Maintenance Methods Research Articles

Failure Prediction of Coal Mine Equipment Braking System Based on Digital Twin Models

The primary function of a mine hoist is the transportation of personnel and equipment, serving as a crucial link between underground and surface systems. The proper functioning of key components such as work braking and safety braking is essential for ensuring the safety of both personnel and equipment, thereby playing a critical role in the safe operation of coal mines. As coal mining operations extend to greater depths, they introduce heightened challenges for safe transportation, compounded by increased equipment loss. Consequently, there is a pressing need to enhance safety protocols to safeguard personnel and materials. Traditional maintenance and repair methods, characterized by routine equipment inspections and scheduled downtime, often fall short in addressing emerging issues promptly, leading to production delays and heightened risks for maintenance personnel. This underscores the necessity of adopting predictive maintenance strategies, leveraging digital twin models to anticipate and prevent potential faults in mine hoists. In summary, the implementation of predictive maintenance techniques grounded in digital twin technology represents a proactive and scientifically rigorous approach to ensuring the continued safe operation of mine hoists amidst the evolving challenges of deepening coal mining operations. In this study, we propose the integration of a CNN-LSTM algorithm within a digital twin framework for predicting faults in mine hoist braking systems. Utilizing software such as AMESim 2019 and MATLAB 2016b, we conduct joint simulations of the hoist braking digital twin system. Subsequently, leveraging the simulation model, we establish a fault diagnosis platform for the hoist braking system. Finally, employing the CNN-LSTM network model, we forecast failures in the mine hoist braking system. Experimental findings demonstrate the effectiveness of our proposed algorithm, achieving a prediction accuracy of 95.35%. Comparative analysis against alternative algorithms confirms the superior performance of our approach.

Three-Dimensional Meso-Structure-Based Model for Evaluating the Complex Permittivity of Asphalt Concrete.

Microwave heating is an emerging alternative pretreatment method for road maintenance in cold seasons. The thermal behavior of asphalt pavement under microwave heating is mainly determined by the complex permittivity of the asphalt mixture. In this study, an innovative approach for calculating the complex permittivity of an asphalt mixture based on a three-dimensional meso-scale heterogeneous structure was proposed. A series of experiments was conducted to verify the accuracy of this approach. The effect of porosity, void size, moisture content and aggregate gradation on the complex permittivity for an asphalt mixture were computationally analyzed based on the validated approach. Moreover, the applicability of commonly used classical dielectric models was analyzed. The classical Lichtenecker-Rother (LR) dielectric model was modified on the basis of simulation data for various conditions. The results showed that the real part of the complex permittivity decreased with the increase in porosity. Some sudden change in the imaginary part of the complex permittivity was observed within the frequency range from 2.6 GHz to 3.9 GHz. A larger air void size would lead to a larger frequency at which sudden change occurs. The real part and imaginary part of the complex permittivity tend to be smaller when more coarse aggregates are replaced with fine aggregates. Both the real part and the imaginary part of the complex permittivity increase with higher moisture content due to the stronger dielectric property of water. Each 1% increase in moisture content would lead to about a 3~4% increase in the real part of the complex permittivity. The determination coefficients R2 for the real part and the imaginary part of the complex permittivity fitted by the modified Brown model were the maximum values, which were 0.922 and 0.980, respectively. The method presented in this study is useful for transportation agencies to optimize microwave heating during winter maintenance.

AI-based technology to prognose and diagnose complex crack characteristics of railway concrete sleepers

Railway concrete sleepers are key safety-critical components in ballasted railway tracks. Due to frequent high-intensity impact loadings from train-track interaction over irregularities together with hostile environmental conditions, complicated characteristics of various crack patterns can incur on railway concrete sleepers, which will decrease their durability and service life overtime. Early warning of those cracks can help railway engineers to plan and schedule for renewal and maintenance timely and effectively. This study thus explores the artificial intelligence application of YOLOv5OBB (YOLOv5 with Oriented Bounding Box output) in the identification and classification of cracks in railway sleepers into three distinct types: longitudinal, transverse, and inclined, based on their specific crack angles, which have not been investigated in the past. The identification of crack angles is the novelty of this study. Recognising the various types of cracks is critical, given their varying causes and degrees of severity. Current corrective maintenance methods pose considerable safety risks to workers and exhibit low efficiency, underscoring the need for a more autonomous and efficient solution. This study marks a significant stride towards revolutionising railway maintenance, evidenced by an impressive mAP (Mean Average Precision) of 0.72 for crack detection and a 92% accuracy rate for angle detection. These promising results substantiate our study's potential to pioneer advancements in railway infrastructure maintenance.

Deep learning-based digital twin for intelligent predictive maintenance of rapier loom

The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from “regular maintenance” and “post-event maintenance” to “predictive maintenance”. In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of “combining the real with the virtual and controlling the real”. Firstly, a digital twin system structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom.

Assessment of Intelligent Unmanned Maintenance Construction for Asphalt Pavement Based on Fuzzy Comprehensive Evaluation and Analytical Hierarchy Process

Conventional human-involved maintenance methods for asphalt pavement pose significant challenges when applied to high-traffic road sections, often leading to congestion and safety risks, as well as reduced maintenance efficiency. In recent years, explorations into unmanned construction technology for newly constructed expressways have yielded beneficial and encouraging results. However, its application in road maintenance in more complex environments still needs to be expanded. In this study, an intelligent unmanned maintenance technology for asphalt pavement was applied to the Lilong Highway in Zhejiang Province, China, and the compactability, thickness, surface smoothness, permeability coefficient, and constructure depth of maintenance road sections were measured. Then, based on fuzzy comprehensive mathematics and the analytic hierarchy process, a comprehensive evaluation was performed on the intelligent unmanned maintenance technology, considering the aspects of road quality, safety, application, and socio-economic benefits. The results show that the road quality of intelligent unmanned maintenance technology can meet the road specification requirements. In addition, the membership degree of unmanned maintenance technology in the excellent grade is the highest, reaching 0.805, and the quantified value for the overall evaluation of the application effectiveness of unmanned maintenance technology is 92.10. This means that the final comprehensive evaluation result of unmanned maintenance technology is rated as excellent. The research findings provide decision-makers with valuable insights into the unmanned automation maintenance challenges faced by asphalt pavement, enabling them to implement appropriate measures to elevate the maintenance standards of road transportation.

Geometric batch optimization for packing equal circles in a circle on large scale

The problem of packing equal circles in a circular container is a classic and famous packing problem, which is well-studied in academia and has a variety of applications in industry. The problem is computationally challenging, and researchers mainly focus on small to moderate-scale instances with the number of circular items n less than 320 in the literature. In this work, we aim to solve this problem on large scale, and it is the bottleneck for most global optimization problems. Specifically, we propose a novel geometric batch optimization method that makes batch gradient descent based on the geometric locations of packing items. This method not only can speed up the convergence process of continuous optimization significantly but also reduce the memory requirement during the program’s runtime. Then, we propose a heuristic search method, called solution-space exploring and descent, that can discover a feasible solution efficiently on large scale. Besides, we propose an adaptive neighbor object maintenance method to maintain the neighbor structure applied in the continuous optimization process. In this way, we can find high-quality solutions on large-scale instances within reasonable computational times. Extensive experiments on the benchmark instances sampled from n= 300 to 1000 show that our proposed algorithm outperforms the state-of-the-art algorithms and performs excellently on large-scale instances. In particular, our algorithm found 10 improved solutions among the 21 well-studied moderate-scale instances and 95 improved solutions among the 101 sampled large-scale instances. Furthermore, our geometric batch optimization, heuristic search, and adaptive maintenance methods are general and can be adapted to other packing and continuous optimization problems.

Enhancing Predictive Maintenance in Water Treatment Plants through Sparse Autoencoder Based Anomaly Detection

The deployment of Machine Learning (ML) for improving Water Treatment Plants (WTPs) predictive maintenance is investigated in the present article. Proactively detecting and fixing functional difficulties which might cause catastrophic effects has historically been an endeavour for reactive or schedule-based maintenance methods. Anomaly Detection (AD) in WTP predictive maintenance frameworks is the primary goal of this investigation, which recommends a novel approach based on autoencoder (AE)-based ML models. For the objective of examining high-dimensional time-series sensor data collected from a WTP over a long time, Sparse Autoencoders (SAEs) are implemented. The data collected involves an array of operational measurements that, evaluated together, describe the plant's overall performance. With the support of the AE, this work aims to develop a practical framework for WTP operation predictive maintenance. Anomalies are all system findings from testing that might result in flaws or malfunctions. The research article analyses January and July 2023 WTP data from Jiangsu Province China. The AE paradigm had been evaluated using F1-scores, recall, accuracy, and precision. SAE has substantially improved AD functionality.

Modeling Dynamics of Human Gut Microbiota Derived from Gluten Metabolism: Obtention, Maintenance and Characterization of Complex Microbial Communities.

Western diets are rich in gluten-containing products, which are frequently poorly digested. The human large intestine harbors microorganisms able to metabolize undigested gluten fragments that have escaped digestion by human enzymatic activities. The aim of this work was obtaining and culturing complex human gut microbial communities derived from gluten metabolism to model the dynamics of healthy human large intestine microbiota associated with different gluten forms. For this purpose, stool samples from six healthy volunteers were inoculated in media containing predigested gluten or predigested gluten plus non-digested gluten. Passages were carried out every 24 h for 15 days in the same medium and community composition along time was studied via V3-V4 16S rDNA sequencing. Diverse microbial communities were successfully obtained. Moreover, communities were shown to be maintained in culture with stable composition for 14 days. Under non-digested gluten presence, communities were enriched in members of Bacillota, such as Lachnospiraceae, Clostridiaceae, Streptococcaceae, Peptoniphilaceae, Selenomonadaceae or Erysipelotrichaceae, and members of Actinomycetota, such as Bifidobacteriaceae and Eggerthellaceae. Contrarily, communities exposed to digested gluten were enriched in Pseudomonadota. Hence, this study shows a method for culture and stable maintenance of gut communities derived from gluten metabolism. This method enables the analysis of microbial metabolism of gluten in the gut from a community perspective.

An integrated framework of preventive maintenance and task scheduling for repairable multi-unit systems

Preventive maintenance and task scheduling are both crucial activities in industrial practice. Although they are interdependent and mutually influential, most existing studies typically analyze them separately. In this paper, an integrated framework of preventive maintenance planning and task scheduling is explored. On one hand, while most scholars focus on the system as a whole in scheduling studies, we further take into account the degradation and maintenance of multiple units during the task scheduling process. On the other hand, instead of only using "direct replacement" as the main maintenance method, we further consider the repairability of the unit by incorporating imperfect maintenance effects into the maintenance modeling. Additionally, the lifecycle partitioning method is proposed to analyze the maintenance groups for multiple units at the completion of scheduling tasks and their corresponding probabilities. Based on this foundation, an integrated decision model is established with the preventive maintenance threshold and scheduling task sequence as decision variables to minimize the total weighted expected completion time. Finally, a novel genetic algorithm based on hybrid encoding is proposed, and the effectiveness of integrated decision-making was validated using a practical example.

The Dual Role of Culture in Evolutionary Play: Anthropogenic Expansion Vs Destruction of Biodiversity

A large number of Green discourse blames the human species for the current ecological crisis. However, this description of humanity as the ecocidal culprit serves to conceal the role of humans, in both past and contemporary pre-industrial societies, as custodians of biodiversity. Indigenous societies are known to have conserved their natural resource base for posterity, by instituting cultural norms and institutions against exhaustive resource use. In addition, pre-industrial societies also increased biodiversity on taxic and genetic levels, by domestication of many wild biota, and on the ecosystem level with agroforestry. While Darwin gave much importance to the process of domestication of plants and animals by means of artificial selection, modern science and agriculture curricula tend to neglect this aspect of the history of human civilization. The novel taxa, created in the process of domestication, are characterised by many morphological and behavioural traits never found in the wild progenitor species. Further selection of favourable traits of the new species created an abundance of distinctive crop landraces and animal breeds. The increment in diversity at the ecosystem, taxic and genetic levels, by the process of domestication and in ancient agroforestry systems, began to reverse with industrial development over the past two centuries. Indeed, industrial development has been the chief driver of the continuing process of biodiversity erosion and habitat loss worldwide. Industrial agricultural systems, signposted by the Green Revolution (GR), has severely truncated the on-farm crop species and genetic diversity, which characterised traditional multi-crop farming and agroforestry systems in native agrarian cultures. Over the past six decades, the continual replacement of hundreds of landraces with a handful of GR cultivars, combined with the institutional apathy toward in situ landrace conservation efforts, has led to the disappearance of the importance of genetic purity of landraces from breeding programs and heirloom crop conservation discourse. Most of the modern farmers, predominantly dependent on the industrial supply of crop seeds, have forgotten the methods of genetic purity maintenance, resulting in the rapid loss of the hundreds of crop landraces with distinctive properties, which were selected centuries ago for diverse agronomic, gustatory, and aesthetic qualities. A recognition of the value of the custodian role of ecosystem people in creating and conserving biodiversity, vis-a-vis the current industrial decimation of biodiversity on all levels, will likely promote biodiversity conservation ethos in modern societies, and the value of genetic purity of the extant crop landraces.

Application of Omics Techniques in Skin Homeostasis Evaluation

In recent years, skin homeostasis has been paid more attention by cosmetics industry. Many factors can damage skin homeostasis and affect skin health and beauty. Therefore, maintaining skin homeostasis is the basis of cosmetic efficacy, but there is a lack of corresponding evaluation methods. At present, some efficacy claims related to skin homeostasis, such as moisturizing and repairing, are only evaluated from a certain aspect of the skin, which is not completely consistent with the overall concept of homeostasis. With the development of omics technologies especially cutaneous lipidomics, cutaneous microbiome and proteomics technologies, which are based on the systematic concept to systematically analyze the lipidomics, microbiome and proteome that affect the skin. Based on a variety of omics research techniques, combined with steady-state correlation target analysis, more comprehensive and in-depth analysis can be conducted, which is a potential emerging evaluation method for skin homeostasis maintenance. In this paper, the future development direction of the evaluation method of homeostasis maintenance is forecasted, which provides a theoretical basis for the development of cosmetics for maintaining skin homeostasis.

An intelligent schedule maintenance method for hydrogen fuel cell vehicles based on deep reinforcement learning considering dynamic reliability

Schedule maintenance activities for hydrogen fuel cell vehicles are aimed to keep them in better condition in long-term operation. In this paper, we develop a reinforcement learning based schedule maintenance strategy, which is used to comprehensively consider the balance between safety and maintenance cost, so as to find the optimal maintenance strategy. A multi-level framework is established with remaining useful life of key components, fault tree analysis, and logistics cost model to generate an exploration environment that can express the operational stability, the failure rate of components, and the cost of repair and storage. The trained agent combines a deep neural network to explore the optimal strategy under the dynamic reliability of key components. Finite steps are the constraints. Safety rates, maintenance cost, and episode of operation are incorporated into a multi-objective reward function. The hydrogen supply circuit of fuel cell vehicle is simulated via Python. The trained agent is compared with traditional time-based schedule maintenance strategy and corrective maintenance strategy. The results show that the reinforcement learning based schedule maintenance agent has optimized the total reward, cost control and accident rate by 77%, 59% and 5%, respectively, compared with the traditional time-based schedule maintenance strategy. In the safe operation management of fuel cell vehicles, efficient and stable decision-making ability has been achieved.

Predictive Maintenance Framework for Fault Detection in Remote Terminal Units

The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which may diminish its performance or even lead to its breakdown, rendering it non-operational. Lately, predictive maintenance methods have been considered for industrial systems, such as power generation stations, as a proactive measure for preventing failures. Such methods use data gathered from industrial equipment and Machine Learning (ML) algorithms to identify data patterns that indicate anomalies and may lead to potential failures. However, industrial equipment exhibits specific behavior and interactions that originate from its configuration from the manufacturer and the system that is installed, which constitutes a great challenge for the effectiveness of ML model maintenance and failure predictions. In this article, we propose a novel method for tackling this challenge based on the development of a digital twin for industrial equipment known as a Remote Terminal Unit (RTU). RTUs are used in electrical systems to provide the remote monitoring and control of critical equipment, such as power generators. The method is applied in an RTU that is connected to a real power generator within a Public Power Corporation (PPC) facility, where operational anomalies are forecasted based on measurements of its processing power, operating temperature, voltage, and storage memory.

Resilience-oriented approach of dynamic production and maintenance scheduling optimisation considering operational uncertainty

ABSTRACT One of the main challenges in operating multistate manufacturing systems (MMSs) is maintaining stable and robust production against various disruptions. Therefore, an urgent need exists for an operation and maintenance (O&M) method that optimises MMS resilience, i.e. the capability of withstanding or recover from disruptions of various sources. Consequently, this study proposes an integrated resilience-oriented production and maintenance scheduling approach for MMSs. This approach enhances MMS resilience by reinforcing its adaptivity to the variation in production requirements. Based on a conceptual investigation of operational uncertainty and its mechanism of disruption, this study devotes to (i) formulating a performance loss-based resilience measurement with consideration of operational uncertainties and (ii) proposing a reinforcement learning-based approach to schedule MMS operation and maintenance activities for MMS components of different performance states. An industrial case study of a ferrite phase shifter manufacturing system is subsequently conducted to validate the proposed approach. Results demonstrate the effectiveness of the proposed approach in the resilience optimisation of MMSs.

Better Catchment of Workforce for Elevating Workplace Performance Integrating AI – A Systematic Literature Review

Combined team of skilled and unskilled employees refers Workforce in any organisation, being the key factor behind anenterprise significant growth integrated with Artificial Intelligence (AI) and Generative AI at various industrial sectors. Theiroverall development rolls on possible with the workforce existing with agility and ability to conduct difficult workplace tasks in a hassle-free, dutiful manner. Mitigating challenges are present within most organizations that even the leaders and legendary stakeholders face in establishing proper managerial strategic methods in successful maintenance of the workforce etiquettes with credibility. Human resources have a powerful and consistent momentum within them that creates more chaos in managing them, and that unique feature is creative thinking ability eventually. A general reaction of human minds regarding any change implemented in an organisation is to create resistance with equilibrium. Manager’s prioritized commanding skillin human resource management technique is listening to employees’ issues and suggesting options to help them cope with it in a simple way.The study has focused on conducting a systematic review of workforce management articles published in globally acclaimed management journals between 2018 and 2024. The articles’ objectives vary, and the thesis papers contain various methodical approaches for deriving the concluding sections. On Summarising, the findings have covered different aspects, such as the enablers and barriers present within an organisation, and have significant relevance in workforce management. On the other hand, some articles have covered the impact of human psychology in managing a workforce containing people from different cultures and boundaries. The role of leadership is significantly important in managing the workforce. Eminent workforce maintenance is directly associated with improving the employees’ job satisfactionpost recruitment, effective participation in enforcing workplace regulations, and the human resource management system integrated with AI.

Analisis interval waktu perawatan preventif pada Paper Machine dengan metode reliability centered maintenance (RCM)

The maintenance process and preventive measures on paper machines need to be studied further, this will affect the production process and paper machine performance. This study aims to determine the criticality of the drying machine failure and determine the preventive measures that must be taken in the maintenance of the dryer and determine the time interval for the maintenance of the dryer machine components. To overcome this problem, this research was made using the Reliability Centered Maintenance (RCM) method. RCM can be interpreted as a process used in determining what to do for the machine, wherein in the RCM methodology there is one FMEA calculation in dealing with critical components in the dryer. Based on data processing and analysis using the RCM method, the maintenance time interval results from the maintenance of the dryer component. The results found are drums/cylinders with maintenance intervals of 82.7 hours, components of all pipes with maintenance intervals of 91.1 hours, and gear components with maintenance intervals of 110.7 hours. The implications for the company's factory regarding preventive maintenance intervals will have an impact on improving the production process and performance of paper machines through early prevention of machine failures that often occur.

Maximizing Machine Efficiency in Indonesia's Noodle Industry

This study utilizes the Total Productive Maintenance (TPM) method, focusing on the Overall Equipment Effectiveness (OEE) to evaluate machine efficiency within a notable Indonesian noodle manufacturer. In the rapidly growing food manufacturing industry, particularly dry and instant noodles, machine performance is critical for meeting increasing domestic and international market demands. Results reveal that the OEE values are below industry standards, primarily due to speed reductions, idle and minor stoppages, process defects, and breakdown losses. These findings emphasize the urgent need for preventive maintenance strategies to mitigate downtime and enhance productivity, providing valuable insights for global industries facing similar challenges. Highlights: Rapid growth in the food manufacturing industry demands increased productivity. Machine performance is crucial for maintaining optimal production processes. Total Productive Maintenance (TPM) and Overall Equipment Effectiveness (OEE) methods help measure and improve machine efficiency. Keywords: Total Productive Maintenance, Overall Equipment Effectiveness, Six Big Losses.

FulBM: Fast fully batch maintenance for landmark-based 3-hop cover labeling

Landmark-based 3-hop cover labeling is a category of approaches for shortest distance/path queries on large-scale complex networks. It pre-computes an index offline to accelerate the online distance/path query. Most real-world graphs undergo rapid changes in topology, which makes index maintenance on dynamic graphs necessary. So far, the majority of index maintenance methods can handle only one edge update (either an addition or deletion) each time. To keep up with frequently changing graphs, we research the ful ly b atch m aintenance problem for the 3-hop cover labeling, and proposed the method called FulBM . FulBM is composed of two algorithms: InsBM and DelBM, which are designed to handle batch edge insertions and deletions respectively. This separation is motivated by the insight that batch maintenance for edge insertions are much more time-efficient, and the fact that most edge updates in the real world are incremental. Both InsBM and DelBM are equipped with well-designed pruning strategies to minimize the number of vertex accesses. We have conducted comprehensive experiments on both synthetic and real-world graphs to verify the efficiency of FulBM and its variants for weighted graphs. The results show that our methods achieve 5.5 × to 228 × speedup compared with the state-of-the-art method.

Developing a predictive maintenance model for the rehabilitation of roads’ assets

Maintaining road infrastructure is a significant challenge in minimizing public fund waste from reactive maintenance. Preventive maintenance is considered a new era in this field. To achieve cost savings and prolong the life cycle of road networks, it’s crucial to determine their lifespan at the time of construction. This article proposes an emerging predictive methodology to select road network rehabilitation policy. This methodology facilitates long-term planning based on technical and budgetary perspectives to identify the most cost-effective rehabilitation policy for the road network’s assets over the planning horizon. To accomplish this, the Pavement Rehabilitation Predictive Model (PRPM) is developed for long-term forecasting of future asset conditions as the network deteriorates over time. It takes into account effective planning for different rehabilitation policies predicts asset deterioration, and aids in making informed decisions by identifying the best rehabilitation policy for pavement sections. An empirical example is used to illustrate the proposed approach in detail, based on the evaluation of three scenarios of the rehabilitation policies. The results indicate that the prediction model is highly efficient, resulting in an average budget savings of up to 20% compared to other relevant maintenance methods.

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

The installed solar capacity in the European Union has expanded rapidly in recent years. The production of these plants is stochastic and highly dependent on the weather. However, many factors should be considered together to estimate the expected output according to the weather forecast so that these new PV plants can operate at maximum capacity. Plants must be operated in coordination with maintenance operations and considering actual energy market prices. Various methods have recently been developed in the literature, ranging from the most impactful artificial-intelligence-based generation estimation methods to various diagnostic and maintenance methods. Moreover, the optimal operational and maintenance strategy usually depends on market regulation, and there are many concerns related to the distribution system operator. This review article aims to summarize and illustrate the challenges of operating and maintaining solar power plants and the economic and technical importance of these problems.