Parallel Search Research Articles

Robust 3D coordinate transformation based on genetic algorithm intelligent weighting

In order to improve the solution quality of 3D coordinate transformation parameters, a robust optimization model is obtained by integrating the 3D coordinate transformation model within the overall framework of the genetic algorithm in this paper. The minimum of the weighted sum of squares of the point residuals of the common points is taken as the objective function for optimization, and the parallel search is started from the initial population, and the updating process of the solution set is used to generate new and better individuals, and the adaptive weight combination corresponding to the optimum of the fitness function is obtained at the end of the generation iteration. The generated simulated data and measured data were analyzed using the Hefei Light Source tunnel control network as an application scenario. The results show that the method in this paper can automatically reduce the influence of low-quality data on the model, and compared with the two robust conversion models of IGG III iterative weighting, the method in this paper is not affected by the empirical parameters in determining the weight combinations and the weighted sum of squares of the residuals of the solved points are smaller, so it can effectively improve the quality of the solved conversion parameters.

A parallel deep adaptive large neighbourhood search algorithm for distributed heterogeneous hybrid flow shops with mixed-model assembly scheduling

ABSTRACT Nowadays, manufacturing enterprises must have fast response and flexible production capabilities to meet personalized and diversified market demands. Mixed-model production and distributed production have become the preferred production methods for enterprises. This article studies a distributed heterogeneous hybrid flow shop scheduling problem with a mixed-model assembly line (DHHFSP-MMAL), which consists of manufacturing and assembly stages. The DHHFSP-MMAL is modelled by a mixed integer linear programming (MILP) model. Three constructive heuristics and a parallel deep adaptive large neighbourhood search (PDALNS) problem are presented. A constructive heuristic with a group strategy is employed to obtain an initial solution. Several deep destroy-and-repair operators are proposed where problem-specific greedy local search methods are applied. The PDALNS assigns weights to destroy-and-repair operators to guide the selection of operators. The parallel computing technique is introduced to increase the efficiency of training. The experiments demonstrate that the PDALNS algorithm is an efficient and effective algorithm for solving the DHHFSP-MMAL problem.

CMOS-Integrated Ternary Content Addressable Memory using Nanocavity CBRAMs for High Sensing Margin.

The development of data-intensive computing methods imposes a significant load on the hardware, requiring progress toward a memory-centric paradigm. Within this context, ternary content-addressable memory (TCAM) can become an essential platform for high-speed in-memory matching applications of large data vectors. Compared to traditional static random-access memory (SRAM) designs, TCAM technology using non-volatile resistive memories (RRAMs) in two-transistor-two-resistor (2T2R) configurations presents a cost-efficient alternative. However, the limited sensing margin between the match and mismatch states in RRAM structures hinders the potential of using memory-based TCAMs for large-scale architectures. Therefore, this study proposes a practical device engineering method to improve the switching response of conductive-bridge memories (CBRAMs) integrated with existing complementary metal-oxide-semiconductor (CMOS) transistor technology. Importantly, this work demonstrates a significant improvement in memory window reaching 1.87 × 107 by incorporating nanocavity arrays and modifying electrode geometry. Consequently, TCAM cells using nanocavity-enhanced CBRAM devices can exhibit a considerable increase in resistance ratio up to 6.17 × 105, thereby closely approximating the sensing metrics observed in SRAM-based TCAMs. The improved sensing capability facilitates the parallel querying of extensive data sets. TCAM array simulations using experimentally verified device models indicate a substantial sensing margin of 65× enabling a parallel search of 2048 bits.

Design and optimization of a combined supercritical CO2 recompression Brayton/Kalina cycle by using solar energy and LNG cold energy

Solar energy and LNG cold energy have broad application scenarios as two renewable sources, but their complementary potential has not been fully explored. To fill this gap, this paper constructs a novel combined cycle that utilizes solar energy and recovers LNG cold energy to generate power. This combined cycle consists of a supercritical carbon dioxide recompression Brayton cycle, an ammonia-water mixture Kalina cycle, and an LNG cryogenic power system. A systematic evaluation was conducted on the thermodynamics and exergoeconomic of the model, and the simulation results under basic operating conditions show that the first law efficiency, second law efficiency, net power output and total unit cost reach 55.73 %, 47.72 %, 13.709 MW and 27.44 $·GJ−1 respectively. The genetic algorithm and elitist non-dominated sorting genetic algorithm are used to optimize the system performance. By using the parallel direct search method, the maximum first law efficiency, as well as second law efficiency and a minimum total unit cost of 59.45 %, 52.23 % and 24.81 $·GJ−1 are obtained respectively. The optimal first law efficiency, second law efficiency, and total unit cost of the system, which are 53.52 %, 50.33 % and 26.44 $·GJ−1 respectively, are searched simultaneously from the multi-objective optimization results by LINMAP decision-making method. Furthermore, this paper analyzes the exergy flow rate of the system and heat transfer processes of the heat exchangers under optimal operating condition. A total of 23,059.35 kW of LNG cold energy is fully utilized by the system in the LNG gasification process. The exergy loss ratio and cost ratio of each equipment analysis demonstrates that the heat exchangers (HX) have the largest percentage both exergy loss ratio and cost ratio, especially the HX4.

Quantum Implementation of AIM: Aiming for Low-Depth

Security vulnerabilities in the symmetric-key primitives of a cipher can undermine the overall security claims of the cipher. With the rapid advancement of quantum computing in recent years, there is an increasing effort to evaluate the security of symmetric-key cryptography against potential quantum attacks. This paper focuses on analyzing the quantum attack resistance of AIM, a symmetric-key primitive used in the AIMer digital signature scheme. We present the first quantum circuit implementation of AIM and estimate its complexity (such as qubit count, gate count, and circuit depth) with respect to Grover’s search algorithm. For Grover’s key search, the most important optimization metric is depth, especially when considering parallel search. Our implementation gathers multiple methods for a low-depth quantum circuit of AIM in order to reduce the Toffoli depth and full depth (such as the Karatsuba multiplication and optimization of inner modules; Mer, LinearLayer).

Parallel Beam Search Algorithms for Domain-Independent Dynamic Programming

Domain-independent dynamic programming (DIDP), a model-based paradigm based on dynamic programming, has shown promising performance on multiple combinatorial optimization problems compared with mixed integer programming (MIP) and constraint programming (CP). The current DIDP solvers are based on heuristic search, and the state-of-the-art solver, complete anytime beam search (CABS), uses beam search. However, the current DIDP solvers cannot utilize multiple threads, unlike state-of-the-art MIP and CP solvers. In this paper, we propose three parallel beam search algorithms and develop multi-thread implementations of CABS. With 32 threads, our multi-thread DIDP solvers achieve 9 to 39 times speedup on average and significant performance improvement over the sequential solver, finding the new best solutions for two instances of the traveling salesperson problem with time windows. In addition, our solvers outperform multi-thread MIP and CP solvers in four of the six combinatorial optimization problems evaluated.

NeuronBridge: an intuitive web application for neuronal morphology search across large data sets

BackgroundNeuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome’s structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.ResultsHere, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.ConclusionsNeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org.

Prediction of Ship Painting Man-Hours Based on Selective Ensemble Learning

The precise prediction of painting man-hours is significant to ensure the efficient scheduling of shipyard production and maintain a stable production pace, which directly impacts shipbuilding cycles and costs. However, traditional forecasting methods suffer from issues such as low efficiency and poor accuracy. To solve this problem, this paper proposes a selective integrated learning model (ISA-SE) based on an improved simulated annealing algorithm to predict ship painting man-hours. Firstly, the improved particle swarm optimization (MPSO) algorithm and data grouping techniques are employed to achieve the optimal selection and hyperparameter optimization of base learners, constructing a candidate set of base learners. Subsequently, the simulated annealing algorithm is improved by adding random perturbations and using a parallel perturbation search mechanism to enhance the algorithm’s global search capability. Finally, an optimal set of base learners is composed of the candidate set utilizing the ISA-SE model, and a heterogeneous ensemble learning model is constructed with the optimal set of base learners to achieve the precise prediction of ship painting man-hours. The results indicate that the proposed ISA-SE model demonstrates improvements in accuracy, mean absolute error, and root mean square error compared to other models, validating the effectiveness and robustness of ISA-SE in predicting ship painting man-hours.

A parallel variable neighborhood search for [formula omitted]-neighbor facility location problems

In this paper, we employ the less is more approach to develop a Parallel Variable Neighborhood Search (VNS) algorithm for the α-neighbor p-center problem (αNpCP) and the α-neighbor p-median problem (αNpMP). The αNpCP and the αNpMP are generalizations of the p-center (pCP) and p-median (pMP) problems, respectively. In the α-neighbor problems, one seeks to open p facilities and assign each of the n customers to their closest α ones. The objective is to minimize the maximum distance of a customer to its αth facility, in the case of the αNpCP, and the sum of the distances from each customer to their α nearest facilities, in the case of the αNpMP. Our VNS adapts simple but efficient algorithms and data structures from the pCP and pMP literature to the αNpCP and αNpMP context. We also introduce an updated objective function for the αNpCP, which adds more information to the solution cost and helps the VNS to escape from local optima. Several experimental tests show that our VNS outperforms more complex state-of-the-art algorithms. Regarding the αNpCP, on 120 instances derived from the OR-library set, our algorithm improved best-known solutions for 22, with an average improvement of 34.26%; the overall gap on the 120 instances is 6.18% in favor of our algorithm. Moreover, on 231 instances derived from the TSPLIB set, we improved the solutions for 115, with an average improvement of 5.30%, and an overall improvement gap of 2.47% for all 231 instances. Considering the αNpMP results, our heuristic obtained better results than a heuristic from literature in all 80 instances tested, finding optimal solutions in all these instances.

Search engine Performance optimization: methods and techniques

Background With the rapid advancement of information technology, search engine optimisation (SEO) has become crucial for enhancing the visibility and relevance of online content. In this context, the use of cloud platforms like Microsoft Azure is being explored to bolster SEO capabilities. Methods This scientific article offers an in-depth study of search engine optimisation. It explores the different methods and techniques used to improve the performance and efficiency of a search engine, focusing on key aspects such as result relevance, search speed and user experience. The article also presents case studies and concrete examples to illustrate the practical application of optimisation techniques. Results The results demonstrate the importance of optimisation in delivering high quality search results and meeting the increasing demands of users. Conclusions The article addresses the enhancement of search engines through the Microsoft Azure infrastructure and its associated components. It highlights methods such as indexing, semantic analysis, parallel searches, and caching to strengthen the relevance of results, speed up searches, and optimise the user experience. Following the application of these methods, a marked improvement was observed in these areas, thereby showcasing the capability of Microsoft Azure in enhancing search engines. The study sheds light on the implementation and analysis of these Azure-focused techniques, introduces a methodology for assessing their efficacy, and details the specific benefits of each method. Looking forward, the article suggests integrating artificial intelligence to elevate the relevance of results, venturing into other cloud infrastructures to boost performance, and evaluating these methods in specific scenarios, such as multimedia information search. In summary, with Microsoft Azure, the enhancement of search engines appears promising, with increased relevance and a heightened user experience in a rapidly evolving sector.

Пермь – земля задняя

The study serves as a follow-up to the article that provided a historiographical review of the debate surrounding the etymology of the toponym Perm. About 40 geographical objects with Permian names found on the territory of Finland and 13 regions of the Russian Federation were involved in the historical-geographical research. Various kinds of “back lands” (Perm) are evenly distributed throughout the studied territory, and parallel searches for “front lands” did not yield results, which forces us to question the version of a drifting toponym. At the same time, an abundance of Permian hydronyms is recorded, although this should seem paradoxical, because Perm is the “back land”. The use of historical-geographical methods made it possible to clarify the etiology (origin) of the names of the Permian series, which should be understood not just as the back ground, but the land on the river bottoms, in the upper reaches of rivers, in areas of concentration of overland sections of waterways. Portages of varying intensity of use are recorded in the vicinity of 30 “perm”, in 19 cases it was not possible to detect portages. Exceptions to the rule are fixed in the southeastern periphery of the studied area, in villages and villages named Permyaki and Perminov, which are nominated by origin or by the surname of the first settlers. In ancient times, the most important transit and transport interchanges were carefully fixed on the ground by Perm toponyms and hydronyms, which is why the areas of concentration of the most important transport routes (Perm and Permtsa) attracted special attention of the Novgorod and later Moscow authorities as nodes of strength in the colonized territory.

Enhanced Global Optimization With Parallel Global and Local Structures for Real-Time Control Systems

In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To efficiently optimize such systems, in this paper, we develop a parallel global optimization framework that combines direct search methods with parallel Bayesian optimization. It consists of an iterative global and local search that searches broadly through the entire global space for promising regions and then efficiently exploits each local promising region. We prove the asymptotic convergence properties of the proposed framework and conduct several numerical experiments to illustrate its empirical performance. We also provide a real-time control problem to illustrate the efficiency of our proposed algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by a collision avoidance problem of vessels aided with onboard agent-based simulations. The simulation on one vessel can predict potential conflicts with other vessels on a pre-defined trajectory. In heavy congestion regions, the environment is highly dynamic and thus it is difficult to find a much safer alternative trajectory if collision is predicted on the current one. Moreover, for such real-time decisions, the control system should be quick in response to improve safety. The proposed metamodel based algorithm is designed for quick decision in such highly dynamic systems. The algorithm employs a decomposition of the response surface to better handle the multi-modal surface resulting from the highly dynamic environment. Specifically, it first looks at the large-scale trend globally (filter out the many local fluctuations that may otherwise trap the algorithm) to locate potential promising regions and then proceeds to this local regions for more detailed local search. To make quick decisions, it uses fast direct search algorithms in the local search phase and applies a parallel search scheme to enjoy the abundant computing power. Both the theoretical analysis and the simulation studies demonstrate that the proposed algorithm can provide better decisions quickly. We also note that this algorithm is not limited to real-time control or simulation-based system. In the case where each run of the experiment is expensive and the budget is limited for the final decision and when the response function is multi-modal, this algorithm can hopefully become a quite efficient and competitive approach. The multi-modal responses have broad applications in the area of control, planning and operations research, such as robot navigating and reinforcement learning.

UAV search coverage under priority of important targets based on multi-location domain decomposition

&lt;abstract&gt; &lt;p&gt;In recent years, the coverage path planning (CPP) of unmanned aerial vehicles (UAVs) has attracted attention in reconnaissance, patrol, and search and rescue efforts, aiming to plan the paths for UAVs to cover a specified area as efficiently as possible. This paper proposes a UAV path fast coverage model to prioritize important targets with domain composition based on the starting point and location of the targets, combined with the domain decomposition strategy of important targets. Considering the constraints of the number of UAVs, the number of operators, and the flight time, the parallel search strategy can plan the coverage scheme with the shortest search time for the search range, and further obtain the coordinate points and path coordinates of the UAV turning. Finally, through multiple simulation experiments in four maps of various islands, the proposed method is verified to have an improved performance compare to the two track path coverage algorithms methods in terms of the coverage efficiency and the time complexity, thus providing a more scientific basis for the path coverage research of multi-target searches.&lt;/p&gt; &lt;/abstract&gt;

Low-carbon optimal scheduling for multi-source power systems based on source-load matching under active demand response

To reduce the source-load uncertainty and carbon emission levels of the power system, this study proposes a novel low-carbon economic stochastic optimization scheduling model. Firstly, to partially compensate for the instability of wind power (WP) and photovoltaic power (PV) generation, enhance energy efficiency, and reduce system carbon emissions, the introduction of concentrating solar power (CSP) as one of the power sources on the generation side. Secondly, to optimize load characteristics, a peak–valley time period division method based on fuzzy theory and an active demand response model for electric load using the Logistic fuzzy function are introduced on the load side. Thirdly, taking into account the role of the carbon trading market, a ladder-type carbon trading mechanism (LCTM) is introduced to control carbon emissions and reduce overall economic costs. Finally, considering WP-PV output fluctuations, the degree of matching between power output and load, WP-PV utilization, system economic efficiency, and carbon trading factors, with the objective of maximizing the tracking of the load curve and minimizing comprehensive system economic costs, a multi-objective stochastic optimized scheduling model for power systems is established. A multi-objective Runge–Kutta algorithm based on a population-based parallel search mechanism (PSMORUN) is proposed to solve the active demand response model and the low-carbon stochastic optimization scheduling model. Additionally, constraint repair techniques are employed to handle the complex constraints of the models. To verify the effectiveness of the proposed model, a 10-generator power system, including a WP farm, a PV plant, and a CSP plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the proposed stochastic optimal scheduling model has higher safety, better economy, and lower carbon emissions.

Partial‐depth search and search pattern comparisons

AbstractThis paper compares consumers' optimal search behaviors and firms' strategic reactions in prices under two modes of search: parallel search and sequential search, with the incorporation of partial search depth that measures the amount of information to evaluate a product. Our analysis shows a series of new results: when products are partially evaluated, search depth is greater under sequential search than under parallel search; consumers search for (on average) more products either under parallel search or sequential search, depending on whether partial‐depth search is chosen; the equilibrium price is higher under parallel (sequential) search when search costs are high (low). Interestingly, parallel search might yield higher equilibrium prices with more products searched, compared with sequential search. This challenges the conventional wisdom in the search literature that more products searched lower prices because of the intensified competition.

Parallel path detection for fraudulent accounts in banks based on graph analysis.

This article presents a novel parallel path detection algorithm for identifying suspicious fraudulent accounts in large-scale banking transaction graphs. The proposed algorithm is based on a three-step approach that involves constructing a directed graph, shrinking strongly connected components, and using a parallel depth-first search algorithm to mark potentially fraudulent accounts. The algorithm is designed to fully exploit CPU resources and handle large-scale graphs with exponential growth. The performance of the algorithm is evaluated on various datasets and compared with serial time baselines. The results demonstrate that our approach achieves high performance and scalability on multi-core processors, making it a promising solution for detecting suspicious accounts and preventing money laundering schemes in the banking industry. Overall, our work contributes to the ongoing efforts to combat financial fraud and promote financial stability in the banking sector.

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In this paper, the global static path is planned using a parallel line scan search. When encountering unknown obstacles, the improved Bug algorithm is used for local dynamic path planning according to the sensor detection information. This paper first sets up the safe expansion area to ensure the safety of the USV during the obstacle avoidance process and optimizes the movement direction considering the operation and behavior characteristics of the USV. To meet the requirement of USV steering, the Bezier curve is used to smooth the path points, which greatly improves the smoothness of the path. In this paper, the multi-mode switching strategy of the Bug algorithm based on obstacle boundary width obtained by the sensor is proposed, which ensures no area omissions and meets the requirement of search area coverage during the process of bypassing obstacles. The simulation results show that the improved Bug algorithm can maintain a safe distance along the obstacle boundary to bypass the obstacle. Moreover, the improved Bug algorithm effectively improves the path oscillation phenomenon of traditional Bug and shortens the path length and operating time. Finally, through the global search path planning simulation and comparison experiments, the effectiveness of the proposed method is verified.

A concurrent design optimization framework for IMSFRP composite structures considering material and structural parameters simultaneously

Injection molded short fiber reinforced polymer (IMSFRP) composite is a widely used lightweight material in the automobile industry. Due to the integrated molding characteristics of IMSFRP composite structures, its lightweight potential can be fully exploited through the simultaneous design of material and structural parameters. However, the efficient material-structure parallel optimization design method for IMSFRP composite structures has not been fully investigated. In this study, a framework of lightweight design containing the concurrent optimization of material and structure parameters is proposed, and it has been successfully applied to an automobile IMSFRP composite liftgate inner. Firstly, considering the skin-core-skin (SCS) structure of the material, a layered model is built up and a parameterized constitutive model is established to efficiently obtain material properties for various material parameters. Then, a parameter extraction and mapping method is proposed to map fiber distribution information to the structural analysis model to improve the accuracy of structural analysis. In addition, based on the Kriging model and a newly developed parallel boundary search particle swarm optimization algorithm, a lightweight design framework of composite liftgate inner is proposed to consider the material and structural design variables simultaneously. The results show that the proposed framework can solve the concurrent optimization design problem effectively and achieve a weight reduction of 10.5 % for the composite liftgate inner under multiple working conditions.

An Improved Method to Search All Minimal Paths in Networks

Minimal paths (MPs) play an important role in system reliability analysis, such as binary network reliability evaluation and multistate network reliability evaluation. Searching all MPs is an NP-hard problem. The direct-search-based algorithm implements the depth-first search (DFS) mechanism to find all MPs, which is a simple and efficient method. The current efficient DFS algorithm is based on backtracking and linked list by nodes. In this article, we propose several improvements to speed up the current algorithm. First, we find that the relative positional information of the network can be used to avoid visiting some unnecessary nodes. Thus, we improve the algorithm by incorporating the point in the polygon algorithm to avoid visiting nodes surrounded by other nodes in a network. Second, we further improve the current backtracking condition in which the time complexity of implementing each backtrack is reduced. In addition, we find that different search branches from a similar node are independent and can be executed in parallel. A parallel search mechanism is proposed and incorporated to speed up the searching process. Through computational experiments on several benchmark networks and real transportation networks, we demonstrate that the improved algorithm is more efficient than the existing algorithm. The proposed algorithm becomes more advantageous as the size of the network grows.

Patient-Reported Outcome Measures Used in Randomized Controlled Trials Following Surgical Intervention for Endometriosis: A Structured Review from the AAGL Practice Guidelines Group

ObjectiveNo consensus currently exists regarding patient-reported outcome measure (PROMs) instruments. This structured review was conducted to identify the PROMs used by randomized controlled trials (RCTs) that evaluated surgical treatment in patients with endometriosis. Data sourcesTwo parallel searches were conducted by a medical librarian using Ovid Medline, Ovid Embase, and Cochrane Library for RCTs published from 2000 to July 2022. One search focused on studies reporting quality of life (QoL), and the second search focused on studies reporting pain and sexual, bowel, and bladder function. Method of study selectionDuring the title and abstract screening and reference check, 600 results were identified on PROMs relating to QoL and 465 studies on PROMs relating to pain, and sexual, bowel and/or bladder function, and an evaluation of 17 and 12 studies conducted, respectively. The inclusion criteria involved selecting randomized controlled trials that focused on surgical intervention and assessing QoL, pain, and sexual, bowel and/or bladder function using patient-reported outcome measures. Tabulation, integration & resultsCovidence software was used to organize and identify duplicate articles through screening. We developed a data extraction form to collect key information about each included study, as well as the pertinent PROMs used in the study. Assessment of the risk of bias of each study was also performed. A total of 19 studies were identified involving 2089 participants and a total of 16 PROMs used across the studies. 9/19 (47%) of studies were rated as having a low risk of bias. There were no high-risk studies identified in this review. ConclusionThis study identified a large number of RCTs in surgical treatment of endometriosis that used various PROMs to assess QoL, pain, bladder, bowel and sexual function. The PROMs used by high-quality RCTs for QoL include EHP-30, EHP-5, SF-36, SF-12, EQ-5D; for bowel-related symptoms KESS, GIQLI and CCIS/Wexner; for bladder-related function BFLUTS, IPSS, PISQ- 12 and USP; and finally for sexual function, PISQ-12 and SAQ. Unlike other domains, only one tool (Visual Analogue Scale; VAS) was the dominant PROM used for the assessment of pain. Additionally, the use of more than one PROM in each study to assess different aspects of patient's health and pain symptoms did not become prevalent until after 2015.