Biological function depends on the composition as well as structure of the organism, that is the organization of interactions between its parts. In multicellular organisms, the complex communication between cells is crucial for the coordination of cellular functions (growth, differentiation, apoptosis), compartmentalization of cellular processes within the organism, and the integration of cells into functioning higher levels of structural organization. Here we focus on the interaction among cells, as the basic level of biological organization, capable of a remarkable degree of autonomy and self-maintenance. While a wealth of data on putative cell interactions has recently become available, its biological interpretation depends on expectations we have for the structure of these interaction networks. This contribution is an attempt at formulating basic questions we may ask about cell interaction data. We build on the understanding that cells fulfill two general functions: the integrity-maintaining and the organismal service function. We derive the expected patterns of cell interactions considering two intertwined aspects: the immediate functional and the evolutionary. Based on these, we propose several guidelines for analysis and interpretation of transcriptional cell-interactome data.

The present paper deals with research of maintenance strategy and maintenance process improvement procedure to increase the efficiency and quality of the production system. It includes the design of maintenance performance indicators relevant to today's manufacturing systems to track improvements in the sustainable development of the manufacturing system. Describes the basic principles of maintenance strategy with linkages to the manufacturing system and selecting the right strategy for the organization. The paper emphasizes maintenance management auditing and quality implementation in maintenance. Further, there is the process of changing the maintenance strategy described. This process includes reassessing the criticality of machines and equipment and their design units, then resource and capacity planning and input for maintenance management. It is described the impact of changing the maintenance management organization and maintenance capacity in the light of the change of strategy and the evaluation of maintenance choice alternatives, together with an analysis of the optimization procedure. The paper concludes by presenting the achieved impact of maintenance strategy change on the production efficiency and production quality of the selected enterprise.

U.S. Navy Surface Ship depot-level maintenance periods of performance were studied to develop a method for predicting maintenance durations. The need for the method has been highlighted by Navy leadership in recent media posts describing unacceptable maintenance delays and this research provides practitioners and decisionmakers with a reliable estimating tool. This study helps by putting forth a method that defines the rate of work accomplishment based on relevant variables. Using ordinary least squares models, this research revealed that the size of the contract obligation and the amount of shipyard work occurring simultaneously in the market are key variables in determining depot maintenance durations. With the knowledge found here, the next logical step is an optimization model for each U.S. Navy surface ship homeport.

Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to trigger maintenance before a possible breakdown; however, much less focus has been devoted to the use of such PM predictions as feedback in automated process control mechanisms. They usually integrate preventive solutions to protect the machines, usually causing downtimes. The premise of this study is to develop a holistic adaptive process scheduling mechanism that incorporates PM analysis as a safety component to optimize the operation mode of an industrial process toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. As PM is largely a data-driven approach; hence, relies on the setup, we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time adaptive process scheduling mechanism. It schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, according to the abnormal readings. To demonstrate the pipeline on action, we implement our approach on a small-scale conveyor belt system utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control provides an efficient process with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 dof delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data is available. Due to the sequential nature of the data, non-linearity of the system, and correlations between parameter time series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method is capable of calculating RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

Buildings are subject to intrinsic and extrinsic failures at all stages of the construction process, i.e., design to occupancy (i.e., use) as several failure types can affect them. There are three basic steps for researching building failures: listing, cataloging, and solving/deciding whether highlighting the least invasive and lowest cost intervention. Therefore, this research presents some methods used in construction for the intervention prioritization and seeks to illustrate the most suitable methodology for different settings failure x diagnosis x intervention. Therefore, the most traditional tools are shown: BASICO, SWIFT, FEMEA, AAF, and GUT matrix, where a critical assessment of the strengths and weaknesses is carried out. After that, a case study on maintenance services for the moisture on the building facades was performed. Finally, the GUT matrix and the FMEA method are the best results

The Canal of Aragon and Catalonia (CAC) is 134 km long and irrigates 105,000 ha (131 irrigation user communities) and it is own by the River Ebro’s Water Agency. The aqueducts are located between km 67 and 71 of the canal and were designed by the Civil Engineer Félix de los Ríos Martín in 1907. The cross-section of both aqueducts, Coll de Foix and Capedevila, was extended within the framework of the project by Fernando Hué Herrero in 1962 in order to reach design flows of 26.1 m3/s and 25.7 m3/s, respectively. The structural performance of the aqueducts has been satisfactory, nevertheless the hydraulic capacity has reduced over the years. As a result, the irrigation user communities have expressed the need to extend the cross-section of the aqueducts to meet the irrigation demands. Given the age of the structure and the different design considerations at the time, it is paramount to verify the structural reliability of the aqueducts in the new load configuration. Therefore, the objective of the paper is to present the structural safety analysis conducted and describe the new extended cross-section for both aqueducts (maintaining the original structural typology).

Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predicting the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means (FCM) and Hotelling’s T-squared (T2) have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the T2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T2 labels achieves more than 94% accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision.

Infection with the malaria parasite Plasmodium, remains a major cause of illness and death world-wide. The development of evasion strategies by the parasite from host immunity and its adaptability to antimalarial drugs has raised the urgency for developing new strategies to combat this disease. One promising avenue is targeting telomeres – sequences found at chromosome termini– and telomerase – the enzyme that catalysis synthesis of telomeres using an intrinsic RNA template. As telomeres shorten critically, cells undergo replicative senescence or apoptosis. Plasmodium, lacking telomerase activity in its human stages, exploits alternative mechanisms, accelerating telomere shortening, aging, and immune evasion. Chemical telomerase inhibitors show promise as antimalarials. In this review, we examine the potential of targeting this system in malaria therapy, with telomerase inhibitors offering a novel approach. A systematic search on PubMed using Boolean techniques identified 246 relevant articles from 1985 to March 31, 2024. After filtering, 43 articles met inclusion criteria, supplemented by snowball sampling. The telomerase inhibitor drug strategy is widely applicable against cancer. However, given the drugability of telomerase and the absolute requirement for active telomere maintenance in the vast majority of parasites including plasmodium, telomerase and telomere maintenance could well represent the Achilles heel of the parasite

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.

The core reasoning task for datalog engines is materialization, the evaluation of a datalog program over a database alongside its phys- ical incorporation into the database itself. The de-facto method of computing it, is through the recursive application of inference rules. Due to it being a costly operation, it is a must for datalog engines to provide incremental materialization, that is, to adjust the computation to new data, instead of restarting from scratch. One of the major caveats, is that deleting data is notoriously more involved than adding, since one has to take into account all possible data that has been entailed from what is being deleted. Differential Dataflow is a computational model that provides efficient incremen- tal maintenance, notoriously with equal performance between ad- ditions and deletions, and work distribution, of iterative dataflows. In this paper we investigate the performance of materialization with three reference datalog implementations, out of which one is built on top of a lightweight relational engine, and the two others are differential-dataflow and non-differential versions of the same rewrite algorithm, with the same optimizations.

Burnout is common in many countries and is associated with several other problems, such as depression, anxiety, insomnia and memory deficits, and prospectively it predicts long-term sick-leave, cardiovascular disease and death. Clinical burnout or its residual symptoms often last several years and a common assumption is that recovery takes a long time by nature despite full time sick-leave and absence of work stress. Literature suggests models that hypothetically explain the development, but not maintenance, of the syndrome. Based on cognitive and behavioral principles and stress theory this paper describes a theoretical model explaining how clinical burnout can develop and be maintained. While the development of clinical burnout is mainly explained by prolonged stress reactions and disturbed recovery processes due to work related stressors, maintenance of the syndrome is particularly explained by prolonged stress reactions and disturbed recovery processes due to the new context of experiencing burnout and being on sick-leave. Worry about acquired memory deficits, passivity and excessive sleep, shame, fear of stress reactions, and the perception of not being safe are examples of responses that can contribute to the maintenance. The model has important implications for research and how to intervene clinical burnout.

Centrifugal pumps are widely employed in the oil refinery industry due to their efficiency and effectiveness in fluid transfer applications. The reliability of pumps plays a pivotal role in ensuring uninterrupted plant productivity and safe operations. Analysis of failure history data shows that bearings have been identified as critical components in oil refinery pump groups. However, traditional reliability estimation theories may not apply when data is limited or subject to right censoring. This paper addresses the complexity of estimating the Weibull distribution parameters using the maximum-likelihood method under the abovementioned conditions. The likelihood equation lacks an explicit analytical solution, necessitating the use of numerical methods for resolution. The proposed approach presented in this article leverages the Expectation-Maximization (EM) algorithm for estimating the Weibull distribution parameters. This method provides more accurate estimates of failure rates and probabilities by accounting for limited and censored data. The findings are demonstrated through a case study, showcasing the practical application of the proposed approach.

Wind power is one of the most important sources of renewable energy available today. A large part of the wind energy cost is due to the cost of maintaining wind power equipment. When a wind turbine component fails to function, it might need to be replaced under the less than ideal circumstances. This is known as corrective maintenance. To minimize unnecessary costs, a more active maintenance policy based on the life expectancy of the key components is preferred. Optimal scheduling of preventive maintenance activities requires advanced mathematical modeling. In this paper, an optimal preventive maintenance algorithm is designed using the renewal-reward theorem. In the multi-component setting, our approach involves a new idea of virtual maintenance which allows us to treat each replacement event as a renewal event even if some components are not replaced by new ones. The proposed optimization algorithm is applied to a four-component model of a wind turbine and the optimal maintenance plans are computed for various initial conditions. The modeling results showed clearly the benefit of PM planning compared to pure CM strategy (about 30% lower maintenance cost).

Using data-driven models to solve predictive maintenance problems has been prevalent for original equipment manufacturers (OEMs). However, such models fail to solve two tasks that OEMs are interested in: (1) Making the well-built failure prediction models working on existing scenarios (vehicles, working conditions) adaptive to target scenarios. (2) Finding out the failure causes, furthermore, determining whether a model generates failure predictions based on reasonable causes. This paper investigates a comprehensive architecture towards making the predictive maintenance system adaptive and interpretable by proposing (1) an ensemble model dealing with time-series data consisting of a long short-term memory (LSTM) neural network and Gaussian threshold to achieve failure prediction one week in advance and (2) an online transfer learning algorithm and a meta learning algorithm, which render existing models adaptive to new vehicles with limited data volumes. (3) Furthermore, the Local Interpretable Model-agnostic Explanations (LIME) interpretation tool and super-feature methods are applied to interpret individual and general failure causes. Vehicle data from Isuzu Motors, Ltd., are adopted to validate our method, which include time-series data and histogram data. The proposed ensemble model yields predictions with 100% accuracy for our test data on engine stalling problem and is more rapidly adaptive to new vehicles with smaller error following application of either online transfer learning or the meta learning method. The interpretation methods help elucidate the global and individual failure causes, confirming the model credibility.

The significance of virtual reality (AR/VR/MR) technology stands out because it can be used in various construction fields such as urban design, construction review, maintenance and education, etc. As to prove this, conglomerates such as Facebook, Google, Sony, Microsoft, and Samsung are increasing their investments to preoccupy the virtual reality market and are competing to dominate the contents and platform market. Virtual reality technology has a concept that collectively refers to VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality), but technically, the two technologies are strictly separated. While VR technology is an immersive virtual environment using computer graphic technology, AR technology is a more advanced technology that combines real data and VR data, and MR technology is a technology that combines both AR and VR. In Korea, Republic of, the use of BIM (Building Information Modeling), a 3D information model, becomes mandatory and based on this, the demand for a new market where VR/AR/MR technologies and advanced sensing equipment are combined increases. This study implemented a building maintenance platform using AR based on 5G, and developed matching technology between BIM of the building maintenance platform and AR equipment. Besides this study implemented the technology to enhance the matching rate of the matching technology and carried out the process to enhance the matching rate empirically through continuous tests. As a result, the initial target of 90% matching rate could be improved to 96%. Based on this, it is expected that effective EV (Optimum Life Cycle Cost) could be achieved at a lower cost compared to the previous one by using it for maintenance monitoring after construction and completion of the building, and can be used as an effective solution in the aspect of building maintenance as well.

The economic development and infrastructure of a nation are closely interrelated. In addition, public trust in national infrastructure facilities is closely linked to the preservation of the advantages provided by these facilities to the public. Since the 1970s, Korea has achieved exponential economic growth over a short period of time and the number of infrastructure facilities has increased correspondingly. This compressed economic development has been underpinned by the national infrastructure, whose safety and usability have been excluded from the scope of the development. However, after around 30 years, structural deterioration coupled with general insensitivity to safety in today’s society has considerably reduced public trust in using the infrastructure. Realistically, policies that mainly focus on developing new technologies related to infrastructure construction have led to practical limitations that discourage the development of technologies for maintenance or inspection. Furthermore, current maintenance works face certain limitations caused by various reasons: insufficient budget, increasing number of infrastructure facilities requiring maintenance, shortage of manpower, and rapidly increasing number of aging infrastructure facilities. To overcome these limitations, a new approach is required that is different from general inspection methods under the existing rules and regulations. In this context, this study aimed to explore the efficiency of bridge inspection and maintenance by unmanned aerial vehicles (UAVs) that could observe inaccessible areas, could be conveniently and easily controlled, and could offer high economic benefits. To this end, various tests were performed on elevated bridges, and suitable UAV images were obtained. The obtained UAV images were inspected by using machine vision technology, thereby excluding subjective evaluations by humans. Methods for enhancing the objectivity of the inspection were also discussed. The test results showed that both the efficiency and objectivity of the proposed method were better than those of the existing bridge maintenance and inspection methods.

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

With the rapid development of global offshore wind power, the demand for offshore wind power operation and maintenance is also increasing. Wisdomization of offshore wind farms is a practical need to improve the operation level and benefit of offshore wind farms. This paper first introduces the current development situation and characteristics of global offshore wind power, and expounds the current situation and main challenges of offshore wind power operation and maintenance market. Therefore, our paper discusses the innovation of offshore wind power operation and maintenance from the aspects of operation and maintenance management of offshore wind power, monitoring and analysis technology of units, far-reaching wind field monitoring and operation and maintenance risks. Then, combined with information technology and lean management concept, a smart operation and maintenance management platform for wind farms in far-reaching sea areas is built to explore centralized and intelligent operation and maintenance management mode, improve operation and maintenance efficiency of wind farms in far-reaching sea areas, and minimize operation and maintenance costs. Finally, through the research on the characteristics of 5G technology, combined with the practical experience of operation and maintenance, and in view of the characteristics of offshore wind farms, we analyze and propose several typical application scenarios of 5G technology in the intelligent operation and maintenance of offshore wind farms, which provides a new solution for the efficient operation and maintenance of offshore wind farms.

Background: Student-specific stress is pervasive and impairs health and attainment. Experimentally induced self-affirmation can reduce stress, but little is known about the mechanisms underlying this effect, and the role of individual differences. The present study explores the relationship between spontaneous self-affirmation, student-specific stress, and overlapping constructs of positive self-regard during the COVID-19 pandemic. Methods: The study investigated inter-relationships between spontaneous self-affirmation, student-specific perceived stress and potential covariates including self-esteem, self-integrity and habitual positive self-thought. University students (N = 151) aged 18 to 50 responded to an online questionnaire including the Spontaneous Self-Affirmation Measure, College Student Stress Scale, Rosenberg Self-Esteem Scale, Habitual Index of Positive Thinking and Self-Integrity Scale. Results: Unexpectedly, regression analysis revealed that spontaneous self-affirmation was a positive predictor of student-specific stress, seemingly driven by relational affirmations. Self-esteem and habitual self-thought were negative predictors of student-specific stress, whilst self-integrity was not a predictor. Conclusions: Habitual positive self-thought may offer a sustainable solution to student-specific stress, but spontaneous self-affirmation does not. A reduction in defensive responses, elicitation of negative emotions, or use of conditional relationships in relational affirmation

: Nanotechnology options to road surface maintenance offers several advantages compared to traditionally used materials. The small particle sizer of hydrophobic Nano-Silane modified Nano-Polymers (NSNP) enables these nanotechnology products to deeply penetrate existing road surfaces, sealing micro-cracks and render surfacings to be water-resistant for extended periods of time. In comparison, traditionally used products contain minimum partial sizes of about 1 – 5 microns, that provide a superficial protection that wears off in a relatively short period of time. These traditional products are often associated with vehicle contamination while drying and requires the re-instatement of road markings. None of these disadvantages are associated with applicable NSNP technologies that are quick drying, with no vehicle contamination risks and is equivalent to a “clear-seal” requiring no reinstatement of road markings. In a similar vein, pot-hole repairs can be done using applicable, easy to use, pre-packed and treated pot-hole repair kits that are water-repellent and quick-drying at a fraction of the costs of conventional cold-mix products. Resurfacing using NME binder slurries can be done labour-intensively on a pre-treated NSNP surfacing, restoring cracked surfacing and providing a water-resistant long-lasting protective layer without the removal of existing cracked areas. The implementation of nanotechnology solutions for road surface maintenance operations is directly associated with ease of use, labour-intensive operations, prevention of considerable deterioration in riding quality due to removal and manual re-instatement of cracked surfaces, time and cost savings and a reduction in the risk of water damage to the sub-structure. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in a harsh environment with excess temperature and humidity, vibration, fatigue and load. A breakdown or malfunction in one of these machineries can significantly impact the vessel’s operation and safety and consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This research presents the economic and technical benefits of predictive maintenance over to traditional preventive maintenance, and repair by replacement approaches in the maritime domain. By leveraging modern technology and Artificial Intelligence, we can analyze real-time operating conditions of machinery, enabling early detection of potential damages and allowing for effective planning of future maintenance and repair activities. In this paper, we propose and develop a Convolutional Neural Network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry.

Arabic is present in the Arab world and beyond. It is used as an official (or co-official) language in the Arab World (this refers to the twenty-two member states of the Arab League). Also, Arabic has the status of a national language in Mali, Niger, and Senegal. Besides, it is spoken in linguistic enclaves in Nigeria, Cyprus, Turkey, Uzbekistan, Iran, and Afghanistan. In addition, it is employed as the liturgical language in Muslim countries and countries with a Muslim minority all over Africa and Asia. As a result of the migration to Western countries since the end of the nineteenth century, Arabs and Arabic has been present in such countries. The presence of Arabs in Western countries raises the following question: Are these Arabs language maintainers or language shifters? The present article is an attempt to answer this question through reviewing a number of studies that have dealt with Arabic language maintenance among the Arabs living in Western countries (namely the United States of America, the United Kingdom, Canada, Australia, New Zealand, Sweden, Greece, and France), deducing from these studies the major trends in Arabic language maintenance among these Arabs, and providing a critique of the studies.

This paper proposes a solution for the development of microclimate monitoring for Low Voltage / High Voltage switchgear using the PRTG Internet of Things (IoT) platform. This IoT-based real time monitoring system can enable predictive maintenance to reduce the risk of electrical station malfunctions due to unfavorable environmental conditions. The combination of humidity and dust can lead to unplanned electrical discharges along the isolators inside a low or medium voltage electric table. If no predictive measures are taken, the situation may deteriorate and lead to significant damage inside and outside the switchgear cell. Thus, the mentioned situation can lead to unprogrammed maintenance interventions that can conduct to the change of the entire affected switchgear cell. Using a low-cost and efficient system, the climate conditions inside and outside the switchgear is monitored and transmitted remotely to a monitoring center. From the results obtained using a 365-day time interval, we can conclude that the proposed system is integrated successfully in the switchgear maintaining process, having as result the reducing of maintenance costs.

Efficient quality evaluation provides support for the timely and good maintenance of the lane line marking. This paper searches and optimizes the back propagation(BP) network model which referred to the analytic hierarchy process(AHP) model structure, as well as the number of nodes in the middle layer network. Based on this, a comprehensive evaluation method of multi-dimensional lane line quality such as shape, color and contrast is established. The experimental results show that the parameters of the model are more simplified, and the scoring and classification results of lane lines are more accurate.

The need to consider sustainability has substantially increased the complexity of implementing construction and infrastructure projects and new management practices have emerged during the past decade to tackle the global sustainability challenges, where the engagement and coordination of broader competences from stakeholders throughout the supply chain is required. This new project management paradigm has been accompanied by greater attention to the concept of collaborative business arrangements, often called partnering, that has emerged in construction and infrastructure projects to improve project deliveries. However, there are uncertainties about the optimal strategy to foster, integrate and maintain the required collaboration, particularly in sustainable management practices in infrastructure maintenance projects. This paper addresses these uncertainties, based on a single case study of an infrastructure maintenance contract involving an extensive collaborative business arrangement. The findings reveal that different collaborative practices affect diverse aspects of sustainable project management. Further, the extensive collaborative business arrangement has promoted sustainable deliveries based upon organizational learning and continuous improvements. Thus, this study offers an encouraging example of how extensive collaboration can be fostered and play a key role in sustainable project management practices.

The paper presents a case study and a model for condition monitoring of Diesel engines’ oil of urban buses, through the accompaniment of the evolution of its degradation, with the objective to implement a predictive maintenance policy. Along time, because the usage, there is some decay in the lubricant properties. However, in normal functioning conditions, the lubricants properties, at the time the manufacturers recommend its changing, regardless of they are within the safety limits. Then, based on the accompaniment of the lubricants’ oil condition, the intervals of oil replacement can be enlarged what implies the availability increasing and the corresponding production increasing of the equipment. The model presented in this paper shows its potential to be spread to other types of equipment and organisations that want can implement similar maintenance policies, to achieve the best availability based on the real equipment health conditioning conditions

Background: The financial austerity policy imposed to Portugal by Troika - the European Commission, European Central Bank and International Monetary Fund in 2011, led to a tremendously social crisis. Under these dominant trends in Europe, which were felt especially in certain countries, such as Portugal, we feel ourselves forced into an analysis and reflection on the impact of this conjuncture in social workers, especially analyzing the role played by social workers and its critical consciousness relative to this neoliberal model and imposed austerity policies. Methods: This article results from an analysis of empirical data collected after the entry of Troika in Portugal, during 2013. Were conducted 26 in-depth interviews to front line social workers, with the purpose of obtaining a narrative of their daily work practice and with the concern of encouraging reflection on them. Results: Analysis shows the predominance of a maintenance role at the expense of more emancipatory approaches and thus, this results point to the relationship of this maintenance role with the sociological paradigm of social regulation and shows also, how social work is still attached to a conservative agenda. Secondly, one finds a weak critical reflection among social workers, being very afraid to speak out against the system, taking on a submissive attitude and subservient to the policies of social cuts imposed. The analysis then raises the challenge for social workers to develop a critical reflexivity Conclusion: The results highlight several important observations in terms of social woker role and that a critical activity is urgently required to social workers.

Every firm is devoted to polishing and fine-tuning its maintenance strategy. More exactly, when we develop robust and efficient preventive and conditional maintenance approaches, our objective is to combine performance and robustness to increase maintenance decision-making. This, in turn, leads to a reduction in system failures and a subsequent drop in related expenses. This impulse drives the origin of this essay: to provide fresh criteria that allow us to improve and optimize maintenance techniques. Furthermore, our study has been realistically utilized via the deployment of condition-based maintenance (CBM) techniques, which are commonly regarded as a critical lever that allows firms to gain a competitive advantage in the context of the fourth industrial revolution. Combining both perfect and imperfect maintenance actions into CBM strategies appears to be an effective technique to obtain high economic performance and robustness in the maintenance strategies of the companies. Following this idea, we propose and optimize in this paper a set of criteria enabling the combined assessment of the mean economic performance and the resilience of various kinds of maintenance techniques. The advantage of the proposed criterion is that it adapts to different types of maintenance strategies and provides access to a simple and relevant evaluation model. Thanks to this new criterion, we will be able to choose the better maintenance strategies that have more performance and robustness between these strategies for a system with more or less stable behavior. The Monte Carlo Method is incorporated into the comparative assessment of maintenance methods, adding a layer to the evaluation of the performance and robustness of each adaptation in decision-making.

In this paper I tentatively answer 50 questions sampled from a pool of over 10,000 weekly questions formulated by students in a course entitled “The Self”. The questions pertain to various key topics about self-processes, such as self-awareness, self-knowledge, self-regulation, self-talk, self-esteem, and self-regulation. The students’ weekly questions and their answers highlight what is currently know about the self. Answers to the student questions also allow for the identification of some recurrent lessons about the self. Some of these lessons include: all self-processes are interconnected (e.g., prospection depends on autobiography), self-terms must be properly defined (e.g., self-rumination and worry are not the same), inner speech plays an important role in self-processes, controversies are numerous (are animals self-aware?), measurement issues abound (e.g., self-reflection as an operationalization of self-awareness), deficits in some self-processes can have devastating effects (e.g., self-regulatory deficits may lead to financial problems), and there are lots of unknowns about the self (e.g., gender differences in Theory-of-Mind).

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

A comprehensive Energy Audit of a Maintenance facility was performed to assess its energy performance and identify scope for improvement. The facility’s Energy Use Intensity (EUI) for 2022 was 404 kWh/m2 — more than double the Benchmark EUI for Maintenance facilities (151 kWh/m2) recommended by EnergyStar. Furthermore, the Load Factor for 2022 was 0.22, which is lower than the recommended minimum of 0.75 for an efficient building. The audit encompassed an in-depth evaluation of the building's structural and operational characteristics, comprising HVAC systems, lighting, building envelope, and energy-intensive machinery. An energy model of the building was developed to emulate the baseline building energy performance for 2022. Following the energy model's development and validation, an analysis was conducted to identify energy-intensive areas and opportunities for optimization. Energy Efficiency Measures were then formulated, focusing on improving energy efficiency while focusing on energy consumption reduction and GHG emission reduction. Results demonstrated the potential of Energy Audits and Modeling to enable significant reductions in energy consumption and promote sustainable building practices. Among the Energy Efficiency Measures considered, re-sizing and decarbonizing HVAC equipment contributed the most to energy savings, with a 100% decrease in natural gas and 37% decrease in electricity use annually.

Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating Artificial Intelligence (AI) across various applications to enhance production processes. We can cite two critical areas where AI can play a key role in industry such as product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.

Rail, as the cleanest and greenest high-volume transport, has a central role in the decarbonizing, and it is expected to become the backbone of future mobility in the world. It is worthy to highlight that rail generates the lowest CO2 emissions and energy consumption in operation with respect to the other transportation modes but during construction and maintenance phases CO2 emission, energy consumption and other environmental impacts are significant and need to be carefully assessed and properly mitigated. This paper, based on an extensive literature review, provides a comprehensive framework of trends and challenge in railway sustainability. Attention is focused on track and related materials, maintenance strategies, and methods of assessment of the sustainability. Results show that improvement of materials and practices used in construction and appropriate strategies in maintenance, supported by effective monitoring of the state of the track, can reduce the negative effects on the environment and society and contributes to make this transportation mode greener. Proper methods for the assessment of the sustainability, (LCA, Circularity Index) help to quantify the potential of environmental enhancement of different solutions and constitute effective and indispensable tools in the decision-making process.

In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

Vitrimers are covalent network materials, comparable in structure to classical thermosets. Unlike normal thermosets, they possess a chemical bond swap mechanism that makes their structure dynamic and suitable for activated welding and even autonomous self-healing. The central question in designing such materials is the trade-off between autonomy and material stability: The swap mechanism facilitates the healing but it also facilitates creep, which makes the perfectly stable self-healing solid a hard goal to reach. Here, we address this question for the case of self-healing vitrimers made from star polymers. Using coarse-grained molecular dynamics simulations, we study the adhesion of two vitrimer samples and find that they bond together on timescales that are much shorter than the stress relaxation time. We show that the swap mechanism allows the star polymers to diffuse through the material through coordinated swap events, but the healing process is much faster and does not depend on this mobility.

Self-assembly is a spontaneous process through which macroscopic structures are formed from basic microscopic constituents (e.g. molecules or colloids). By contrast, the formation of large biological molecules inside the cell (such as proteins or nucleic acids) is a process more akin to self-organization than to self-assembly, as it requires a constant supply of external energy. Recent studies have tried to merge self-assembly with self-organization by analyzing the assembly of self-propelled (or active) colloid-like particles whose motion is driven by a permanent source of energy. Here we present evidence that points to the fact that self-propulsion considerably enhances the assembly of polymers: self-propelled molecules are found to assemble into polymer-like structures, the average length of which increases towards a maximum as the self-propulsion force increases. Beyond this maximum, the average polymer length decreases due to the competition between bonding energy and disruptive forces that result from collisions. The assembly of active molecules might have promoted the formation of large pre-biotic polymers that could be the precursors of the informational polymers we observe nowadays.

This paper introduces actual considerations on the progressive disuse of residential space in the Alpine territory, considering possible actions. Nowadays, the building complexes built around the 60s and 70s (symbol of the mass tourism) fell to be suffering and searching of a new identity: the generation of owners, who bought them, has aged and the propensities of the new generations for holiday in those places have changed, which, moreover, now offer fewer opportunities for leisure, particularly in winter. Due to the great attention (and seeming opportunities) of current incentive policies towards the energy improvement of the existing stock, authors investigate the private conveniences of transformations through the refurbishment of these buildings. Starting from the study of the territory and the dynamics of local population, the research analyses a possible set of energy works, based on the 110% Superbonus tax incentive, introduced by L.D. 34/2020. A large part of the insights is focused on technical and economic feasibility study of the possible actions, following a process based on the evolution of the legislation. The work carried out is based on a specific case study, located in a small municipality in Piedmont mountain area, consisting of three apartment blocks of mostly second homes. The adopted methodology lends itself as a guide for the feasibility study of the Superbonus and, used as a tool for policy evaluation, leads to interesting results from the point of view of both private and public convenience.

In this paper, I propose a test for machine self-awareness inspired by the Turing test. While my test is plain and simple, it offers a much-needed alternative to ungrounded speculation while still capturing the essence of self-awareness. I discuss how to apply the test to Large Language Models and conduct experiments on GPT-3.5 that suggest it is not self-aware. I discuss the implications of machine self-awareness and relate it to questions about meaning and understanding. Finally, I present some next steps for studying self-awareness in machines.

The present paper reports an exact approach quantifying the electromagnetic contribution to the anomalous magnetic moment occurring in isolated system comprised of non-composite particle carrying elementary electric charge. Essential averaging procedure and regularization of the electromagnetic field potentials necessary when quantifying the electromagnetic self-interactions and when deriving equations of motion without singularities and obeying the conservation laws are thoroughly discussed. The study shows that the dynamics of the considered system is associated to unique classical transcendental equations of motion satisfied by the particle's velocity and the electromagnetic contribution to the anomalous $g$-factor known from the quantum electrodynamics. The equations of motion lead to an exact analytical expression for the anomalous $g$-factor that provides more accurate result than that calculated with the aid of quantum electrodynamics. It matches the experimentally measured value reported in the literature to a one part per trillion. We obtain a_e=0.00115965218000(65) thus reveling the potential of non-perturbative, non-probability methods in predicting the electron's anomalous g-factor.

Background: The adoption of self-care behaviors among patients with arterial hypertension (AH) plays an important role in the management of their health condition. However, a lack of scales assessing self-care is observed. We aimed to develop and validate the Hippocratic hypertension self-care scale. Methods: From a pool of questions derived from a literature review, 18 items were included in the scale and reviewed by a committee of experts. Participants indicate the frequency at which they follow the self-behavior prescribed in each statement on a five-point Likert scale. Data were collected between April 2019 and December 2019. Re-sults: 202 consecutive adult patients with AH were enrolled in the study. The internal consistency of the scale was found to be 0.807, using Cronbach’s alpha coefficient. An exploratory factor analysis identified two do-mains that accounted for 92.94% of the variance of the scale items; however, each sub-scale could not be used as an independent scale. Finally, the test- retest of the scale showed a significant strong correlation (r=0.0095, p<0.001). Conclusion: Our data indicate that the scale is a reliable and valid tool for assessing self-behaviors in patients with AH. Health professionals can use it in their clinical practice to improve the management of patients’ health condition.

: BACKGROUND: The prevalence of smoking among students is a growing concern, particular-ly among medical students. Being associated with stress, competitiveness, and various social in-fluences, smoking can have detrimental effects on self-esteem and self-efficacy and may create a vicious cycle that may contribute to students’ well-being and academic performance out-comes.METHODS: A cross-sectional study on 456 medical students was conducted via an online survey using structured questionnaires on socio-demographic characteristics, smoking habits, nicotine dependence, self-esteem, and self-efficacy. RESULTS: The prevalence of smoking was 31.1% The scores of self-esteem and self-efficacy were positively correlated with smoking habits (W1=0.957, W2=0.975, P<0.05). Students presented rather low smoking addiction with male stu-dents having slightly higher scores than female students on self-esteem and self-efficacy, mili-tary students had higher self-esteem scores than civilians with physical activity having a posi-tive correlation with non-smoking behavior. CONCLUSION: According to our results, self-esteem and self-efficacy were slightly positively correlated with smoking habits. Furthermore, there was no statistically significant association between other social factors and smoking. Our results may develop a theoretical basis for medical students’ psychological variables studying for further implementation of university educational programs and preventive interventions em-powering students towards an overall better quality of life, addressing, among others, smoking behaviors.

Self-enhancement (SE) is often overlooked as a fundamental cognitive ability mediated via the Pre-Frontal Cortex (PFC). Here we present research that establishes the relationship between the PFC, SE, and the potential evolved beneficial mechanisms. Specifically, we believe there is now enough evidence to speculate that SE exists to provide significant benefits and should be considered a normal aspect of the self. Whatever the metabolic or social cost, the upside of SE is great enough that it is a core and fundamental psychological construct. Furthermore, though entirely theoretical, we suggest that a critical reason the PFC has evolved so significantly in Homo sapien is to, in part, sustain SE. We therefore elaborate as to its proximate and ultimate mechanisms.

While the desire to uncover the neural correlates of consciousness has taken numerous directions, self-face recognition has been a constant in attempts to isolate aspects of self-awareness. The neuroimaging revolution of the 1990’s bought about systematic attempts to isolate the underlying neural basis self-face recognition. These studies, including some of the first fMRI (functional Magnetic Resonance Imaging) studies, revealed a right hemisphere bias for self-face recognition in a diverse set of regions including the insula, the Dorsal Frontal Lobe, the Temporal Parietal Junction and Medial Temporal Cortex. Confirmation of these data (which are correlational) was provided by TMS (Transcranial Magnetic Stimulation) and patients in which direct inhibition or ablation of right hemisphere regions leads to a disruption or absence of self-face recognition. These data are consistent with a number of theories including a right hemisphere dominance for self-awareness and/or a right hemisphere specialization for identifying significant social relationships including to oneself.

Building defects are very prevalent and contribute significantly to a building's economic value. There are numerous information sources on building defects that have a lot potential for learning more about building defects. This study aimed to identify the information sources used in previous building defect studies and to identify the motivation for carrying out such studies. To fulfil this aim a scoping study was carried out. The information sources identified included insurance companies, private databases, questionnaire surveys, lawsuits, building surveys, client complaint forms, and maintenance reports. This study found that insurance company and client complaint forms include the largest collection of real building defect cases, but such databases may lack detailed descriptions of the causes of the defects. The main purposes of building defects studies identified here included design challenges, identification of defects, building maintenance management, quality management, systematization in data collection, providing an overview of typical defects, and classifying defects. Identification was found to be the most common purpose, indicating that the industry wants to learn more. This study identified research gaps in the climate perspective in relation to building defects. Most of the studies focused on the economical perspective, and none focused on the carbon footprint perspective.

Modern engineering faces challenges in ensuring technical standards for service, durability, and sustainability. Political, administrative, and budgetary factors, coupled with climate change, pose tasks to structural integrity, affecting industries and economies. Marine infrastructures represent a strategic asset of the country as it handles a large part of the economic exchanges. This article analyzes five essential factors that play a fundamental role in the performance analysis of coastal structures: Chloride-induced corrosion, degradation models, maintenance strategies, monitoring, and climate change. Starting with reinforcement corrosion, considered as the main cause of distress, particularly in coastal zones for the long-term behavior of structures. Additional pressure from the influences of climate change is becoming evident and extreme, leading to reduction in capacity. To guarantee the lifespan, degradation models contribute by estimating the long-term performance of the asset as a strategic piece to the development of effective maintenance solutions. Additionally, quantifying the condition of the structure from monitoring data, plays a crucial part that provides information on the current situation of the structure. Finally, this review summarizes the challenges associated with the maintenance of aging marine structures considering aspects such as corrosion, monitoring, and the future challenges this area will face due to climate change.

Forest road pavement needs an evaluation methodology based on a comprehensive assessment of road conditions. This research was conducted to evaluate the performance of a method for rating the surface condition of forest roads and eventually to adapt the method to the situation prevailing in a forest road network. The rating method selected as the basis for this experiment was the pavement condition index (PCI) developed by the U.S. Army Corps of Engineers. A 53 km of forest roads were selected contained the most influential factors and conditions variability. Eventually, 201 road segments were delineated between 150-300 m in length. Within the given segments, sample plots were set 20 m in length consecutively. It was concluded that the panel scores for distress and surface condition of sample unit and section differed from forest road pavement condition index (FRPCI) and PCI. Linear regression was used to derive equations between distress and PCI score to determine effective FRPCI parameters that provide a numerical rating for the condition of road segments within the road network, where 0 worlds are the worst possible condition, and 100 is the best possible condition best. Also, regression analysis showed the FRPCI model with a 0.87 correlation for the total of the road is a performance index used for the first time in forest roads. This study showed a range of FRPCI from 7.8 to 96.3, different from PCI and URCI ratings (0.85-45 and 1.2-53). The FRPCI index helps forest managers in road maintenance, harvesting, and planning to use road information.

Interpretation of increased gain in the bovine animals is difficult to be validated due to inherent genetic variation to meet their requirement for energy and protein, and those may relate to the bull species, e.g., Thai swamp buffalo. Therein, the study aimed at investigating and predicting protein requirement systems, with providing abundant energy intake 2.20 Mcal/kg DM for maintenance and growth of Thai swamp buffaloes using the comparative prolonged feeding trial for 90 days. Sixteen bull Thai swamp buffaloes at the initial (Age: 18-24 months; BW: 233 ± 25.0 kg) were as-signed into four treatment groups, four buffaloes each, fed 5.42, 6.96, 8.94, and 10.71% DM crude protein (CP). CP intake, BW, and physiological fluid were determined. The net CP requirements for maintenance and growth of Thai swamp buffaloes were 5.41 g CP/kg W0.75 and 0.46 g CP/g average daily gain (ADG), respectively. Our results indicated that CP requirement increases, when BW increases. An increased dietary CP resulted in an increased number at blood urine nitrogen (N), N absorption, total volatile fatty acid, urinary purine derivative, and the microbial N. Notably, the net CP requirement for growth of Thai swamp buffalo was higher than it reported in NRC, but the maintenance was lower.

In the manufacturing industry, orders are typically scheduled and delivered through batches, and the probability of machine failure under high-load operation is high. On this basis, we focus on a single machine batch scheduling problem with a maintenance interval (SMBSP-MI). The studied problem is expressed by three-field representation as 1|B,MI|\sum{F_j+\mu}m, and the optimization objective is to minimize total flow time and delivery costs. Firstly, 1|B,MI|\sum{F_j+\mu}m is proved to be NP-hard by Turing reduction. Secondly, shortest processing time (SPT) order is shown the optimal scheduling of SMBSP-MI, and a dynamic programming algorithm based on SPT (DPA-SPT) with the time complexity of O(n^3T_1) is proposed. A small-scale example is designed to verify the feasibility of DPA-SPT. Finally, DPA-SPT is approximated to a fully-polynomial dynamic programming approximation algorithm based on SPT (FDPAA-SPT) by intervals partitioning technique. The proposed FDPAA-SPT runs in O(\frac{n^5}{\varepsilon^2})\ time with the approximation (1+\varepsilon).

This study investigated the breakdown trend in an automated production with an aim to recommend the application of Reliability-centered maintenance (RCM) for an improved productivity via a new preventive maintenance (PM) program. Individual Section-forming machine (ISM): a glass blowing machine for making glass bottles was used as the case study of an automated production system. The machine parts and the working mechanisms were analysed with a special focus on methods of processes and procedures that will enable the ISM maintenance department run more effective and achieve its essential goal of ensuring effective machine operation and reducing machine downtime. In this work, information is provided on the steps and procedures to identify critical components of the ISM using Failure Modes and Effect Analysis (FMEA) as a tool to develop an optimal maintenance program based on the reliability data of the equipment’s functional components. A relationship was established between the failure rate of the machine components and the maintenance costs such that using the recommended PM program, an evidence of improvement in the machine’s availability, safety and cost-effectiveness will result into an increase in the company’s profit margin.

For offshore wind power generation, accessibility is one of the main factors that has great impact on operation and maintenance due to constraints on weather conditions for marine transportation. This paper presents a framework to explore the accessibility of an offshore site. At first, several maintenance types are defined and taken into account. Next, a data visualization procedure is introduced to provide an insight into the distribution of access periods over time. Then, a rigorous mathematical method based on finite state Markov chain is proposed to assess the accessibility of an offshore site from the maintenance perspective. A five-year weather data of a marine site is used to demonstrate the applicability and the outcomes of the proposed method. The main findings show that the proposed framework is effective in investigating the accessibility for different time scales and is able to catch the patterns of the distribution of the access periods. Moreover, based on the developed Markov chain, the average waiting time for a certain access period can be estimated. With more information on the maintenance of an offshore wind farm, the expected production loss due to time delay can be calculated.

We report the development of an extrinsic self-healing coating system that shows no fluorescence from the intact coating, yellowish fluorescence in cracked regions, and greenish fluorescence in healed regions, thus allowing the separate monitoring of cracking and healing of coatings. This fluorescence monitoring self-healing system consisted of a top coating, an epoxy matrix resin containing mixed dye-loaded in single microcapsule. The dye-loaded microcapsules consisted of a poly(urea-formaldehyde) shell encapsulating a healing agent containing MAT-PDMS and styrene, a photo-initiator and a mixture of two dyes, one that fluoresces only in the solid state (DCM) and a second that fluoresces dramatically increased in the solid than solution state (4-TPAE). A mixture of the healing agent, photo-initiator and the two dyes was yellow due to fluorescence from DCM. On UV curing of this mixture, however, the color changed from yellow to green and the fluorescence intensity increased due to fluorescence from 4-TPAE in the solid state. When a self-healing coating embedded with microcapsules containing the DCM/4-TPAE dye mixture was scratched, the damaged region exhibited a yellowish color that changed to green after healing. Thus, the self-healing system reported here allows the separate monitoring of cracking and healing based on changes in fluorescence color.

Background: The adoption of self-care behaviors among patients with congestive heart failure (CHF) is essential for the management of their health condition. However, there is a lack of tools for estimating self-care in CHF patients. We aim to develop and validate the Hippocratic heart failure self-care scale (HHFSCS). Methods: the scale includes 22 items which are reviewed by a committee of experts. Individuals indicate the frequency at which they follow each self-behavior on a five-point Likert scale. Adult patients with CHF (n=250) from a General Hospital, in Athens, were participated in the study from June 2020 to March 2021. Reliability coefficients and Explanatory Factor Analysis (EFA), using a Varimax rotation and the Principal Components Method were used to assess the psychometric measurements. Results: The Cronbach’s alpha coefficient of the HHFSCS was 0.807. The exploratory factor analysis identified two domains that accounted for 88.44% of the variance of the scale items; however, each sub-scale could not be used as an independent scale. Finally, the test-retest showed a significant strong correlation (r=0.973, p&lt;0.001). Conclusions: The HHFSCS is a reliable and valid tool for assessing self-behaviors in CHF patients. Health professionals can use it in their clinical practice to improve the management of patient’s health conditions.

Online education allows learners to develop knowledge and skills flexibly and conveniently—such observation among students whose characteristics involve student engagement, self-regulation, and self-efficacy. However, studies to characterize Filipino online learners seem lacking. Thus, this study aimed to characterize science education tertiary students in the Philippines concerning their online student engagement (OSE), self-regulated learning (SRL), and online learning self-efficacy (OLSE). The unprecedented events brought by COVID-19 pandemic also urged the implementation of online modalities while there is no available information on students’ online learning profiles. Hence, researchers used a survey research employed through ex post facto approach to determine the effects of the demographic profile on OSE, SRL, and OLSE. The survey was participated by N=373 respondents who answered the questionnaire with informed consent administered via Google Forms. Results revealed that OSE indicators moderately characterized students, while SRL and OLSE indicators are true of them, substantiated by the overall mean M=3.85(SD=0.90), M=3.86(SD=0.92), and M=3.14(SD=0.73), respectively. Also, multivariate tests showed no significant effect among the independent groups (p&gt;0.05), except for gender and OLSE interaction (p&lt;0.05) so, only in OLSE was a significant difference found in gender. In conclusion, Filipino online learners are moderate across aspects of student engagement, self-regulation, and self-efficacy.

Secure attachment, developed through consistent relationships with attachment figures in childhood, is a crucial factor in fostering healthy interpersonal relationships and a positive self-perception. This research aimed to assess how perceived security, as an indicator of secure attachment, predicts adult self-concept through the mediating effects of resilience and positive self-esteem. A quantitative, cross-sectional, and non-probabilistic study was conducted with 383 participants, of whom 76.2% were females, aged between 17 and 86 years (M = 28.52, SD = 12.23). Correlational analyses revealed significant positive associations between perceived security, self-concept, resilience, and positive self-esteem. Path analysis demonstrated that resilience and positive self-esteem sequentially mediate the relationship between perceived security during childhood and adult self-concept (Total Effect R2 = .34, F(1,381) = 193, p < .001; Indirect effect of sequential mediation b = .02, CI 95% [.01, .04]). Findings suggest that the ability to cope with ad-versity and personal acceptance underlie the impact of perceived security on self-concept. It is recommended to promote not only secure attachment in family interventions but also resilience and self-esteem in programs targeting adolescents, youth, and adults.

Purpose: To identify the impact of dealing with COVID-19 patients in clinical areas on nurses' professional self-concept and self-confidence. Background: Professional self-concept is considered a critical factor in the recruitment/retention process in nursing, nursing shortage, career satisfaction, and academic achievements. Professional self-confidence is also a crucial determinant in staff satisfaction, reducing turnover, and increasing work engagement. Design: Descriptive, comparative study. Methods: The study was conducted between February to May 2021 by utilizing a convenience sampling technique. A total of 170 nurses from two facilities were recruited from two COVID-19 and non-COVID-19 designated facilities. The level of professional self-concept and self-confidence was assessed by utilizing the Nurses' Self-Concept Instrument and Self-Confidence Scale. Results: The professional self-concept level among the exposed group to COVID-19 patients was lower than the comparison group, while the professional self-confidence level among the exposed group to COVID-19 patients was similar to the comparison group. On the other hand, the satisfied staff and those who received professional training in dealing with COVID-19 patients reported a higher level of professional self-concept. Conclusions: Dealing with COVID-19 patients has an impact on professional self-concept; the exposure group was lower than those who did not deal with COVID-19 patients, while the professional self-confidence level among the exposed group was similar to the comparison group. Getting professional training in dealing with COVID-19 patients and being satisfied at work were significant factors in improving the professional self-concept. Policymakers should create strategies that target the improvement of professional training in dealing with COVID-19 patients.

Background: Level of perceived competence as a basic psychological need could trigger achievement of diabetes self-management goals. Due to lack of a specific data collection tool to measure level of self-competence among Persian speaking patients with diabetes this study was conducted for cross-cultural adaptation and psychometric assessment of the Persian version of Perceived Competence Scale for Diabetes (PCSD-P). Methods: Standard translation/back-translation procedure was carried out to prepare a preliminary draft of the PCSD-P. Content and face validity of the early draft were checked by an expert panel including 15 scholars in the field of health education and promotion as well as nursing education with experience of working and research on diabetes. The final drafted questionnaire was completed by 177 randomly selected patients with type 2 diabetes. Based on the collected data structural validity of the contrived version was appraised using exploratory and confirmatory factor analysis (EFA, CFA). Cronbach's alpha and Intraclass Correlation coefficients (ICC) were used to check the scale’s reliability and internal consistency. ; (3) Results: The estimated measures of Content Validity Index (CVI= 0.95) and Content Validity Ratio (CVR= 0.8) were in the range of acceptable recommended limits. The EFA analysis results demonstrated a single factor solution according to the items’ loadings for the component. The model fit indices i.e. RMSEA= 0.000, CFI=1, TLI=1, GFI= 0.998, NFI= 0.999 RFI= 0.995 confirmed consistency of the hypothesized one-factor solution. Values of the internal consistency and reliability coefficients were also in the vicinity of acceptable range (α= 0.892, ICC=0. 886, P= 0.001). Conclusions: The study findings revealed good internal validity and applicability of the PCSD-P to measure degree of self-competence among Persian speaking type 2 diabetes patients to manage the chronic disease. Due to unrepresentativeness of the study sample future cross-cultural test of PCSD-P on diverse and broader Persian speaking populations is recommended.

This paper addresses the development of knowledge and assessment-centered learning approaches within a reflective learning framework in a first year physics class in a university faculty. The quality of students’ reflections was scored using a Self-reporting Reflective Learning Appraisal Questionnaire at the end of each learning approach. The results showed the differences between the approaches based on reflections on the learning control through self-knowledge, by connecting experience and knowledge, as well as through self-reflection and self-regulation. Assessment-centered activities fundamentally help students identify aspects of their attitudes towards, as well as regulate, their sustainability learning education.

Artificial intelligence (AI) has found extensive applications in the sustainability domain. The municipal solid waste incineration (MSWI) process can achieve waste-to-energy (WTE) goals through the assistance of automation and information technologies. In theory, AI can facilitate the intelligent operation of the MSWI process, simultaneously achieving low pollution emissions and high production efficiency. This study provides an overview of AI applications in the MSWI process. Addressing fundamental challenges, the optimal control of the MSWI process is categorized into four aspects: modeling, control, optimization, and maintenance. Subsequently, this study conducts a detailed analysis and discussion of AI applications in each of these technological domains. In conclusion, this paper outlines the future research directions and provides an outlook on the fundamental issues pertaining to AI applications in the optimal control of the MSWI process.

The present work provides continuity with studies where currently the industrial market is characterized by a highly variable demand in terms of quantities and flexibility of manufacturing or mass customization, which translates into a more demanding production context in terms of the continuous changes required to the production systems, the effect of which results in an increase in the fatigue subjected to the machines that make up the production systems. On the other hand, current production systems tend to use highly communicative and sensorized cyber-physical systems; these characteristics can be used to integrate them into decision-assisted systems in order to improve the availability of the industrial plant. The assisted system developed focuses on collecting and taking advantage of historical knowledge of industrial plant failures and breakdowns. By ideally integrating the Reliability Centered Maintenance (RCM) methodology and Case-Based Reasoning (CBR) algorithms implemented in a Java application, it is possible to design maintenance plans adjusted to a real and changing operational context of any industrial plant. As a result, faster and more accurate decisions are made, as they are based on data. This article focuses on improving certain aspects of the developed assisted system by adding more value to it by incorporating Fuzzy Logic (FL) techniques. The aim is to improve the way of entering information about risk factors and their relative importance, by incorporating natural language, instead of a numerical score, resulting in increased precision in the calculation of the Risk Priority Number (RPN) of the new cases incorporated into the assisted system.

The rapid growth of the aviation industry has progressively heightened the importance of airport maintenance management. There is an increasing need for a comprehensive system that considers the diverse requirements of various stakeholders. Existing research has primarily focused on the technological advancements of optimization models, with limited applicability in practice. In this study, we introduce the Condition Cases Optimization (CCO) to address these limitations while incorporating multi-facility and multi-year network optimization models. We developed condition index, Serviceability Level, integrated assessment indices and performance models for decision-making criteria. We categorize conditions into three classes and propose a practical decision-making strategy that can flexibly adapt to budget constraints. Sensitivity analysis highlights the impact of initial budget, maintenance methods, costs, and thresholds on decision outcomes. Through a case study we validate the effectiveness and practicality of the CCO method as an efficient decision-making tool.

The IPCC’s sixth assessment report projects 15% to 43% (44 EJ/y – 310 EJ/y) of global primary energy to be generated by biomass in 2050 across multiple GHG mitigation scenarios. That report also emphasises the importance of electrification to meet GHG reduction targets. With increased reliance on electric power, and increased appeal to biomass, bioenergy for electricity is expected to play a major role in future energy markets. What makes the bioenergy solution more attractive is its reported reasonable Energy Return on Investment (EROI). However, generation at large scale is projected to be greatly dependent on crops and plantations. This shifts the GHG emissions concern to be concerns over land use and other emissions integrated in the bioenergy lifecycle. It is therefore vital to know whether the potential of electricity generation from biomass outweighs environmental impact of bioenergy. This paper evaluates the potential of biomass electricity mainly generated from short rotation woody crops combustion in generating green energy. This is done using the “Green EROI (EROIg)” quantification methodology, which indicates the net energy generated to society after investing in ecosystem maintenance energy (ESME). ESME is a non-monetary weighting mechanism of an entity’s different lifecycle environmental impacts. This study found that the EROIg of bioelectricity is marginally larger than unity when converted to its primary equivalent form (EROIg-PE) which indicates that the technology is somewhat energetically viable if its production was to be green. Three design options were proposed to improve bioenergy’s EROIg performance, these include adding 20% waste wood in the combustion mix, staggered harvesting and plantation to achieve annual harvest and pelletizing wood. This approach appeared to improve the EROIg especially for pelletizing, due to its simultaneous reduction in storage and transport costs, making the production energetically and environmentally viable even at a 1 : 1 secondary : primary ratio with an EROIg of 1.11 and an EROIg-PE of 3.17. We conclude with the discussion of the multiple indirect advantages of growing crops that can be used for energy generation, and a discussion on how this technique can be used alongside others to help them generate cleaner energy while facing the current global climate, biodiversity and waste issues.

Despite the important role of offshore renewable energies in the energy transition, the economical viability is still unclear. Therefore, an appropriate site selection is crucial. Besides the energy potential, the impact of operation and maintenance (O&M) aspects on the location can be critical. Traditional accessibility assessment metrics do not allow a comprehensive evaluation. Therefore, the present paper suggests a novel, technology-informed metric, incorporating the overall set of most critical aspects, i.e. metocean conditions, visibility due to sunlight and sea fog, system failures, and O&M logistics. Among the different aspects, limited visibility is shown to be highly relevant with a reduction of up to 60% in accessibility. The study assesses accessibility in 5 different locations across Europe. On the one hand, accessibility is shown to be less sensitive to long-term resource variations with a reduction of 5% in the last 6 decades. On the other hand, accessibility is shown to be inversely proportional to the energy potential overall, meaning that as energy potential increases, accessibility is reduced, increasing downtime, reducing final energy generation and increasing the final cost of energy. As a consequence, site selection should combine energy potential and accessibility assessments, which is enabled by the technology-informed metric presented here.

Current bridge inspection practices rely on paper-based data acquisition, digitization, and multiple conversions in between incompatible formats to facilitate data exchange. This practice is time-consuming, error-prone, cumbersome, and leads to information loss. One aim for future inspection procedures is to have a fully digitized workflow that achieves loss-free data exchange, which lowers costs and offers higher efficiency. On the one hand, existing studies proposed methods to automatize data acquisition and visualization for inspections. These studies lack an open standard to make the gathered data available for other processes. On the other hand, several studies discuss data structures for exchanging damage information through out different stakeholders. However, those studies do not cover the process of automatic data acquisition and transfer. This study focused on a framework that incorporates automatic damage data acquisition, transfer, and a damage information model for data exchange. This enables inspectors to use damage data for subsequent analyses and simulations. The proposed framework shows the potentials for a comprehensive damage information model and related (semi-)automatic data acquisition and processing.

Worldwide, millions of kilometres of sewers are constructed from concrete pipes. Unfortunately, concrete sewers are susceptible to corrosion from biogenic hydrogen sulphide, and though they may pass visual inspection, their ability to hold together under load may be degraded. This paper presents the design of a teleoperated robot with a protractible probe, that allows an operator to apply a localised load to selected points within a concrete sewer pipe. We report findings from laboratory and field trials of our prototype, with initial results suggesting that this approach has the potential to contribute useful information to sewer maintenance planning.

Denture wearers often complain about jeopardized function and reduced quality of life due to lack of prosthesis’ retention. Implant retained mandibular overdentures, on 2 non-connected implants (2IOD) are well-proven solutions to overcome these issues. We prospectively assessed 69 patients and scrutinized clinical records until at least 7 years of function. Thirty-six were retained on Locator ® Abutments (LA) and thirty-tree on Ball Abutments (BA). Both systems were compared regarding the type, amount and total cost of required maintenance. One implants was lost, yielding 98.7% survival after 7 years. In total 438 technical issues occurred: 121 (27.35%) in BA and 317 (72.4%) in LA. Out of these, 343 events (78%) were solved chairside: 191 (43.6%) were replacements of retention caps, 113 (25.8%) were minor acrylic repairs, in 26 (5.9%) pressure ulcers had to be relieved and 13 (3%) related to abutments. LA required 179 insert replacements compared to 12 in the BA group. The overall initial treatment cost was 3850 euro. The average total maintenance cost in relation to the initial cost for the LA and BA groups was 19.11% (range 0% - 82.24%) and 18.91% (range 0% - 113.26%) respectively (P=0.540). Conclusions: The 7-years maintenance costs for a 2IOD is acceptable when the patient is regularly checked and professionally maintained. Most events are easily solvable chairside, but a few patients required more expensive interventions, regardless of the type of attachment used.

Effective maintenance of railways requires a comprehensive assessment of the actual condition of the construction materials involved. In this regard, Ground-Penetrating Radar (GPR) stands as a viable alternative to the invasive and time-consuming traditional techniques for the inspection of these infrastructures. This work reports the experimental activities carried out on a test-site area within a railway depot in Rome, Italy. Specifically, a 30 m-long railway section was divided into 10 sub-sections reproducing different various physical and structural conditions of the track-bed. In more detail, combinations of varying scenarios of fragmentation and fouling of the ballast were reproduced. The set-up was then investigated using different multi-frequency GPR horn antenna systems. The effects of the different physical conditions of ballast on the electromagnetic response of the material were analysed for each scenario using time- and frequency-domain signal processing techniques. Parallel to this, modelling was provided to estimate fouling content. Interpretation of results has proven the viability of the GPR method in detecting signs of decay at the network level, thereby proving this technique to be worthy for implementation in asset management systems.

It is well known that working PV plants show several maintenance needs due to wiring and modules degradation, mismatches, dust and PV cells defects and faults. There are a wide range of studies that show the theoretical and some laboratory tests of how these circumstances may affect the PV production. Thus, it results mandatory to evaluate the whole PV plant performance and, then, it’s payback time, profitability and environmental impact or carbon footprint. However, very few studies include a systematic procedure to quantify and supervise the real degradation effects and faults impacts on the field. In this paper, the authors first conduct a brief review of the most frequent PV faults and degradation that can be found on real conditions operative PV Plants. Then, they propose and develop an innovative Geographic Information System application to locate and supervise them. The designed tool has been applied to either a large PV plant of 108 kWp and a small PV plant of just 9 kWp installed on a home rooftop. For the large PV plant, 24 strings of PV modules have been modelized and introduced into the GIS application and every module in the power plant has been studied including voltage, current, power, series and parallel resistance, fill factor, normalized PV curve to STC, thermography and visual analysis. For the small PV installation 3 strings of PV panels have been studied identically. It must be noticed that PV modules in this case include power optimizers. The precision of the study allows the researchers to locate and supervise up to a third part of every PV cell in the system, which are adequately georreferenciated. The developed tool allows both the researchers and the investors to increase control on the PV plant performance and conducts to a better planification of maintenance actuations and to evaluate several PV modules replacement strategies in a preventive maintenance programme. Found PV faults include hot spots, snail tracks, EVA discoloration, PV cells fractures, busbars discoloration, bubbles and Si discoloration.

Today, issues related to people’s mental health and well-being have been described as a challenge for society, globally as well as in Sweden. This calls for new approaches to mental health promotion. The aim was to evaluate the adequacy of the content and structure, describing experiences of study circles as a means of supporting participants' self-care and self-compassion. The overall design is a descriptive, QUAL+quan design, where quantitative and qualitative results are integrated. Five participants participated in a focus group interview, of whom four completed questionnaires. One individual interview was conducted with the study circle leader. Study circles can be an arena for mental health promotion as learning and sharing of experience contributes to a sense of coherence, as well as self-compassion and a genuine concern for one’s own and others' wellbeing, but not considered an alternative to psychiatric care, for those in need of professional services. Study circles can be a possible means to support self-care and thereby promote mental health in the general population, and a valuable contribution to public health. However, in addition to modifications of the content, further research is needed on the qualifications for study circle leaders as well as the dissemination of study circles.

Self-compacting concrete (SCC) production is a complex operation that requires finding a good combination and a suitable dosage of each of its constituents. Several formulation methods have been developed to meet the workability requirements of SCC. In this paper, the relationships between the fresh properties of SCC based on local metakaolin (MK) and the spreading of its mortar was investigated. The results showed the existence of good correlations between the spreading of the mortar and the fresh state properties of the related SCC and that the MK substitution rate did not affect these correlations. The choice of the mortar flow rate should target appropriate fresh properties of the SCC and a viscosity enhancing agent (VEA) could be used if the mix is too fluid and present an instability.

Based on the sensitive sum frequency generation cross-correlation frequency-resolved optical gating (SFG-XFROG) measurement setup, besides the pulse broadening, blue shift, red shift and obvious pulse acceleration, we observed the soliton evolution when the low energy soliton pulse with wavelength of 1555nm transmit through the Si photonic crystal waveguide. The measurements were nicely matched with the simulation results, which are achieved with an optimized nonlinear Schrödinger equation (NLSE) modeling. The effects of various parameters of the silicon photonic crystal waveguides and the incident pulses on the pulse transmission were also analyzed, including the nonlinear effects and dispersion such as the self-phase modulation (SPM), self-steepening (SS) and intra-pulse Raman scattering(IRS). The results help us understand further the ultra-fast nonlinear dynamics of soliton in silicon-based waveguides, and even open a novel way for soliton-based functional elements in CMOS-compatible platforms.

Building on the Sustainable Happiness Model, this study examined how congruency between ideal self-image and brand image influence a sense of happiness. The findings show that when ideal self-image and ideal social self-image are congruent with brand image a sense of happiness can be enhanced through brand identification and positive emotions. This study contributes to literature as it reveals the mechanism of how congruency between ideal self-image and brand image positively affect happiness.

Purpose: This study aims to analyze the factors that affect the behavior of user-defined avatars in different virtual environments, and compare the differences in public self-awareness, self-expression, and emotional expression among customized avatars in multiple virtual contexts. Methods: Using a between-subjects experimental design, two random groups of participants were asked to customize avatars for themselves in two contexts, a multiplayer online social game (MOSG) and a virtual meeting (VM). i.e. a relaxed and a serious social environment. Results: When subjects perceived a more relaxed environment, the customized avatars had less self-similarity, and the subjects exhibited a stronger self-disclosure willingness and enhanced avatar wishful identification; nevertheless, public self-consciousness was not increased. When subjects perceived a more serious environment, the customized avatars exhibited a higher degree of self-similarity, and the subjects exhibited a greater self-presentation willingness, along with enhanced identification of avatar similarity, and increased public self-consciousness. Conclusions: Participants expressed positive emotions, suggesting that avatars play a positive role in various virtual contexts. The virtual context affects the self-similarity of user-customized avatars, and avatar self-similarity affects self-presentation and self-disclosure willingness, and these factors will affect the behavior of the user-customized avatar. This study contributes suggestions to the Metaverse avatar customization platform design.

The capacitor-mismatch among diverse defects caused by the manufacturing process variations significantly affects the linearity of capacitor-array used to implement the capacitive digital-to-analog converter (CDAC) in the successive-approximation-register (SAR) analog-to-digital converter (ADC). Accordingly, the linearity of the SAR ADC is limited by that of capacitor-array, resulting in serious yield loss. This paper proposes an efficient foreground self-calibration technique to enhance the linearity of the SAR ADCs by mitigating the capacitor-mismatch based on the split ADC architecture along with variable capacitors. In this work, two ADC channels (i.e., ADC1 and ADC2) for the split ADC architecture include their capacitive DACs (CDACs) whose binary-weighted capacitor-arrays consist of variable capacitors. A charge-sharing SAR ADC is used for each ADC channel. In the normal operation mode, their digital outputs are averaged to be the final ADC output, as in a conventional split ADC. In the calibration mode, every single binary-weighted capacitor for the two ADCs is sequentially calibrated by making parallel or/and antiparallel connection among two or thee capacitors from the two channels. For instance, because the capacitors of the CDACs ideally exhibit the binary-weighted relation as Cn=2×Cn−1, variable capacitor Cn of ADC1 can be updated to be closest to the sum of Cn−1 of ADC1 and Cn−1 of ADC2 for the calibration. For the process, the two capacitor-arrays of the two ADCs can be reconfigured to be connected to each other, so that Cn of ADC1 can be connected with two of Cn−1 of ADC1 and ADC2 in antiparallel. The two voltages at the top and the bottom plates of the CDAC are compared by a comparator of ADC1, and the comparison results are used to update Cn. This process is iterated, until Cn is in agreement with the sum of two of Cn−1. Finally, all the capacitors can be calibrated in this way to have the binary-weighted relation. The simulation results based on the proposed work with a split SAR ADC model verified that the proposed technique can be practically used, by showing that the total-harmonic-distortion and the signal-to-noise-and-distortion ratio were enhanced by 21.8-dB and 6.4-dB, respectively.

Background: This study aimed to develop and evaluate a dietary self-care promotion program for hemodialysis patients based on Hurley's self-efficacy model. Methods: The needs of hemodialysis patients were identified, and an online community-based dietary self-care promotion program was developed with expert evaluations. 44 hemodialysis patients were randomly assigned to an experimental group (n=22) and a control group (n=22). The program was applied to the experimental group for four weeks. Before and after the intervention, as well as four weeks after the intervention, dietary self-efficacy, dietary self-care behavior, interdialytic weight gain, serum phosphorus, and serum potassium were assessed in both groups. Results: Significant differences over time and between groups were observed in dietary self-efficacy, interdialytic weight gain, serum phosphorus, and serum potassium. The interaction between time and group was also significant. While no significant differences were found over time in dietary self-care behavior, significant group differences and interaction effects between time and group were identified. Conclusions: The results indicate that the dietary self-care promotion program using an online community for hemodialysis patients is more effective in enhancing dietary self-efficacy and dietary self-care behavior compared to conventional treatment. This suggests the clinical applicability of the proposed dietary self-care promotion program.

In recent decades, considerable attention has been focused on the design and development of surfaces with defined or tunable properties for a wide range of applications and fields. To this end, self-assembled monolayers (SAMs) of organic compounds offer a unique and straightforward route of modifying and engineering the surface properties of any substrate. Thus, alkane-based self-assembled monolayers constitute one of the most extensively studied organic thin-film nanomaterials, which have found wide application in antifouling surfaces, control of wettability or cell adhesion, sensors, optical devices, corrosion protection, organic electronics, among many other applications. Some of which have led technological transfer to industry. Nevertheless, recently, aromatic-based SAMs have gained in importance as functional components, particularly in molecular electronics, bioelectronics, sensors, etc., due to their intrinsic electrical conductivity and optical properties, opening up new perspectives in these fields. However, some key issues affecting device performances still need to be lifted off to ensure their full use and access to novel functionalities such as memories, sensors, or active layers in optoelectronic devices. In this context, we will present herein recent advances of p-conjugated systems-based self-assembled monolayers (e.g., push-pull chromophores) as active layer and their applications.

Health self-tracking is an ongoing trend as software and hardware evolve, making the collection of personal data not only fun for users but also increasingly interesting for public health research. In a quantitative approach we studied German health self-trackers (N=919) for differences in their data disclosure behavior by comparing data showing and sharing behavior among peers and their willingness to donate data to research. In addition, we examined user characteristics that may positively influence willingness to make the self-tracked data available to research and propose a framework for structuring research related to self-measurement. Results show that users' willingness to disclose data as a "donation" more than doubled compared to their "sharing" behavior (willingness to donate= 4.5/10; sharing frequency= 2.09/10). Younger men (up to 34 years), who record their vital signs daily, are less concerned about privacy, regularly donate money, and share their data with third parties because they want to receive feedback, are most likely to donate data to research and are thus a promising target audience for health data donation appeals. The paper adds to qualitative accounts of self-tracking but also engages with discussions around data sharing and privacy.

The recommendations of the panel of experts on the further development of the ‘High-Tech Strategy’ of the Government of the Federal Republic of Germany designate the biologization of economic processes along with digitization as the central driver of societal change. Various fields such as the 'biologization of materials research' were then defined in the 'Bio-Agenda' in order to walk the path from biology to innovation. In view of this perspective, we describe how the use of biological materials and mechanisms can lead to resource conservation, the production of intelligent materials and new technological and medical applications. Our strategy, based on research on ‘Biointerfaces in Technology and Medicine’, aims at the development of modular biohybrid systems that could be used as 'biofactories of the future' for sustainable production processes. To achieve this goal, in a first phase already known technologies have to be further refined and integrated in order to obtain and apply compartmentalized reaction systems on different length and time scales. In a second phase, the resulting functional units will be employed to develop dissipative systems useful for biomedical applications and advanced production processes. From a technical point of view, future success in creating 'something entirely new' will depend crucially on robust and complementary research capabilities. Since the close connection of engineering and life sciences at KIT provides an excellent basis for this endeavor, we consider the above perspectives to be feasible.

Toby Walsh in “The Singularity May Never Be Near” gives six arguments to support his point of view that technological singularity may happen but that it is unlikely. In this paper, we provide analysis of each one of his arguments and arrive at similar conclusions, but with more weight given to the “likely to happen” probability.

This study uses a wind turbine case study to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real time condition monitoring, predictive analytics, and health management of selected components of Wind turbines in a wind farm.. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by Machine Learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as Condition Monitoring and Predictive Maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real time sensor data, and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.

With the widespread use of electric machines there is a growing need to extract information from the machines to optimize their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of an squirrel cage induction motor rotor physical variables. The system comprises: circuit to acquire desirable rotor variable(s) value(s) and send it to the computer; a rectifier and power storage circuit that besides converting an alternating current in continuous also store energy for a certain amount of time to wait the motor engine shutdown; and magnetic generator that harvest energy from rotating field to power the circuits mentioned above. The embedded system is set on the rotor, making it difficult to power the system because it is rotating, problem solved with the construction of the magnetic generator eliminating the need of using batteries or collector rings and send data to the computer using a wireless NRF24L01 module. For the proposed system validation a temperature sensor (DS18b20) was used, variable known as the most important when identifying the need for maintenance and control systems. Tests were made getting satisfactory results proving the viability of using sensors on the rotor.

Dementia is a public health priority, in which memory impairments are prevalent and affects an individual’s daily activities. Research has tried to find ways to support memory problems of individuals with dementia. Self reference effect (SRE) has been shown to benefit memory in healthy, non-dementia group. The study aimed to investigate if SRE will improve memory performance of individuals with dementia. We used ecologically valid Ownership Procedure and the Remember/Know Paradigm to examine the Self-Reference Effect (SRE) on memory. It was hypothesised that participants living with dementia will perform differently in the recall of items that were self-referentially encoded compared to items other-referentially encoded and that the memory will be different in control participants than in those that live with dementia. The research found that self-referential processes did not have effect on memory in individuals with dementia. We suggest that impaired self-awareness may have contributed to the lack of this effect. However, we had a small sample size and these findings should be treated with caution.

Q-switched laser radiation at wavelengths of 355 nm, 532 nm, and 1064 nm from a Nd: YAG laser was used to generate plasma in laboratory air at the target surface made of compressed nano-silver particles of size 95 ± 10 nm. The emitted resonance spectra from the neutral silver at wavelengths of 327.9 nm and 338.2 nm indicate existence of self-reversal in addition to plasma self-absorption. Both lines were identified in emission spectra at different laser irradiation wavelengths with characteristic dips at the un-shifted central wavelengths. These dips are usually associated with self-reversal. Under similar conditions, plasmas at the corresponding bulk silver target were generated. The recorded emission spectra were compared to those obtained from the nano-material target. The comparisons confirm existence of self-reversal of resonance lines that emerge from plasmas produced at nano-material targets. This work suggests a method for recovery of the spectral line shapes and discusses practical examples. In addition, subsidiary calibration efforts that utilize the Balmer series Hα-line reveal that other Ag I lines at 827.35 nm and 768.7 nm are optically thin under variety of experimental conditions and are well-suited as reference lines for measurement of the laser plasma electron density.

Good health and the promotion of well-being for all is the third of the 17 Global Goals included in the 2030 Agenda for Sustainable Development. Contributing to this goal, the current study aimed to examine the relationships between one kind of athletes’ well-being, namely state organic self-talk, with personality traits, and basic psychological need satisfaction and frustration within their sport. Athletes (N = 691; mean age 21.65) from a variety of individual (n = 270) and team sports (n = 421) completed a multisection questionnaire capturing the targeted variables. Three-step hierarchical regression analyses revealed that: In step 1, all personality traits were to some extent a significant predictor of athletes’ organic, spontaneous self-talk dimensions and goal-directed self-talk functions. In step 2, need satisfaction significantly contributed to all spontaneous self-talk dimensions and goal-directed self-talk functions (except for creating functional deactivated states) over and above personality. Finally, in step 3, need frustration significantly contributed to negative spontaneous self-talk dimensions, and to all goal-directed self-talk functions (except for instruction) over and above personality and need satisfaction. Overall, our results indicate the importance of personality traits as personal antecedents, and perceptions of basic psychological need satisfaction and frustration as social-environmental antecedents, in shaping athletes’ state organic self-talk.

Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and rare survey has been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human error and to simplify management, self-management CPS (SCPS) is a wise choice. And to achieve dependable self-management, systematic solution is necessary to verify the design and to guarantee the safety of self-adaptation decision, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focus on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means; then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaption, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaption, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

Artificial Intelligence (AI) models are expected to have a great impact in the manufacturing industry, optimizing time and resource cost by enabling applications such as predictive maintenance (PM) of production machines. A necessary condition for this is the availability of high quality data collected as close as possible to the process in question. With the advent of 5G equipped multi sensor platforms (MSPs), high sampling rate data can be collected and transmitted for processing in real time. This poses a data security challenge, since this data may give valuable insight into confidential business information of companies. Federated learning (FL) enables the training of AI models with data from multiple sources without it leaving the shop floor, by utilizing distributed computing resources available on premise. This paper introduces an architecture of FL based on data collected from 5G MSPs for enabling PM in industrial environments and discusses its potential benefits and challenges.

Technology-enabled maintenance via sensors and the internet of things (IoT) technologies is seen as the “new maintenance strategy”. The aim is to use these technologies to focus on unnecessary breakdowns and increase levels of asset availability where the existing of a dedicated team to maintain or data on asset condition is limited, useless or full of noise. This paper presents the integration of various sensing nodes within an automated laser cutting machine for monitoring of critical components’ conditions. The integrated system provides a centralised platform for data collection from various sensors and provides early failure detection mechanism based on specified set points with alert mechanisms utilising a custom dashboard for data visualisation. The research highlights the required stages for the identification of the appropriate components and sensory devices for the development of efficient monitoring environment.

We analyze damage propagation modeling of turbo-engines in a data-driven approach. We investigate subspace tracking assuming a low dimensional manifold structure and a static behavior during the healthy state of the machines. Our damage propagation model is based on the deviation of the data from the static behavior and uses the notion of health index as a measure of the condition. Hence, we incorporate condition-based maintenance and estimate the remaining useful life based on the current and previous health indexes. This paper proposes an algorithm that adapts well to the dynamics of the data and underlying system, and reduces the computational complexity by utilizing the low dimensional manifold structure of the data. A significant performance improvement is demonstrated over existing methods by using the proposed algorithm on CMAPSS Turbo-engine datasets.

Plants and its corresponding botanical preparations have been used for centuries due to their remarkable potentialities in both treatment and prevention of numerous affections. Hundreds of biologically active constituents are present in each whole plant matrice, working in synergism and conferring both its own protection against invaders and even providing promissory bioactive effects for human beings. The worldwide population has devoted increasing attention and preference by medicinal plants use for health promotion and disease prevention, and more recently for oxidative-stress related disorders protection. Indeed, oxidative stress-related disorders, like cardiovascular and (neuro) degenerative disorders, and even cancer have raised exponentially. Although oxidative stress is in itself intrinsic to our own metabolism, allarming sources of free radicals are daily affecting us. In fact, plant-derived bioactives present a broad spectrum of biological effects, and its antioxidant, anti-inflammatory and more recently anti-aging effects have been considered a hot topic among the medical and scientific community. Nonetheless, and although its numerous biological effects, it should not also be forgotten that some bioactive molecules are prone to oxidation and can even exert pro-oxidant effects. In this sense, the objective of the present review is to provide a detailed overview on plant-derived bioactives in oxidative stress-related disorders. Specifically, the role of phytochemicals as antioxidants and pro-oxidant agents is carefully addressed, as is its therapeutic relevance in disease prevention. Finally, an eye-opening look in overall evidence in humans is also ensured.

The high demand for liquefied natural gas (LNG) requires more LNG carriers (LNGCs) to be in operation. During transportation, there is a high risk due to the required extremely low temperatures and the explosive nature of LNG cargo. Moreover, when there is a lack of experience in operating old LNGCs, there is a serious concern regarding operational accidents. A systematic maintenance strategy, especially for LNG cargo containment systems, is crucial for maintaining safe LNG transportation at sea. The purpose of this study is to develop preventive LNG cargo containment system maintenance models by using LNGC dock specifications from LNGCs of various ages. The dock specifications from a conventional LNGC repairing dock were analyzed using natural language processing techniques in order to develop preventive maintenance models of the LNG cargo containment system. From these results, and by considering the ship’s age, it was found that for young LNGCs the priority for repair should focus on checking routine consumable spare parts by tank inspections, whereas for older LNGCs the focus should be on tank condition maintenance rather than on other facilities. These results are expected to be useful in the development of a maintenance strategy of preventive LNG cargo containment systems in maritime LNG transportation.

DNA methylation, one of the major epigenetic mechanisms, plays critical roles in regulating gene expression, genomic stability and cell lineage commitment. Establishment and maintenance of DNA methylation in mammals is achieved by two groups of DNA methyltransferases: DNMT3A and DNMT3B, which are responsible for installing DNA methylation patterns during gametogenesis and early embryogenesis, and DNMT1, which is essential for propagating DNA methylation patterns during replication. Both groups of DNMTs are multi-modular proteins, containing a large N-terminal regulatory region in addition to the C-terminal methyltransferase domain. Recent structure-function investigations of the individual domains or large fragments of DNMT1 and DNMT3A have revealed the molecular basis for their substrate recognition and specificity, intramolecular domain-domain interactions, as well as their crosstalk with other epigenetic mechanisms. These studies highlight a multifaceted regulation for both DNMT1 and DNMT3A/3B, which is essential for the precise establishment and maintenance of lineage-specific DNA methylation patterns in cells. This review summarizes current understanding of the structure and mechanism of DNMT1- and DNMT3A-mediated DNA methylation, with emphasis on the functional cooperation between the methyltransferase and regulatory domains.

Symmetry-breaking is a phenomenon that is observed in various contexts, from the early universe to complex organisms, and is considered a key puzzle in understanding the emergence of life. The presence of enantiomeric amino acids and proteins highlights its critical role. However, the origin of symmetry-breaking has yet to be comprehensively explained, particularly from an energetic standpoint. This article explores a novel approach by considering energy dissipation, specifically lost free energy, as a crucial factor in elucidating symmetry-breaking. Through a thorough thermodynamic analysis applicable to enantiomeric systems at various scales, from elementary particles to aggregated matter like crystals, we present experimental findings that establish a direct correlation between non-equilibrium free energy and energy dissipation during formation processes. Results emphasize the pivotal role of energy dissipation not only as an outcome but as the trigger for symmetry-breaking. This insight suggests that understanding the origins of complex systems, from cells to living beings and the universe itself, requires a lens focused on non-equilibrium processes, ultimately facilitating the formation of non-racemic systems.

Only by understanding the ability to take third-person perspective can we begin to elucidate the neural processes responsible for one’s inimitable conscious experience. The current study examined differences in hemispheric laterality during a first-person perspective (1PP) and third-person perspective (3PP) taking task, when using Transcranial Magnetic Stimulation (TMS). Participants were asked to take either the 1PP or 3PP when identifying the number of spheres in a virtual scene. During this task, single-pulse TMS was delivered to the motor cortex of both the left and right hemispheres of 10 healthy volunteers. Measures of TMS-induced motor-evoked potentials (MEPs) of the contralateral abductor pollicis brevis (APB) were employed as an indicator of lateralized cortical activation. The data suggest that the right hemisphere is more important in discriminating between 1PP and 3PP. These data add a novel method for determining perspective taking and add to the literature supporting the role of the right hemisphere in meta representation.

This study is a synthesis of 159 articles that were selected for their relevance to comprehend key aspects of the Massive Open Online Courses (MOOCs) phenomenon, from a discourse analysis perspective. Since 2011, MOOCs are expanding worldwide so that the number of subscribers outpointed 101 million at the end of 2018. This paper explores the question whether the MOOCs are the embodiment of the global one-world classroom or whether, instead, they represent a low-cost alternative tailored to a segment that doesn´t have enough time or resources to attend a brick-and-mortar college. In addition, the review tackles the link between motivation and low completion rates. Finally, we discuss the need to devise better methods to assess the pedagogical value of MOOCs.

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share several commonalities: historic, conceptual, methodological, and philosophical. It was possible to develop them only because of information technology, while their origins can be traced back to cybernetics. In this perspective, I'll revise the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. The paper poses more questions than answers, but hopefully it will be useful to align efforts in these fields towards overcoming --- or accepting --- their limits.

This study addresses the critical challenge of Speech Enhancement (SE) in noisy environments, where the deployment of Deep Neural Networksolutions on microcontrollers is hindered by their extensive computational demands. Focusing on this gap, our research introduces a novel SE method optimized for MCUs, employing a 2-layer GRU model that capitalizes on perceptual speech properties and innovative training methodologies. By incorporating self-reference signals and a dual strategy of compression and recovery based on the Mel scale, we develop an efficient model tailored for low-latency applications. Our GRU-2L-128 model demonstrates a significant reduction in size and computational requirements, achieving a 14.2× decrease in model size and a 409.1× reduction in operations compared to traditional DNN methods like DCCRN, without compromising performance. This advancement offers a promising solution for enhancing speech intelligibility in resource-constrained devices, marking a pivotal step in SE research and application.

The main consequence of radiation damage on a Silicon-Photomultiplier (SiPM) is a significant increase in the dark current. If the SiPM is not adequately cooled, the power dissipation causes it to heat up, which in turn affects its performance based on the temperature. In order to investigate this heating effect, experiments were carried out using a KETEK SiPM glued on an Al2O3 substrate, as well as HPK SiPMs that were either glued onto an Al2O3 substrate or a flexible PCB. The assemblies were connected either directly to a temperature-controlled chuck on a probe station or through layers of materials with well-defined thermal resistance. A DC-mode LED was used to illuminate the SiPM, and the resulting SiPM current was measured to determine the steady-state temperature. This temperature depended on the power dissipated in the multiplication region of the SiPM and the thermal resistance. This information can be utilized to adjust the SiPMs working point, taking into account the effects of self-heating. Similarly, this approach can be applied to packaged SiPMs that have an unknown thermal contact to a heat sink.

The evolution of sentence representation learning, especially with parse tree encoders, has shown remarkable progress. Traditional approaches predominantly rely on recursive encoding of tree structures, which impedes parallel processing capabilities. Additionally, these methods often overlook the significance of dependency tree arc labels. To overcome these limitations, we introduce the Syntax-Enhanced Transformer (SET), incorporating a novel dual-attention mechanism that integrates relation-focused attention alongside traditional self-attention. This design effectively encodes both dependency and spatial positional relationships within sentence dependency trees. Our approach innovatively incorporates syntactic information into the Transformer framework without compromising its inherent parallelizability. The SET demonstrates superior or comparable performance to contemporary methods across various sentence representation tasks, significantly enhancing computational efficiency.

This article is a scoping review analyzing advances in experimental studies on bacterial self-healing of concrete. From Scopus, ScienceDirect, Web of Science and Google Scholar search engine databases, 54 studies were selected for analysis, and an increasing trend of publications since 2015 was found, with India as the country with the largest research presence. Different ma-terials are used as replacement or admixture in concrete, such as fibers, particles and recycled material from construction waste. It was found that the most commonly used bacteria are Bacillus subtilis and Bacillus sphaericus, which can improve the strength of concrete by generating calcite and sealing cracks. In addition, it was found that the presence of materials such as fibers, ashes and construction recyclates can protect bacteria in an alkaline environment. The designs usually in-clude more than two experimental groups and a control. In summary, this study highlights the importance of bacterial self-healing in concrete and provides information on advances in this field of research.