Search Procedure Research Articles

BWBEV: A Bitwise Query Processing Algorithm for Approximate Prefix Search

We tackle the challenge of conducting an approximate prefix search within datasets of strings. We explore using a bit-parallelism technique to compute the edit distance between distinct strings and illustrate its adaptation for an approximate prefix search procedure referred to as BWBEV. This technique employs a unary representation of edit vectors alongside bitwise operations to efficiently update these vectors during the edit distance computation. We also show how to apply our new bit-parallelism technique strategy to online edit distance computation between strings without index structure. Our experiments with BWBEV applied to approximate prefix search for a query autocompletion task revealed a substantial acceleration of over 36% when contrasted against state-of-the-art methods.

Model for Predicting Maize Crop Yield on Small Farms Using Clusterwise Linear Regression and GRASP

Planting a crop involves several key steps: resource assessment, crop selection, crop rotation, planting schedules, soil preparation, planting, care, and harvesting of crops. In this context, estimating the productivity of a crop based on available information, such as expected climatic conditions and agricultural practices, helps farmers reduce the uncertainty of their investment. In Colombia, maize is the fourth most important crop in the country. Significant efforts are required to improve productivity in traditional and technified production systems. In this sense, this research proposes and evaluates an approach called Clusterwise Linear Regression (CLR) to predict the crop maize yield in small farms, considering data on climate, soil, fertilization, and management practices, among others. To develop the CLR model, we conducted the following steps: data collection and preparation, clustering using k-means, cluster optimization with Greedy Random Adaptive Search Procedure (GRASP), and performance evaluation. The cluster optimization process allows the identification of clusters with similar characteristics and generates multiple linear regression models with mixed variables that explain the yield of the farms on each cluster. The Simulated Multiple Start Annealing (MSSA) metaheuristics were also evaluated, but the results of GRASP were the best. The results indicate that the proposed CLR approach is more effective than the linear and nonlinear algorithms mentioned in the literature, such as multiple lasso linear regression, random forests, XGBoost, and support vector machines. These algorithms achieved an accuracy of 70%. However, with the new CLR model, a significantly improved accuracy of 87% was achieved with test data. The clusters’ studies revealed key factors affecting crop yield, such as fertilization, drainage, and soil type. This transparency is a benefit over black-box models, which can be harder to interpret. This advancement can allow farmers to make better decisions about the management of their crops.

Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm Optimization

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality.

Software architecture-based self-adaptation in robotics

Context:Robotics software architecture-based self-adaptive systems (RSASSs) are robotics systems made robust to runtime uncertainty by adapting their software architectures. The research landscape of RSASS approaches is multidisciplinary and fragmented, with many aspects still unexplored or ineffectively shared among communities involved. Objective:We aim at identifying, classifying, and analyzing the state of the art of existing approaches for RSASSs from the following perspectives: (i) the key characteristics of approaches and (ii) the evaluation strategies applied by researchers. Method:We apply the systematic mapping research method. We selected 37 primary studies via automatic, manual, and snowballing-based search and selection procedures. We rigorously defined and applied a classification framework composed of 32 parameters and synthesize the obtained data to produce a comprehensive overview of the state of the art. Results:This work contributes (i) a rigorously defined classification framework for studies on RSASSs, (ii) a systematic map of the research efforts on RSASSs, (iii) a discussion of emerging findings and implications for future research, and (iv) a publicly available replication package. Conclusion:This study provides a solid evidence-based overview of the state of the art in RSASS approaches. Its results can benefit RSASS researchers at different levels of seniority and involvement in RSASS research.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Prediction of crystal structures and superconductivity of actinium borides at high pressures

Abstract To investigate potential compounds that may emerge in actinium borides at high-pressure conditions. In this work, we carried out systematic crystal structure search procedure on the Ac-B compounds from 0 to 300 GPa conditions based on evolutionary algorithms and density functional theory (DFT). We discovered a stable phase AcB6 at 0 GPa with common structure of hexaboride and three phases AcB, AcB8 and Ac2B11 at higher pressures in the Ac-B system. Our results show that Ac2B11 exhibits a Vickers hardness of approximately 31 GPa and a superconducting transition temperature Tc of 14.4 K at 0 GPa. The structure of Ac2B11 resembles a middle phase evolving from the hexaboride AcB6 towards a class of pentaboride structures, characterized by the formation of a B22 cage structure resulting from further boron enrichment based on the B20 cage, or derived from the partial dimerization of the B24 cage found in hexaborides. We believe that this configuration may hold significant potential as a parent matrix for the design of interesting ultra-hard materials with relatively high superconducting transition temperatures.

A meta-heuristic extension of the Lagrangian heuristic framework

Lagrangian heuristics for discrete optimization work by modifying Lagrangian relaxed solutions into feasible solutions to an original problem. They are designed to identify feasible, and hopefully also near-optimal, solutions and have proven to be highly successful in many applications. Based on a primal-dual global optimality condition for non-convex optimization problems, we develop a meta-heuristic extension of the Lagrangian heuristic framework. The optimality condition characterizes (near-)optimal solutions in terms of near-optimality and near-complementarity measures for Lagrangian relaxed solutions. The meta-heuristic extension amounts to constructing a weighted combination of these measures, thus creating a parametric auxiliary objective function, which is a close relative to a Lagrangian function, and embedding a Lagrangian heuristic in a search procedure in the space of the weight parameters. We illustrate and make a first assessment of this meta-heuristic extension by applying it to the generalized assignment and set covering problems. Our computational experience show that the meta-heuristic extension of a standard Lagrangian heuristic can significantly improve upon solution quality.

Contextual cuing survives an interruption from an endogenous cue for attention.

Three experiments explored how the repetition of a visual search display guides search during contextual cuing under conditions in which the search process is interrupted by an instructional (endogenous) cue for attention. In Experiment 1, participants readily learned about repeated configurations of visual search, before being presented with an endogenous cue for attention towards the target on every trial. Participants used this cue to improve search times, but the repeated contexts continued to guide attention. Experiment 2 demonstrated that the presence of the endogenous cue did not impede the acquisition of contextual cuing. Experiment 3 confirmed the hypothesis that the contextual cuing effect relies largely on localized distractor contexts, following the guidance of attention. Together, the experiments point towards an interplay between two drivers of attention: after the initial guidance of attention, memory representations of the context continue to guide attention towards the target. This suggests that the early part of visual search is inconsequential for the development and maintenance of the contextual cuing effect, and that memory representations are flexibly deployed when the search procedure is dramatically interrupted.

Urogynecologic Care for Women Who Are Incarcerated

In the United States, approximately 180,700 women are incarcerated across jails and prisons, comprising a demographic with an aging population and a higher growth rate compared to men. Despite this demographic trend, research into urogynecologic care for women who are incarcerated is notably lacking, with few studies addressing pelvic floor disorders such as incontinence, and even fewer focusing on access to treatment options like vaginal pessaries or surgical interventions. Women who are incarcerated may face unique challenges in obtaining urogynecologic care, including limited access to medical evaluations, invasive search procedures affecting intravaginal device use, and inadequate hygiene resources, all of which hinder effective management strategies. Barriers to nonsurgical treatments like pelvic floor physical therapy are exacerbated by transportation logistics, while access to surgical interventions is impeded by complex approval processes within correctional facilities. In addition, managing incontinence with limited access to hygiene products and bathroom facilities further compromises the health and dignity of incarcerated women. Urgent attention and advocacy are needed to address disparities in research and clinical care for incarcerated populations. As a first step, we urge clinicians to acquaint themselves with the correctional facilities in their areas and the current health care pathways for women incarcerated in these facilities. It is our collective responsibility to ensure equitable and compassionate care for this vulnerable population.

Experimental Design and Modeling for Forward-Inverse Maps

In customer-driven design of systems or products, one has performance targets in mind and would like to determine values for product or system parameters that meet such targets. Engineering computer simulation models predict performance given design parameter values; meeting a target is done iteratively through an optimization search procedure, typically by optimizing a regression, neural network or other type of approximation metamodel of the computer model. We pose the thesis that, since forward metamodel construction is a key part of this strategy, constructing an inverse metamodel directly is advantageous. The inverse metamodel obviates the need for optimization in many settings. One can design experiments that allow simultaneous fitting of forward and inverse metamodels. We discuss the potential for this strategy, the connection with the calibration problem, and some of the issues that must be resolved to make the approach practical.

Employment Effects of Skills Trainings in Sub-Saharan Africa: A Systematic Review of Recent Randomized Controlled Trials

AbstractThis study provides a comprehensive systematic review of recent randomized controlled trials (RCTs) to evaluate the employment effects of skills training programs in sub-Saharan Africa. The review focuses on studies conducted between 2019 and 2024, a period marked by a significant increase in the number of RCTs of training interventions in this region, and we thus fill a gap left by earlier reviews that did not reflect this recent surge in experimental studies. We employ the standard SPIDER approach for defining search terms and the PRISMA procedure for search and selection to systematically analyze the impact of these training programs on employment outcomes. The findings reveal a shift towards a more optimistic assessment compared to previous analyses, suggesting that many recent skills training programs do contribute to improving employment-related outcomes.

Modified Double Inertial Extragradient-like Approaches for Convex Bilevel Optimization Problems with VIP and CFPP Constraints

Convex bilevel optimization problems (CBOPs) exhibit a vital impact on the decision-making process under the hierarchical setting when image restoration plays a key role in signal processing and computer vision. In this paper, a modified double inertial extragradient-like approach with a line search procedure is introduced to tackle the CBOP with constraints of the CFPP and VIP, where the CFPP and VIP represent a common fixed point problem and a variational inequality problem, respectively. The strong convergence analysis of the proposed algorithm is discussed under certain mild assumptions, where it constitutes both sections that possess a mutual symmetry structure to a certain extent. As an application, our proposed algorithm is exploited for treating the image restoration problem, i.e., the LASSO problem with the constraints of fractional programming and fixed-point problems. The illustrative instance highlights the specific advantages and potential infect of the our proposed algorithm over the existing algorithms in the literature, particularly in the domain of image restoration.

An advanced symbol detection approach for MIMO-FBMC/OQAM scheme based on migrating birds optimization algorithm

Application of multiple-input multiple-output (MIMO) technology to the filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) system provides great improvements on its robustness to channel fading effects. Nevertheless, the unique qualities of MIMO-FBMC/OQAM scheme do not make any sense without solving the symbol detection problem on its receiver part. To this end, an efficient symbol detecting strategy having the capability of recovering the transmitted symbol sequences with the highest accuracy is needed for the related system. Maximum likelihood (ML) detector is known with its excellent symbol detection performance in the literature. However, due to the usage of exhaustive search procedure, the computational complexity of the ML detector reaches extremely high levels with the increase of antenna size and modulation order. On the other hand, in the case that the exhaustive search procedure is substituted with the symbol optimization process, it becomes possible to achieve a large amount of complexity reduction in the conventional ML scheme without compromising too much on its symbol detection performance. In order to carry out an efficient symbol optimization in discrete space, we propose migrating birds optimization (MBO) algorithm based on cyclic bit flipping procedure in this paper. By virtue of employing the proposed MBO algorithm reinforced by the cyclic bit flipping mechanism for optimizing the symbol sequences, the computational complexity of the conventional ML is reduced to quite low levels. The percentages of complexity reduction achieved by the proposed scheme over the classical ML for 4 × 4, 6 × 6 and 8 × 8 MIMO configurations are equal to 29.688%, 87.188% and 98.299%, respectively. In addition to these huge complexity gains, an efficient symbol detection performance quite near to that of conventional ML is obtained thanks to the usage of aforementioned MBO-based symbol optimization procedure.

Hierarchical porous metamaterial for sound absorption

Absorption of sound is of great importance in several engineering applications, from room acoustics to engine noise reduction. Porous media are practical and commonly used to address this issue. However, their performance is limited at large wavelengths, where more material is required. Inspired by recursive patterns at multiple scale lengths, typical of many biological systems, we propose here a new design based on hierarchical multilayered porous materials. Two hierarchical designs are investigated: periodic and gradient. In both cases it is found that the increase of the hierarchy level allows to simultaneously (i) increase the absorption, and (ii) reduce the quantity of porous material required. Both the cases of normal and oblique incidences are examined. The absorption coefficient is calculated via the transfer matrix method and the finite element method, supporting measurements in an impedance tube. The porous media are described through the JCA model. An optimization algorithm based on the metaheuristic Greedy Randomized Adaptive Search Procedure is developed to optimize the organization of the porous layers, and practical tips are given on the most suitable choice of parameters (static air-flow resistivity, angle of incidence) to obtain the highest absorption amplification at desired low frequencies.

LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection Sensors

In this paper, the concept of symmetry is used to design the efficient inference of a fall-detection algorithm for elderly people on embedded processors—that is, there is a symmetric relation between the model’s structure and the memory footprint on the embedded processor. Artificial intelligence (AI) and, more particularly, Long Short-Term Memory (LSTM) neural networks are commonly used in the detection of falls in the elderly population based on acceleration measures. Nevertheless, embedded systems that may be utilized on wearable or wireless sensor networks have a hurdle due to the customarily massive dimensions of those networks. Because of this, the algorithms’ most popular implementation relies on edge or cloud computing, which raises privacy concerns and presents challenges since a lot of data need to be sent via a communication channel. The current work proposes a memory occupancy model for LSTM-type networks to pave the way to more efficient embedded implementations. Also, it offers a sensitivity analysis of the network hyper-parameters through a grid search procedure to refine the LSTM topology network under scrutiny. Lastly, it proposes a new methodology that acts over the quantization granularity for the embedded AI implementation on wearable devices. The extensive simulation results demonstrate the effectiveness and feasibility of the proposed methodology. For the embedded implementation of the LSTM for the fall-detection problem on a wearable platform, one can see that an STM8L low-power processor could support a 40-hidden-cell LSTM network with an accuracy of 96.52%.

Scheduling multi-skill technicians and reassignable tasks in a cloud computing company

We investigate a multi-skill technician and reassignable task scheduling problem in a cloud computing company. In the problem, multi-skill technicians are assigned to process a large number of tasks from customer requests in a certain scheduling horizon. The tasks are allowed to be reassigned to another technician multiple times, and one technician can process multiple tasks in parallel. The company not only focuses on processing efficiency, but also expects to improve customers’ experience and technicians’ satisfaction. We characterize the feasible solutions and introduce a weighted objective with three metrics: processing efficiency, response delay, and workload balance. An effective two-stage hierarchical optimization method embedded in a greedy randomized adaptive search procedure framework is proposed. In the first stage, initial solutions are generated by a greedy randomized construction procedure, and then improved by local search with an ejection chain operator to optimize processing efficiency. In the second stage, two local search procedures with five operators for improving response delay or workload balance are designed. Computational experiments are conducted to evaluate the effectiveness of our algorithm. The results show that the proposed algorithm is competent in fast computing a schedule of high quality. It also reveals that reassignments are helpful in reducing response delay and balancing workloads in the scheduling.

Efficacy of Different Warming Methods for Perioperative Hypothermia Prevention on Body Temperature and Shivering Among Transurethral Resection Patients: A Systematic Review and Meta-Analysis Study

PurposeThe present study attempts to assess the impacts of distinct warming strategies employed to avert perioperative hypothermia on core body temperature and shivering among transurethral resection patients. MethodsWe performed a comprehensive search of Turkish and English keywords across a range of databases, including PUBMED, Web of Science, Cochrane Library, SCOPUS, Ovid, EBSCOhost, Yöktez, DergiPark, and TR Index, to identify studies on the subject published between January 1, 2001-2024. The search procedure yielded 11 studies to be recruited for meta-analysis. We analyzed the data using the trial version of Comprehensive Meta-Analysis Software (CMA). The degree of heterogeneity was quantified using Higgins I2 tests, while we assessed publication bias through Kendall’s Tau and Egger’s regression analyses. ResultsThis meta-analysis focuses exclusively on the effects of forced air warming and irrigation fluid warming, as only these methods provided sufficient data to calculate effect size. Our findings demonstrated that while forced air warming significantly affected preoperative (g = 0.279), intraoperative (15th minute, T4) (g = 0.845), postoperative (g=0.647) body temperature and postoperative shivering (g = -2.279), irrigation fluid warming had a significant impact on postoperative body temperature (g = 1.007) and intraoperative shivering (g = 0.188). ConclusionOverall, our results indicated that forced air warming and irrigation fluid warming can serve as a preventive measure against hypothermia and shivering among transurethral resection patients. Given that other forms of warming could not be included in this research, further studies are recommended to explore scholarly evidence on the efficacy of these methods.

Truss sizing optimum design using a metaheuristic approach: Connected banking system

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the “No Free Lunch theorem”. Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

НЕЙРОМЕРЕЖЕВІ МАТЕМАТИЧНІ МОДЕЛІ ЛЮДИНОМАШИННОГО СПІЛКУВАННЯ ТА РОЗПІЗНАВАННЯ

The study is devoted to the neural network interpretation of the task of human-machine communication and recognition by multiple criteria, considered as a task of assignment. The main goal is to reduce this problem to a standard form, where the number of criteria groups is equal to the number of ranked documents. The study defined the architecture of a neural network and proposed the use of a network of binary neurons, which is a matrix of a certain dimension. The proposed ranking model is based on a neural network that contains arbitrary feedback. This allows the excitation to be transmitted back to the neuron, which contributes to the repeated performance of its function. However, in dynamic neural networks instability occurs, which is manifested in a random change in the states of neurons without reaching stationary states. The question of stability of the dynamics of such systems remains open. The considered discrete Hopfield neural network has the following characteristics: one layer of elements, each element is connected to all others, but not to itself; only one element is updated per stage; elements are updated in random order, but each is updated with the same frequency; the output function is binary (value "0" or "1"). A Hopfield neural network is recurrent: the output of the network is reused as input until a steady state is reached. After starting, the neural network changes its state, gradually moving to a stable mode, which allows identifying a plan for evaluating the process of human-machine communication according to a set of criteria. Random search procedures are used to refine the results. The proposed energy function is minimized to ensure that the constraints are met and the problem is solved. The constructed function reaches a minimum only in the states corresponding to the assignment plans. The definition of the network parameters is carried out by comparing the obtained functions with the energy function in general form. The practical implementation of the model demonstrated that Hopfield's neural network can be successfully applied to document ranking in human-machine communication and recognition systems, providing high accuracy and efficiency in solving ranking problems.

Scheduling as a Constraint Satisfaction Problem (Using the Example of Open-Pit Minе Production Scheduling Problem)

The research described in the work is aimed at developing methods for Scheduling. The fundamental disadvantage of the existing methods of Mixed-Integer Linear Programming in application to the problems under consideration is the fact that they are too demanding on the amount of RAM. The difficulty of applying local search procedures to such high-dimensional problems is to develop an effective way to find an acceptable initial approximation and determine the neighboring state transition function, which would allow achieving the optimum fast enough. In the Operations Research Theory, adding additional conditions to a problem can lead to a fundamental change in the problem-solving scheme. The methods proposed in the study are implemented within the framework of the Constraint Programming Paradigm which makes it possible to represent the subject domain dependencies saving RAM, as well as to provide the ability to step-by-step take into account heterogeneous problem conditions without essentially changing the scheme of finding solutions. A significant part of the research deals with methods of logical inference on constraints to reduce the search space and speed up the computational process. The approach to scheduling is illustrated by the Open-Pit Mine Production Scheduling Problem, which was first proposed to be solved as a Constraint Satisfaction Problem. In order to find the first feasible solution, a «greedy» search method is proposed, the result of which can be improved using the developed hybrid method. Both methods rely on original procedures of inference on constraints. The proposed approach has proven its efficiency for block models with sizes of tens and hundreds of thousands of blocks.

Greedy randomized adaptive search procedure for supply chain network resilience optimization considering risk diffusion

Supply disruption risks occurring at individual nodes in a supply chain network can propagate to other nodes through the links in the network affecting the overall performance of the supply chain. In this context, mitigation strategies are commonly implemented to respond to these disruptions and improve the resilience of the supply chain. This study proposes a mathematical model to optimize the resilience of the supply chain network by selecting critical nodes and identifying the appropriate type of mitigation strategies to be implemented under a limited budget. The resilience of the supply chain network is evaluated by the degree of propagation of risks from individual nodes to the entire supply chain network, using a risk diffusion model where a disruption risk decreases exponentially as it progresses downstream in the supply chain. A greedy randomized adaptive search procedure (GRASP)-based solution approach was developed to solve the proposed formulation and its performance was compared to other solution procedures for different scenarios showing it to be the most effective and robust. A summary of the insights developed from the solutions obtained for the different scenarios is presented along with directions for future research.