Future Maintenance Research Articles

Language attitudes towards French: A mixed-method investigation on potential Chinese immigrants in Ontario and Quebec Canada

This mixed-methods study aims to explore the language perceptions of Chinese individuals who live in Canada and consider immigration, specifically focusing on their attitudes towards French. The goal is to understand their attitudes towards French and, ultimately, increase policy makers’ awareness of the future maintenance of French in Canada and re-evaluating the current language teaching approach. Seventy-eight Chinese participants from Quebec or Ontario regions completed two questionnaires that were derived from the Belief about Ethnolinguistic Vitality framework, followed by a semi-structured interview conducted with sub-sampled nine participants to explore reasons behind their attitudes. In terms of analyses and results, although Quebec participants believed that French would become more commonly valued and used in the future, both Ontario and Quebec participants claimed that French would not be as essential as English, and they had more positive attitudes towards the English acquisition because of its great regional power and instrumental benefits. Additionally, a multi-regression analysis demonstrated that attitudes towards French engagement were affected by sociocultural differences and language proficiency but were not affected by educational contact (French language course). The findings of thematic analysis indicate that problems about the monolingual teaching approach led to a negative view of French lessons and even a negative attitude towards engaging with the French language. Finally, practical implications and suggestions were provided in order to enhance their attitudes towards French engagement in Canada.

A Corrosion Maintenance Model Using Continuous State Partially Observable Markov Decision Process for Oil and Gas Pipelines

This paper proposes a continuous state partially observable Markov decision process (POMDP) model for the corrosion maintenance of oil and gas pipelines. The maintenance operations include complex and extensive activities to detect the corrosion type, determine its severity, predict the deterioration rate, and plan future inspection (monitoring) schemes and maintenance policy. A POMDP model is formulated as a decision-making support tool to effectively handle partially observed corrosion defect levels. It formulates states as the pipeline’s degradation level using a probability distribution. Inline inspection (ILI) methods estimate the latest state of the pipeline, which also defines the initial state of the optimization process. The set of actions comprises corrosion mitigation operations. The errors associated with the ILI method are used to construct the observation function for the model. The sum of inspection, maintenance operations, and failure costs for a given state and action are formulated as rewards. Numerical experiments are made based on data collected from the literature. The results showed that different policies, whether derived from solvers (theoretical) or determined from practical experience, can be compared to identify the best maintenance alternative using the model. It was also observed that the choice of the solvers is important since they affect the discounted rewards and the run time to obtain them. The model approximates the parameters and uncertainty associated with the propagation of corrosion, proficiency of inspection methods, and implementation of maintenance policies. Overall, it can be applied to improve the maintenance decision-making process for the oil and gas pipeline as it incorporates the stochastic features of the operation.

Point Cloud-Based Historical Building Information Modeling (H-BIM) in Urban Heritage Documentation Studies

Integrating building information modeling (BIM) technology into historical and cultural heritage structures has resulted in historic building information modeling (H-BIM), an effective method for managing and documenting invaluable historical artifacts. H-BIM has the potential to aid with the design, restoration, and maintenance activities of historical buildings by properly integrating parametric data and deliberately assessing semantic information, thus progressing the 3D model’s performance. H-BIM also aims to conduct cultural heritage (CH) studies in a systematic manner by combining 3D modeling techniques and information management. The first step in this process is to conduct a suitable geomatic survey to build accurate 3D models. The most efficient modeling method is to employ 3D point cloud data that are acquired using the terrestrial laser scanning method. This study proposes a manual modeling strategy for urban historical buildings that falls within the scope of the H-BIM methodology. The proposed modeling strategy follows a workflow that inserts point cloud data into the BIM environment in order to create a 3D urban cultural heritage model using ontologies applied with the H-BIM methodology. With this objective in mind, the H-BIM model development platform Revit was used to model the object elements of the inheritance structure in 3D and save them in the BIM library. It is very important to model building elements separately in 3D modeling studies. The resulting BIM product sheds light on fundamental data for the future planning, design, operation, and maintenance processes of the building. Users can obtain data suitable for H-BIM by adding graphical and non-graphical data.

Evaluating the Residential Outdoor Environment for the Elderly in Shuiqinggou, Qingdao, China using the Seniors’ Outdoor Survey Tool

As China’s elderly population grows, residential areas need to provide an outdoor environment for their physical and mental health in good condition. Many studies have found that better outdoor environments in residential areas can improve the health of the elderly and increase their happiness. The Seniors’ Outdoor Survey (SOS) tool was developed as an effective and reliable assessment tool to evaluate the needs and preferences of the elderly in outdoor spaces. This study used the SOS tool to evaluate Shuiqinggou Park in Qingdao, Shandong Province, China. The outdoor environment is divided into three parts for five domains of evaluation. The final average score of the whole environment is 84.35, the percentage is 86.9% compared with the total score, the Inter-rater ICC among trainers is 0.917, and the T-test value is 0.799. Through the results of the tools, it can be concluded that Shuiqinggou Park is a favorable outdoor space suitable and safe for the elderly. The study’s findings can help project managers and designers identify the essential outdoor characteristics for future construction and maintenance prioritization.

Applicability and Cost Implication of Labor-Based Methods for Sustainable Road Maintenance (SRM) in Developing Countries

Regular maintenance keeps roads in a good working condition for users. Road maintenance can be conducted using labor and machine based methods. In this study, applicability and costs of labor-based, namely employment intensive (EI), labor intensive (LI), and labor-based equipment-supported (LBES) road maintenance methods were investigated with a case study in Tigray region, northern Ethiopia. The objectives of this study were therefore to (a) identify road defects and outline the corresponding labor-based maintenance methods, (b) compare relative costs of the three labor-based road maintenance methods, and (c) explore indicators of sustainable road maintenance (SRM) practices for developing countries. The results of this study showed that the labor-based methods are suitable for maintaining a wide range of road defects such as pavement cracks, bleeding, potholes, and rutting. Results of the cost analysis revealed that the LI and EI were 6% and 36% cheaper than the LBES method for repairing shoulder damage in asphalt roads. Roadside bush clearing using LBES resulted in 40% and 147% cost saving compared to EI and LI methods, respectively. Average share of labor cost compared to combined cost of labor and equipment in EI, LI, and LBES was 97%, 79%, and 37%, respectively. Unit rates of maintenance activities for different labor-based methods were also developed for Tigray region. The developed road maintenance unit rates will be useful for estimating cost of labor-based methods in Tigray and other similar regions in Ethiopia. In this study, 45 suitability indicators under 10 categories and three themes (social, economic, and environmental) were developed to improve future road maintenance practices in developing countries. Finally, it is recommended to use SRM practices to increase sustainability of road infrastructure in developing countries.

HBIM AS A TOOL FOR THE ANALYSIS AND CONSERVATION OF ARCHITECTURAL HERITAGE. CASE STUDY: THE RAME TOWER'S DIGITAL TWIN

Abstract. The Rame Tower, located in the municipality of Los Alcázares (Región of Murcia, Spain), is one of the watchtowers post coasts built in the sixteenth century in an attempt to defend the Spanish coast from attacks by Barbary pirates. Although the tower is protected monument as a BIC (Cultural Heritage Asset) is currently in a state of degradation and neglect and passively suffers the flow of time and vandalism. Like most cultural heritage assets, it is characterized by deep irregularities and heterogeneity, due both to the changes in use over the centuries and to the passage of time. In this research, a 3D survey is first realized by combining different techniques and instrumentation in order to obtain a complete cloud of points, both outside and inside. The cloud of points thus obtained serves as the basis for the realization of the HBIM model of the tower. Thanks to the points cloud you can reach a level of development (LOD) such that you can talk about digital twin of the asset. The HBIM model thus obtained will then be used for the realization of a proposal for recovery and reuse and an example of management and maintenance. The aim is to demonstrate how BIM applied to assets is useful both for its recovery and for future management and maintenance. The research also identifies which problems are found in the modeling of discontinuities typical of cultural heritage in the BIM environment and which solutions can be applied.

International Roughness Index Prediction for Jointed Plain Concrete Pavements Using Regression and Machine Learning Techniques

Rigid pavements are recently gaining vast interest for their durability and large traffic volume endurance. This raises the need for a robust method to predict their long-term performance to support decisions on their future maintenance and rehabilitation requirements. One of the most descriptive parameters of pavement ride quality is the International Roughness Index (IRI). IRI can be used as a performance indicator for the level of serviceability of rigid pavement. This study presents the evaluation of 21 variables covering climate, traffic, and structural parameters in predicting IRI for jointed plain concrete pavement (JPCP) sections using 1,414 data points acquired from the Long-Term Pavement Performance (LTPP) database. It was found that only 10 variables are significant in the IRI prediction for JPCP. Moreover, four modeling techniques were applied, namely, linear regression, multivariate adaptive regression splines, Gaussian process regression, and artificial neural network. It was found that a deep ANN model with a structure of one input layer, three hidden layers, and one output layer [10-36-18-9-1] gives the highest IRI prediction accuracy with root mean square error and coefficient of determination (R2) of 0.117 and 0.92, respectively. Finally, a sensitivity analysis was performed to evaluate the impact of each input variable on the accuracy of IRI prediction for JPCP sections, and it was found that the most influential variables are initial IRI, pavement age, mean and standard deviation of humidity, standard deviation of evaporation, mean and standard deviation of freezing index, pavement compressive strength, ratio between pavement and base layers thickness, and finally an estimated parameter called site factor which is a function of (age, freezing index and soil percentage passing sieve no. 200).

Structural Health Monitoring of Large-Scale Geomembrane Floating Covers Using Solar Energy

High-density polyphenylene geomembranes are utilized as large-scale floating covers in anaerobic water treatment plants for the purpose of collecting renewable biogas energy and preventing the emission of odorous by-products. During degradation, the accumulation of scum, the presence of biogas pockets and sewage can negatively impact the structural integrity of the covers and hinder degradation efficiency. Traditional methods are insufficient in identifying the substances under the covers. In this study, a thermography technique was developed and tested in lab-scale experiments using ambient weather information to inspect the geomembranes. The technique was then applied to a remote monitoring investigation of large-scale floating covers located at a sewage treatment plant. Long-term monitoring of two regions on the covers was conducted using a thermal camera, with ambient weather information recorded to help analyze temperature changes. The acquired thermal image sequences were analyzed, resulting in the creation of a segmented cover map indicating the profiles of substances. The results of the on-site thermal imaging field trials verify the effectiveness of the developed remote sensing thermography technique, which has the potential to assist in future asset maintenance, remove scum efficiently and enhance anaerobic degradation efficiency at sewage water treatment plants.

Multi-scenario simulation analysis of cultivated land based on PLUS model—a case study of Haikou, China

Land-use changes have received increasing attention since the 1990s. Cultivated land is a vital land resource with both natural and economic features. Socioeconomic development profoundly affects the spatiotemporal evolution of cultivated land. In turn, the spatiotemporal change of cultivated land also reflects the socioeconomic development trend. This study took Haikou City, which has the highest degree of urbanization in Hainan Province, as the study area, analyzed the spatiotemporal evolution characteristics of its cultivated land resources during the past 20 years of rapid socioeconomic development, and predicted the future spatiotemporal evolution trends of its cultivated land resources on this basis, to provide scientific reference for the rational use and sustainable development of the cultivated land resources in Haikou. This study applied the single land-use dynamic degree (SLUDD) and land-use transfer matrix to explore the spatiotemporal evolution of cultivated land in Haikou from 2000 to 2020. The patch-generating land-use simulation (PLUS) model was introduced to predict the patterns of cultivated land under different development scenarios in 2030. The results showed that: (1) although rapid socioeconomic development has contributed to Haikou's urbanization rate over the past 20 years, the accompanying expansion of construction land has caused damage to cultivated land resources, especially in urban suburbs. Site conditions, differential land benefits, and urban economic radiation may be potential influencing factors leading to the loss of peri-urban cultivated land; (2) it was verified that the PLUS model applies to the land-use patterns prediction in Haikou; and (3) the simulation results of both natural and urban development scenarios not only predicted the continued loss of cultivated land in the original zone but also exposed new sites of cultivated land loss. Although construction land expansion is an inevitable trend, there is still hope that the expansion rate can be controlled, for example, under the cultivated land conservation scenario. The simulation results of the three scenarios have essential reference values for the future maintenance of cultivated land resource security in Haikou.

Mental Health Literacy Intervention to Reduce Stigma Toward Mental Health Symptoms and Disorders in Women Rugby Players: A Feasibility Study

This feasibility study aimed to evaluate the effectiveness of an educational intervention on stigma toward mental health symptoms and disorders, mental health literacy, and help-seeking intentions among U.K. semielite women rugby players. Seven semielite women rugby players participated. An A-B-A single-case experimental research design was used to assess stigma toward mental health symptoms and disorders, mental health literacy, and help-seeking intentions at baseline, intervention, and follow-up phases. The intervention was successful in enhancing the players’ mental health literacy and reducing stigmatizing attitudes toward seeking professional psychological help. Acknowledging the study’s small sample size, the findings revealed that there is a need for scaffolding to support future developments, advancements, and maintenance of mental health support in women’s rugby. Practical implications of future findings from a larger-scale study may lead to policy reformation across the game to inform and improve systemic mental health support for women rugby players.

Evaluation of the Spanish-Language Cancer Educational Webinar Series "Vamos a educarnos contra el cáncer" with the RE-AIM Framework.

The COVID-19 pandemic disrupted healthcare for patients with chronic diseases, including cancer. Barriers to healthcare increased, especially for racial and ethnic minorities. While many institutions developed webinars to educate community members, few webinars used a community-based participatory approach, employed a theory-based engagement design, and were evaluated. This manuscript reports the outcomes of "Vamos a educarnos contra el cáncer," a 2021 webinar series. Monthly educational webinars were conducted in Spanish on cancer-related topics. The presentations were delivered by Spanish-speaking content experts from different organizations. Webinars were conducted using the video conferencing platform Zoom. Polls were launched during the webinar to collect data and evaluate each webinar. The RE-AIM model of reach, effectiveness, adoption, implementation, and maintenance was used to evaluate the series. The SAS Analytics Software was used for analysis and data management. Two hundred ninety-seven people participated with over 3000 views of the webinar recordings (Reach); 90% rated the sessions as good or excellent (Effectiveness); 86% agreed to adopt or improve a cancer-related behavior, and 90% reported willingness to adopt or improve a cancer-related action for someone else (Adoption); 92% reported feeling engaged (Implementation). The series has produced a resource library, manual of operations, and agreement of the Hispanic/Latino Cancer Community Advisory Board (CAB) to continue the webinar series in the future (Maintenance). Overall, these results highlight the impact of this webinar series and provide a standard approach to planning, delivering, and evaluating webinars as a strategy for cancer prevention and control in a culturally appropriate manner.

Optimizing system reliability through selective maintenance allocation: A novel multi‐objective programming approach using neutrosophic fuzzy concept

AbstractSelective maintenance refers to the problem of decision‐making on how to maintain a multi‐component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likelihood of success for future maintenance tasks. In this article, we proposed a multi‐objective decision‐making model for maximizing the system reliability of a multi‐component systems. The objective functions represent the reliability of the sub‐systems arranged in series comprising of components arranged in parallel forms. The cost and total time incurred in the repair/replacement activities are considered as two sets of constraints in the proposed model. Fuzzy logic is incorporated to deal with the inherent uncertainty that exists in the parameters involved in the proposed model. To solve the uncertain multi‐objective selective maintenance model, we have proposed four‐valued neutrosophic technique. The proposed solution methodology combines the concept of four‐valued neutrosophic fuzzy set with fuzzy goal programming technique. Four linear membership functions for the degrees of truth, uncertainty, contradiction, and false are used to obtain the compromise solution through the proposed technique. To determine the feasibility and applicability of the proposed model and solution methodology, a case problem of multi‐component system of an aircraft gas turbine engine is studied.

Ageing-Oriented Prosthetic Treatment Plan: A Case Report

Today, older adults are routinely classified into different categories based on their degrees of independence. However, due to the extreme heterogeneity of this group, and the fact that ageing is a progressive process, older adults may require customized prosthetic treatment. Older people, during ageing, may quickly pass into a frail condition, with loss of independence and of the manual skills necessary for proper hygiene maintenance. With this in mind, complete arch rehabilitations can make an important contribution to a patient’s quality of life. New treatment strategies are needed which take into account the priorities of ageing. The aim of this case report is to show an adaptable prosthetic treatment protocol that is useful for older adults. This protocol provides individual satisfaction and also aids forecasting of the patient’s future functional and maintenance needs.

BIM interoperability in the maintenance planning process for existing buildings

One of the main difficulties inherent to the process of maintenance and management of existing assets is the lack of plans and project documents that allow a study and even an understanding of how the structure of the building works. In addition to the lack of records of changes made that may have altered the structure of heritage buildings over time. This makes it difficult to analyze interventions, to treat possible problems that may evolve into more worrying pathologies that put at risk the useful life of the property. Older buildings require more frequent inspections in order to carry out interventions that can extend their service life. Having all records of maintenance and inspections is essential for better planning future interventions. The usage of BIM technology in maintenance planning and management of existing buildings is becoming increasingly popular. This study proposes a methodology to efficiently use BIM technology in the phase of use, operation, and maintenance of buildings. Navisworks Manage software is used as a key alternative for information visualization and management, taking advantage of the interoperability inherent in the BIM process through IFC. The methodology involves the collection of data, modeling of the structure, linking maintenance sheets to each respective structural element of the 3D model, visualization of the 3D model in Navisworks Manage, and generation of 2D projects with indications of problems identified in the field. The proposed methodology improves the accuracy and dynamic nature of the maintenance planning process, allowing better control of inspection and maintenance records over time. The ease and security of data stored in real-time in online spreadsheets ensure the recording of information in an accessible and integrated way, which can be used in planning and reform processes over time. It enables the adoption of a preventive approach that favors and provides essential information for future maintenance. The proposed methodology can be adapted and reproduced for application in other design disciplines like architecture and complementary designs. The proposed methodology was used for the visualization and documentation of the 3D model referring to the as-built of SG-12. This case study is part of the set of General Services Sheds of the University of Brasília, which besides being an old building in the process of being listed as cultural heritage, has undergone several changes over time. The methodology developed and the result of the documentation of the data are presented in detail.

A stochastic optimization approach for optimal Tail Assignment with knowledge-based predictive maintenance

AbstractRecent developments in the field of data analysis offer enormous potential to derive predictions of future aircraft maintenance necessities from observable aircraft attributes. This procedure is called Predictive Maintenance. The Predictive Maintenance generates considerable business benefits compared to alternatives like reactive or preventive maintenance for various reasons. Nevertheless, recognizing a maintenance necessity is only the first step, since the execution of the maintenance action has to fit into the airlines operational schedule. An essential step to realize financial benefits is to integrate Predictive Maintenance into the airlines optimization processes. To contribute to this task, we expand a well-known Tail Assignment model for the assignment of a potentially heterogeneous fleet of aircraft to a schedule of flights by a number of part failure scenarios. This results in a stochastic mixed integer linear optimization program, for which an optimization algorithm with solution guarantees is developed. This optimization algorithm is based on Benders Decomposition, which is concretized for the Tail Assignment problem and optimized for this task. Using this algorithm, a large proportion of the recovery costs for almost all instances tested is saved. The algorithm solves all instances in a reasonable amount of time. The algorithm uses a state-of-the-art mixed-integer linear solver, implementing a decomposition-based solution procedure for stochastic programs, called the L-shaped method. To demonstrate the potential of the approach, we benchmark our results using four strategies, motivated by commonly used preventive, reactive and passive approaches. Our approach leads to considerable cost savings when compared to each of the four benchmark approaches. The algorithms are tested on a set of instances with up to 80 flights based on a pre-pandemic schedule of a larger German airline. To the best of our knowledge, this article presents the first attempt to implement an exact optimization approach integrating malfunction predictions of the form presented into a Tail Assignment model.

Assessment of the impacts of climat change on water supply system pipe failures

Climate change is projected to have profound impacts on the resilience and sustainability of built infrastructure. This study aims to understand the impacts of climate change on water supply systems and to facilitate adaptive actions. A premium database maintained by the Cleveland Water Division, Cleveland, Ohio, USA is analyzed. It contains 29,621 pipe failure records of 51,832 pipes over the past 30 years, representing one of the largest dataset in current literature. From the database, pipe failure rate models have been developed for water pipes made of different types of materials at different ages. The influence of climate (temperature and precipitation) on fragility of water pipes are obtained. Based on the developed climate-fragility failure rate models, the impacts of climate change on the water systems located in different geographic regions are evaluated by predicting the failure rate and number of failures in the water systems in the next 80 years (2020 to 2100). Climate models are used to predict weather under different climate change scenarios. The results demonstrate that the impacts of climate change on water supply system are likely complicated and are dependent upon factors such as the geographic location, pipe material, pipe age, and maintenance strategies. Water pipes in the cold regions may experience fewer number breaks due to the warmer weather and less severe winter, whereas those located in the hot regions may experience more failures associated with more corrosion. Different pipe replacement strategies are compared, which demonstrate the importance of considering the aging of water supply system in future maintenance decisions. This study enriches current understandings on the impacts of climate change on the water systems. The results will help water utilities to design climate change adaptation strategies.

Patient perceptions and knowledge of Deep Brain Stimulation (DBS) in Parkinson’s disease (PD) (KNOW-DBS) (P5-11.015)

<h3>Objective:</h3> Identify existing knowledge gaps about DBS in PD patients with an intent to improve health outcomes. <h3>Background:</h3> DBS is a safe and effective treatment for patients with advancing PD with motor fluctuations and dyskinesia. However, the surgical decision making involves a multistep complex process. In spite of adequate counseling by the neuromodulation team, patients may still experience confusion about this intricate therapy. Revealing those existing knowledge gaps is vital to improve patient understanding of this therapy with device self-adjustment alongside medical management. <h3>Design/Methods:</h3> Questionnaires have a long history of utility in PD, however studies have not specifically assessed patient knowledge of DBS in PD. A survey was generated using commercial software and is being administered to patients over the next 6 months. The 23-point questionnaire utilizes Likert response options to explore patient knowledge of DBS. <h3>Results:</h3> 17 subjects thus far have completed the survey. The majority (64.7%) erroneously believed that DBS will alter the natural course of their PD. Most (82.4%) believe the efficacy of DBS is similar to optimal pharmacotherapy, would improve tremor (88.2%), slowness (58.8%), stiffness (64.7%), dyskinesia (52.9% ), and walking (52.9%). When asked about the potential side effects of DBS, most (94.1%) patients knew about the possibility of brain bleed or speech impairment, 76.5% acknowledged the potential for stroke and cognitive impairment, and 88.2% chose infection as a possible adverse event. Finally, 100% of respondents correctly knew that DBS does not mean that all PD medications can be eliminated, that future maintenance procedures are likely, and DBS has been shown to improve quality of life. <h3>Conclusions:</h3> Although a lot of patients demonstrated sound knowledge about DBS, misconceptions are still indeed prevalent. Identification of these knowledge gaps is vital towards improving health literacy and the findings described herein will form the basis for effective educational interventions. <b>Disclosure:</b> Dr. Chitnis has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Abbott neuromodulation. Dr. Chitnis has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Skypass Foundation. Dr. Chitnis has received personal compensation in the range of $10,000-$49,999 for serving as an officer or member of the Board of Directors for Skypass Foundation. Dr. Chitnis has received publishing royalties from a publication relating to health care. Mr. Haque has nothing to disclose. Ms. Ahmed has nothing to disclose. Dr. Oblack has nothing to disclose. The institution of Dr. Salinas has received research support from North Texas VA Health Care System New Investigator Award. Dr. McDaniels has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for NeuroChallenge. Nader Pouratian has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Boston Scientific. Nader Pouratian has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Abbott. Nader Pouratian has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Sensoria Therapeutics. Nader Pouratian has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for BrainLab. Mr. Yazdani has nothing to disclose.

Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model

The stability of artificial sand-binding vegetation determines the success or failure of restoration of degraded ecosystem, accurately evaluating the stability of artificial sand-binding vegetation can provide evidence for the future management and maintenance of re-vegetated regions. In this paper, a novel data-driven evaluation model was proposed by combining statistical methods and a neural network model to evaluate the stability of artificial sand-binding vegetation in the southeastern margins of the Tengger Desert, where the evaluation indexes were selected from vegetation, soil moisture, and soil. The evaluation results indicate that the stability of the artificially re-vegetated belt established in different years (1956a, 1964a, 1981a, and 1987a) tend to be stable with the increase of sand fixation years, and the artificially re-vegetated belts established in 1956a and 1964a have almost the same stability, but the stability of the artificially re-vegetated belt established in 1981a and 1987a have a significant difference. The evaluation results are reliable and accurate, which can provide evidence for the future management of artificial sand-binding vegetation.

Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts

Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5min. We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.

Failure analysis and data-driven maintenance of road tunnel equipment

The operation and maintenance of each technological system are essential parts of the life cycle. This article describes the result of the analysis of diagnostic data from tunnel equipment and its use. The analysis was carried out with the main objective of finding relations that could be further applied as a specific approach for the diagnostic procedure and possibly for future predictive maintenance of tunnel equipment. The article summarises the research of the project focused on the suitability of diagnostic data for potential use of predictive data-driven maintenance of technological parts in road tunnels. Data records were captured in the technological and tunnel control systems deployed in the Czech tunnels. The relations between warning and failure notifications were analysed together with the selected physical conditions of tunnel equipment during the operation by using measurements of technical parameters such as undervoltage, temperature, etc. Additionally, the impact of these parameters was monitored and evaluated according to the life cycle of the equipment. This resulted in a sequence analysis of states and events detected on particular equipment. In the article, the relations between measured technical parameters and failures are presented, together with the solution proposal that could be applied for failure prediction and enhancements of road tunnels operation and its technological systems by applying predictive maintenance of tunnel equipment. The presented outputs show that the cause of VMS failures can be the reaching of a critical device temperature or undervoltage, that are measurable, detectable even before the failure itself, which can be potentially predicted in this way.