Search Method Research Articles

Proximal Policy Optimization with Population-based Variable Neighborhood Search Algorithm for Coordinating Photo-Etching and Acid-Etching Processes in Sustainable Storage Chip Manufacturing

In the complex process of manufacturing storage chips, the photo-etching and acid-etching stages play a crucial role, significantly affecting energy consumption and environmental impact. This paper introduces a novel Bi-Level Programming Model for Storage Chip Manufacturing (BLPM-SCM) aimed at optimizing the coordination between these two stages. The upper-level model focuses on minimizing the time it takes to complete wafer production, while the lower-level model seeks to reduce the number of acid-etching tanks used, thereby balancing production efficiency with resource utilization. To address the inherent complexity of the bi-level model, we present a hybrid meta-heuristic algorithm that combines Proximal Policy Optimization (PPO) with a Population-based Variable Neighborhood Search (PVNS) method. The PPO-PVNS algorithm enhances the intensification phase by adaptively selecting shaking and local search strategies, while PVNS supports the diversification phase, ensuring comprehensive exploration of the search space through iterative updates of the solution population. Extensive numerical experiments demonstrate the algorithm's superior performance and generalization capabilities in optimizing the manufacturing process. It significantly improves the coordination between the photo-etching and acid-etching stages, achieving dual optimization of energy consumption and environmental benefits. Furthermore, this study provides valuable insights and decision-making tools for industry practitioners, offering innovative solutions for scheduling optimization in the semiconductor sector and promoting more sustainable and efficient production practices.

A novel automated neural network architecture search method of air target intent recognition

Modern air battlefield operations are characterized by flexibility and change, and the battlefield evolves rapidly and intricately. However, traditional air target intent recognition methods, which mainly rely on manually designed neural network models, find it difficult to maintain sustained and excellent performance in such a complex and changing environment. To address the problem of the adaptability of neural network models in complex environments, we propose a lightweight Transformer model (TransATIR) with a strong adaptive adjustment capability, based on the characteristics of air target intent recognition and the neural network architecture search technique. After conducting extensive experiments, it has been proved that TransATIR can efficiently extract the deep feature information from battlefield situation data by utilizing the neural architecture search algorithm, in order to quickly and accurately identify the real intention of the target. The experimental results indicate that TransATIR significantly improves recognition accuracy compared to the existing state-of-the-art methods, and also effectively reduces the computational complexity of the model.

Improved search method for classified reusable components on cloud computing

Improved search method for classified reusable components on cloud computing

Optimal Intermediate Pressure Investigation in a CO₂ Transcritical Distributed Compression Refrigeration Cycle{fr}Recherche sur la Pression Intermédiaire Optimale dans un Cycle de Réfrigération à Compression Distribuée Transcritique au CO₂

The CO2 transcritical distributed compression cycle proposal provides a novel approach for refrigerant cooling in traditional transcritical cycles. This paper employed an exhaustive search method to derive the correlation formula for the optimal intermediate pressure. From the perspectives of thermodynamic performance and economics, a comparative analysis was conducted between the cycle established under the optimal intermediate pressure obtained in this study, the cycle based on equal compression ratios in traditional two-stage compression cycles, and the cycle established using the optimal secondary compression ratio method based on low-pressure stage discharge pressure mentioned in previous literature study. The research results indicate that the system's COP calculated using the obtained optimal intermediate pressure correlation method can be improved by up to 7.26% and 5.32%, respectively, compared to the traditional and literature-based methods. The exergy loss with the optimal intermediate pressure method is less than with the other two methods. The entransy dissipation rate of the system obtained using the optimal intermediate pressure correlation method is 24.61% and 50.14% lower than that of the traditional and literature-based methods, respectively. The investment cost of the main components using the optimal intermediate pressure correlation method is about 5% higher than that of the traditional method and about 1% higher than that of the optimal pressure ratio method. However, the total annual cost rate of the system is the lowest. The research enriches and improves the theory of the CO₂ transcritical distributed compression cycle, thereby facilitating the advancement of practical applications based on this cycle theory.

Train assignment and handling capacity arrangement in multi-yard railway container terminals: An enhanced adaptive large neighborhood search heuristic approach

Expanding terminal scale and constructing multiple railway handling yards have become popular strategies for leading railway container terminals globally to cope with the ever-increasing handling volume. Although such an approach greatly enhances the terminal’s productivity, it also intensifies handling operations and introduces additional inter-yard interactions, complicating terminal management. To address these challenges, this paper investigates an integrated optimization approach for multi-yard railway container terminals, which feature both rail-road and rail-rail container transshipment operations. The train assignment plan for each yard and the handling capacity arrangement for each train are jointly optimized while considering inter-yard container transits, workload allocation for yards, and safety requirements in train shunting. This problem is formulated as a nonlinear programming model, with the objective to minimize operational delay and service time for each incoming train, and to minimize workload differences and container transit volume among yards. To efficiently solve this problem, this research develops an enhanced adaptive large neighborhood search (EALNS) heuristic, which includes several customized operators and feasibility repair methods, and is further enhanced with a local search method and backtracking mechanism compared to the standard ALNS framework. Computational experiments with different data scales and problem settings demonstrate the superiority of the EALNS in terms of solution quality and stability compared with three other solution methods. Additionally, practical insights for terminal operations are drawn through detailed analysis of different infrastructure configurations, transshipment train characteristics, and unit cost settings.

Dynamic pricing and inventory model for perishable products with reference price and reference quality

Reference price (quality) is a benchmark point used by consumers to make price (quality) judgements. Combining reference price and reference quality with dynamic pricing and inventory management, this paper applies differential equations theory to construct a optimal control model for perishable products when the quality of products declines exponentially. The demand of products depends on price, quality, reference price and reference quality, among which reference price and reference quality are influenced by consumers’ past memory. The aim is to maximize the retailer’s profit during the period. The conclusions are as follows. Firstly, a optimal dynamic pricing and inventory model for perishable products with reference price and reference quality is constructed, and the model is extended to dual decision variables, stochastic demand, time-dependent effects, competitive and infinite planing horizon setups. Secondly, through the Pontryagin maximum principle, the analytical expression of the optimal dynamic price is derived. Thirdly, a linear search method to solve the optimal static price is proposed. Finally, the sensitivity of the main parameters is analyzed and the corresponding management enlightenments are given. By comparison of dynamic and static pricing, we find that dynamic pricing can achieve more profits and take a shorter selling period. In addition, for the sales problem of perishable products affected by both reference price and reference quality, retailers should adopt skimming pricing strategy (the optimal price decreases with time). Furthermore, to obtain more profit, retails should strive to increase the sensitivity coefficients of two types of reference, and the reference quality memory coefficient, while to decrease the reference price memory coefficient.

A systematic scanning method to locate cryptic terrestrial species

When studying wild animals, consideration must be given to potential detrimental effects of the study technique, particularly if techniques may affect behaviour or energy expenditure. Many small terrestrial species occupy cryptic habitats, the characteristics and locations of which may be poorly understood. To study these habitats, researchers must be able to locate them, but must also consider the potential for disturbance of the organisms and the impacts this may have. Here, we developed and tested a novel, non-invasive method of locating the cryptic hibernation nests of passive integrated transponder (PIT) tagged hazel dormice Muscardinus avellanarius. The use of a powerful PIT tag scanner combined with a systematic search technique resulted in the location of nine wild hibernating dormice. Camera trap recordings indicated no external dormouse activity following detections, indicating minimal disturbance. In addition, eleven PIT tags no longer inside a dormouse were detected on the forest floor during searches. This study demonstrates a non-invasive alternative to techniques such as radio-collaring for small mammals, and highlights potential uses of PIT tags in research beyond identification of individuals, particularly in understanding fine-scale habitat selection.-A systematic search method enabled location of cryptic terrestrial species-The use of PIT tags allows detection with minimal disturbance

Measurements of F1‐ Region Ionosphere State Variables at Arecibo Through Quasi Height‐Independent Exhaustive Fittings of the Incoherent Scatter Ion‐Line Spectra

AbstractWe discuss an exhaustive search approach to fit the incoherent scatter spectrum (ISS) in the F1‐region for molecular ion fraction (fm), ion temperature (Ti), and electron temperature (Te). The commonly used “full profile” approach for F1‐region measurements parameterizes the molecular ion fraction as a function of altitude and fits all the related heights for the state variables. In our approach, we fit the ISS at each height for fm, Ti, Te, and ion velocity (Vi) independently. Our exhaustive search method finds all the major local minima at each altitude. Although a parameterized function is used to guide the algorithm in finding the best solution, the fitting parameters retain their local characteristics. Despite that fitting fm, Ti, and Te without constraints requires Doppler shift to be accurately determined and the ISS signal‐to‐noise ratio higher than the full‐profile method, simulations show that Ti, Te, and fm can be recovered within a few percent accuracy with a moderate signal‐to‐noise ratio. We apply the exhaustive search approach to the Arecibo high‐resolution incoherent scatter radar data taken on 13 September 2014. The derived ion and electron temperatures are sensitive enough to reveal thermosphere gravity waves commonly seen in the electron density previously. Our method is more robust than previous height‐independent fitting methods. Comparison with another Arecibo program indicates our results are likely more accurate. Simultaneous high‐resolution measurements of Ti, Te, fm, Vi, and electron concentration (Ne) in our approach open new opportunities for synergistic studies of the F1‐region dynamics and chemistry.

Ventilation in school and students’ health after outbreak of COVID-19: A systematic literature review

Ventilation is one of the factors that support the health of students needed to support the goals of education. The objective of this study is to analyze how ventilation at school impacts the health of students. Scopus and Web of Science were used to retrieve published articles on ventilation at school and students’ health. The search method used the keywords “ventilation” OR “Indoor air pollution” OR “Indoor environment quality” AND “School” AND “Student” OR “Children” AND “Health”, from 2019- May 2023. The ventilation in schools researched were ventilation methods, insufficient or lack of ventilation, and the frequency of opening windows. The air quality parameters studied in classrooms were Nitrogen Dioxide (NO2); Particulate matter 10 (PM10); Particulate matter 2,5 (PM2.5); Particulate matter (PM1); Ozone (O3), benzene (C6H6), and carbon dioxide (CO2). The results showed that the diseases identified in students as a result of poor ventilation were tiredness, bronchitis, symptoms of asthma, risk of infection, rhinitis, etc. We conclude that as ventilation reduces indoor air pollutants, thus related to student health, it should be prioritized to fulfil the requirements for the health of students.

An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems

Knowing the type of buried object before excavation prevents unnecessary excavation. Moreover, it saves time and money. In this study, an experiment set was prepared for the detection of buried objects. The experimental set was composed of an antenna that sends and receives electromagnetic waves in a wide frequency band, software that records and processes reflections, and a sandbox. In the study, metallic and non-metallic objects with different depths, sizes and shapes were buried in this sand pool and measurements were taken along a profile. 2D images were created from the measurements and image processing techniques were applied to these images. Classification algorithms were used to detect the type of bruied object from processed images. To increase the success of the algorithms, correlation-based attribute selection (CFS) and Principal Component Analysis (PCA) were used as attribute selection techniques. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Harmony search (HA), and Evolutionary search (EA), which are among the metaheuristic optimization algorithms, were preferred as search methods in attribute selection with CFS. The performance of the algorithms was analyzed using the 10-fold cross-validation method. As a result, it was understood that the use of the PCA algorithm in attribute selection increases the classification success more than metaheuristic algorithms. The most successful among the classification algorithms used is the Random tree algorithm. After PCA, the accuracy value of this algorithm was 95.8 Therefore, a hybrid approach is proposed in which PCA and Random tree algorithms are used in the software embedded in the measurement system.

Side Searching and Object Improvement Methods for Unconstrained Face Recognition Environment

Side Searching and Object Improvement Methods for Unconstrained Face Recognition Environment

Research on the Identification of Rock Mass Structural Planes and Extraction of Dominant Orientations Based on 3D Point Cloud

The different spatial distribution forms of rock mass structural planes create weak zones in the rock mass, which is also a key factor in controlling rock mass stability. Accurately and efficiently identifying rock mass structural planes and obtaining their dominant orientations is critical for rock mass engineering design and construction. Traditional surveying methods for high and steep rock mass structural planes pose high safety risks, offer limited data, and make comprehensive statistical analysis difficult. This paper utilizes complex rock mass surface 3D point cloud data obtained through 3D laser scanning technology and uses the Hough space transform method to calculate the normal vectors of the 3D point cloud. Based on the difference in normal vectors and surface variation, region growing segmentation is applied to identify and extract rock mass structural planes. Additionally, the fast search and density peak clustering method (CFSFDP) is used for clustering analysis of the rock mass structural planes to obtain dominant orientations. This method was applied to a highway’s high and steep rock slope, successfully identifying 281 structural planes and two sets of dominant structural planes. The orientation of the dominant structural planes identified through RocScience Dips 7.0 analysis showed a deviation of no more than ±3°, complying with engineering standards. The research results offer a feasible solution for the identification of high and steep rock mass structural planes and the extraction of the orientation of dominant structural planes.

Wave Height Estimation with a Short Return Period

Bisection and grid search methods are proposed as wave height estimation techniques for short return periods (hereafter RP; reference, one year) using long-term wave monitoring data. The proposed method is compared and evaluated with the estimation results using GP (generalized Pareto) and GEV (generalized extreme value) distribution functions, which are widely used as extreme value analysis methods. The wave height data used for the estimation were KMA Ulleungdo Ocean Buoy’s wave height data observed for 12 years from 2012 to 2023. The estimation results show that the annual frequency wave height is 4.55 m, the 90% confidence interval (±5%) is [4.18, 4.69] (m), and the confidence interval for the RP is [0.58, 1.42] (years). The difference from the GP and GEV estimation results (4.61 m and 4.53 m, respectively) was statistically ‘no significance difference.’ The method proposed in this study can estimate design variables for RPs without assumptions on candidate extreme distribution functions or parameter estimation procedures, so it can be used to estimate wave heights for short RPs of one year or less when observation data of approximately ten years or more are available.

Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network.

Due to the uncertainty of the subsurface environment and the complexity of parameters, particularly in feature extraction from input data and when seeking to understand bidirectional temporal information, the evaluation and prediction of the rate of penetration (ROP) in real-time drilling operations has remained a long-standing challenge. To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. Initially, data are denoised using the Savitzky-Golay filter, and five correlation coefficient methods are employed to select input features, with principal component analysis (PCA) used to reduce model complexity. Subsequently, a sliding window approach transforms the time series into a two-dimensional structure to capture dynamic changes, constructing the model input. Finally, the ROP prediction model is established, and search methods are utilized to identify the optimal hyperparameter combinations. Compared with other neural networks, CBT-LSTM demonstrates superior performance metrics, with MAE, MAPE, RMSE, and R2 values of 0.0295, 0.0357, 9.3101%, and 0.9769, respectively, indicating the highest predictive capability. To validate the model's robustness, noise was introduced into the training data, and results show stable performance. Furthermore, the model's predictive results for other wells achieved R2 values of 0.95, confirming its strong generalization ability. This method provides a new solution for ROP prediction in real-time drilling operations, assisting drilling engineers in better planning their operations and reducing drilling cycles.

Mетод визначення зміни просторового положення об'єкта орбітального сервісу з невідомою формою

This paper presents a method for determining the pose of an on-orbit service object (target) with an unknown surface shape based on processing target surface point clouds. It is assumed that a point cloud is obtained using a sensor in the form of a lidar-based system onboard the service spacecraft. The algorithm of the method operates with a simplified description of the cloud; it uses only the coordinates of the cloud points without identifying any surface features. The idea of the method is as follows. From all cloud points obtained at a certain time instant, select points that are close to a certain degree to a given set of planes arranged in space in a certain way. From the point cloud corresponding to the next time instant, select points that are close to the same set of planes, but moved in space in a known way. By convention, let the arrangement of the projections of the selected cloud points onto the given planes be called a pattern of the intersection of the point clouds with the planes. By varying the displacement of the set of planes, maximize the coincidence of the intersection patterns of the first and second clouds of points. The degree of coincidence of the intersection patterns is characterized by a specified objective function whose arguments are target pose parameters. The target displacement parameters are found as the arguments of the objective function that maximize it. A variant of the objective function is proposed. A test example of the application of the proposed method is considered. A global optimization method known as the direct search method was used to find the arguments of the objective function. Test calculations were performed, and they confirmed the workability of the algorithm and determined its scope of applicability. The computational burden was not considered: the goal was to verify the workability of the method in principle. The advantage of the proposed method is its ability to determine a change in the pose of an object with an unknown surface shape. The proposed method may be considered as a source of observation data for a filtering procedure when estimating the parameters of the relative motion of a target with an unknown surface shape.

Review on Lane Detection Techniques

Abstract—Lane detection in current automobiles is extremely popular; it has become an inseparable part of the vehicle’s set, as it is considered indispensable nowadays to prevent an accident and limit the risk of injury or even death on the road. This review article addresses some of the different systems of line measuring features and characteristics, strengths, and weaknesses. This article starts by reviewing the literature and showing the different methods, methods, and techniques that are employed in the research line. Then, it continues with the description of the main elements of the search line, such as camera, image processing algorithms, and alerts, regarding the variety of functions that may be embodied in a line lane search, such as lane departure warning, cruise control, and automatic emergency braking. Compared the advantages and disadvantages of various camera, radar-based and lidar-based line search methods and set a comparison of accuracy, reliability, cost, and performance in different driving styles. In this report, it also discusses challenges and limitations of line calling like bad weather conditions, road signs, and poor performance, along with the risks and disadvantages of over-reliance on lane detection of the vehicles. Index Terms—Lane detection, reliability, robustness, semantic splitting, effectiveness

FUNCTIONAL ASSESSMENT METHODS FOR SPINAL CORD INJURY PATIENTS IN REHABILITATION PROCESS – A SYSTEMATIC REVIEW

Background: Spinal cord injury (SCI) leads to neurological damage that causes motor, sensory and autonomic neurologic functions decrease and/or loss. Evaluating the functionality of persons with SCI, especially in cases of incomplete injury, requires professional experience and skill. Objectives: This study aimed to gain a systematic overview of the functional assessment methods for SCI patients in the rehabilitation process. Search Methods: Systematic electronic searches were carried out using different databases from which 3489 registers were retrieved. Selection criteria: Studies that used tools to evaluate patients with SCI motor function as the manual test, Graded and Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP), ASIA Impairment Scale (AIS), Neuromuscular Recovery Scale (NRS), Walking Index SCI (WISCI), Walking Index SCI II (WISCI-II), functional independence measure (FIM) or Spinal Cord Independence Measurement III (SCIM-III). Reviews, animal studies, articles with more than ten years, theses and dissertations and/or out of the object of study were excluded. Data collection and analysis: Two experienced professionals searched the databases. All titles were read for the first selection. Afterward, the abstracts were read, and another number of articles were excluded. In the end, ten articles met the inclusion criteria of this study. Main results: We identified different tools used for the functional assessment of SCI persons, but 9 out of 10 studies did not detail the results by segment, and none presented a manual test for trunk evaluation. Robotic therapy combined with conventional therapy brings good results. Final Considerations: Ten studies that met inclusion criteria were identified. It was noticed that more in-depth studies are needed detailing the tests by segment, especially on trunk functionality in people with different levels of SCI.

VNS-BA*: An Improved Bidirectional A* Path Planning Algorithm Based on Variable Neighborhood Search.

The A* algorithm is an effective method for path planning; however, it has certain drawbacks, such as a high number of turns, low planning efficiency, and redundant searches. To address these issues, this paper proposes an improved bidirectional A* global path planning algorithm based on a variable neighborhood search strategy, named VNS-BA*. The new algorithm first employs an 8-11-13 neighborhood search method for node expansion. Then, the bidirectional search strategy is optimized by using the current nodes of the opposite path and the global target point, enabling the two paths to meet in the middle of the map. Finally, redundant turns are removed from the path, and cubic spline interpolation is applied to achieve local smoothing at the turns. The effectiveness of the improved algorithm was validated on different maps and compared with A* and its three derived improved versions. The simulation results indicate that VNS-BA* shows significant improvements in terms of the number of path turns, turn angles, and planning efficiency.

Public trust in national electronic health record systems: A scoping review of qualitative studies

Abstract Background Having public trust in national electronic health record systems (EHRs) is essential for the successful implementation of electronic health records within a country’s healthcare system. Without trust, people are less likely to consent to sharing their health data through an EHR and government stakeholders will have difficulties promoting uptake and facilitation of EHRs. Research investigating public trust in EHRs is limited, however. In response, we conducted a scoping review in order to gain clarity on the influences and themes behind public trust in EHRs, which can support the implementation of EHRs. Methods We reviewed 27 qualitative studies focusing on public trust in EHRs. Studies were identified between January 2022 and June 2022 and were ascertained using an inclusive search method and guided by the PRISMA-ScR checklist. In an iterative process, conceptual themes were derived, and detail the promoters and outcomes of public trust in EHRs. Results Three major themes that describe the promoters of public trust in EHRs and two major themes that detail the outcomes once public trust in EHRs exists were ascertained from the literature. Additional findings bring forth the consequential role of healthcare actors in the public trust building process. Conclusions Results show comprehension, autonomy, and data protection are key concepts that help build and consolidate public trust in EHRs, while the public and the healthcare system benefit from public trust in EHRs. Furthermore, health system actors can be supportive or detrimental to the implementation of EHRs. Their effect is dependent on their activities within the health care system and how the public perceives those activities. Based on: Papadopoulos K, von Wyl V, Gille F. What is public trust in national electronic health record systems? A scoping review of qualitative research studies from 1995 to 2021. DIGITAL HEALTH. 2024;10. doi:10.1177/20552076241228024 Key messages • This research clarifies the conceptualizations that make up the trust relationship between the public and EHRs. • The findings can assist researchers, policymakers, and other health system actors in attaining a better understanding of the intricacies of public trust in EHRs.

GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search

Biological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to the query networks. Great progress has been achieved in biological network similarity searches. However, it remains a challenge to mine the biological network information fully to improve the accuracy of query results without increasing time overheads. In this study, we propose a biological network similarity search method based on graph neural networks named GBNSS, which combines topological and biological information (GO annotations) of biological networks into graph neural networks to find topologically and biologically similar biological networks in the database. Additionally, GBNSS is a topology-free biological network similarity search method with an arbitrary network structure. The experimental results on four benchmark datasets show that GBNSS outperforms the existing methods in terms of computational efficiency and search accuracy. Case studies further demonstrate that GBNSS is capable of searching similar networks in real-world biological networks.