu2e Experiment’s production target, whereby the goal is to extract a uniform slice of the circulating 1e12 protons in the DR over 25,000 turns (43 ms). The DR contains 3 harmonic sextupoles that excite a third-integer resonance and three fast, tune-ramping quadrupole magnets that drive the horizontal tune towards the 29/3 resonance. In our initial work, the surrogate model trains on a semi-analytical simulation provided in the same format as live data. Using Reinforcement Learning (and other potential ML methods), the trained surrogate acts as the “environment” in which a simple ML control agent could learn to dynamically adjust the quadrupole ramp at 430 break points within the 43 microsecond spill window. The controller will be hosted on a dedicated Arria 10 FPGA. In this work, we report the accuracy and fidelity of the surrogate model in comparison to the response dynamics of the physics simulator.

Narayanan, Aakaash [Fermilab]↗

Enhanced delivery of engineered Fe-Mn binary oxides in heterogeneous porous media for efficient arsenic stabilization

Heterogeneity in sediment and aquifer is universal, resulting in preferential flows of injected materials in the high permeability regions and forming flow by-passed zones in the low permeability regions during in-situ subsurface remediation. This adverse effect can considerably delay the completion of remedial operations and significantly increase the cost. Column experiments were designed and conducted to study the transport of starch- and starch-xanthan gum modified Fe-Mn binary oxide particles (SFM and SXFM) in saturated heterogeneous porous media and to reveal the particles’ arsenic (As) stabilization performance. Fine-in-Coarse (FIC) and Coarse-in-Fine (CIF) patterns of heterogeneous packings were set up in the columns. Testing results demonstrated that starch-xanthan gum dual treatment on Fe-Mn binary oxides successfully improved the particles’ migration capability in heterogeneous porous media and their distribution uniformity attributed to the profound shear thinning behavior of xanthan gum solution. The addition of xanthan gum to the system increased the viscosity and shear thinning property of SXFM suspension, making it a better candidate for delivery. Both SFM and SXFM stabilized As in heterogeneously packed sediment collected from a contaminated site, with SXFM showing better stabilization performance than SFM. The stabilization effects of SXFM were 90.7-97.0%, compared to 82.0-95.2% of SFM.

Yan, Xiulan↗

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. Here, in this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Droning on to Delivery: Examining the Energy Impacts of Using Drones for Moving Goods

The demand for fast, localized delivery has grown significantly in recent years. Whether for cheesy snacks, prepared food, medical solutions, or business deliveries, fast and efficient delivery is increasingly a demand and differentiator. Drone delivery in the freight sector offers to revolutionize last-mile logistics and improve services. This research analyzes the impacts of drone energy for delivery operations, aiming to compare various types of drones (large and small, rotary and VTOL) and various types of business methods. The project executes novel open-air and laboratory-based testing to look at the impacts of weights, operations, temperatures and weather conditions. The study combines this real-world experimental data with fleet optimization mathematical models to assess energy consumption of different scenarios as well as examine the minimum fleet size and additional battery requirements. These fleet optimization models compare energy between types of deployments with existing delivery methods. The model was also extended to look at mixed fleets of aerial drones and ground vehicles to accommodate different restrictions on drones or when weather prohibits their use. The analysis provided insights into factors such as drone design, payload weight, flight distance, weather conditions and operational parameters and impacts of each. It showed how to combine different types of vehicles to reduce energy and improve services. And it showed that drone speed, routing restrictions, and unfavorable weather significantly influence energy consumption. Our open experiment data and optimization models can assist stakeholders and industry in understanding drone package delivery and offers keys to improving deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Delivered complementation in planta (DCIP) enables measurement of peptide-mediated protein delivery efficiency in plants

Abstract Using a fluorescence complementation assay, Delivered Complementation in Planta (DCIP), we demonstrate cell-penetrating peptide-mediated cytosolic delivery of peptides and recombinant proteins in Nicotiana benthamiana. We show that DCIP enables quantitative measurement of protein delivery efficiency and enables functional screening of cell-penetrating peptides for in-planta protein delivery. Finally, we demonstrate that DCIP detects cell-penetrating peptide-mediated delivery of recombinantly expressed proteins such as mCherry and Lifeact into intact leaves. We also demonstrate delivery of a recombinant plant transcription factor, WUSCHEL (AtWUS), into N. benthamiana. RT-qPCR analysis of AtWUS delivery in Arabidopsis seedlings also suggests delivered WUS can recapitulate transcriptional changes induced by overexpression of AtWUS. Taken together, our findings demonstrate that DCIP offers a new and powerful tool for interrogating cytosolic delivery of proteins in plants and highlights future avenues for engineering plant physiology.

59 BASIC BIOLOGICAL SCIENCES↗

Packaged delivery of CRISPR–Cas9 ribonucleoproteins accelerates genome editing

Effective genome editing requires a sufficient dose of CRISPR–Cas9 ribonucleoproteins (RNPs) to enter the target cell while minimizing immune responses, off-target editing, and cytotoxicity. Clinical use of Cas9 RNPs currently entails electroporation into cells ex vivo, but no systematic comparison of this method to packaged RNP delivery has been made. Here we compared two delivery strategies, electroporation and enveloped delivery vehicles (EDVs), to investigate the Cas9 dosage requirements for genome editing. Using fluorescence correlation spectroscopy, we determined that >1300 Cas9 RNPs per nucleus are typically required for productive genome editing. EDV-mediated editing was >30-fold more efficient than electroporation, and editing occurs at least 2-fold faster for EDV delivery at comparable total Cas9 RNP doses. We hypothesize that differences in efficacy between these methods result in part from the increased duration of RNP nuclear residence resulting from EDV delivery. Our results directly compare RNP delivery strategies, showing that packaged delivery could dramatically reduce the amount of CRISPR–Cas9 RNPs required for experimental or clinical genome editing.

60 APPLIED LIFE SCIENCES↗

Do parcel lockers reduce delivery times? Evidence from the field

Common-carrier parcel lockers have emerged as a secure, automated, self-service means of delivery consolidation in congested urban areas, which are believed to mitigate last-mile delivery challenges through reducing out-of-vehicle delivery times and consequently vehicle dwell times at the curb. However, little research exists to empirically demonstrate the environmental and efficiency gains from this technology. In this study, we designed a nonequivalent groups pre-test/post-test control experiment to estimate the causal effects of a parcel locker on delivery times in a residential building in downtown Seattle, WA. Here, the causal effects are measured in terms of vehicle dwell time and the time delivery couriers spend inside the building, through the difference-in-difference method and using a similar nearby residential building as a control. The results showed a statistically significant decrease in time spent inside the building, and small yet insignificant reduction in delivery vehicle dwell time at the curb. The locker was also well received by the building managers and residents.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Nanoparticle cellular internalization is not required for RNA delivery to mature plant leaves

Rapidly growing interest in the nanoparticle-mediated delivery of DNA and RNA to plants requires a better understanding of how nanoparticles and their cargoes translocate in plant tissues and into plant cells. However, little is known about how the size and shape of nanoparticles influence transport in plants and the delivery efficiency of their cargoes, limiting the development of nanotechnology in plant systems. In this study we employed non-biolistically delivered DNA-modified gold nanoparticles (AuNPs) of various sizes (5-20 nm) and shapes (spheres and rods) to systematically investigate their transport following infiltration into Nicotiana benthamiana leaves. Generally, smaller AuNPs demonstrated more rapid, higher and longer-lasting levels of association with plant cell walls compared with larger AuNPs. We observed internalization of rod-shaped but not spherical AuNPs into plant cells, yet, surprisingly, 10 nm spherical AuNPs functionalized with small-interfering RNA (siRNA) were the most efficient at siRNA delivery and inducing gene silencing in mature plant leaves. These results indicate the importance of nanoparticle size in efficient biomolecule delivery and, counterintuitively, demonstrate that efficient cargo delivery is possible and potentially optimal in the absence of nanoparticle cellular internalization. Additionally, our results highlight nanoparticle features of importance for transport within plant tissues, providing a mechanistic overview of how nanoparticles can be designed to achieve efficacious biocargo delivery for future developments in plant nanobiotechnology.

59 BASIC BIOLOGICAL SCIENCES↗

Partner with a Third-Party Delivery Service or Not? A Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic

Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions such as whether to partner with online platforms, to form their own delivery team, or both. In this paper, we develop an integrated prediction-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first use the susceptible-infected-recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using predicted demand samples, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to show results for helping restaurants build contingency plans under rapid market changes. Our method can be used under unexpected demand surges, various infection/vaccination status, and demand patterns. Here, our results show that a restaurant can benefit from partnering with third-party delivery platforms when (i) the subscription fee is low, (ii) customers can flexibly decide whether to order from platforms or from restaurants directly, (iii) customers require more efficient delivery, (iv) average delivery distance is long, or (v) demand variance is high.

97 MATHEMATICS AND COMPUTING↗

Delivering Clean Air in Denver: Propane Trucks and Infrastructure in Mail Delivery Application

In 2021, Drive Clean Colorado, a Denver-based Clean Cities coalition, initiated the deployment of five Class 6 medium-duty propane autogas delivery trucks into a United States Postal Service (USPS) mail delivery contractor fleet with the goal of providing a proof-of-concept demonstration for mail delivery fleets nationwide. Funded by the U.S. Department of Energy's Office of Energy Efficiency & Renewable Energy, the project, titled "Delivering Clean Air in Denver: Propane Trucks and Infrastructure in Mail Delivery Application," enabled the purchase and deployment of the propane-powered trucks, refueling infrastructure, and supporting analysis by the National Renewable Energy Laboratory (NREL). Key objectives of the analysis included calculating the propane vehicles' emissions reductions, analyzing the costs and operational performance of the propane fleet compared to conventional diesel-powered vehicles, and evaluating the viability of propane as a clean and cost-effective alternative to conventional diesel trucks (AFDC 2023b, 2023c). The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a clean and cost-effective alternative to conventional diesel trucks. This proof-of-concept demonstration is expected to lead to improved understanding of the performance attributes, costs, and operational issues to inform technology adoption decisions, helping to spur market transformation toward lower-emission truck fleets. By reducing the risk of first adoption, potential exists to transform the U.S. Postal Service (USPS) mail delivery system into a lower-carbon national fleet. This technical report details the vehicle duty cycle characterization and fleet cost and performance comparison performed by the National Renewable Energy Laboratory, in addition to estimated emissions reductions for delivery operations.

33 ADVANCED PROPULSION SYSTEMS↗

Poly( N -vinylpyrrolidone)- block -Poly(dimethylsiloxane)- block -Poly( N -vinylpyrrolidone) Triblock Copolymer Polymersomes for Delivery of PARP1 siRNA to Breast Cancers

Nearly 20% of HER2-positive breast cancers develop resistance to HER2-targeted therapies requiring the use of advanced therapies. Silencing RNA therapy may be a powerful modality for treating resistant HER2 cancers due to its high specificity and low toxicity. However, the systemic administration of siRNAs requires a safe and efficient delivery platform because of siRNA’s low stability in physiological fluids, inefficient cellular uptake, immunoreactivity, and rapid clearance. We have developed theranostic polymeric vesicles to overcome these hurdles for encapsulation and delivery of small functional molecules and PARP1 siRNA for in vivo delivery to breast cancer tumors. The 100 nm polymer vesicles were assembled from biodegradable and non-ionic poly(N-vinylpyrrolidone) 14 -block-poly(dimethylsiloxane) 47 -block-poly(N-vinylpyrrolidone) 14 triblock copolymer PVPON 14 -PDMS 47 -PVPON 14 using nanoprecipitation and thin-film hydration. We demonstrated that the vesicles assembled from the copolymer covalently tagged with the Cy5.5 fluorescent dye for in vivo imaging could also encapsulate the model drug with high loading efficiency (40%). The dye-loaded vesicles were accumulated in tumors after 18 h circulation in 4TR breast tumor-bearing mice via passive targeting. We found that PARP1 siRNA encapsulated into the vesicles was released intact (13%) into solution by the therapeutic ultrasound treatment as quantified by gel electrophoresis. Additionally, the PARP1 siRNA-loaded polymersomes inhibited the proliferation of MDA-MB-361TR cells by 34% after 6 days of treatment by suppressing the NF-kB signaling pathway, unlike their scrambled siRNA-loaded counterparts. Finally, the treatment by PARP1 siRNA-loaded vesicles prolonged the survival of the mice bearing 4T1 breast cancer xenografts, with the 4-fold survival increase, unlike the untreated mice after 3 weeks following the treatment. These biodegradable, non-ionic PVPON 14 -PDMS 47 -PVPON 14 polymeric nanovesicles capable of the efficient encapsulation and delivery of PARP1 siRNA to successfully knock down PARP1 in vivo can provide an advanced platform for the development of precision-targeted therapeutic carriers, which could help develop highly effective drug delivery nanovehicles for breast cancer gene therapy.

60 APPLIED LIFE SCIENCES↗

Targeted nonviral delivery of genome editors in vivo

Cell-type-specific in vivo delivery of genome editing molecules is the next breakthrough that will drive biological discovery and transform the field of cell and gene therapy. Here, we discuss recent advances in the delivery of CRISPR-Cas genome editors either as preassembled ribonucleoproteins or encoded in mRNA. Both strategies avoid pitfalls of viral vector-mediated delivery and offer advantages including transient editor lifetime and potentially streamlined manufacturing capability that are already proving valuable for clinical use. We review current applications and future opportunities of these emerging delivery approaches that could make genome editing more efficacious and accessible in the future.

59 BASIC BIOLOGICAL SCIENCES↗

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Drone energy consumption↗

Intracellular Biomacromolecule Delivery by Stimuli-Responsive Protein Vesicles Loaded by Hydrophobic Ion Pairing

Proteins can perform ideal therapeutic functions. However, their large size and significant surface hydrophilicity and charge prohibit them from reaching intracellular targets. These chemical features also render them poorly encapsulated by nanoparticles used for intracellular delivery. In this work, a novel combination of protein vesicles and hydrophobic ion pairing (HIP) was used to load protein cargo and achieve cytosolic delivery to overcome the limitations of previous protein vesicle properties. Protein vesicles are thermally self-assembling nanoparticles made from elastin-like polypeptide (ELP) fused to an arginine-rich leucine zipper and a globular protein fused to a glutamate-rich leucine zipper. To impart stimuli-responsive disassembly, physiological stability, and small size, the ELP sequence was modified to include histidine and tyrosine residues. HIP was used to load and release protein cargo requiring endosomal escape for cytosolic function. HIP vesicles enabled delivery of cytochrome c, a cytosolically active protein, and a significant reduction in viability in both a traditional two-dimensional (2D) human cancer cell line culture and a biomimetic three-dimensional (3D) organoid model of acute myeloid leukemia. By examining the uptake of positively and negatively charged fluorescent protein cargos loaded by HIP, this work revealed the necessity of HIP for cytosolic cargo delivery and how HIP loading influences protein vesicle self-assembly and disassembly using microscopy, small-angle X-ray scattering, and nanoparticle tracking analysis. HIP protein vesicles have the potential to broaden the use of intracellular proteins as therapeutics for various diseases and extend protein vesicles to deliver other biomacromolecules, as the strategy developed here resulted in the first cytosolic protein cargo delivery using protein vesicles.

59 BASIC BIOLOGICAL SCIENCES↗