Historical Condition Research Articles

Maya Angelou’s Selected Poems: A Reflection on Racism and Segregation

This paper focuses on the issues of racism and segregation in Maya Angelou’s selected poems. As an African American, Maya Angelou in her poems and autobiographical novels demonstrates the historical, social and economic condition of her community, especially till the 1970’s. To argue on these grounds, power structure between the white and the black, or African American is also important to discuss. In this paper, six poems of Maya Angelou which are “Still I Rise”, “Caged Bird”, “Alone”, “Harlem Hopscotch”, “Phenomenal Woman” and “My Guilt” will be discussed and analyzed to discover and rediscover the themes of racism and segregation. So, this paper examines the historical reality of Maya Angelou’s community, important features, facts and information found in her poetry, along with the way of segregation in a racist society. Simultaneously, this paper explores the socio-economic context of the blacks as a base for racism and segregation and the apparent mentions of racism and segregation in Maya Angelou’s poems.

Climate change impacts on hydrology and water resources in East Africa considering CMIP3, CMIP5, and CMIP6

Climate change impacts on the hydrology and water resources in East Africa require proper planning of regional adaptation measures. Past studies on the subject matter tended to focus on catchment instead of regional scale. To support actionable policy regarding regional planning of water resources management, this paper reviewed studies conducted in East Africa based on CMIP3, CMIP5, and CMIP6. Compared to the historical condition, temperature of East Africa over the mid- to late 21st century will increase by varying extents over the range 0.17–7°C with about 2.6°C on average. Future precipitation over East Africa will increase by 8.5% on average. Changes in discharge based on CMIP3, CMIP5, and CMIP6 will vary from −25.3 to +60.5%, −42.5 to 129%, and −23.4 to 69%, respectively. Ensemble means of the changes in discharge across the study area based on CMIP3, CMIP5, and CMIP6 will be by 25.2, 19.2, and 19.1%, respectively. Future peak river discharge across East Africa will increase by 8% on average. These results are important for planning regional climate change adaptation across East Africa. To support local or catchment adaptation, results of climate change impacts of temperature, precipitation and discharge of about 50 catchments across East Africa can be found summarized in this review paper. It is recommended that both regional and local policies or approaches should be considered for climate change adaptation to ensure a balanced, effective and sustainable framework for climate resilience in support of measures for tackling broad and specific needs cohesively.

B - 32 Neurological Problems in Boxers during the 20th Century: a Review and Synthesis

Abstract Objective There are no validated clinical diagnostic criteria for chronic traumatic encephalopathy or traumatic encephalopathy syndrome (TES). To understand the historical clinical condition, its applicability to modern day athletes, and the pathogenesis of clinical problems, we examined the literature in boxers from the 20th century, with specific attention to neurological findings and characteristics of clinical disease progression. Data Selection Data were extracted for 243 boxers included in 45 articles published between 1928 and 1999, including cases from articles originally published in German. The presence or absence of 22 neurological signs and features were extracted. Data Synthesis The most common neurological problems were slurring dysarthria (49%), gait disturbances (44%), and memory loss (36%), with several other problems that were less frequent, including hyperreflexia (25%), ataxia (22%), increased tone (19%), and extensor Babinski sign (16%). Frank dementia appeared in some cases (17%). There were significantly fewer neurological deficits reported in boxers who fought in the latter part of the 20th century compared to boxers who fought earlier in the century. For most cases, there were no comments about whether the neurological problems were progressive (145, 60%). A progressive condition was described in 71 cases (29%) and a stationary or improving condition was described in 27 cases (11%). Canonical neurodegenerative disease-like progression was described in 15 cases (6%). Conclusions Neurological problems associated with boxing-related neurotrauma during the 20th century are the foundation for present day TES. However, the clinical signs and features in the 20th century differ in most ways from the modern criteria for TES.

Machine learning-based technology for asphalt concrete pavement performance decision-making in hot and humid climates

Highway state agencies incur significant budget savings through optimal allocation of pavement Maintenance, Rehabilitation, and Reconstruction (MR&R) activities. These activities require robust prediction models that can handle large-scale, real-world data and can forecast pavement performance in the long run. Unfortunately, the traditional performance prediction models have been questionable in terms of efficiency and accuracy, are based on a limited number of explanatory variables, and are designated to predict short-term (up to five years) pavement conditions. Therefore, the goal of this study was to propose a machine learning-based technology that can predict the field performance by up to 11 years of Asphalt Concrete (AC) overlays placed on asphalt pavements in Southern states in the US based on key project conditions. The proposed technology resulted from assessing the prediction accuracy of machine learning algorithms, including Decision-Tree (DT), eXtreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and ensemble-learning method, in forecasting the Pavement Condition Index (PCI) as the pavement performance indicator. For each algorithm, six models were developed sequentially based on historical pavement condition data collected from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. The six models learned from 892 log miles of randomly placed AC overlay sections in Louisiana. The output of these models was the future PCI of AC overlays at a biannual rate from one to 11 years. The findings showed that XGBoost and ensemble learning showed similar performance during model training and were further evaluated using the testing dataset. During model testing, the ensemble learning method yielded higher prediction accuracy than other algorithms, with R2 values decreasing from 0.77 at age 1 to 0.67 at age 11, Root Mean Square Error (RMSE) values increasing from ±1.65 to ±4.74, and Mean Absolute Error (MAE) increasing from 1.24 to 4.59. The resulting models can be used by state agencies to replace traditional prediction techniques.

Digital Tools for the Preventive Conservation of Built Heritage: The Church of Santa Ana in Seville

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM’s efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns.

Local spatial and temporal relation discovery model based on attention mechanism for traffic forecasting

Recognizing the evolution pattern of traffic condition and making accurate prediction play a vital role in intelligent transportation systems (ITS). With the massive increase of available traffic data, deep learning-based models have attracted considerable attention for their impressive performance in traffic forecasting. However, the majority of existing approaches neglect to model of asynchronously dynamic spatio-temporal correlation and fail to consider the impact of historical traffic data on future condition. Additionally, the attribute of deep learning method presents challenges in interpreting the explicit spatiotemporal relationships. In order to enhance the accuracy of traffic prediction as well as extract comprehensive and explainable spatial–temporal relevance in traffic networks, we propose a novel attention-based local spatial and temporal relation discovery (ALSTRD) model. Our model firstly implements feature representation learning to effectively express latent input traffic information. Then, a local attention mechanism structure is established to model asynchronous dependencies of historical input data. Finally, another attention network and the Pearson Correlation Coefficient method are introduced to extract the elaborate influence of the historical traffic condition of neighboring roads on the future condition of the target road. The experiment results on several datasets demonstrate that our model achieves significant improvements in prediction accuracy compared to other baseline methods, which can be attributed to its ability to extract the fine-grained correlation among historical traffic data and capture the dynamic association between past and future data. In addition, the incorporation of attention mechanism and Pearson Correlation Coefficient promotes the model’s ability to elucidate spatiotemporal correlations among traffic data, thereby providing a more robust explanation.

Hydroclimatic scenario generation using two-stage stochastic simulation framework

Climate change poses significant challenges for decision-making processes across a range of sectors. From the water resources planning and management perspective, the interest is often in evaluating the performance of a water supply system in a future state considering the potential changes in rainfall and streamflow characteristics. With observed climate change signals, scenario-based projections of rainfall and streamflow simulations are crucial for evaluating the potential impacts of climate change on water resource systems. Given the complexity of the existing approaches, their applications for generating scenario-based projections of streamflow and rainfall are limited. We developed a non-parametric bootstrapping approach, NPScnGen, for future scenario generation of any hydroclimatic variable. The developed approach is flexible and can be used with any physical hydrological or data-driven stochastic model that provides simulations of hydroclimatic variables of interest for the historical climate condition. In NPScnGen, samples of any set of time-series characteristics, such as mean and standard deviation, are generated from a multivariate Gaussian process for the considered scenario, and then bootstrapping is performed to select the closest sample from the historical simulation of that hydroclimatic variable. We have also proposed a modified wavelet-based model, Wavelet-HMM, and used that model to synthetically generate historical climate time-series as a baseline. We present the application of the developed framework consisting of historical climate simulation and future climate projection approaches on rainfall and streamflow datasets for the Tampa Bay region in Florida.Plain Language Summary: Water resources managers require a wide range of hypothetical but potential changes in hydroclimatic variables such as streamflow and rainfall to evaluate the sustenance of water supply systems in future. Existing scenario generation approaches are limited by either the complexity of statistical models or dependency on climate models which have their own limitations. In such a scenario, the developed non-parametric scenario generation framework in this study, NPScnGen, can be very useful. The developed framework can be applied with any sophisticated time-series generation model that can generate synthetic hydroclimatic traces for baseline climate condition, and it is also flexible in generating a wide range of potential climate change scenarios. We show the application of the framework on both streamflow and rainfall datasets.

Enhancing pavement performance prediction with traffic speed deflection data

ABSTRACT Pavement performance modelling is an essential part of pavement management. Past research has shown that a structural indicator can be included in pavement performance models to capture differences in performance between weak and strong pavements. Weak pavements are expected to deteriorate more rapidly than strong pavements, as consistently shown in past research. The work presented in this paper uses the Traffic Speed Deflectometer (TSD) to calculate multiple structural indexes and compares their ability to differentiate between poor and well performing pavements. Historical condition data from the US National Parks pavement network is used in this assessment. Two approaches for calculating the subgrade resilient modulus and two approaches for calculating the effective structural number from the TSD results are compared. The temperature corrected centre deflection, surface curvature index and ratio of effective to required structural number are all calculated from the TSD data. Performance models are developed for two condition indexes and the International Roughness Index (IRI). In all cases, the addition of the structural indexes improved the quality of predictions, and the IRI prediction showed the most improvement from including the indexes. Ultimately, it was shown that the TSD testing results can be used to inform the pavement management process.

Challenges in Restoring Mediterranean Seagrass Ecosystems in the Anthropocene

The intense human pressures in the Anthropocene epoch are causing an alarming decline in marine coastal ecosystems and an unprecedented loss of biodiversity. This situation underscores the urgency of making ecological restoration a global priority to recover degraded ecosystems. Meadows of the endemic Mediterranean seagrass Posidonia oceanica have lost more than half of their original extent in the last century, necessitating immediate conservation and management measures, supported by active restoration interventions. This paper explores new opportunities and provides specific recommendations to enhance restoration as a fundamental strategy for reversing the decline of P. oceanica ecosystems in the Mediterranean Sea. When a return to a historical pristine reference condition may not be feasible in the short term or desirable given current environmental conditions and uncertainty, transplanting the tolerant and fast-growing seagrass species Cymodocea nodosa could facilitate natural recolonization. This would occur through secondary ecological succession, benefiting the sensitive and slow-growing species P. oceanica. Future global and local efforts should primarily focus on proactive management to prevent further alterations by planning appropriate conservation measures in a timely manner to mitigate and reverse global changes. As a secondary step, restoration programs can be implemented with a focus on ‘target-oriented’ rather than ‘reference-oriented’ conditions, aiming to establish ecosystems capable of sustaining the future rather than replicating the historical environment.

Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin

The reliability evaluation of the subsea control module(SCM) is the key to ensure the safety and stability of subsea oil-gas production. The failure probability and reliability of SCM components are dependent on time and working conditions. To analyze the SCMs’ reliability considering varying working conditions, this paper proposed a new digital twin and dynamic Bayesian network(DBN) based model utilizing historical working condition data in reliability analysis. In the proposed framework, critical working condition data is obtained by sensor-based Digital Twin(DT) simulation and used for dynamically updating the parameters in the DBN reliability analysis model. The reliability evaluation of an actual SCM electric system was carried out. The results revealed the most probable failure mode and the most vulnerable components in the system. Finally, the fault prediction based on the back-forward analysis capacity of the proposed method was conducted to predict the probability of the faulty device when unexpected failures occur.

Characterize and analysis of meteorological and hydrological drought trends under future climate change conditions in South Wollo, North Wollo, and Oromia Zones, in Ethiopia

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

Hindcasting estuary ecological states using sediment cores, modelled historic nutrient loads, and a Bayesian network

Estuaries are impacted by catchment land use changes, driving degradation associated with eutrophication and alterations in sediment dynamics. Estuarine ecological monitoring has typically covered only recent periods, so magnitudes and trajectories of degradation are often poorly described. Here, we develop a multi-method approach to hindcast historic estuary condition. We determined geochemical properties in dated sediment cores from two sites in the eutrophic New River Estuary (NRE), Aotearoa New Zealand. Mud, organic matter, carbon, and nitrogen (N) contents increased from ~1847 to 2019 (~20 to 100%, ~1 to ~7%, ~0.2 to ~3%, and ~0.03 to ~0.37% respectively). Historic land use modelling shows increasing catchment loads and water column total N rising from 165 to 663 mg m-3, with more rapid change in the late 20th and early 21st centuries. Strong correlations (R2 = 0.93-0.95) were found between sediment N content and water column N concentrations. Trophic indicators hindcasted by a Bayesian Belief Network worsened from the mid-20th to 21st centuries. This degradation is associated with increased sheep, beef and dairy farming in the catchment. Our approach identifies nutrient and sediment loadings associated with ‘tipping points’ for indicators and can inform policy by identification of hazardous levels of contaminant loading for other estuaries.

Research on energy-saving control of agricultural hybrid tractors integrating working condition prediction.

To address the issues of tractors using too much fuel and not being energy efficient, a predictive control strategy based on Pontryagin's minimum principle integrating working condition prediction is proposed for agricultural hybrid tractors. The Dongfanghong 1804 tractor is being used for research. Firstly, the main parameters of the hybrid drive system are determined and modeled. Secondly, based on the adaptive cubic exponential forecasting method, the working condition information for a period of time in the future is predicted through historical working condition information. Furthermore, combining the predicted working conditions information, the goal is to minimize the total energy consumption cost of the entire machine. Motor power and diesel engine power are control variables. The battery state of charge is a state variable. Subsequently, a predictive control strategy based on Pontryagin's minimum principle integrating working condition prediction is proposed. Finally, the simulation test is carried out based on the MATLAB simulation platform. Research indicates: under plowing conditions, compared with the power following control strategy, the proposed predictive control strategy can effectively manage the performance of the diesel engine and motor, ensuring they operate at their most efficient level. The total energy consumption costs of the power following control and predictive control strategies are 37.17 China Yuan (CNY) and 33.67 CNY, respectively. The cost of energy used is decreased by 9. 42%, which helps make tractor field plowing more efficient and economical.

TICI: a taxon-independent community index for eDNA-based ecological health assessment.

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n=16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R2=0.82), and TICI variation between sample replicates was minimal (CV = 0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.

Fugitivas de la violencia: emociones y resistencias de mujeres en condición de desplazamiento forzado

Women in the process of forced displacement experience violence during their transit, due to their historical gender condition, place of origin, age, schooling, economic conditions, etc. The purpose of this work was to know the emotions and resistance of women in the process of forced displacement who reside in shelters in Tijuana. The study was qualitative, interpretative, descriptive, and cross-sectional. To obtain the information, the field diary and semi-structured interviews with five collaborators were used. In the qualitative analysis of content, experiences of violence were found from their places of origin, the journeys and in the shelters; the emotions they reported were guilt, fear and anger; however, the hope of a better life for them and their families, the alliances with other women, even when they are intermittent, allow them to resist critical moments and continue their search for a dignified life.

Jak nadać sens nowej kondycji historycznej? Taynna Marino rozmawia z Markiem Tammem i Zoltánem Boldizsárem Simonem

Marek Tamm and Zoltán Boldizsár Simon, in dialogue with Taynna M. Marino, discuss some of the main dilemmas and challenges of contemporary historical theory, from the scientific status and so-called crises within the discipline to discussions about new forms of temporality and historicity that can respond to the technoscientific, ecological and socio-political changes we are facing today. In this conversation, the authors emphasize the historians’ role in making history relevant for the future and the efforts to redefine historical knowledge to encompass diverse forms of life (more-than-human, better-than-human, nonhuman) and tackle disconnected pro-spects of the future. Finally, they call attention to the importance of a fruitful dialogue between historians and theorists of history and of collaborating with scholars from other sciences to develop new ways of making sense of the new historical condition.

Evaluation of the historic wooden structure condition based on the results of non-destructive tests

Deformation of wooden roof structures is the effect of strength parameters reduction caused by biological corrosion, passage of time, looseness in joints and assembly inaccuracies. It is a significant problem, especially in buildings built several hundred years ago. Over many years of operation, historical structures are exposed to various loads, not only from their own weight or from external environmental influences (wind, snow). There are also accidental loads such as fires or warfare. Repairs and reconstructions, carried out as a result of accidental damages, are also important for the building structure. Renovation processes often influence on changes in the static scheme of the originally constructed structural system. Rheological phenomena of structural elements and joints have a significant impact on the redistribution of internal forces.In the paper the thorough condition evaluation of the historical church building is presented. The evaluation is performed with the use of non-destructive testing (NDT) methods. The analyzed church building was built in 1579–1601. Throughout its history, it was rebuilt after a fire when the structure of the roof and ceiling above the main nave was reconstructed. Then the roof covering was changed from heavy ceramic tiles to light sheet metal covering, and finally the sheet metal was replaced with metal roof tiles. In the meantime, the roof structure suffered damage resulting from the war, and a part of the roof structure had to be replaced. The current condition of the roof truss was analyzed using finite element method (FEM) software. The influence of the condition of carpentry joints on the deformation of the roof truss was analyzed. The duration of load (DOL) effect on the deformation of structural elements, as well as the influence of biological corrosion and uneven distribution of wood strength parameters in individual structural elements were considered. Based on the presented detailed analysis of the condition of the historic wooden structure using NDT methods, suggestions regarding the general applicable area in historic structures were formulated.

Marx’s Critical Theory of Slavery

Abstract Marx’s critical theory of slavery is the operational subtext throughout his critique of political economy. For Marx, the movement from modern slavery to capital represents a historical transition of significance, not only (or foremost) as an empirical transition but also as a transformation of social substance. Marx reveals why, in retrospect, production based on slavery, as logical configuration, must give way to the generalising logic of wage labour. Marx’s critical theory of slavery historicises wage labour (qua category) as the dissolution and inversion of enslaved labour (qua category). Plantation slavery production in the colonised territories initiates a qualitative development that constitutes the historical condition for the emergence of a mode of sociality that we can call ‘value’. Marx’s theoretical account of modern slavery is a lever in his broader critique of capital and is fundamentally distinct from empirical, historical accounts.

Abdelmalek Sayad: from the double absence to presentification as historical condition of migration

Abstract (it) Facendo dialogare Sayad con altrɜ autorɜ, quest’articolo propone il concetto di presentificazione intendendolo come un uso politico del tempo da parte degli Stati-nazione, funzionale all’assoggettamento delle persone. Le politiche confinarie europee, infatti, pongono le soggettività migranti in regimi di incertezza legale e precarietà temporale, utilizzando il tempo come un’arma per astrarle dalla Storia, collocandole in un presente reiterato. La presentificazione produce assenza dal campo storico, mentre le persone tentano di farsi presenza. Questa condizione viene infatti sfidata dalle soggettività migranti, che provano a riappropriarsi del proprio tempo, attivando processi di soggettivazione, emergenti dalle pieghe dei regimi di assoggettamento. Il “provvisorio che dura” produce qualcosa laddove si fa presenza attraverso forme di attivazione politica che interrompono la ricorsività prodotta dai regimi di frontiera. Questo articolo muove quindi dalla dialettica tra campi di forza all’interno dei quali si gioca la partita del soggetto: da una parte, presentificazione e individuazione, dall’altra soggettivazione e storicizzazione.

Enhancing aircraft engine remaining useful life prediction via multiscale deep transfer learning with limited data

Abstract Predicting the remaining useful life (RUL) of the aircraft engine based on historical data plays a pivotal role in formulating maintenance strategies and mitigating the risk of critical failures. None the less, attaining precise RUL predictions often encounters challenges due to the scarcity of historical condition monitoring data. This paper introduces a multiscale deep transfer learning framework via integrating domain adaptation principles. The framework encompasses three integral components: a feature extraction module, an encoding module, and an RUL prediction module. During pre-training phase, the framework leverages a multiscale convolutional neural network to extract distinctive features from data across varying scales. The ensuing parameter transfer adopts a domain adaptation strategy centered around maximum mean discrepancy. This method efficiently facilitates the acquisition of domain-invariant features from the source and target domains. The refined domain adaptation Transformer-based multiscale convolutional neural network model exhibits enhanced suitability for predicting RUL in the target domain under the condition of limited samples. Experiments on the C-MAPSS dataset have shown that the proposed method significantly outperforms state-of-the-art methods.