Running Time Research Articles

Time-Efficient RSA over Large-Scale Multi-Domain EON.

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra's shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called "Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)", which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.
In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

A feedback matrix based evolutionary multitasking algorithm for high-dimensional ROC convex hull maximization

Multi-objective evolutionary algorithms have shown their competitiveness in solving ROC convex hull maximization. However, due to “the curse of dimensionality”, few of them focus on high-dimensional ROCCH maximization. Therefore, in this paper, a feedback matrix (FM)-based evolutionary multitasking algorithm, termed as FM-EMTA, is proposed. In FM-EMTA, to tackle “the curse of dimensionality”, a feature importance based low-dimensional task construction strategy is designed to transform the high-dimensional ROCCH maximization task into several low-dimensional tasks. Then, each low-dimensional task evolves with a population. To ensure that the low-dimensional task achieves a better ROCCH, an FM-based evolutionary multitasking operator is proposed. Specifically, for each low-dimensional task i, the element FM(i,j) in feedback matrix is defined to measure the degree that the low-dimensional task j could assist task i. Based on it, an FM-based assisted task selection operator and an FM-based knowledge transfer operator are developed to constitute the evolutionary multitasking operator, with which the useful knowledge is transferred among the low-dimensional tasks. After the evolution, the best ROCCHs obtained by the low-dimensional tasks are combined together to achieve the final ROCCH on the original high-dimensional task. Experiments on twelve high-dimensional datasets with different characteristics demonstrate the superiority of the proposed FM-EMTA over the state-of-the-arts in terms of the area under ROCCH, the hypervolume indicator and the running time.

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

Quantification of Epoxyeicosatrienoic acids Enantiomers: The development of reliable and practical liquid chromatography mass Spectrometry assay

Epoxyeicosatrienoic acids (EETs) are increasingly recognized as key metabolites in the arachidonic acid (AA) metabolic pathway. EETs are epoxy derivatives of AA with two chiral centers formed by cytochrome P450 (CYP) enzymes. EETs have reported biological activities as racemates; however, knowledge on specific optical isomers of EET is lacking. A main reason is the absence of practical assay to quantify EETs isomers associated with specific pathological conditions and enzymes. The reported underivatized chiral LC-MS/MS assays utilize different mobile phases and flow rates or required long run times to achieve separation of EET stereoisomers. Others incorporated a derivatization step before the separation of EETs in their assays. Therefore, the objective of this study was to develop and validate a stereoselective assay for the simultaneous quantitation of underivatized EET enantiomers using Liquid Chromatography Mass Spectrometry (LC-MS/MS) with an optimum baseline separation using binary mobile phase and gradient elution. Herein, we report the development and validation of an LC-MS/MS assay, and its application to quantify the formation of EET enantiomers mediated by human liver microsomes. Assay linearity extends over 10–600 ng/mL with r2 > 0.99 for all EETs enantiomers. The inter-run accuracy was within ± 15 %, and precision was ≤ 15 %, and < 20 % for the LLOQ. The matrix effect for the current assay was within ≤ ±20 %, and the mean recovery for quantitative methods was 70–125 %. The assay proved to be reliable and practical for chiral analysis.

Development and Validation of an Improved HPLC-MS/MS Method for Quantifying Total and Unbound Lenalidomide in Human Plasma.

This study aimed to develop a fully validated HPLC-MS/MS method for quantifying total and unbound lenalidomide concentrations in human plasma. Unbound concentrations were measured using plasma ultrafiltrate prepared with Amicon® Centrifugal Filters. Lenalidomide and lenalidomide-d5 (internal standard) were extracted from 50 μL of human plasma using liquid-liquid extraction. Chromatography was conducted with a Halo® C18 column using 0.1% formic acid and methanol (20:80, v/v) as the mobile phase. The mass spectrometer was operated in a positive ion mode with an electrospray ionization interface and multiple reaction monitoring modes. Calibration curves were linear over the range of 5 to 1000 ng/mL (r2 > 0.996) for both the total and unbound lenalidomide. For total lenalidomide concentrations, between-run precision (coefficients of variation) and accuracy were 1.70-7.65% and 94.45-101.10%, respectively. For unbound concentrations, inter-day precision and accuracy were 1.98-10.55% and 93.95-98.48%, respectively. We developed a highly reproducible, sensitive, and efficient bioanalytical method using a smaller volume of plasma sample (50 μL) with a relatively short run time (2.5 min). The proposed analytical method was successfully applied to measure total and unbound lenalidomide concentrations at various time points in multiple myeloma patients with renal impairment.

Extracting efficient exact real number computation from proofs in constructive type theory

Abstract Exact real computation is an alternative to floating-point arithmetic where operations on real numbers are performed exactly, without the introduction of rounding errors. When proving the correctness of an implementation, one can focus solely on the mathematical properties of the problem without thinking about the subtleties of representing real numbers. We propose a new axiomatization of the real numbers in a dependent type theory with the goal of extracting certified exact real computation programs from constructive proofs. Our formalization differs from similar approaches, in that we formalize the reals in a conceptually similar way as some mature implementations of exact real computation. Primitive operations on reals can be extracted directly to the corresponding operations in such an implementation, producing more efficient programs. We particularly focus on the formalization of partial and nondeterministic computation, which is essential in exact real computation. We prove the soundness of our formalization with regards to the standard realizability interpretation from computable analysis and show how to relate our theory to a classical formalization of the reals. We demonstrate the feasibility of our theory by implementing it in the Coq proof assistant and present several natural examples. From the examples we have automatically extracted Haskell programs that use the exact real computation framework AERN for efficiently performing exact operations on real numbers. In experiments, the extracted programs behave similarly to native implementations in AERN in terms of running time and memory usage.

Assessment of Nine Real-Time PCR Kits for African Swine Fever Virus Approved in Republic of Korea.

The African swine fever virus (ASFV) causes severe disease in wild and domestic pigs, with high mortality rates, extensive spread, and significant economic losses globally. Despite ongoing efforts, an effective vaccine remains elusive. Therefore, effective diagnostic methods are needed to rapidly detect and prevent the further spread of ASF. This study assessed nine commercial kits based on real-time polymerase chain reaction (PCR) approved in the Republic of Korea using the synthesized ASFV plasmid, 20 food waste samples, and artificially spiked samples (ASSs). The kits were evaluated for their diagnostic sensitivity, specificity, cost per reaction, and reaction running time. In addition, the results were compared with those of the World Organization for Animal Health (WOAH) standard methods. Three commercial kits (VDx® ASFV qPCR Kit, Palm PCR™ ASFV Fast PCR Kit, and PowerChek™ ASFV Real-time PCR Detection Kit Ver.1.0) demonstrated the highest sensitivity (100 ag/μL), cost-effectiveness (less than KRW 10,000), and shortest running time (less than 70 min). These kits are suitable for the monitoring, early diagnosis, and prevention of the spread of ASF. This is the first report on the performance comparison of ASFV diagnostic kits approved in the Republic of Korea, providing valuable information for selecting kits for testing with food waste samples.

Two-Dimensional Liquid Chromatography Tandem Mass Spectrometry Untangles the Deep Metabolome of Marine Dissolved Organic Matter.

Dissolved organic matter (DOM) is an ultracomplex mixture that plays a central role in global biogeochemical cycles. Despite its importance, DOM remains poorly understood at the molecular level. Over the last decades, significant efforts have been made to decipher the chemical composition of DOM by high-resolution mass spectrometry (HR-MS) and liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS). Yet, the complexity and high degree of nonresolved isomers still hamper the full structural analysis of DOM. To address this challenge, we developed an offline two-dimensional (2D) LC approach using two reversed-phase dimensions with orthogonal pH levels, followed by MS/MS data acquisition and molecular networking. 2D-LC-MS/MS reduced the complexity of DOM, enhancing the quality of MS/MS spectra and increasing spectral annotation rates. Applying our approach to analyze coastal-surface DOM from Southern California (USA) and open-ocean DOM from the central North Pacific (Hawaii), we annotated in total more than 600 structures via MS/MS spectrum matching, which was up to 90% more than that in iterative 1D LC-MS/MS analysis with the same total run time. Our data offer unprecedented insights into the molecular composition of marine DOM and highlight the potential of 2D-LC-MS/MS approaches to decipher the chemical composition of ultracomplex samples.

Web log mining techniques to optimize Apriori association rule algorithm in sports data information management

To optimize the current college sports data information management system, this study combines the Apriori association rule algorithm with web application development technology to upgrade the management system. Firstly, this study explores novel log mining techniques in genetic algorithms and web application development technology. Secondly, by integrating log mining techniques to optimize the Apriori algorithm, associations between sports data and information are discovered. Through the optimized algorithm, this study identifies key association rules of sports data information and validates the optimized system’s reliability and effectiveness through experiments. The experimental results show that the running time of the traditional Apriori algorithm exponentially grows with the increase in information volume, while the optimized execution efficiency is improved by approximately 10–15%. Additionally, the average retrieval accuracy of this optimized system can reach 98.3%, although the retrieval time also increased by 23%. Therefore, the technology and algorithms proposed in this study have certain application value in the sports information management system and contribute to the optimization of data information management in this field.

The effects of inflation, shortages, and partial backlogs on products that deteriorate over time in response to varied demand

This study aims to determine an optimal policy to deal with a situation where a retailer should have enough stock to meet customer demands and prevent supply loss and deterioration. In this study, we demonstrate the impact of partial backlogging, shortages, and inflation on decaying items to provide the most relevant solutions to this problem. The model uses a two-parameter Weibull deterioration distribution with an exponential demand rate. Numerical results and sensitivity analysis are combined with a graphical demonstration, and the proposed approach is reliable and accurate, providing a new explanation for a different type of system. This paper determines the total cost and optimal production run time by developing an inventory model. Sensitivity analysis is carried out to demonstrate the proposed model.

Influence of cartilage defects and a collagen gel on integrity of corresponding intact cartilage: a biomechanical in-vitro study.

Numerous cartilage repair procedures have been developed for focal lesions to minimize suffering and possibly prevent the development of osteoarthritis with a focus on so-called one-step procedures. The aim of this work was to investigate the effects of both focal cartilage defects and a biomaterial (ChondroFiller) on the corresponding articular cartilage. On a friction test stand, 18 porcine osteochondral cylinders were tested in six experimental setups under cyclic loading (33 N) against a friction partner in saline solution. The friction partner (cartilage, bone, cartilage defect, cartilage defect with ChondroFiller) and the running times (1 hour and 6 hours) were varied. The damage to the osteochondral cylinders was assessed histologically using a visual damage classification. The cartilage versus bone group showed severe cartilage damage in both the one-hour and six-hour experiments, with an average damage score of 3.5. Damage in the cartilage versus cartilage defect group was moderate, with damage values of 2.5 (1 h) and 2.67 (6 h). The cartilage versus cartilage defect with ChondroFiller group showed a damage value of 2.67 for the one-hour and 2.5 for the six-hour trials. Even focal grade IV cartilage lesions can lead to significant damage to the corresponding cartilage in vitro. The damage could not be reduced by the use of ChondroFiller, likely because of the initial instability of this biomaterial. Therefore, a biomaterial must be stable in the beginning with regard to full weight-bearing, or joint loading should be delayed until stable filling of the defect is achieved.

Echo State Network and Sparrow Search: Echo State Network for Modeling the Monthly River Discharge of the Biggest River in Buzău County, Romania

Artificial intelligence (AI) has become an instrument used in all domains with good results. The water resources management field is not an exception. Therefore, in this article, we propose two machine learning (ML) techniques—an echo state network (ESN) and sparrow search algorithm–echo state network (SSA-ESN)—for monthly modeling of the water discharge of one of the biggest rivers in Romania for three periods (S, S1, and S2). In both models, R2 was over 0.989 on the test and training sets and the mean absolute error (MAE) varied between 4.4826 and 7.6038. The performance of the SSA-ESN was similar, but the ESN had the shortest run time. The influence of anomalies on the models’ quality was assessed by running the algorithms on a series without the aberrant values, which were detected by the seasonal hybrid extreme studentized deviate (S-H-ESD) test. The results indicate that removing the anomalies significantly improved both models’ performance, but the run time was increased.

An Algorithm for Clustering with Confidence-Based Must-Link and Cannot-Link Constraints

We study here the semisupervised k-clustering problem where information is available on whether pairs of objects are in the same or different clusters. This information is available either with certainty or with a limited level of confidence. We introduce the pair-wise confidence constraints clustering (PCCC) algorithm, which iteratively assigns objects to clusters while accounting for the information provided on the pairs of objects. Our algorithm uses integer programming for the assignment of objects, which allows us to include relationships as hard constraints that are guaranteed to be satisfied or as soft constraints that can be violated subject to a penalty. This flexibility distinguishes our algorithm from the state of the art, in which all pair-wise constraints are considered hard or all are considered soft. We developed an enhanced multistart approach and a model-size reduction technique for the integer program that contribute to the effectiveness and efficiency of the algorithm. Unlike existing algorithms, our algorithm scales to large-scale instances with up to 60,000 objects, 100 clusters, and millions of cannot-link constraints (which are the most challenging constraints to incorporate). We compare the PCCC algorithm with state-of-the-art approaches in an extensive computational study. Even though the PCCC algorithm is more general than the state-of-the-art approaches in its applicability, it outperforms the state-of-the-art approaches on instances with all hard or all soft constraints in terms of both run time and various metrics of solution quality. The code of the PCCC algorithm is publicly available on GitHub. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: The research of D. S. Hochbaum was supported by the AI Institute NSF Award [Grant 2112533]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0419 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0419 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Managing Resources for Shared Micromobility: Approximate Optimality in Large-Scale Systems

We consider the problem of managing resources in shared micromobility systems (bike sharing and scooter sharing). An important task in managing such systems is periodic repositioning/recharging/sourcing of units to avoid stockouts or excess inventory at nodes with unbalanced flows. We consider a discrete-time model; each period begins with an initial inventory at each node in the network, and then, customers (demand) materialize at the nodes. Each customer picks up a unit at the origin node and drops it off at a randomly sampled destination node with an origin-specific probability distribution. We model the above network inventory management problem as an infinite horizon discrete-time discounted Markov decision process (MDP) and prove the asymptotic optimality of a novel mean-field approximation to the original MDP as the number of stations becomes large. To compute an approximately optimal policy for the mean-field dynamics, we provide an algorithm with a running time that is logarithmic in the desired optimality gap. Lastly, we compare the performance of our mean field-based policy with state-of-the-art heuristics via numerical experiments, including experiments using Austin scooter-sharing data. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02023 .

An abinitio approach to the Hugoniot.

The Hugoniot is the equation of state of a shock-compressed material and is a key part of high-pressure physics. One way of calculating it is via the Hugoniostat that has significant computational advantages over direct calculation via non-equilibrium molecular dynamics. We introduce a number of improvements to the Hugoniostat, which significantly reduce the run time and the number of atoms required for converged results. Consequently, abinitio Hugoniot calculations are tractable. We illustrate the benefits through simple model potentials and with density functional theory calculations of argon.

Integrity management system of hydrogen pipeline based on big data mining model optimized by improved whale algorithm

ABSTRACT In order to improve the integrity management level of the hydrogen pipeline, the intelligent information management system of the hydrogen pipeline based on big data intelligent mining model is proposed. The improved whale optimization algorithm is proposed by applying the adaptive weight update formula and quantum revolving door operation. Finally, a hydrogen pipeline is selected to carry out abnormal data detection, and the running time with different nodes and different clustering algorithms optimized by different intelligent algorithms is obtained, and the results show that the proposed Gaussian clustering optimized by an improved whale optimization algorithm can effectively find out abnormal data of integrity management of hydrogen pipeline. Monitoring precision of the proposed model is improved effectively, and the running time of the proposed model is also shortened; therefore, the proposed model can be an effective means for integrity management of the hydrogen pipeline.

Prediction of carbon dioxide levels in the European Alps based on machine learning algorithms

With the rapid development of world industrialization, the continuous increase of global carbon dioxide emissions has led to the gradual deterioration of the ecological environment and the obvious aggravation of the greenhouse effect. In this paper, the carbon dioxide content in the air over the European Alps is taken as the research object, and the RNN and LSTM neural network prediction models are respectively used to compare and predict it. The results show that the fitting effect of LSTM is better than that of RNN, and the fitting effect of the prediction model will also improve with the increase of iteration times and sample size. Since carbon monoxide and methane in the air will cause changes in carbon dioxide content, this paper adds the two factors into the LSTM prediction model as influencing factors, and the goodness-of-fit can reach 0.95, which is higher than the prediction results when there are only one influencing factor or no influencing factor. In order to reduce the running time, XGBoost, LightGBM and random forest algorithms are respectively used in this paper and Bayesian optimization algorithm is used to predict the carbon dioxide content. The results show that the prediction effect is slightly lower than LSTM. Therefore, this paper takes the above three algorithms as the base model. Linear regression experiments are carried out for the meta-model's Stacking fusion algorithm. The goodness-of-fit can reach 0.92, which significantly improves the prediction effect compared with the base model. Finally, the sensitivity analysis of the Stacking fusion model is carried out in this paper. The experimental results show that the model has strong stability.

Enhancing energy-efficient building design: a multi-agent-assisted MOEA/D approach for multi-objective optimization

Energy-efficient building design is often challenged by multiple optimization problems due to contradictory objectives that are often hard to balance, so an effective optimization method should be thoroughly considered. Accordingly, a multi-objective evolutionary algorithm is then proposed. Firstly, the multi-agent auxiliary objective evolutionary algorithm for building energy efficiency model is established. According to model result analysis, the proposed algorithm runs fastest for 1640s with the average running time of 1710s in a single-room building, comparing to the least running time of 1680s for the multi-objective particle swarm optimization algorithm. In multi-room buildings, the proposed algorithm runs from 3350s to 3650s, with the average running time of 3500s. In conclusion, the model proposed in this study can comprehensively consider multiple objectives such as energy consumption, cost, comfort, etc. No matter in single-room or multi-room buildings, the model demonstrates superior performance and stability to realize comprehensive optimization of energy conservation design.

Parallel adaptive large neighborhood search based on spark to solve VRPTW

Aiming at the multi-objective vehicle path planning problem with time windows (VRPTW), a Spark-based parallel Adaptive Large Neighborhood Search algorithm (Spark-ALNS) is proposed to solve it. The main design of the 4-point strategy: (1) Design a new simulated annealing algorithm cooling strategy to achieve a better jump out of the local optimal solution. (2) Adopt CW initialization to accelerate the convergence speed. (3) Use three destruction operators and three repair operators to implement local path optimization. (4) A new parallel strategy is proposed to improve the algorithm’s accuracy and reduce the running time. To illustrate the algorithm’s effectiveness, the arithmetic example in Solomon is used as an example. The experimental results show that the proposed Spark-ALNS can find better solutions, get the known optimal solutions for 41 out of 56 instances, and find new optimal solutions for 31 algorithms, which outperforms other evolutionary algorithms. The runtime is 3–5 times better than other parallel algorithms and is able to solve VRPTW effectively.