Open-source Software Framework Research Articles

Drone Kit-Python for Autonomous Quadcopter Navigation

Using Python scripts over the MAVLink protocol, developers can use the open-source DroneKit Python software framework to enable autonomous drone operations. This framework provides excellent flexibility and power to facilitate automated drone control. The built quadcopter has an X configuration and uses a DJI F450 frame with some modifications. Interestingly, the drone has legs made of aluminum on both sides to help with smooth takeoffs and landings. The frame is 45 cm diagonal length and 30 cm vertical height. The drone was given an additional weight in a 15 x 18 x 12.5 cm box. The propeller used in this investigation is a 9x6 carbon-based model. The x2216 1400kV brushless motor that is being used is from Sunnysky, and it comes with an Electronic Speed Controller (ESC) with a 30A rating. A 4-cell 14.8V Lithium-Polymer (Li-Po) battery with a 7200mAh capacity powers the drone. Apart from that, the drone weighs 1573g in total. The results are obtained by self-measurement and flight measurement data (FMU). Six attempts were made, and the results showed that the second flight had the longest flight time and the highest altitude. In particular, the Flight Measurement Unit (FMU) reported that the flight lasted 81 seconds and reached an altitude of 0.93 meters. In contrast, the self-measurement data reported that the flight lasted 85 seconds and reached an altitude of 1.5 meters.

Development and deployment of data-driven turbulence model for three-dimensional complex configurations

Abstract In recent years, the synergy between artificial intelligence and turbulence big data has given rise to a new data-driven paradigm in turbulence research. Data-driven turbulence modeling has emerged as one of the forefront directions in fluid mechanics. Most existing studies focus on feature construction, selection, and the development of modeling frameworks, often overlooking the practical deployment and application of trained models. This paper examines the entire process from model construction to real-world deployment, using data-driven turbulence modeling for high Reynolds number flows over complex three-dimensional configurations as a case study. Key stages include data generation, input-output feature construction, model training, model compilation and optimization, deployment, and validation. We successfully implemented the entire workflow in a heterogeneous supercomputing environment and, through mixed programming techniques, integrated the resulting turbulence model into the Platform for Hybrid Engineering Simulation of Flows (PHengLEI) open-source software framework. This allowed for mixed-precision simulations, with the main equations solved in double precision and the turbulence model in half precision. The new computational framework was validated through large-scale parallel numerical simulations on grids with tens of millions of elements for three-dimensional complex configurations. The results highlight the efficiency of our model deployment, with overall computational efficiency improving by 13.35% and the turbulence model’s solution speed increasing by approximately 3.9 times. The accuracy of the computations was also confirmed, with the average relative error in the lift and drag coefficients calculated by the data-driven turbulence model within 3%. Across various computing nodes, the relative error in the computed aerodynamic coefficients remained within 1%, demonstrating the framework’s scalability. Notably, our contributions have been incorporated as a case study in the latest PHengLEI open-source project5 5 https://forge.osredm.com/PHengLEI/PHengLEI-TestCases/tree/master/Y02_ThreeD_M6_Unstruct_Branch_Ascend. .

Enhancing Non-Player Characters in Unity 3D using GPT-3.5

This case study presents a comprehensive integration process of OpenAI's GPT-3.5 large language model into Unity 3D to enhance non-player characters (NPCs) in video games and interactive applications. The study aims to develop an architecture and open source software framework that enables NPCs to engage in dynamic real-time interactions with players and other characters. The background and motivation for the study are provided, highlighting the existing limitations of traditional NPC programming and the potential of advanced natural language models such as GPT-3.5 to overcome these limitations. The methodology section outlines the step-by-step process, covering framework design and preparation, core architecture development, humanoid avatar integration and animation, and important feature extensions. The progression of framework design and implementation is described, emphasizing key architectural concepts, design patterns, and essential classes and interfaces. The results of the case study are discussed, focusing on the valuable insights gained and the implications for future advancements. Lessons learned from the integration process are shared, along with suggestions for potential improvements and directions for future research. This case study provides a practical resource for game developers and researchers interested in leveraging advanced natural language processing capabilities to create more immersive and interactive NPC experiences in Unity 3D environments.

The Design and Real-Time Optimization of an EtherCAT Master for Multi-Axis Motion Control

To address the issues of low bandwidth, weak real-time performance, and poor synchronization in traditional fieldbuses for multi-axis motion control, a solution for the implementation of an EtherCAT master based on the IgH EtherCAT Master open-source software framework and an embedded hardware platform is proposed. On a hardware platform centered around the AM64x Sitara processor, a Linux real-time operating system based on the Xenomai real-time kernel is constructed, and the IgH master framework is ported to realize a high-performance EtherCAT master. The configuration process of the EtherCAT bus is detailed, a master application program is developed, and methods for the real-time performance optimization of the master—such as exclusive CPU usage by the master process and the optimization of the network card driver—are proposed. Finally, experiments are conducted on a six-axis servo control platform, with the packet analysis of the periodic EtherCAT data frames sent by the master. The experimental results show that the optimized master, under a high-speed communication cycle of 500 microseconds, maintains maximum jitter within 20 microseconds and average jitter within 1 microsecond, meeting the requirements for high-precision multi-axis motion control.

Biophysical essentials – A full stack open-source software framework for conserved and advanced analysis of patch-clamp recordings

Background and ObjectivesPatch-Clamp recordings allow for in depth electrophysiological characterization of single cells, their general biophysical properties as well as characteristics of voltage- and ligand-gated ionic currents. Different acquisition modes, such as whole-cell patch-clamp recordings in the current or voltage clamp configuration, capacitance measurements or single channel recordings from cultured cells as well as acute brain slices are routinely performed for these purposes. Nevertheless, multipurpose transparent and adaptable software tools to perform reproducible state-of-the-art analysis of multiple experiment types and to manage larger sets of experimental data are currently unavailable. MethodsBiophysical Essentials (BPE) was developed as an open-source full stack python software for transparent and reproducible analysis of electrophysiological recordings. For validation, BPE results were compared with manually analyzed single-cell patch-clamp data acquired from a human in vitro nociceptor-model and mouse dorsal root ganglia neurons. ResultsWhile initially designed to improve time consuming and repetitive analysis steps, BPE was further optimized as a technical software solution for entire workflow processing including data acquisition, data preprocessing, normalization and visualization and of single recordings up to stacked calculations and statistics of multiple experiments. BPE can operate with different file formats from different amplifier systems and producers. An in-process database logs all analysis steps reproducible review and serves as a central storage point for recordings. Statistical testing as well as advanced analysis functions like Boltzmann-fitting and dimensional reduction methods further support the researchers' needs in projects involving electrophysiology techniques. ConclusionsBPE extends beyond available patch-clamp specific, open source – and commercial analysis tools in particular because of reproducible and sharable analysis workflows. BPE enables full analysis from raw data acquisition to publication ready result visualizations – all within one single program. Thereby, BPE significantly enhances transparency in the analytical process of patch-clamp data analysis. BPEs function scope is completely accessible through an easy-to-use graphical user interface eliminating the need for programing language proficiency as required by many community patch-clamp analysis frameworks and algorithms.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies.

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

Gauss–Newton Runge–Kutta integration for efficient discretization of optimal control problems with long horizons and least-squares costs

This work proposes an efficient treatment of continuous-time optimal control problems with long horizons and nonlinear least-squares costs. In particular, we present the Gauss–Newton Runge–Kutta (GNRK) integrator which provides a high-order cost integration. Crucially, the Hessian of the cost terms required within an SQP-type algorithm is approximated with a Gauss–Newton Hessian. Moreover, L2 penalty formulations for constraints are shown to be particularly effective for optimization with GNRK. An efficient implementation of GNRK is provided in the open-source software framework acados. We demonstrate the effectiveness of the proposed approach and its implementation on an illustrative example showing a reduction of relative suboptimality by a factor greater than 10 while increasing the runtime by only 10%.

Design of storage benchmark kit framework for supporting the file storage retrieval

An open-source software framework called the storage benchmark kit (SBK) is used to store the system benchmarking performance framework. The SBK is designed to perform any storage client or device using any data type as a payload. SBK simultaneously helps number of readers as well as writes to the storage system of large amounts of data as well as allows end-to-end latency benchmarking for multiple writers and readers. The SBK uses standardized performance measures for comparing and evaluating various storage systems and their combinations. Distributed file systems, distributed database systems, single or local node databases, systems of object storage, platforms of distributed streaming and messaging, and systems of key-value storage are the storage solutions supported by SBK. The SBK supports various storage systems like XFS, Kafka streaming storage systems, and Hadoop distributed file system (HDFS) performance benchmarking. The experimental results show that a proposed method achieves execution time of 65.530 s, 40.826 s and 30.351 s for the 100k, 500k and 1000k files respectively which ensures better improvement than the existing methods such as simple data interface and distributed data protection system.

Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method

Multiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. The solutions to these problems are known as Pareto optimal solutions, which are mathematically incomparable. Thus, a decision maker must be employed to provide preferences to find the most preferred solution. However, decision makers often lack support in providing preferences and insights in exploring the solutions available. We explore the combination of learnable evolutionary models with interactive indicator-based evolutionary multiobjective optimization to create a learnable evolutionary multiobjective optimization method. Furthermore, we leverage interpretable machine learning to provide decision makers with potential insights about the problem being solved in the form of rule-based explanations. In fact, we show that a learnable evolutionary multiobjective optimization method can offer advantages in the search for solutions to a multiobjective optimization problem. We also provide an open source software framework for other researchers to implement and explore our ideas in their own works. Our work is a step toward establishing a new paradigm in the field on multiobjective optimization: explainable and learnable multiobjective optimization . We take the first steps toward this new research direction and provide other researchers and practitioners with necessary tools and ideas to further contribute to this field.

Modeling and Fundamental Dynamics of Vacuum, Gas, and Antisolvent Quenching for Scalable Perovskite Processes.

Hybrid perovskite photovoltaics (PVs) promise cost-effective fabrication with large-scale solution-based manufacturing processes as well as high power conversion efficiencies. Almost all of today's high-performance solution-processed perovskite absorber films rely on so-called quenching techniques that rapidly increase supersaturation to induce a prompt crystallization. However, to date, there are no metrics for comparing results obtained with different quenching methods. In response, the first quantitative modeling framework for gas quenching, anti-solvent quenching, and vacuum quenching is developed herein. Based on dynamic thickness measurements in a vacuum chamber, previous works on drying dynamics, and commonly known material properties, a detailed analysis of mass transfer dynamics is performed for each quenching technique. The derived models are delivered along with an open-source software framework that is modular and extensible. Thereby, a deep understanding of the impact of each process parameter on mass transfer dynamics is provided. Moreover, the supersaturation rate at critical concentration is proposed as a decisive benchmark of quenching effectiveness, yielding ≈ 10-3 - 10-1s-1 for vacuum quenching, ≈ 10-5 - 10-3s-1 for static gas quenching, ≈ 10-2 - 100s-1 for dynamic gas quenching and ≈ 102s-1 for antisolvent quenching. This benchmark fosters transferability and scalability of hybrid perovskite fabrication, transforming the "art of device making" to well-defined process engineering.

Experimental Prediction Method of Free-Field Sound Emissions Using the Boundary Element Method and Laser Scanning Vibrometry

Acoustic emissions play a major role in the usability of many product categories. Therefore, mitigating the emitted sound directly at the source is paramount to improve usability and customer satisfaction. To reliably predict acoustic emissions, numerical methods such as the boundary element method (BEM) are employed, which allow for predicting, e.g., the acoustic emission into the free field. BEM algorithms need appropriate boundary conditions to couple the sound field with the structural motion of the vibrating body. In this contribution, firstly, an interpolation scheme is presented, which allows for appropriate interpolation of arbitrary velocity data to the computational grid of the BEM. Secondly, the free-field Helmholtz problem is solved with the open-source BEM software framework NiHu. The forward coupling between the device of interest and BEM is based on the surface normal velocities (i.e., a Neumann boundary condition). The BEM simulation results are validated using a previously established aeroacoustic benchmark problem. Furthermore, an application to a medical device (knee prosthesis frame) is presented. Furthermore, the radiated sound power is evaluated and contextualized with other low-cost approximations. Regarding the validation example, very good agreements are achieved between the measurements and BEM results, with a mean effective pressure level error of 0.63 dB averaged across three microphone positions. Applying the workflow to a knee prosthesis frame, the simulation is capable of predicting the acoustic radiation to four microphone positions with a mean effective pressure level error of 1.52 dB.

Potentiality of automatic parameter tuning suite available in ACTS track reconstruction software framework

Particle tracking is among the most sophisticated and complex part of the full event reconstruction chain. A number of reconstruction algorithms work in a sequence to build these trajectories from detector hits. Each of these algorithms use many configuration parameters that need to be fine-tuned to properly account for the detector/experimental setup, the available CPU budget and the desired physics performance. Few examples of such parameters include the cut values limiting the search space of the algorithm, the approximations accounting for complex phenomena or the parameters controlling algorithm performance. The most popular method to tune these parameters is hand-tuning using brute-force techniques. These techniques can be inefficient and raise issues for the long-term maintainability of such algorithms. The opensource track reconstruction software framework known as “A Common Tracking Framework (ACTS)” offers an alternative solution to these parameter tuning techniques through the use of automatic parameter optimization algorithms. ACTS come equipped with an auto-tuning suite that provides necessary setup for performing optimization of input parameters belonging to track reconstruction algorithms. The user can choose the tunable parameters in a flexible way and define a cost/benefit function for optimizing the full reconstruction chain. The fast execution speed of ACTS allows the user to run several iterations of optimization within a reasonable time bracket. The performance of these optimizers has been demonstrated on different track reconstruction algorithms such as trajectory seed reconstruction and selection, particle vertex reconstruction and generation of simplified material map, and on different detector geometries such as Generic Detector and Open Data Detector (ODD). We aim to bring this approach to all aspects of trajectory reconstruction by having a more flexible integration of tunable parameters within ACTS.

Predictions for electromagnetic counterparts to Neutron Star mergers discovered during LIGO-Virgo-KAGRA observing runs 4 and 5

ABSTRACT We present a comprehensive, configurable open-source software framework for estimating the rate of electromagnetic detection of kilonovae (KNe) associated with gravitational wave detections of binary neutron star (BNS) mergers. We simulate the current LIGO-Virgo-KAGRA (LVK) observing run (O4) using current sensitivity and uptime values as well as using predicted sensitivites for the next observing run (O5). We find the number of discoverable kilonovae during LVK O4 to be ${ 1}_{- 1}^{+ 4}$ or ${ 2 }_{- 2 }^{+ 3 }$, (at 90 per cent confidence) depending on the distribution of NS masses in coalescing binaries, with the number increasing by an order of magnitude during O5 to ${ 19 }_{- 11 }^{+ 24 }$. Regardless of mass model, we predict at most five detectable KNe (at 95 per cent confidence) in O4. We also produce optical and near-infrared light curves that correspond to the physical properties of each merging system. We have collated important information for allocating observing resources for search and follow-up observations, including distributions of peak magnitudes in several broad-bands and time-scales for which specific facilities can detect each KN. The framework is easily adaptable, and new simulations can quickly be produced in response to updated information such as refined merger rates and NS mass distributions. Finally, we compare our suite of simulations to the thus-far completed portion of O4 (as of 2023, October 14), finding a median number of discoverable KNe of 0 and a 95 percentile upper limit of 2, consistent with no detections so far in O4.

Optihood – a multi-objective analysis and optimization framework for building energy systems at neighborhood scale

Strategic planning of future energy systems in a district is essential to fully exploit the benefits from sector coupling. In particular, new solutions may arise when a group of buildings is considered rather than individual buildings. Therefore, in order to identify optimal production and storage technologies within a district, new tools are needed. In this paper we present the open-source software framework optihood, which allows for multi-objective optimization and analysis of energy systems for neighborhoods. The tool assists researchers and, in a future stage, energy planners in the design of energy systems as well as in optimizing their operation strategy. After introducing the tool, its applicability is demonstrated with a case study, wherein we investigated the supply of a small neighborhood of four residential buildings with space heat, domestic hot water and electricity demands. The optimizer was allowed to consider heat pumps (geothermal/air-source), PV, batteries and hot water storage tanks as available technologies. We show comparisons between optimal solutions in terms of total annualized cost for the buildings considered (a) together as a group and (b) individually.

Electrochemical Methods in the Cloud: FreiStat, an IoT-Enabled Embedded Potentiostat.

Embedded potentiostats enable electrochemical measurements in the Internet-of-Things (IoT) or other decentralized applications, such as remote environmental monitoring, electrochemical energy systems, and biomedical point-of-care applications. We report on Freiburg's Potentiostat (FreiStat) based on the AD5941 potentiostat circuit from Analog Devices, together with custom firmware, as the key to precise and advanced electrochemical methods. We demonstrated its analytical performance by various cyclic voltammetry measurements, advanced techniques such as differential pulse voltammetry, and a lactate biosensor measurement with currents in the nA range and a resolution of 54 pA. The FreiStat yielded analytical results comparable to benchtop devices and outperformed current commercial embedded potentiostats at significantly lower cost, smaller size, and lower power consumption. Decentralized corrosion analysis by a Tafel plot using the IBM Cloud showed its applicability in a typical IoT scenario. The developed open-source software framework facilitates the integration of electrochemical instrumentation into applications utilizing machine learning and other artificial intelligence. Together with the affordable and highly capable embedded potentiostat, our approach can leverage analytical chemistry toward increasingly important, more widespread and decentralized applications.

An open-source software framework for the integrated simulation of structures in fire

The traditional methods to understand the development of elevated temperature in a structure, and also the associated structural response, are not representative of realistic fire scenarios. To provide a more accurate and realistic reflection of the fire development, the current paper develops a generic middleware which interfaces between the computational fluid dynamics (CFD) software Fire Dynamics Simulator (FDS) and the finite element (FE) analysis software OpenSees. This framework enables a fully integrated simulation of a realistic fire scenario including the heat transfer through the structure and the resulting thermo-mechanical response. The proposed framework is open-source and freely available and therefore can be used and further developed by researchers and practicing engineers and customised to their requirements. This paper shows validation against two sets of experimental results and one real fire incident. A number of different types of thermal boundary conditions such as gas temperatures and heat fluxes, are obtained from the CFD analysis and are then used in the subsequent heat transfer and thermo-mechanical analysis. The primary advantage of this computational tool is that it provides consultants and designers with the means to undertake large-scale projects requiring performance-based fire engineering solutions.

Eleven key measures for monitoring general practice clinical activity during COVID-19: A retrospective cohort study using 48 million adults' primary care records in England through OpenSAFELY.

The COVID-19 pandemic has had a significant impact on delivery of NHS care. We have developed the OpenSAFELY Service Restoration Observatory (SRO) to develop key measures of primary care activity and describe the trends in these measures throughout the COVID-19 pandemic. With the approval of NHS England, we developed an open source software framework for data management and analysis to describe trends and variation in clinical activity across primary care electronic health record (EHR) data on 48 million adults.We developed SNOMED-CT codelists for key measures of primary care clinical activity such as blood pressure monitoring and asthma reviews, selected by an expert clinical advisory group and conducted a population cohort-based study to describe trends and variation in these measures January 2019-December 2021, and pragmatically classified their level of recovery one year into the pandemic using the percentage change in the median practice level rate. We produced 11 measures reflective of clinical activity in general practice. A substantial drop in activity was observed in all measures at the outset of the COVID-19 pandemic. By April 2021, the median rate had recovered to within 15% of the median rate in April 2019 in six measures. The remaining measures showed a sustained drop, ranging from a 18.5% reduction in medication reviews to a 42.0% reduction in blood pressure monitoring. Three measures continued to show a sustained drop by December 2021. The COVID-19 pandemic was associated with a substantial change in primary care activity across the measures we developed, with recovery in most measures. We delivered an open source software framework to describe trends and variation in clinical activity across an unprecedented scale of primary care data. We will continue to expand the set of key measures to be routinely monitored using our publicly available NHS OpenSAFELY SRO dashboards with near real-time data. This research used data assets made available as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058).The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157).

Seismic response of soil-shield tunnel systems in sandwiched liquefiable soil deposits

Long-extended underground structures such as shield-driven tunnels may be constructed through sandwiched liquefiable soil profiles, which can compromise their integrity during strong earthquakes. In this study, the influence of sandwiched liquefiable soil layer on the seismic response of shield tunnels under ground motions with different frequency contents is evaluated through nonlinear numerical simulations. Six soil-shield tunnel finite element models with different sandwiched liquefiable soil profiles are developed using the open-source software framework OpenSees. The solid–fluid fully coupled dynamic effective stress analysis method is adopted for the nonlinear seismic analysis of liquefiable soil. The numerical results demonstrate that the frequency contents of earthquake motion and the spatial location of the sandwiched liquefiable soil profile have great influence on the dynamic responses of the segmental tunnel. In all cases studied, the bottom half of the tunnel embedded in the liquefiable soil layer experienced the most unfavorable condition for the seismic performance. The deformations and internal forces of the segmental joints around the tunnel foot are remarkably higher than at other joints under the same ground motion intensity. Moreover, the Kobe motion with the low-frequency content would significantly amplify the seismic responses of both the site and the segmental tunnel compared to the Chi-Chi motion with the high-frequency content in different sandwiched liquefiable soil deposits. It is also found that the racking ratio of the non-liquefiable soil layer is greater than that of the liquefiable soil layer, which means that the surrounding soil with large shear deformation pushes the underground structure leading to larger adverse lateral deformation of the structure.

Learned multiphysics inversion with differentiable programming and machine learning

We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-source software framework for computational geophysics and, more generally, inverse problems involving the wave equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, the software is designed to be both readable and scalable, allowing researchers to easily formulate problems in an abstract fashion while exploiting the latest developments in high-performance computing. The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning.

Real-Time Remote Control and Autonomous Navigation of a Two-Wheeled Robot with ROS and Raspberry Pi 4

Abstract: The development of autonomous vehicles has been the subject of considerable research and development in recent years. The project aims to develop a 2-wheeled autonomous car using a Robotic Operating System (ROS) with lidar and camera sensors as inputs. The car's control system will be designed to make decisions based on the information provided by these sensors, allowing it to navigate and avoid obstacles in real-time. The project will begin by selecting appropriate hardware components such as motors, batteries and sensors and integrating them into a functional prototype. Lidar and camera sensors will be used to detect and localize the position of the car, detect obstacles and determine their distance from the vehicle. The control system will be developed using ROS, an open source software framework for robot development, and will include algorithms for navigation, obstacle avoidance and decision making. The car will be programmed to follow a predetermined route. Finally, the autonomous car's performance will be evaluated through various tests, including its ability to follow a specific path and avoid obstacles. The results of these tests will be analyzed, and possible improvements will be identified. The outcome of this project is expected to provide a proof-of-concept for the development of autonomous vehicles using ROS with lidar and camera sensors as input. This technology has the potential to revolutionize the transportation industry by providing safer and more efficient transportation solutions