Precision Requirements Research Articles

Bayesian optimization for state engineering of quantum gases

Abstract State engineering of quantum objects is a central requirement for precision sensing and quantum computing implementations. When the quantum dynamics can be described by analytical solutions or simple approximation models, optimal state preparation protocols have been theoretically proposed and experimentally realized. For more complex systems such as interacting quantum gases, simplifying assumptions do not apply anymore and the optimization techniques become computationally impractical. Here, we propose Bayesian optimization based on multi-output Gaussian processes to learn the physical properties of a Bose-Einstein condensate within few simulations only. We evaluate its performance on an optimization study case of diabatically transporting the quantum gas while keeping it in its ground state. Within a few hundred executions, we reach a competitive performance to other protocols. While restricting this benchmark to the well known Thomas-Fermi approximation for straightforward comparisons, we expect a similar performance when employing more complex theoretical models, which would be computationally more challenging, rendering standard optimal control theory protocols impractical. This paves the way for efficient state engineering of complex quantum systems including mixtures of interacting gases or cold molecules.

Chip Appearance Inspection Using Machine Learning Techniques

In recent years, the increasing demand for chips has put forward higher requirements for chip production quality and production efficiency. After the chip manufacturing is completed before leaving the factory, it needs to be detected after packaging, which makes the development of chip detection and localization system difficult due to the small size and large number of chips and the high precision requirements for them. Therefore, this paper aims to design a fast, highprecision chip detection and positioning system for the positioning and detection needs of the chip packaging process, the chip detection and positioning system for in-depth research, to achieve the accurate positioning of the chip as well as the deformation produced by the detection of the main work is as follows: the first chapter is mainly about why this paper is mainly aimed at discovering the problems in the chip packaging process. The second chapter is about how to take pictures of the chip. Chapter 3 focuses on the pre-processing of the pictures. Chapter 4 focuses on determining whether the chip is deformed through different machine learning and deep learning techniques. Finally, the conclusion section summarizes the whole paper.

Advancements in Key Technologies for Vibration Isolators Utilizing Electromagnetic Levitation

With the advancement of manufacturing, the precision requirements for various high-precision processing equipment and instruments have further increased. Due to its noncontact nature, simple structure, and controllable performance, electromagnetic levitation has broad application prospects in ultra-precision instruments and ground testing of aerospace equipment. Research on vibration isolation technology using electromagnetic levitation is imperative. This paper reviews the latest research achievements of three types of passive isolators and five active isolation actuators. It also summarizes the current research status of analytical methods for passive isolators and the impact of isolator layout. This study explores current isolators’ achievements, such as the development of passive isolators that generate negative stiffness and require mechanical springs for uniaxial translational vibrations, single-function actuators, and control systems focused on position and motion vibration control. Based on the current isolators’ characteristics, this review highlights future developments, including focusing on passive isolators for heavy loads and multi-axis isolation, addressing complex vibrations, including rotational ones, and developing methods to calculate forces and torques for arbitrary six-DOF movements while improving speed. Additionally, it emphasizes the importance of multifunctional actuators to simplify system structures and comprehensive control systems that consider more environmental factors. This provides significant reference value for vibration isolation technology using electromagnetic levitation.

Study of Five-Hundred-Meter Aperture Spherical Telescope Feed Cabin Time-Series Prediction Studies Based on Long Short-Term Memory–Self-Attention

The Five-hundred-meter Aperture Spherical Telescope (FAST), as the world’s most sensitive single-dish radio telescope, necessitates highly accurate positioning of its feed cabin to utilize its full observational potential. Traditional positioning methods that rely on GNSS and IMU, integrated with TS devices, but the GNSS and TS devices are vulnerable to other signal and environmental disruptions, which can significantly diminish position accuracy and even cause observation to stop. To address these challenges, this study introduces a novel time-series prediction model that integrates Long Short-Term Memory (LSTM) networks with a Self-Attention mechanism. This model can hold the precision of feed cabin positioning when the measure devices fail. Experimental results show that our LSTM-Self-Attention model achieves a Mean Absolute Error (MAE) of less than 10 mm and a Root Mean Square Error (RMSE) of approximately 12 mm, with the errors across different axes following a near-normal distribution. This performance meets the FAST measurement precision requirement of 15 mm, a standard derived from engineering practices where measurement accuracy is set at one-third of the control accuracy, which is around 48 mm (according to the accuracy form the official threshold analysis on the focus cabin of FAST). This result not only compensates for the shortcomings of traditional methods in consistently solving feed cabin positioning, but also demonstrates the model’s ability to handle complex time-series data under specific conditions, such as sensor failures, thus providing a reliable tool for the stable operation of highly sensitive astronomical observations.

Application of Two-Dimensional NMR for Quantitative Analysis of Viscosity in Medium–High-Porosity-and-Permeability Sandstones in North China Oilfields

The viscosity of crude oil plays a pivotal role in the exploration and development of oil fields. The predominant reliance on laboratory measurements, which are constrained by manual expertise, represents a significant limitation in terms of efficiency. Two-dimensional nuclear magnetic resonance (NMR) logging offers a number of advantages over traditional methods. It is capable of providing faster measurement rates, as well as insights into fluid properties, which can facilitate timely adjustments in oil and gas development strategies. This study focuses on the loose sandstone reservoirs with high porosity and permeability containing heavy oil in the Huabei oilfield. Two-dimensional nuclear magnetic resonance (NMR) measurements and analyses were conducted on saturated rocks with different-viscosity crude oils and varying oil saturation levels, in both natural and artificial rock samples. This study elucidates the distribution patterns of different-viscosity crude oils within the two-dimensional NMR spectra. Furthermore, the T1 and T2 peak values of the extracted oil signals were employed to establish a model correlating oil viscosity with NMR parameters. Consequently, a criterion for determining oil viscosity based on two-dimensional NMR was formulated, providing a novel approach for estimating oil viscosity. The application of this technique in the BQ well group of the Huabei oilfield region yielded an average relative error of 15% between the actual oil viscosity and the computed results. Furthermore, the consistency between the oil types and the oil discrimination chart confirms the reliability of the method. The final outcomes meet the precision requirements for practical log interpretation and demonstrate the excellent performance of two-dimensional nuclear magnetic resonance (NMR) logging in calculating oil viscosity. The findings of this study have significant implications for subsequent exploration and development endeavors in the research area’s oilfields.

Quantum Physical Unclonable Function Based on Multidimensional Fingerprint Features of Single Photon Emitters in Random AlN Nanocrystals

AbstractPhysical unclonable function (PUF) has emerged as a unique physical'fingerprint' that is inherently difficult to replicate. It shows tremendous application value in various hardware security areas such as identity authentication, chip anticounterfeiting, communication encryption, blockchain, etc. However, with the rapid development of 3D nanoprinting, classical PUFs constructed with disordered micro‐nanostructures face tremendous threats from physical cloning attacks. Herein, this study proposes and demonstrates the utilization of room‐temperature single‐photon emitters derived from atomic defects in randomly distributed pyramidal aluminum nitride (AlN) nanocrystals as a novel quantum PUF to resist physical cloning attacks. The fabrication of this quantum PUF on silicon (Si) wafers enables seamless integration with silicon photonic integrated circuits. The multidimensional fingerprint features of the single‐photon emitters are highly sensitive to the lattice parameters of the uneven AlN nanocrystals. Furthermore, each single photon emitter can work as a quantum random number generator to ensure the fundamental unpredictability of PUFs. The subatomic precision requirement coupled with unpredictable quantum emission behavior makes it practically impossible to attack the proposed quantum PUF, providing a promising solution for information security in the post‐quantum era.

3D Voxel-based Path Planning for AVs in Dynamic Complex Environments

Abstract. With the rapid advancement of science and technology, autonomous driving technology has been widely applied in various fields such as the automotive industry, intelligent warehousing, and emergency rescue. Traditional 2D path planning has limitations in dealing with the complex and dynamic environments of the real world, making it difficult to meet the high precision and complex path planning requirements of autonomous vehicles. With the development of data acquisition technology, large-scale point cloud data has become easily accessible, which can better extract the three-dimensional complex of the environment. Nevertheless, the unstructured nature and vast amount of point cloud data make path planning on it extremely challenging. Voxel models can effectively compress point cloud data and provide neighboring topological structures. In view of this, we propose a voxel-based framework for the path planning of autonomous vehicles in dynamic and complex 3D environments, which involves converting point cloud data into voxels and extracting navigation spaces. Subsequently, deep reinforcement learning techniques are utilized to achieve obstacle avoidance and navigation on the voxel map. It is expected that this framework will contribute to the development of 3D path planning and enhance the utilization of point cloud data in the navigation field.

From data silos to seamless integration and coordination: a data-asset centric approach to smart hospital facility management

PurposeExisting hospital building operations involve numerous information technology applications and complex building systems; therefore, an intelligent facility management (FM) platform is required to ensure their continuous operation. To address the persistent issues of data silos, inefficient data interoperability, and workflow incoordination that have been identified in the current body of FM practice and literature, the present study develops a data-asset (DA) centric FM platform specifically designed for hospital buildings.Design/methodology/approachThis study proposes a semi-customized approach to develop the DA-centric FM platform for hospital buildings. To elucidate the precise function requirements of the hospital FM platform, focus group interviews are employed. By seamlessly integrating the as-built BIM model, IoT sensor data and FM workflow data, the BIM-based DA model with a data transfer mechanism is developed. The development of the FM platform with function modules in a case study is guided by a five-tier architecture and the coordination theory (CT). The case study provides an in-depth introduction to the applications of DA management, space management and maintenance management modules.FindingsThe capabilities of the developed DA-centric hospital FM platform are validated through the case application and user satisfaction survey, which assess data quality, automation level, operation efficiency, flexibility and functionality. For hospital FM activities, this DA-centric FM platform realizes data integration and seamless transformation, optimizes workflow coordination and enhances operation performance.Originality/valueThe initial scholarly contribution is the establishment of the BIM-based DA model, which serves as the data middle platform for continuous data integration, transmission and sharing within the FM platform. Subsequently, under the guidance of the CT, the business process of function modules is designed, improving the intra-module and inter-module workflow coordination. The developed DA-centric FM system along with its performance benchmarking application, assists facility managers and decision-makers in implementing smart operations for hospital buildings and achieving the management goals of safety, efficiency, energy savings and convenience.

Eccentric load capacity of ultra-high strength concrete-filled steel tubular lattice short columns

To explore the behavior of ultra-high strength concrete filled steel tubular (UHSC-FST) lattice short columns under eccentric compressive loads, experimental analyses on four specimens and 276 finite element models using ABAQUS were conducted. The study focused on the effects of steel tube wall thickness, concrete strength, steel tube strength, and load eccentricity on failure modes, load-displacement curves, and ultimate load-bearing capacity. Results showed that for columns with minor eccentricity-induced compressive failure, the reduction coefficient of ultimate load capacity has a weak correlation with the parameters. However, for columns with significant eccentricity-induced tensile failure, the reduction coefficient increases with steel tube thickness and strength and decreases with concrete strength. The center-to-center distance of the columns has a minor effect. Based on these findings, a comparative analysis of existing standards was conducted, leading to the development of an accurate method for calculating the ultimate load-bearing capacity of eccentric CFST lattice columns. This method is applicable to various cross-sectional dimensions, material strengths, and steel tube wall thicknesses, with calculation errors within 10 %, ensuring it meets engineering precision requirements.

Macro-Nanoporous Film with Cauliflower-Shaped Fibers for Highly Efficient Passive Daytime Radiative Cooling.

Passive daytime radiative cooling (PDRC) is a simple and effective cooling approach that does not consume any extra energy just by highly reflecting shortwave sunlight and highly radiating infrared heat through the atmospheric windows. In recent years, the application of photonic coolers and metamaterials for PDRC has been studied. However, they usually have complex processes and high precision requirements, which seriously limit large-scale fabrication. In this paper, a high-performance polyvinylidene difluoride-hexafluoropropylene (PVDF-HFP) PDRC fiber film was prepared by a simple and efficient electrospinning method combined with phase separation, and the obtained PVDF-HFP fibers have a cauliflower-shaped macro-nanoporous structure. The cauliflower-shaped structure provides more scattering sites of the fibers, and the fiber film with the macro-nanoporous morphology has a high scattering ability in the solar region, resulting in a high solar reflectivity of 99.65%. The PVDF-HFP porous film possesses an emissivity of 90.44% in the atmospheric window, and it can reach a maximum cooling temperature of 10.2 °C during the daytime. In addition, the excellent mechanical strength provides a guarantee for its large-scale practical application. This study offers an effective improvement strategy for the spectral performance of polymer fiber films, which is meaningful for green cooling management and reduction of carbon emission.

An improved two-phase rate transient analysis method for multiple communicating multi-fractured horizontal wells in shale gas reservoirs: Field cases study

The reduction in well spacing for multi-fractured horizontal wells (MFHWs) and the increase in infill drilling exacerbate the risk of fracture communication. This presents substantial challenges for the rate transient analysis of the parent–child well system. This study proposes an improved two-phase straight-line analysis method to evaluate the linear flow parameters of multiple communicating MFHWs in shale gas reservoirs. Considering shale gas adsorption–desorption mechanisms, nonuniform length of fractures, stress-dependent effects, and matrix shrinkage effects, two-phase productivity equation is established within the dynamic drainage area (DDA). Subsequently, we develop a three-well square root of time plot based on DDA correction and propose a rigorous workflow for evaluating linear flow parameters in single-well, two-well, and three-well systems. The straight lines on the parent well's square root of the time plot exhibit varying degrees of jumps, depending on the frequency of fracture communication. After communication, it is necessary to adjust the cumulative production and production time of the parent well to accurately recalculate the average pressure within the drainage area. Numerical simulations are employed to generate a series of cases under different reservoir conditions to validate the accuracy of the proposed model. The results show that the model estimates fracture half-lengths with errors within 8%, meeting the precision requirements for field applications. Additionally, the method exceeds numerical simulations in computational speed. Two field case studies in the WY Basin, China, further illustrate the effectiveness and practicality of the proposed method, providing theoretical support for hydraulic fracturing construction design and development planning.

Stage-IV cosmic shear with Modified Gravity and model-independent screening

We forecast constraints on minimal model-independent parametrisations of several Modified Gravity theories using mock Stage-IV cosmic shear data. We include nonlinear effects and screening, which ensures recovery of General Relativity on small scales. We introduce a power spectrum emulator to accelerate our analysis and evaluate the robustness of the growth index parametrisation with respect to two cosmologies: ΛCDM and the normal branch of the DGP model. We forecast the uncertainties on the growth index γ to be of the order ∼ 10%. We find that our halo-model based screening approach demonstrates excellent performance, meeting the precision requirements of Stage-IV surveys. However, neglecting the screening transition results in biased predictions for cosmological parameters. We find that the screening transition shows significant degeneracy with baryonic feedback, requiring a much better understanding of baryonic physics for its detection. Massive neutrinos effects are less prominent and challenging to detect solely with cosmic shear data.

Ship model self-propulsion instrument development and test verification

Abstract Aimed at the characteristics of high precision requirements and large environmental changes of the thrust and torque coupling test in the self-propulsion test of the ship model, the paper carries out the design of the instrument of the ship model test, the calculation of structural strength, the design of anti-jamming of the thrust and torque coupling, and the selection and realization of thrust and torque module. Based on the above, the instrument was calibrated with sensitivity coefficient, high and low temperature, and dynamic stability tests. Besides this, a self-propulsion test at the design speed point of a ship was carried out to verify the accuracy and environmental adaptability. From the static calibration result, the coefficient of determination is 1.0. The max zero drift of thrust and torque is 0.11% FS and 0.05% FS respectively from the low-high temperature test, and the max zero drift of torque under different revolutions is 0.23% FS from the dynamic test. In addition to the above, the max deviation between Maric-SP01 and R31 of thrust and torque is less than 0.78% and 0.65% respectively from the ship model self-propulsion test. So, in one word, the instrument has good reliability and accuracy and can meet the requirements of the ship model self-propulsion test.

Greenhouse gas column observations from a portable spectrometer in Uganda

Abstract. The extensive terrestrial ecosystems of tropical Africa are a significant store of carbon and play a key but uncertain role in the atmospheric budgets of carbon dioxide and methane. As ground-based observations in the tropics are scarce compared with other parts of the world, recent studies have instead made use of satellite observations assimilated into atmospheric chemistry and transport models to conclude that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for up to a third of global methane growth, and that the tropical Africa region dominates net carbon emission across the tropics. These studies critically rely on the accuracy of satellite datasets, such as those from the Orbiting Carbon Observatory-2 (OCO-2), the Greenhouse gases Observing SATellite (GOSAT), and the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), along with results from atmospheric transport models, over a geographical region where there are little independent data to test the robustness of published results. In this paper we present the first ground-based observations of greenhouse gas column concentrations over East Africa, obtained using a portable Bruker EM27/SUN Fourier transform infrared (FTIR) spectrometer during a deployment covering the first few months of 2020 in Jinja, Uganda. We operated the instrument near autonomously by way of an automated weatherproof enclosure and observed total atmospheric column concentrations of the greenhouse gases carbon dioxide and methane, as well as carbon monoxide, a useful proxy for emissions from incomplete combustion processes in the region. We discuss the performance of the combined enclosure and spectrometer system that we deployed in Jinja to obtain these data and show comparisons of our ground-based observations with satellite datasets from OCO-2 and Orbiting Carbon Observatory-3 (OCO-3) for carbon dioxide and TROPOMI for methane and carbon monoxide, whilst also comparing our results with concentration data from the GEOS-Chem and Copernicus Atmosphere Monitoring Service (CAMS) atmospheric inversions that provide a means of increasing spatial and temporal coverage where satellite data are not available. For our measurement period, we find mean differences in XCO2 between OCO-2 and the EM27/SUN of −0.29 % and between OCO-3 and the EM27/SUN of −0.28 %. In the case of TROPOMI, the mean difference in XCH4 that we find between TROPOMI and the EM27/SUN is −0.44 %, whilst for XCO the mean difference is −5.65 %. In each of these cases, the mean difference observed between the satellite and ground-based column concentrations is either close to or within the precision and accuracy requirements for the respective missions. With regard to the model and reanalysis comparisons with the EM27/SUN column concentrations, we see mean differences from the EM27/SUN of a global GEOS-Chem inversion for XCO2 of −0.08 %, a regional high-resolution GEOS-Chem inversion for XCH4 of −0.22 %, and the CAMS global reanalysis for XCO of −9.79 %. Our results demonstrate the value of ground-based observations of total column concentrations and show that the combined EM27/SUN and enclosure system employed would be suitable for acquisition of the longer-term observations needed to rigorously evaluate satellite observations and model and reanalysis calculations over tropical Africa.

The One-Dimensional Flow Pressure Loss Correction Model Based on the Particle Flow through Concrete Bend

The rheological behavior of concrete pumping in the bend section is complex. The existing conversion calculation for pumping pressure loss based on the straight pipe section often fails to meet engineering precision requirements. This paper develops a pressure loss correction model for a concrete pumping bend section based on a new one-dimensional flow model for the straight pipe section simulation of particle flow; the study analyzes the impact of parameters such as elbow shape and pumping flow rate on pressure loss. An equivalent conversion relationship between the bend section and the straight section is established. The comparison with the measured pressure values from the project shows that the relative error between the pressure loss values calculated by the correction model and the actual measurements is 15.8%.

Curriculum Design and Sim2Real Transfer for Reinforcement Learning in Robotic Dual-Arm Assembly

Robotic systems are crucial in modern manufacturing. Complex assembly tasks require the collaboration of multiple robots. Their orchestration is challenging due to tight tolerances and precision requirements. In this work, we set up two Franka Panda robots performing a peg-in-hole insertion task of 1 mm clearance. We structure the control system hierarchically, planning the robots’ feedback-based trajectories with a central policy trained with reinforcement learning. These trajectories are executed by a low-level impedance controller on each robot. To enhance training convergence, we use reverse curriculum learning, novel for such a two-armed control task, iteratively structured with a minimum requirements and fine-tuning phase. We incorporate domain randomization, varying initial joint configurations of the task for generalization of the applicability. After training, we test the system in a simulation, discovering the impact of curriculum parameters on the emerging process time and its variance. Finally, we transfer the trained model to the real-world, resulting in a small decrease in task duration. Comparing our approach to classical path planning and control shows a decrease in process time, but higher robustness towards calibration errors.

Vision-based reinforcement learning control of soft robot manipulators

Purpose This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator. Design/methodology/approach A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection. Findings Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models. Research limitations/implications Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities. Practical implications By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems. Originality/value This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

Finite element analysis for the measurement error of electrostatic accelerometer due to the electrode misalignment

Abstract The electrostatic accelerometer is the key payload of many space missions, such as satellite gravity measurements, space gravitational experiments, with a typical precision requirement from nano-g to femto-g. The measurement error model analysis is an important issue for the electrostatic accelerometer with numerous error sources. The traditional method by theoretical analysis is complicated to evaluate the complete measurement error models quantitatively especially when the machining and assembly errors of the sensor head are considered. Limited by the earth gravity and seismic noise which is much higher than the intrinsic noise, the complete error model of electrostatic accelerometers can also hardly be tested on ground. In this paper, the method by using finite element analysis of the sensor head is studied. The simulation model for an electrostatic accelerometer sensor head is built, and the multi-conductor capacitance matrix data is simulated. The accuracy of the capacitance simulation is evaluated in three ways including the convergence check of the capacitance with the mesh size, the symmetry verification, and the sensitivity comparison with the theoretical model. Finally, the accelerometer measurement model is analyzed based on the simulation data, using the case of rotating installation misalignment of the upper electrode as an example. The measurement model and error items of one horizontal axis and one rotating axis are quantitatively evaluated. The method proposed in this paper provide a new effective approach to the measurement model analysis of the electrostatic accelerometers in complex working scenarios both in orbit and on ground, which could be helpful to enhance the performance and the efficiency of application.

Uncertainty quantification of acoustic metamaterial bandgaps with stochastic material properties and geometric defects

Acoustic metamaterials are a subject of increasing study and utility. Through designed combinations of geometries with material properties, acoustic metamaterials can be built to arbitrarily manipulate acoustic waves for various applications. Despite the theoretical advances in this field, however, acoustic metamaterials have seen limited penetration into industry and commercial use. This is largely due to the difficulty of manufacturing the intricate geometries that are integral to their function and the sensitivity of metamaterial designs to material batch variability and manufacturing defects. Capturing the effects of stochastic material properties and geometric defects requires empirical testing of manufactured samples, but this can quickly become prohibitively expensive with higher precision requirements or with an increasing number of input variables. This paper demonstrates how uncertainty quantification techniques, and more specifically the use of polynomial chaos expansions and spectral projections, can be used to greatly reduce sampling needs for characterizing acoustic metamaterial dispersion curves. With a novel method of encoding geometric defects in a 1D, interpretable, resolution-independent way, our uncertainty quantification approach allows for both stochastic material properties and geometric defects to be considered simultaneously. Two to three orders of magnitude sampling reductions down to ∼100 and ∼101 were achieved in 1D and 7D input space scenarios, respectively. Remarkably, this reduction in sampling was possible while preserving accurate output probability distributions of the metamaterial performance characteristics (bandgap size and location).

The Significance of Provisional Patent Applications in Protecting Early-Stage Inventions: A Legal and Empirical Analysis

This article critically examines the role of Provisional Patent Applications (PPAs) as strategic tools within the intellectual property framework, particularly highlighting their significance in safeguarding early-stage inventions. By allowing inventors to secure a priority date while still developing their inventions, PPAs provide a time frame when additional funding can be sought and further development can be pursued without losing the claim to the original invention date. This is especially important in industries like information and communication technology and the life sciences, where development timelines are lengthy and investment-intensive. PPAs serve not only as a cost-effective initial step in patent filing but also as a protective mechanism against premature disclosure, safeguarding the novelty of the invention. PPAs do not undergo formal examination and do not result in a granted patent unless followed by a standard patent application. However, they establish a legal placeholder that can be crucial for securing further patent rights. This study explores the legal framework of PPAs across various jurisdictions. The analysis also delves into the specific benefits and strategic considerations associated with drafting and filing PPAs, including their role in facilitating additional research and refinement of the invention. In addition, it presents novel empirical evidence on the growth of PPAs in the United States and their use as priorities in regular USPTO and EPO patent applications. Despite their advantages, PPAs come with limitations, such as the limited time available to convert them into standard patent applications (typically one year) to benefit from their priority date and the precise requirements for subsequent standard patent applications, which this article addresses. Through a detailed exploration of both the theoretical framework and practical applications of PPAs, the article contributes to a deeper understanding of their role in broader patent strategies, advocating for their careful and informed use in the innovation process.