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This paper proposes the extension of a prognosis process by means of the integration of maintenance alternative impacts in order to develop a maintenance decision‐making tool.

The deployment of this extended prognosis process follows a methodology based both on probabilistic and on event approaches.

The importance of the maintenance function has increased due to its role in keeping and improving the system availability and safety but also the product quality. To support this new role, the maintenance concept has undergone several major developments to lead to proactive considerations mainly based on prognosis process allowing one to select the best maintenance plan to be carried out.

Studies over the last 20 years have indicated that around Europe the direct cost of maintenance is equivalent to between 4 and 8 per cent of total sales turnover. The indirect cost of maintenance is likely to be a similar amount. Thus, in the countries where modern maintenance practices have yet to be well adopted by industry, the potential savings from modern maintenance are massive. These modern and efficient maintenances imply identifying the root‐cause of component failures, reducing the failures of production systems, eliminating costly unscheduled shutdown maintenances, and improving productivity as well as quality. It means, for the companies, migrating from their traditional reactive approach, which is “fail and fix”, to “predict and prevent”. The advantage of the latter is that maintenance is performed only when a certain level of equipment deterioration occurs. This “proactive” maintenance is mainly based on prognosis process often considered as the Achilles heel, while its goal is fundamental for implementing anticipation capabilities. This paper looks into this issue by proposing the development of an innovative prognosis process integrating the modelling of maintenance actions and their impacts on system performances. It leads to offering a maintenance aided decision‐making tool cable of assisting the decision‐maker in selecting the best maintenance plan to be carried out.

The feasibility of this new prognosis is experimented on the manufacturing Tele‐Maintenance (TELMA) platform supporting the unwinding of metal bobbins.

The paper is based on the idea that management of a national economy is a complex cybernetic system. By considering the Romanian economy the author postulates that the relations between socio‐economic macro‐systems should be expressed in terms of national income. On the basis of the model thus established future developments in various macro‐systems are predicted, e.g. in production, technology, population, labour force, etc. The author then describes a system for the prognosis of social and economic developments in Romania up to the year 2000. In order to achieve this object a Control Commission for Prognosis has been established under the chairmanship of Mr. Nicolae Ceauşescu, the President of Romania.

The purpose of this study is to automatically provide suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains.

Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical components. The major reason is that fault prognosis is an activity that, unlike fault diagnosis, involves a lot of uncertainty and it is not always possible to envision a model for predicting possible faults.

This work proposes a solution based on massive text mining for automatically suggesting prognosis activities concerning mechanical components.

The great advantage of text mining is that makes possible to automatically analyze vast amounts of unstructured information to find corrective strategies that have been successfully exploited, and formally or informally documented, in the past in any part of the world.

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

The purpose of this paper is to create a new method of prognosis based on remaining useful life (RUL) prediction for degradation assessment.

In the present paper the authors describe a new method of prognosis to improve the accuracy of forecasting the system state. This framework of forecasting integrates the model-based information and the hybrid approach, which employs the structured residuals in the first part and the particle filter in the second part.

The performance of the suggested fusion framework is employed to predict the RUL of battery pack in hybrid electric vehicle. The results show that the proposed method is plausible due to the good prediction of RUL, and can be effectively applied to many systems for prognosis.

In this study the authors illustrate how the suggested method can provide an accurate prediction of the RUL over conventional data-driven methods without physical model and classical particle filter with a single damage model.

The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM.

Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed.

In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified.

The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required.

The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

Labyrinth test psychophysiological experiment is elaborated to analyze possible mechanism of the probabilistic prognosis forming and realizing in the condition of essential information uncertainty. The dynamics of the probabilistic prognosis construction and improvement during multi‐stage labyrinth test solving is established. This dynamics is characterized by considerable changes of the subjective probabilities and membership function quantitative indices apparently used in the fuzzy logic of mental activity being applied in decision‐making processes realized in the condition of uncertainty. The possible link between probabilistic prognosis efficiency and some functional peculiarities of cortical hemisphere asymmetry has also been studied.

This study aims to identify the dietary patterns of two groups of subjects (with and without COVID-19), and to assess the relationship of findings with the prognosis of COVID-19 and metabolic risk parameters.

This study included 100 individuals in the age range of 19–65 years. The medical history, and data on biochemical, hematological and inflammatory indicators were retrieved from the files. A questionnaire for the 24-h food record and the food intake frequency was administered in face-to-face interviews, and dietary patterns of subjects were assessed.

In individuals with COVID-19, the hip circumference, the waist-hip ratio and the body fat percentage were significantly higher (p < 0.05), and the muscle mass percentage was significantly lower (p < 0.05). Mediterranean diet adherence screener (MEDAS), dietary approaches to stop hypertension (DASH) and healthy eating ındex-2015 (HEI-2015) scores were low in the two groups. A linear correlation of DASH scores was found with the muscle mass percentage (p = 0.046) and a significant inverse correlation of with the body fat percentage (p = 0.006). HEI-2015 scores were significantly and negatively correlated with body weight, body mass index, waist circumference, hip circumference and neck circumference (p < 0.05). Every one-unit increase in MEDAS, DASH and HEI-2015 scores caused reductions in C-reactive protein levels at different magnitudes. Troponin-I was significantly and negatively correlated with fruit intake (p = 0.044), a component of a Mediterranean diet and with HEI-2015 total scores (p = 0.032).

The limitation of this study includes the small sample size and the lack of dietary interventions. Another limitation is the use of the food recall method for the assessment of dietary patterns. This way assessments were performed based on participants’ memory and statements.

Following a healthy diet pattern can help reduce the metabolic risks of COVİD-19 disease.

Despite these limitations, this study is valuable because, to the best of the authors’ knowledge, it is the first study demonstrating the association of dietary patterns with disease prognosis and metabolic risks concerning COVID-19. This study suggests that dietary patterns during the COVID-19 process may be associated with several metabolic risks and inflammatory biomarkers.

The aim of this paper is to present a discussion on prognosis and mitigation of major landslide zones in an attempt to minimize the impact of such disasters in future. A case study on the sequence of sliding events of Varunavat Parvat, Uttarkashi (India), response of masses and administration and causative factors of sliding events has been presented in detail for prognosis and mitigation of large slide zones.

The prognosis and mitigation strategy discussed is based on the monitoring of mass wasting zones through field investigations and satellite image analysis (of pre‐ and post‐landslide period images) and experiential learning and interaction with village elders in landslide hazard‐prone Himalayan terrain.

The paper finds that Himalayan habitations such as Uttarkashi (which is situated in an area of fragile rocks, complex tectonics, seismic activity and cloud burst‐prone unstable hill slopes with colluvium and old slide zones) should have minimum anthropogenic activity in the form of slope cutting for road or building construction.

The paper reflects the author's individual understanding of causative factors and indications of landslides in Varunavat Parvat area in Uttarkashi township of Uttarakhand (India).

The paper calls for amalgamation of experience‐based local knowledge of villagers of landslide‐prone areas and modern scientific and technical know‐how and above all the coordinated efforts of community and authorities for prognosis and mitigation of large‐scale landslides in the inhabited areas. It has been further emphasized that sensitization and awareness programs and strict implementation of land‐use regulations are vital components of effective mitigation strategy.

In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people’s lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment; however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM).

The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detection.

The present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested.

This study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems – a topic not yet addressed in the literature.