Window Analysis Research Articles

Abandoned coal tunnel survey by multiscale window analysis of Rayleigh waves

Abandoned coal tunnels (ACTs) pose risks to coal mine safety by accumulating gas and water, causing surface subsidence, and endangering infrastructure. Accurate detection of ACTs is vital for advanced prediction and geotechnical hazard warnings. Seismic methods, particularly the surface-wave method, have demonstrated reliability in detecting shallow buried tunnels within complex geological environments, but improvements in accuracy and resolution are still needed. In this paper, we suggest utilizing the multiscale window analysis of surface waves (MWASW) method for ACT surveys. We conduct a synthetic test and a real-world case study to evaluate the feasibility and validity of this approach. The synthetic test on a 2D ACT model reveals that MWASW can reconstruct anomalous structures and identify the presence of an ACT, while also highlighting some limitations of the method. In a real-world scenario, we applied MWASW to a multi-survey line dataset from a coal mine in Shanxi Province, China. The resulting meter-scale resolution pseudo-3D S-wave velocity structure uncovers a primary ACT and two sub-ACTs buried at approximately 60 m, offering valuable insights into their morphology and orientation. To our knowledge, this is the first application of the MWASW method in such a case study, indicating its potential as an emerging alternative for characterizing complex near-surface media, such as abandoned coal tunnels or mined-out areas in coal fields.

Spatial and temporal fishery management assessment using DEA: Case study of spanner crabs in Queensland, Australia

The aim of this case study was to assess potential temporal and spatial differences in productivity measures of vessels operating in the Queensland spanner crab fishery. This fishery’s logbook records of catch and effort data allowed analysis of the impact of fishery management changes on productivity measures. Data envelopment analysis (DEA) with a ‘window analysis’ approach was used to derive estimates for measures of technical efficiency, capacity utilisation and scale efficiency over time for five different spanner crab fishing regions. The results suggest that average technical efficiency and capacity utilisation were relatively low over time and across fishing regions, implying a high level of technical inefficiency and the existence of excess capacity in the fishery. Scale efficiency was found to be high historically but decreased slightly since 2006 for all regions. The results suggest that this decline is likely not caused by the fishery management changes, but instead is due to other factors. Additional data (e.g., revenue, profit, costs, skipper experience) and analysis is needed to assess the causes for the low technical efficiency and capacity utilisation and reasons for the decrease in scale efficiency as a baseline for specific fishery management recommendations. The study shows that temporal and spatial efficiency and productivity analysis of fisheries can help identify potential issues that are not otherwise apparent.

The complete chloroplast genome of Ulmus mianzhuensis with insights into structural variations, adaptive evolution, and phylogenetic relationships of Ulmus (Ulmaceae)

BackgroundUlmus mianzhuensis is an endemic tree species in China with high ornamental and economic value. Currently, little is known regarding its genomic architecture, phylogenetic position, or adaptive evolution. Here, we sequenced the complete chloroplast genome (cp genome) of U. mianzhuensis and further compared the variations in gene organization and structure within Ulmus species to define their genomic evolution, then reconstructed the phylogenomic relationship of 31 related Ulmus species to explore the systematic position of U. mianzhuensis and the utility of cp genome for resolving phylogenetics among Ulmus species.ResultsOur results revealed that all the Ulmus species exhibited a typical quadripartite structure, with a large single copy (LSC) region of 87,170 − 88,408 bp, a small single copy (SSC) region of 18,650 − 19,038 bp and an inverted repeat (IR) region of 26,288 − 26,546 bp. Within Ulmus species, gene structure and content of cp genomes were highly conserved, although slight variations were found in the boundary of SC/IR regions. Moreover, genome-wide sliding window analysis uncovered the variability of ndhC-trnV-UAC, ndhF-rpl32, and psbI-trnS-GCU were higher among 31 Ulmus that may be useful for the population genetics and potential DNA barcodes. Two genes (rps15 and atpF) were further detected under a positive selection of Ulmus species. Comparative phylogenetic analysis based on the cp genome and protein-coding genes revealed consistent topology that U. mianzhuensis is a sister group to U. parvifolia (sect. Microptelea) with a relatively low-level nucleotide variation of the cp genome. Additionally, our analyses also found that the traditional taxonomic system of five sections in Ulmus is not supported by the current phylogenomic topology with a nested evolutionary relationship between sections.ConclusionsFeatures of the cp genome length, GC content, organization, and gene order were highly conserved within Ulmus. Furthermore, molecular evidence from the low variation of the cp genome suggested that U. mianzhuensis should be merged into U. parvifolia and regarded as a subspecies of U. parvifolia. Overall, we demonstrated that the cp genome provides valuable information for understanding the genetic variation and phylogenetic relationship in Ulmus.

Machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study.

Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt. This study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features. Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10s analysis window size. The findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.

Retrospective study on the presence of spasticity-plus syndrome in the advanced practice nurse’s office in multiple sclerosis

Spasticity plus syndrome (SSP) is a clinical diagnosis based on the presence of a compendium of symptoms that has been suggested recently in patients with demyelinating disease. Using this diagnosis might help to avoid polimedication and to offer a better symptomatology management in long-term patients. ObjectiveThe aim of this study is to retrospectively analyze the number of cases compatible with a SSP diagnostic in our nurse clinical-consultancy. MethodologyWe analyzed a time-window from 2002 till 2022 and we included cases compatible with SSP at those 2 time-points as well as the number of symptomatic treatments prescribed also in both time-points. ResultsAll cases in the databased were compatible with SSP and also associated pain (90%), rigidity (83.3%), fatigue (75%) and bladder problems (43.3%). An increase in symptomatic treatments was stated (from 1.4 to 3.9 at the end of the temporal window of analysis), and THC:CBD was prescribed in 88.9% of them. There was no correlation between number of symptoms at the beginning and number of symptomatic treatments at the end of the time-window. No correlation either was found between number of symptomatic treatments and physical disability. ConclusionsSSP is relatively easy to be diagnosed in our consultancy and it should be more frequently stated as it will offer the opportunity to individual management of symptoms and treatments for our patients that could impact their quality of life in the long-term.

Sol-gel method preparation and high-rate energy storage of high-entropy ceramic (FeCoCrMnNi)C porous powder

Among the existing electrochemical capacitive energy storage electrode materials, the (TiNbTaZrHf)C, (VCrNbMoZr)2N, (CoCrFeMnNi)3O4, (FeCoCrMnMg)3O4 and (FeCoCrMnCuZn)3O4 all have excellent capacitive performance, but the energy density is limited due to the narrow potential window. In order to solve the problem of low energy density of electrochemical supercapacitor, a kind of high-entropy carbides (HEMCs) with broad potential window is designed from generalized analysis of electrochemical window of active elements. This high-entropy carbide contains five active elements of Fe, Co, Cr, Mn and Ni. The prepared HEMC-3 has abundant pores and a specific surface area of 417.8 m2 g−1. The large specific surface area makes the HEMCs have high-rate energy storage performance. Electrochemical tests show that the HEMC-3 presents a potential window of [-1.0, 0.6] versus Hg/HgO, and can obtain a specific capacitance of 169.7 F g−1 at a current density of 0.5 A g−1. Due to the designed wide potential window of the prepared HEMCs, the energy density has been greatly improved, which shows a new strategy for developing of electrochemical supercapacitor electrode materials.

Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer

Lung cancer is the world’s second-largest cause of cancer mortality. Patients’ lives can be saved if this malignancy is detected early. Doctors, however, encounter difficulties in detecting cancer in computed tomography (CT) images. In recent years, significant research has been devoted to producing automated lung nodule detection methods that can help radiologists. Most of them use only the lung window in their analysis and generally do not consider the mediastinal windows, which, according to recent research, carry important information. In this paper, we propose a simple yet effective algorithm to analyze multi-window CT images for lung nodules. The algorithm works in three steps. First, the CT image is preprocessed to suppress any noise and improve the image quality. Second, the lungs are extracted from the preprocessed image. Based on the histogram analysis of the lung windows, we propose a multi-Otsu-based approach for lung segmentation in lung windows. The case of mediastinal windows is rather difficult due to irregular patterns in the histograms. To this end, we propose a global–local-mean-based thresholding technique for lung detection. In the final step, the nodule candidates are extracted from the segmented lungs using simple intensity-based thresholding. The radius of the extracted objects is computed to separate the nodule from the bronchioles and blood vessels. The proposed algorithm is evaluated on the benchmark LUNA16 dataset and achieves accuracy of over 94% for lung tumor detection, surpassing that of existing similar methods.

Comparing Market Efficiency in Developed, Emerging, and Frontier Equity Markets: A Multifractal Detrended Fluctuation Analysis

In this article, we investigate the market efficiency of global stock markets using the multifractal detrended fluctuation analysis methodology and analyze the results by dividing them into developed, emerging, and frontier groups. The static analysis results reveal that financially advanced countries, such as Switzerland, the UK, and the US, have more efficient stock markets than other countries. Rolling window analysis shows that global issues dominate the developed country group, while emerging markets are vulnerable to foreign capital movements and political risks. In the frontier group, intensive domestic market issues vary, making it difficult to distinguish similar dynamics. Our findings have important implications for international investors and policymakers. International investors can establish investment strategies based on the degree of market efficiency of individual stock markets. Policymakers in countries with significant fluctuations in market efficiency should consider implementing new regulations to enhance market efficiency. Overall, this study provides valuable insights into the market efficiency of global stock markets and highlights the need for careful consideration by international investors and policymakers.

Multi-Objective Process Optimization of Laser Cladding Co-Based Alloy by Process Window and Grey Relational Analysis

To determine the optimal process parameters for the preparation of a Co-based alloy cladding layer, the experimental research of laser cladding Co-based alloy was carried out based on the optimal process window and grey relational analysis methods with 42CrMo as the substrate. The analysis of variance (ANOVA) was used to explore the influence laws of laser process parameters on the forming characteristics of the cladding layer within the optimal process window range. Furthermore, the optimal process parameter combination was obtained by grey relational analysis, and the experimental verification of the optimization results was conducted. It was found that the process parameter interval determined by the optimal process window was laser power 1300–2100 W, scanning speed 6–14 mm/s, and powder feeding rate 17.90–29.84 g/min. The influence order of each process parameter was: laser power > scanning speed > powder feeding rate. The optimal process parameters of laser power 2100 W, scanning speed 6 mm/s, and powder feeding rate 17.90 g/min were obtained. The experimental verification results of optimal process parameters proved that the grey correlation grade of the optimized parameters was improved by 0.260 compared with the initial parameters and agreed well with the prediction value with an accuracy of 96%. After optimization, the cross-sectional area, the ratio of the width to height, cladding efficiency, and powder utilization rate of the cladding track increased by 4.065 mm2, 1.031, 19.032, and 70.3%, respectively, and the fluctuation ratio decreased by 60.9%. The optimal cladding track was well bonded to the substrate without cracks, holes, and evident element segregation, and included the phases of Cr3C7, CoCx, fcc-Co, and WC.

Can the new energy pilot policy improve China’s urban energy economic efficiency?

The global energy era is accelerating ahead of schedule. At the urban level, can large-scale utilization of renewable energy in cities promote energy consumption reduction and economic cost saving, improving energy economic efficiency (EEE)? Based on balanced panel data for 260 prefecture-level cities in China from 2010 to 2020, we used data envelope analysis (DEA) window analysis and difference-in-differences and propensity score matching to evaluate the effect of the New Energy Demonstration City Program on urban EEE. We found that the policy promoted the improvement of urban EEE. However, there is specific regional heterogeneity in the policy effects, with this policy having a more significant effect on EEE improvements in the Northeast, cities with high pollution levels, and cities with high-tech innovation levels. These findings have significant policy implications for supporting the country in promoting the development and utilization of new energy sources to improve EEE.

Understanding mental health trends during COVID-19 pandemic in the United States using network analysis.

The emergence of COVID-19 in the United States resulted in a series of federal and state-level lock-downs and COVID-19 related health mandates to manage the spread of the virus. These policies may negatively impact the mental health state of the population. This study focused on the trends in mental health indicators following the COVID-19 pandemic amongst four United States geographical regions, and political party preferences. Indicators of interest included feeling anxious, feeling depressed, and worried about finances. Survey data from the Delphi Group at Carnegie Mellon University were analyzed using clustering algorithms and dynamic connectome obtained from sliding window analysis. Connectome refers to the description of connectivity on a network. United States maps were generated to observe spatial trends and identify communities with similar mental health and COVID-19 trends. Between March 3rd, 2021, and January 10th, 2022, states in the southern geographic region showed similar trends for reported values of feeling anxious and worried about finances. There were no identifiable communities resembling geographical regions or political party preference for the feeling depressed indicator. We observed a high degree of correlation among southern states as well as within Republican states, where the highest correlation values from the dynamic connectome for feeling anxious and feeling depressed variables seemingly overlapped with an increase in COVID-19 related cases, deaths, hospitalizations, and rapid spread of the COVID-19 Delta variant.

Hydrodynamic and environmental modelling influence on numerical analysis of an innovative installation method for floating wind

This paper presents a numerical modelling methodology for an innovative installation method of floating wind turbines. The study analyses the influence of hydrodynamic and environmental modelling factors on the dynamics of a multibody system consisting of a catamaran with three wind turbines and a spar foundation with a mooring system. The research focuses on features such as viscous drag on the spar, hydrodynamic interaction between floating bodies, long- and short-crested waves, first- and second-order wave loads, wave directions, and wave peak periods. The study found that hydrodynamic modelling factors had a significant effect on the horizontal motion of the installation system but a negligible effect on vertical motion and mooring system tension. Therefore, it is recommended to consider viscous drag forces, hydrodynamic interaction, and first- and second-order wave forces when developing mechanical damping and control systems to reduce horizontal motion. The study also suggests using both long- and short-crested waves for numerical analysis when estimating the weather window for installation operability analysis. Overall, this study contributes to the development of an efficient and practical installation method for floating wind turbines by analysing the impact of hydrodynamic and environmental factors on the dynamics of the multibody system.

Non-linear elasticity experiment and analysis of acrylic material used for human occupied vehicle

ABSTRACT Acrylic is an ideal material for some components in underwater engineerings, such as observation windows and fully-transparent hulls of Human-Occupied Vehicle (HOV). The safety of these acrylic structures is quite essential for the integrity of the underwater facility and the research on the acrylic performance used in the deep sea is not very systematic. To capture the accurate properties of the acrylic used for these structures, several compression experiments are carried out to identify the compressive elasticity of acrylic under different loading rates, including the elastic region, upper yield point and lower yield point. Non-linear performance and rate-dependent characteristics are found in the experiments. Based on the experimental data, polynomial form of hyper-elasticity is well-matched to describe the non-linear stress and strain diagram. Numerical analysis of acrylic specimen and observation window are conducted based on the hyper-elastic model. The results show good consistency between simulations and experiments. The illustrated material model provides a basis for establishing a more accurate design-by-analysis-methodology for acrylic structures used in underwater facilities.

ФОРМУВАННЯ ЕКСПОЗИЦІЇ ВІТРИН ВІДОМИХ БРЕНДІВ ОДЯГУ ЗАСОБАМИ КОМПОЗИЦІЇ

The article outlines compositional tools used in the design of window displays of popular brand clothing stores. The author analyzes the current state of research of this topic and studies the materials of researchers dealing with the topic of window dressing and its development. The research methodology was developed and the regulatory requirements for window display design were analyzed. The analysis of window display design of well-known clothing brands in the national and foreign practice was carried out, and their comparative analysis was made. The study was supplemented by the analysis of artistic decoration of window displays with artificial lighting elements. Individual features of decoration of window displays of popular brand clothing stores were studied. The relevance of the present research topic is explained by the importance of the window display design as a means of advertising for trade establishments helping to influence the consumer. A high-quality window display design encourages a person to enter a store and definitely motivates to make a purchase. Opening of new trade spots and redesign of interiors of existing stores take place in conditions of active competition. It is important to analyze the world and national trends of design of window displays of famous brands in the sphere of fashion, and their development, to identify common and distinctive features, as well as individual characteristics, with account for the industry and regional context. Window displays are powerful and effective tools of trade advertising. They familiarize passers-by with store goods, novelties, promotions and seasonal changes, and also act as a business card of a shop. Window displays with interesting design help buyers to navigate through new fashion trends. The design primarily affects the visual perception of goods. Quite often, people switch from looking at a skillfully decorated shop window to the product itself as its integral part. That is why the object of advertising affects the psychology of a person more strongly than its image in a photo. Direct visual contact with the product gives a person the most accurate information about its shape, size, color, and texture. Having analyzed the experience of foreign designers in the sphere of window display design, we can note the notable differences in design and formation of the exposition. Window displays of national brands are distinguished by sophistication, minimalism of shapes and colors that are close in tone to the clothing brand itself, while foreign store windows, on the contrary, make “loud” appearance of new collections, have a contrast combination of shapes and colors, aimed at enhancing the impression on customers. They are characterized by an asymmetric structure of the composition, a bright color scheme, use of shiny materials, hyperbolization of dimensions, and accentuation of the central element of the composition. Non-standard plots and colors can be seen in window displays of famous clothing stores. Designers engage famous artists to create installations with the use of lighting and illusion effects to attract the attention of customers. This study supplemented the existing scientific researches in the sphere of window display design of stores of famous clothing brands and comprehensively analyzed the issue of window display design with account for compositional and technical methods and tools.

Dolphins reduce hearing sensitivity in anticipation of repetitive impulsive noise exposures.

The auditory steady-state response (ASSR) was continuously measured in two bottlenose dolphins during impulse noise exposures to determine whether observed head movements coincided with actual changes to auditory system sensitivity. Impulses were generated by a seismic air gun at a fixed inter-pulse interval of 10 s. ASSR amplitudes were extracted from the instantaneous electroencephalogram using coherent averaging within a sliding analysis window. A decline in ASSR amplitude was seen during the time interval between air gun impulses, followed by an elevation in ASSR amplitude immediately after each impulse. Similar patterns were not observed during control trials where air gun impulses were not generated. The results suggest that the dolphins learned the timing of the impulse noise sequences and lowered their hearing sensitivity before each impulse, presumably to lessen the auditory effects of the noise. The specific mechanisms responsible for the observed effects are at present unknown.

Adaptive window space direction laser speckle contrast imaging to improve vascular visualization.

Vascular visualization is crucial in monitoring, diagnosing, and treating vascular diseases. Laser speckle contrast imaging (LSCI) is widely used for imaging blood flow in shallow or exposed vessels. However, traditional contrast computation using a fixed-sized sliding window introduces noise. In this paper, we propose dividing the laser speckle contrast image into regions and using the variance criterion to extract pixels more suitable for the corresponding regions for calculation, and changing the shape and size of the analysis window at the vascular boundary regions. Our results show that this method has a higher noise reduction and better image quality in deeper vessel imaging, revealing more microvascular structure information.

Long-term trends in abundance, phenology, and morphometrics of Little Stint Calidris minuta during autumn migration in southern Sweden, 1946–2020

The Little Stint Calidris minuta is an Arctic wader species that migrates through the Baltic Sea region towards wintering areas in North and West Africa and the Mediterranean region. We use a 75-year trapping series, comprising 4,791 Little Stints on autumn migration, from Ottenby Bird Observatory in Sweden to illustrate long-term trends in abundance, phenology, and morphometrics. Numbers of trapped juveniles dropped from median 31 (mean 74) in 1946–1999 to median 1.5 (mean 3.5) birds in 2000–2020, while the number of adults was generally low and without trends. Rolling window analyses showed that the drop in juveniles started around 1984, and from 1993 onward the median never exceeded seven juveniles/year (25 %-quantile: 0–1; 75 %-quantile: 4–55). Moreover, adult birds advanced their passage on average 0.48 days per year, passing 26 days earlier in 2020 than in 1946. Earlier migration of adults and decreased numbers of juveniles suggest low reproductive output in recent decades. Morphometric data of recaptured birds show that Little Stints on stopover at Ottenby gain fuel at a speed close to the theoretical maximum, strongly indicating that the conditions at the trapping site remain favourable for foraging waders.

Seismic Analysis of RMS Amplitude Attributes to Identify Hydrocarbon Potential in Field "X" of Jambi Province Indonesia

Indonesia has many potential oil and gas basins. Seismic attribute analysis of RMS Amplitude (Root Mean Square) was conducted in the Betung area, Jambi sub-basin, South Sumatra Basin. The reservoir characterisation technique used in this study is rms attribute analysis which can show changes in the lithology of the study layer. Seismic attributes were extracted from 2D pre-stack time migration (PSTM) seismic data. This study also takes BTJ-210 log data as interpretation and determines the reservoir thickness at the target. Based on the seismic attribute analysis with rms, it is verified that the reservoir is indeed detected within the analysis window of 10 ms, 20 ms, 30 ms, 40 ms and the possible distribution of the reservoir is in horizon 3 and horizon 4 which has a high range of amplitude values and is in a high range zone or higher area than the surrounding area. The results of this seismic attribute analysis can show the hydrocarbon prospect zone and the potential for new well development in the study area, which is located in Horizon 3 and Horizon 4 at a depth of 227 to 250 metres south of the study area.

Measuring the economic performance of transition economies: DEA-bootstrapping approach

PurposeThe paper analyzes the relative technical efficiency of the transition economies of the Western Balkans in the period 2007–2021, in comparison with the former countries with a socialist state system, today members of the European Union (EU), based on selected macroeconomic indicators and panel data.Design/methodology/approachData envelopment analysis (DEA), i.e. its extension, DEA Window analysis, is applied. Total technical efficiency, as a prerequisite of economic efficiency, is decomposed into pure technical efficiency (PTE) and scale efficiency (SE). Bootstrapping method and Mann–Whitney U test were used to check the robustness of the obtained results, i.e. efficiency values.FindingsThe results show that in 2020, all observed countries recorded a significant drop in economic efficiency as a result of a general, disproportionate drop in the value of selected macroeconomic variables, which occurred due to the global economic crisis and the slowdown in economic activity caused by the COVID-19 pandemic. This drop in efficiency was significantly greater in the former socialist states, now members of the European Union, which showed their greater sensitivity to global crises. None of the observed economies in the observed period was relatively efficient, that is, at the level of best practice, which occurred primarily as a consequence of the inefficiency of business conditions expressed in the economies of scale.Research limitations/implicationsThe main limitation of this study stems from the very nature of the concept of DEA efficiency, which is relative in nature. Also, the results and their interpretation are also significantly influenced by the choice of model variables, as shown by Lábaj et al. (2013), as well as a small number of decision-making units (DMUs). The mentioned limitations prevent unambiguous interpretation and generalization of the obtained results.Practical implicationsThe study may be of importance to economic policy makers in macroeconomic decision-making. The application of the DEA concept in measuring the technical efficiency of national economies is a useful tool in the analysis of macroeconomic performance and a benchmarking approach for positioning and achieving competitive advantage on the international market.Originality/valueSince research of this type is very limited, the results of this study make a theoretical and empirical contribution to the literature, creating a basis for future research and reexamination. The application of the DEA concept in measuring the technical efficiency of national economies is a useful tool in the analysis of macroeconomic performance and a benchmarking approach for positioning and achieving competitive advantage in the international market.

Multi-Objective Optimization of Maintenance, Rehabilitation, and Reconstruction Decision Making Considering Safety

Traditional pavement maintenance, rehabilitation, and reconstruction (MRR) planning accounts for user cost by maximizing drivers’ comfort, reducing users’ travel delay, and minimizing fuel consumption, while controlling the agency’s costs of undertaking the necessary preservation activities. Safety performance is seldom considered in the MRR planning of pavements. However, the pavement condition has a large impact on the crash frequency and needs to be addressed in the process of pavement MRR planning. Therefore, this study first incorporates safety costs into the MRR planning process as a separate objective. To do so, the MRR planning process seeks to find an optimal maintenance strategy with the lowest agency cost while minimizing crash frequency and user cost based on the resulting pavement condition. A multi-objective optimization approach is proposed to identify the optimal MRR plans that utilize a semi-Markov international roughness index deterioration model developed based on real pavement condition inspection data from Pennsylvania between 2006 and 2018. The results suggest that the agency costs will increase by $9,716 (5.3%) per mile per lane during a 50-year analysis window for pavement starting in good condition when the crash frequency is considered in the MRR planning. This increased agency cost contributes to a reduction in predicted crash frequency by 0.27%. The results can be used to determine the amount agencies need to spend on MRR activities to reduce accidents under different traffic flow and vehicle fleet combinations. The conclusion of this study provided evidence that lower crash frequency can be achieved by developing better MRR planning from the roadway operation perspective.