Adaptation Of Methodology Research Articles

Domain reinforcement feature adaptation methodology with correlation alignment for compound fault diagnosis of rolling bearing

In the long-term operation process, rolling bearings often have multiple single faults or cascade faults, which are coupled with each other to form the compound fault. The complex coupling components of compound fault lead to difficultly building a correlation between compound fault feature and fault class. Moreover, for the transfer learning based on domain adaptation, compound fault feature of source domain is pretty different from that of target domain, thus knowledge of source domain is hard to be transferred into the cross-domain unsupervised learning process of target domain. To solve above mentioned problems well, this paper proposes a domain reinforcement feature adaptation methodology with correlation alignment (CA-DRFA) to complete the cross-domain compound fault diagnosis of bearings. Specifically, a deep reinforcement learning model is improved by being combined with the domain adversarial training way, and meanwhile the correlation alignment metrics is adopted to enhance the generalization performance in feature alignment. In addition, a laboratory experiment and an engineering application are performed to demonstrate that CA-DRFA outperforms other popular cross-domain fault diagnosis methods in cross cutting condition and speed transfer tasks. Overall, CA-DRFA is able to implement cross-domain compound fault diagnosis with high accuracy, and effectively reduce the cost of labeling samples.

Egyptian paediatric kidney transplantation pre-transplant guidance highlights on donor and recipient assessment (R. N. 364)

BackgroundKidney transplantation for chronic kidney disease (CKD) in children is the best treatment option. It needs special medical and surgical expertise highly skilled in management of pediatric age group. Our Egyptian profile for causes of end-stage renal failure (ESRF) in transplanted children reflects prevalence of inherited kidney diseases IKD (43%), urologic causes (26%), glomerulonephritis (GN) (17%), and unknown causes (14%). Renal graft availability remains a great challenge.AimWe need pediatric kidney transplantation (PKT) guideline since children have unique causes for ESRF compared to adults. Their transplant team should be skilled in management of children challenges. Recipients may not have one transplant per life. Long-standing immunosuppression will have its toxicity and need regular monitoring. Lots of data are extracted from adult guidelines lacking paediatric background. Young paediatric nephrologists need short version guidelines rich in educational figures for management plans. Children and their families need Arabic orientation booklets and supportive programmes. National Insurance System sponsors should be guided by National Pediatric Guidelines to minimize the centre’s variations.MethodsOur National Pediatric Guidelines are evidence based adapted from international four source guidelines with permissions [KDIGO-2020, RA/BTS 2022-2018, EAU 2018] that were appraised with Agree 2 plus tool using PIPOH format health questions. We followed the ‘adapted ADAPTE’ CPG formal adaptation methodology that consists of three phases and 24 steps and tools. It was registered on the practice guideline registration international guideline registry with a registration number IPGRP-2023-12-27 CN 312.ResultsSummary includes recommendations for assessment of (1) potential living adult donors for age, medical, surgical, immunologic, familial, metabolic, malignancy, and any donor morbidities and (2) transplant recipient assessment for age, weight, nutritional, psychosocial, immunological, infection states, primary native kidney disease, associated morbidities, the presence of genetic, immunologic, infection, and malignancy risks.ConclusionPediatric kidney transplantation guidelines aim for better donor, recipient, and graft survival. Recommendations are tailored as adopted or adapted statements from evidence-based source guidelines to suit our local pediatric CKD profile.

TDDAM: transformer based deep domain adaptation methodology for lithium-ion battery prognosis

The status of health (SOH) is a vital indicator to characterize the remaining life of lithium-ion batteries (LIBs), and precise prognosis of the SOH is of great importance for battery management systems. In order to prognosis the SOH of LIBs, this paper proposed a Transformer based deep domain adaptation methodology (TDDAM). This paper applies the transformer model, which is widely used in natural language processing and other fields, to the prediction of LIBs. Meanwhile in order to solve the problem of model matching in different types of batteries or different environments, this paper combines domain adaptation method based on the maximum mean discrepancy. Firstly, we extract the data features of LIBs through position encoding and processing of the encoder structure with the multi-head self-attention mechanism as the core. Then, based on the maximum mean discrepancy index, the target domain data and the source domain data features are aligned, and the decoder part of the original transformer model is replaced with a fully connected layer for the prediction of SOH of LIBs in the target domain. This is the first time that a Transformer has been combined with the maximum mean discrepancy to be applied to LIBs prediction. Comprehensive experiments on two CALCE LIBs data showed that the TDDAM achieved smaller prognostic prediction errors over popular SOH diagnostic methods, indicating its great potential as a generic backbone for LIBs prognosis.

A domain adaptation methodology for enhancing the classification of structural condition states in continuously monitored historical domes

A domain adaptation methodology for enhancing the classification of structural condition states in continuously monitored historical domes

Transfer learning with cross-domain adaptation of vibro-acoustic signals for electric motor mechanical fault detection and diagnosis.

Contact-based vibration measurement techniques involving accelerometers are typically utilized for machine health monitoring and manufacturing end-of-line quality control. Acoustic signals, encompassing sound pressure and particle velocity, carry crucial information about the operational status of underlying equipment and can be utilized for detecting various machine faults. Utilizing one-dimensional Convolution Neural Networks (1D-CNN) based on deep learning for processing raw acoustic time signals has proven to be a valuable approach in detecting machinery faults, offering an effective alternative to using traditional accelerometer signals. However, the success of deep learning methods is heavily dependent on the quantity and availability of data for robust network training. When transitioning to new non-contact type sensor technology, manufacturers may face challenges due to insufficient data for training deep learning models. In such cases, leveraging existing historical accelerometer-based data becomes crucial. Transfer learning emerges as a promising solution, allowing deep learning models trained on a source domain (SD) to be applied to a separate but related target domain (TD). This paper hence introduces a domain adaptation methodology by implementing transfer learning, where the SD 1D-CNN network is trained on accelerometer signals, then the trained model weights are transferred to the TD 1D-CNN network that can process acoustic signals.

Enhancing Climate Change Resilience: Assessing Adaptation Needs, and Significance of Monitoring and Evaluation Systems

Losses and damages from climate change related extreme weather events and disasters require the development of adaptation measures to increase resilience to the adverse impacts of climate change. In line with the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement's Global Goal on Adaptation, Parties have developed strategies that include adaptation actions, but there are significant gaps in identification of adaptation needs and the monitoring and evaluation (M&E) of actions to address them. Adaptation M&E systems are critical for measuring the success of adaptation actions, providing feedback from the implementation process, and identifying new actions. There is no global methodology for adaptation M&E. At international climate change negotiations in 2023, it was agreed that countries should operationalize their national adaptation M&E systems by 2030. The aim of this study is to evaluate adaptation M&E methodologies developed by countries at different development levels and to present future policy recommendations for the adaptation M&E system planned to be established in Turkey. The study reveals the necessity of up-to-date socio-economic data as well as climate data in determining adaptation needs and adaptation M&E systems. In Turkey, which is vulnerable to the impacts of climate change, for the success of adaptation actions, besides the rapid operationalization of adaptation M&E system, establishment of the system with an approach that includes all stakeholders in the process and considers adaptation actions as integrated with disaster risk management actions is an important requirement in the context of Turkey's international commitments, national security, and development.

A novel domain adaptation method with physical constraints for shale gas production forecasting

Effective forecasting of shale gas production is essential for optimizing exploration strategies and guiding subsequent fracturing. However, in the new development of shale gas blocks, two main challenges are encountered: (1) data is insufficient, and (2) the dynamic production characteristics of shale gas wells, influenced by factors such as reservoirs and engineering, exhibit complex non-linear and non-stationary features. The inherent black-box nature of deep learning models raises concerns among decision-makers about the reliability of results. The current artificial intelligence model overlooks these factors, resulting in limitations in the accuracy and interpretability of the model. To address these problems, a novel domain adaptation methodology is proposed using physical constraints. First, production data from the source domain is segmented into multiple subdomains to enhance sample diversity. Subsequently, the positive transfer learning subdomains are identified by comparing maximum mean discrepancy (MMD) and global average distance metrics. Then, we integrate all transferable knowledge to create a more comprehensive target model. Finally, by incorporating drilling, completion, and geological data as physical constraints, we develop a hybrid model consisting of a multi-layer perceptron (MLP) and a Transformer, aiming to maximize interpretability, which is proved through comparison of symbolic transfer entropy (STE). The performance of the proposed method is experimentally validated on shale gas production data from two blocks in China. The average RMSE, MAE, and R2 on the target domain are 0.2454 (104 m3/d), 0.1552 (104 m3/d), and 0.88, respectively. These values are significantly superior to the traditional methods. Additionally, we demonstrate the superiority of our method in terms of causality through a comparison with Granger causality. The interpretability of static and dynamic data in the prediction process was studied using zero-value masking and attention mechanisms, respectively. Experimental results demonstrate the effectiveness and superiority of the proposed method for forecasting shale gas production under data insufficiency.

Cultural adaptation of a self-review tool for health promoting universities in Bulgaria.

The Health Promoting University initiative is unknown in Bulgaria, and the health promotion potential of Bulgarian universities has not been studied. In order to examine it, a suitable instrument is needed. The UK Healthy Universities Network provides an accessible Self-Review-Tool (SRT). Aim: To present the process of cultural adaptation of the SRT in Bulgarian language. The standardized WHO methodology for cultural adaptation of instruments was followed in four stages: (1) Two language translations of the instrument into Bulgarian were made; (2) An expert Delphi discussion reached a consensus on specific health promoting (HP) terms, followed by a backward translation; (3) Pilot testing of the tool among university community representatives was conducted through a survey among a small sample, with independent responses to the SRT questionnaire followed by cognitive interviews; (4) Final revision of the instrument. Ten public health experts reached a consensus on the name of the initiative and various HP terms. Ten other respondents pre-tested the tool. Difficulties in responding the SRT concerned the meaning of some HP terms, complex words, the system of answers, limited applicability of some statements. Changes were made to 61 of the total 68 elements in the SRT. All stages of the cultural adaptation were important for the final result. The adapted Bulgarian version of the SRT would be useful to Bulgarian universities that want to make a clear commitment to improving the health of their university community and the wider society.

Access to the European cultural heritage of settlements and towns in the Danube macro-region through interoperable strategies digital transformation

Tangible cultural heritage is a strategic resource for the development of rural and urban settlements. Cooperation with stakeholders from EU countries and regions within the Danube macro-region, such as cultural heritage institutions and competent authorities, allows to agree on the preparation and adaptation of compatible digital innovation strategies in local initiative groups in the Republic of Moldova and Ukraine, as well as on impact mapping through a set of pan-European socio-economic indicators. Objectives of the study: to analyse the available European best practices and methodology for adaptation in the integration of the regions of the Republic of Moldova and Ukraine in the interregional innovation partnership on the smart specialisation "Cultural and Creative Regional Ecosystems" (CCRE-S3) [1]. The overall horizontal objective of the integration into the platform should be to add value to the common characteristics of the cultural and creative sector and to create a permanent discovery mechanism that will carry out activities to identify strategic investments and the use of innovation transfer tools. Previous works: previous concept, research and experience are described in the articles [2] and [3]. Approach: European benchmarking, comparative analysis, bibliographic research. Outcome: recommendations aimed at stimulating new ideas and opportunities related to cultural and creative experiences for local communities and residents by involving several groups of professions that use, apply and implement new technologies in the cultural and creative industries sector. The European Programmes provide resources to support their implementation. In this way, the Danube Strategy participating States [4], by supporting partnerships between the cultural heritage sector and other sectors such as higher education and vocational education and training, creative industries and sustainable cultural tourism, will be able to stimulate innovation for new services and applications in their regions.

A novel approach to work towards gender-responsive urban climate policy

In collaboration with women’s organizations in India, Indonesia, Mexico and South Africa, an approach for a novel gender assessment of urban climate policies was developed and tested. The Gender Assessment and Monitoring of Mitigation and Adaptation (GAMMA) methodology allows for an in-depth analysis of the institutional framework and the mitigation and adaptation policies of cities. Its application by the women’s organizations in 14 pilot cities led to policy recommendations on how to integrate gender equality into urban mitigation and adaptation actions. The results of a monitoring exercise show that the project has made a significant step forward in raising awareness of gender issues and gender-responsive action at the urban level. It provides civil society organizations working on climate justice with a tool to push local governments to work towards low-carbon, resilient, gender-just and inclusive cities. It can also be used by local governments for self-assessment.

Proposed Methodology for Climate Change Adaptation of Water Infrastructures in the Mediterranean Region

Water Infrastructure (WI) is one of the most important sectors of Critical Infrastructure (CI); it encompasses drinking water, wastewater, and stormwater. The WI is vulnerable to the effects of Climate Change (CC) that can disrupt its functionality; thus, it needs to be adapted to CC, especially in the Mediterranean region, where these effects are expected to intensify during the twenty-first century. For this adaptation a Climate Risk and Vulnerability Assessment (CRVA) is required in the development of WI to identify the important climate hazards and to recommend the required measures for the adaptation of the WI to these hazards. In the present work, a CRVA methodology is developed and presented based on a literature survey and the relevant European Commission guidelines; it is structured around five key steps: (1) Description of the WI, (2) Climate change assessment, (3) Vulnerability assessment, (4) Risk assessment, and (5) Assessment of adaptation measures. The application of the methodology is illustrated indicatively to a wastewater system in Greece for which preliminary estimations are performed, results are discussed, and conclusions are drawn. The proposed methodology can be modified for application to other sectors of CI; moreover, is a valuable resource in academic curricula and ongoing research initiatives.

Cross-cultural adaptation and inter-rater reliability of the Schizophrenia proneness instrument adult version (SPI-A) in the Indonesian context

This study aims to adapt the Schizophrenia Proneness Instrument, Adult Version (SPI-A) for the assessment of basic symptoms to the Indonesian context (culturally and linguistically) and analyze the inter-rater reliability of the translated version. Following a specific methodology for cultural adaptation, direct and back-translations were conducted together with cognitive interviews to analyze the comprehensibility of the translated version. A linguistic expert analyzes the resulting version to determine its grammatical and linguistic adequacy. Finally, the interclass correlation (ICC) of the three expert ratings of the samples (N = 9) was analyzed. The direct and back-translation phases showed good conceptual equivalence to the original version. The cognitive interviews revealed items that were challenging to understand and required revision. The final version also considered the judgments of a linguistic expert for grammatical and conceptual improvements. Inter-rater reliability analysis showed an excellent degree of agreement (ICC value: 0.984; 95% CI: 0.950–0.996). The translated SPI-A fits the Indonesian context and can be used in clinical settings to assess basic symptoms in help-seeking individuals in Indonesia.

НАСТАВНИЧЕСТВО КАК ЭФФЕКТИВНАЯ ФОРМА МНОГОУРОВНЕВОЙ АДАПТАЦИИ ПЕРСОНАЛА В ОРГАНИЗАЦИИ ОБРАЗОВАТЕЛЬНОЙ СФЕРЫ

The article discusses the problems of mentoring as an effective form of multi-level adaptation of personnel in the organization of the educational sphere. It is emphasized that the currently existing methodology for personnel adaptation in the organization of the educational sphere is far from perfect, does not have methodological unity, and, in addition, new ideas and approaches are emerging that need generalization and development. This article analyzes existing approaches to defining projects, portfolios and personnel adaptation programs in educational organizations, and classifies management models for personnel adaptation systems in educational organizations.

MODELING OF TWO-PHASE GAS-LIQUID SLUG FLOWS IN MICROCHANNELS

Even though numerical simulation of two-phase flows in microchannels has been attempted by many investigators, most efforts seem to have failed in correctly capturing the flow physics, especially the slug-flow regime characteristics. The presence of a thin liquid film in the order of 10 μm around the bubble (sometimes called gas pocket or gas slug) may be a contributing factor to the above difficulty. Typically, liquid films have a significant effect on the flow field. Thus, there is a strong motivation to employ numerical simulation methods to avoid some of the experimental difficulties. In this paper, the characteristics of two-phase slug flows in microchannels are calculated with the help of the volume-of-fluid (VOF) method. Formation of the slugs for different superficial velocities, capillary numbers, and gas volume fractions are investigated. The minimum mesh resolution required to capture the liquid film surrounding the gas bubble is reported employing a dynamic mesh adaptation methodology with interface tracking. Results are shown to be in good agreement with experimental data and empirical correlations.

Evidence-based Egyptian clinical practice guidelines: for the prevention and management of iron deficiency and iron deficiency anemia in infants, children and adolescents

BackgroundIron deficiency (ID) is the most common nutritional disorder affecting all age groups. Infants and young children are vulnerable to anemia especially iron deficiency anemia (IDA), which represents a public health problem with defined impacts on the health of communities. IDA, however, is a preventable and treatable condition; therefore, early diagnosis represents the cornerstone in protection from its adverse consequences and combating its contributing factors. Several international guidelines for the management of ID/IDA are available, however national guidelines that can be implemented and suits our local needs are lacking.ObjectiveTo adapt the pre-existing high-quality practice guidelines for the screening, prevention and management of ID/IDA in different pediatric age groups, to be available for the national use by general practitioners, pediatricians, and other health care professionals.ResultsThe adaptation group for this guideline included key leaders from different Egyptian institutions. The panel used the ADAPTE methodology for adaptation of guidelines. The panel prioritized the health questions and recommendations according to their importance for clinicians and patients. The procedure included searching for existing guidelines, quality appraisal, and adaptation of the recommendations to the target context of use. The guideline covered all important aspects of detection, identification of infants, children, and adolescents at high risk of developing ID/IDA, proper management, and prevention. The final version of the adapted clinical practice guideline has been made after thorough review of an external review panel and was guided by their official recommendations and modifications. Implementation tools included algorithms, tables, and flow charts to aid decision making in practice, as well as patient-directed information in the Arabic language.ConclusionsThis adapted guideline serves as a tool for the screening, prevention and management of ID/IDA in different pediatric age groups.

MS-FRAN: A Novel Multi-Source Domain Adaptation Method for EEG-Based Emotion Recognition.

Electroencephalogram (EEG), an important physiological signal, has been widely applied in many fields. Among them, EEG-based emotion recognition has gradually become a research hotspot. However, the large distribution differences and variations of EEG signals across subjects make the current research on emotion recognition stuck in a dilemma. In order to resolve this problem, we choose the domain adaptation methodology to explore the inspiration for solutions. In this paper, a Multi-Source Feature Representation and Alignment Network (MS-FRAN) is proposed, in which a new feature learning module named Wide Feature Extractor (WFE) has been designed. The MS-FRAN adopts and improves the strategy of existing multi-source domain adaptation, which possesses two main benefits. For one thing, it can align the distribution of each pair of source and target domains. For another, it can also reduce the distributional differences among the multiple source domains. To demonstrate the effectiveness of MS-FRAN, we have carried out cross-subject experiments on two public benchmark datasets, SEED and DEAP. Experimental results have shown that our method outperforms the related competitive approaches with greater performance for EEG-based emotion recognition.

Similarity-Based Framework for Unsupervised Domain Adaptation: Peer Reviewing Policy for Pseudo-Labeling

The inherent dependency of deep learning models on labeled data is a well-known problem and one of the barriers that slows down the integration of such methods into different fields of applied sciences and engineering, in which experimental and numerical methods can easily generate a colossal amount of unlabeled data. This paper proposes an unsupervised domain adaptation methodology that mimics the peer review process to label new observations in a different domain from the training set. The approach evaluates the validity of a hypothesis using domain knowledge acquired from the training set through a similarity analysis, exploring the projected feature space to examine the class centroid shifts. The methodology is tested on a binary classification problem, where synthetic images of cubes and cylinders in different orientations are generated. The methodology improves the accuracy of the object classifier from 60% to around 90% in the case of a domain shift in physical feature space without human labeling.

Reducing community risk to coastal erosion with managed relocation

As climate change escalates and coastal populations increase, adaptation to ongoing risks to coastal communities due to rising sea levels and storm surges must be reassessed. In Australia, over $25 billion of coastal property is at substantial risk from inundation and erosion. The response by governments to coastal hazards has been to construct artificial sea barriers in a hold-the-line approach of investing in sea walls and beach nourishments1 to protect beachfront residential properties. However, are such structures reducing the long-term risk in an era of climate uncertainty? Is sand renourishment of beaches a sustainable solution to increasing erosion on the coast? Can planned relocations be implemented in planning policies? Sea walls perpetuate ongoing threats to coastal development and a shift in the methodology of land-use planning and adaptation for coastal communities must occur. This would be in line with the Sendai Framework for Disaster Risk Reduction 2015-2030 (UNDRR 2015) goal of strengthening disaster risk governance. A greater emphasis must be placed on strategic planning that directs people away from high-risk beachfronts. This research uses qualitative case studies to understand how local governments in New South Wales address the increasing risks to coastal residential development and the connection between government and risk hierarchies. A framework is proposed to integrate national risk hierarchies into state and local government planning to assist in managing strategic relocations and implementing nature-based solutions.

Culturally adapted safer sex skills building: development andinitial testing ofan HIV prevention intervention forBlack women using substances inthe United States.

Black women who use substances in the United States face unique human immunodeficiency virus (HIV) risk factors. However, interventions addressing cultural factors relevant for preventing HIV risk behaviors among Black women are limited. This project aimed to develop and initially test the effectiveness of a culturally adapted version of safer sex skills building (SSSB), an evidence-based HIV prevention intervention for reducing HIV risk among Black women who use substances. The cultural adaptation procedures involved (i) review of existing literature, (ii) use of Delphi process and theater testing to generate an adapted version, (iii) theater testing of the culturally adapted version and (iv) initial testing. Forty-eight Black women recruited from a substance use treatment clinic in the United States participated in three 2.5-h sessions of a culturally adapted or a generic version of the intervention. More (48%) women in the culturally adapted version self-reported condom use with casual partners compared to women in the generic (29%) version at a 6-week follow-up. The two groups did not differ on self-reported condom use with main partners. The culturally adapted SSSB offers a promising intervention for sexual risk reduction among Black women who use substances. Moreover, the findings advance the field by illustrating a concrete methodology for cultural adaptation.

Novel Ensemble Domain Adaptation Methodology for Enhanced Multi-class Fault Diagnosis of Highly-Connected Fleet of Assets

This paper proposes a novel methodology for enhancing multi-class classification accuracy in fault diagnosis problems among domains with highly-connected fleets of assets using time series data. The approach involves appending specially tailored models to an initial model and incorporating domain adaptation techniques to account for domain variations. The methodology is demonstrated through a case study on fault diagnosis of a fleet of hydraulic rock drills, which presents challenges due to variations in sensor data between different fault classes and individual machines. Results show significant improvements in classification accuracy, both in validation and testing, upon employing ensemble models and applying domain adaptation. While the study is limited to one case study, it lays the groundwork for exploring the applicability of the proposed methodology in other contexts.