A Dear Price to Regret Over:On Mr.Stevens’Dignity and Professionalism in The Remains of the Day

The Remains of the Day is one of Kazuo Ishiguro’s renowned novels.The protagonist Stevens is an English butler whose life is deeply entwined with the concepts of dignity and honor.Set against the backdrop of post-World War II Britain and reflecting on the interwar period,the novel examines Stevens’devotion to his role and the traditional values of English aristocracy.The essay discusses how Stevens’identity is shaped by his unwavering commitment to professionalism and his admiration for British gentleman culture and highlights the identity crisis Stevens faces as he realizes the flawed nature of his employer and the outdated ideals he upheld.Through Stevens’journey of self-reassessment and eventual epiphany,the essay delves into the complexities of his internal struggle to reconstruct his identity,ultimately advocating for a more authentic understanding of dignity and honor.

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

Remaining Useful Life Prediction Method for Multi-Component System Considering Maintenance:Subsea Christmas Tree System as A Case Study

Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health management.However,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating value.The complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL prediction.An RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is proposed.The performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data problems.Based on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance factors.An example of a subsea tree system is given to verify the effectiveness of the proposed method.

Stevens’s Ethical Identity Dilemma in The Remains of the Day

Based on ethical literary criticism,this paper aims to analyze the factors contributing to Stevens’loss of ethical identity.It contends that distorted ethical relationships are the primary driver behind Stevens’s ethical identity dilemma,which includes the abnormal father-son relationship,Stevens’s blind admiration for his master,and his avoidance of Miss Kenton’s feelings.

Dacryocystitis and canaliculitis secondary to residual of epidural catheter remaining in lacrimal duct for 25 years:a case report and literature review

Dear Editor,We present a case of dacryocystitis and canaliculitis secondary to residual of epidural catheter remaining in lacrimal duct for 25y.A 56-year-old male patient was admitted to our medical center on February 16,2023.We obtained the written informed consent from the patient,and this case study was in accordance with the tenets of the Declaration of Helsinki.The main complaint was that the right eye had suffered from persistent tears for more than 25y and discharge for more than 1y.

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’experience.However,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery parameters.This study proposes an electrothermal model-based method to accurately predict battery RCT.Firstly,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery pack.Secondly,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling.Finally,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

Remaining Useful Life for Heavy-Duty Railway Cast Steel Knuckles Based on Crack Growth Behavior with Hypothetical Distributions

The current research on the integrity of critical structures of rail vehicles mainly focuses on the design stage,which needs an effective method for assessing the service state.This paper proposes a framework for predicting the remaining useful life(RUL)of in-service structures with and without visible cracks.The hypothetical distribution and delay time models were used to apply the equivalent crack growth life data of heavy-duty railway cast steel knuckles,which revealed the evolution characteristics of the crack length and life scores of the knuckle under different fracture failure modes.The results indicate that the method effectively predicts the RUL of service knuckles in different failure modes based on the cumulative failure probability curves for different locations and surface crack lengths.This study proposes an RUL prediction framework that supports the dynamic overhaul and state maintenance of knuckle fatigue cracks.

A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.

Remaining oil distribution characteristics in an oil reservoir with ultra-high water-cut

An accurate mapping and understanding of remaining oil distribution is very important for water control and stabilize oil production of mature oilfields in ultra-high water-cut stage.Currently,the Tuo-21 Fault Block of the Shengtuo Oilfield has entered the stage of ultra-high water cut(97.2%).Poor adaptability of the well pattern,ineffective water injection cycle and low efficiency of engineering measures(such as workover,re-perforation and utilization of high-capacity pumps)are the significant problems in the ultra-high water-cut reservoir.In order to accurately describe the oil and water flow characteristics,relative permeability curves at high water injection multiple(injected pore volume)and a semiquantitative method is applied to perform fine reservoir simulation of the Sand group 3e7 in the Block.An accurate reservoir model is built and history matching is performed.The distribution characteristics of remaining oil in lateral and vertical directions are quantitatively simulated and analyzed.The results show that the numerical simulation considering relative permeability at high injection multiple can reflect truly the remaining oil distribution characteristics after water flooding in an ultrahigh water-cut stage.The distribution of remaining oil saturation can be mapped more accurately and quantitatively by using the‘four-points and five-types’classification method,providing a basis for potential tapping of various remaining oil types of oil reservoirs in late-stage of development with high water-cut.

Characteristics of Geological Remains Resources in Bailixia Provincial Geopark in Dazhou City of Sichuan Province

Through the analysis of geological background and geologic structural characteristics in Bailixia Provincial Geopark,the reason why all kinds of characteristic landscapes formed in the geopark was obtained;resources feature evaluation had been conducted on geological remains from the perspective of nature and humanity;geologic remains resources feature of the geopark was illustrated in a systematic way.The paper concluded the features of geologic remains resources in Bailixia Provincial Geopark which included high aesthetic value,rich scientific research value,overall development,and high-grade eco-tourism environment.

The Realization of Stevens’ Autonomy in The Remains of the Day

The Remains of the Day is a masterpiece of Ishiguro Kazuo,the winner of the 2017 Nobel Prize in Literature.Based on a six-day journey,this novel intertwined Stevens’recollections and thoughts,revealing a traditional British butler’s self-deception and self-suppression while confronting with the dilemma of professionalism and personal emotions.This article intends to analyze Stevens’realization of autonomy through his unutterable love toward Miss Kenton in three stages:separateness,competence and emotional autonomy.

Distribution of molluscan remains in the sediment of the Chukchi Sea and its vicinity, the Arctic

The result of an analysis of mollusca remains collected from the Chukchi Sea, Beaufort Sea and Bering Sea in the First Chinese National Arctic Research Expedition, from July to September, 1999 is presented. Seventeen species of mollusca have been identified, which belong to two classes: Bivalvia and Gastropoda. The compositions of the mollusca are very simple. According to the distribution pattern two groups may be distinguished among molluscan species. The Pan-Arctic and circumboreal group comprises Nuculana pernula, N.radiata, Nucula bellotii, Astarte montagui, Seripes groenlandicus, Macoma calcarea, M. moesta alaskana, Liocyrna fluctuosa, Mya pseudoarenaria and Turritella polaris. Three species, Cyclocardia crebricostata, Trichotrois coronata and Argobuccinum oregonense are components of the Pan-Arctic and Pacific boreal group. With regard to feeding habits, detritus feeders dominate. There are 7 species of detritus feeders, i.e., Nuculana pernula, N. radiata, Nucula bellotii, Macoma calcarea, M. moesta alaskana, Macoma sp. and Trichotropis coronata. Detritus feeders are dominant with regard to the numbers of species as well as to the frequency of occurrence. Macoma calcarea is the most abundant species.

The Remains of the Day Under the Perspective of Adaptation

The Remains of the Day is a Booker-winner novel by Kazuo Ishiguro. Stevens is both the protagonist and the narrator of the novel who restrains his feelings and has to live a life of regret and loss. This article provides a glimpse of its character and theme under the perspective of linguistic adaptation.

The development of machine learning-based remaining useful life prediction for lithium-ion batteries

Lithium-ion batteries are the most widely used energy storage devices,for which the accurate prediction of the remaining useful life(RUL)is crucial to their reliable operation and accident prevention.This work thoroughly investigates the developmental trend of RUL prediction with machine learning(ML)algorithms based on the objective screening and statistics of related papers over the past decade to analyze the research core and find future improvement directions.The possibility of extending lithium-ion battery lifetime using RUL prediction results is also explored in this paper.The ten most used ML algorithms for RUL prediction are first identified in 380 relevant papers.Then the general flow of RUL prediction and an in-depth introduction to the four most used signal pre-processing techniques in RUL prediction are presented.The research core of common ML algorithms is given first time in a uniform format in chronological order.The algorithms are also compared from aspects of accuracy and characteristics comprehensively,and the novel and general improvement directions or opportunities including improvement in early prediction,local regeneration modeling,physical information fusion,generalized transfer learning,and hardware implementation are further outlooked.Finally,the methods of battery lifetime extension are summarized,and the feasibility of using RUL as an indicator for extending battery lifetime is outlooked.Battery lifetime can be extended by optimizing the charging profile serval times according to the accurate RUL prediction results online in the future.This paper aims to give inspiration to the future improvement of ML algorithms in battery RUL prediction and lifetime extension strategy.

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development.However,it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries,as well as dynamic operating conditions in practical applications.Moreover,due to insignificant capacity degradation in early stages,early prediction of battery life with early cycle data can be more difficult.In this paper,we propose a hybrid deep learning model for early prediction of battery RUL.The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction.We also design a non-linear correlation-based method to select effective domain knowledge-based features.Moreover,a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost.Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set,but also generalizes well to the secondary test set having a clearly different distribution with the training set.The PyTorch implementation of our proposed approach is available at https://github.com/batteryrul/battery_rul_early_prediction.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

Holocene Floral and Faunal Remains Revealed Concealed Neotectonic Disturbance (Saria Tal, Kumaun Himalaya, India)

Palynological and paleontological investigations supported by the radiocarbon dates of the lacustrine sediments of two profiles from the temperate lake Saria Tal, in Naini Tal District, Kumaun Himalaya, have revealed the presence of a concealed fold at the region. The profile from bore cores represents the upper part of the Late Holocene and the profile from exposed sections from the Middle Holocene to the over middle part of the Late Holocene. The data generated from different investigations have uniformly indicated that the former profile represents normal superposition, while the latter represents the reverse order. The contemporary pollen as well as molluscan zones of both profiles are situated at different elevations but consist of similar bioremains - indicating continuation of the same strata in two profiles. The presence of reverse order of superposition, continuation of the same strata in two profiles at different elevations, and the orientation of biozones, have indicated that the revealed folding is of syncline type. The present study has also given an idea about the origin of this lake.

Fungal Remains from the Subathu Formation of Dogadda, Uttarakhand, India

The present paper deals with the new record of fungal remains from the Subathu Formation exposed along Dogadda-Kotdwar road section in Dogadda, Uttarakhand. The assemblage is composed of 13 species assignable to 10 genera. The important genera are Callimothallus senii, Haplopeltis mucoris, Haplopeltis sp., Parmathyrites sp., Phragmothyrites eocaenicus, Plochmopeltinites sp., Spinosporonites saxenae, Spinosporonites angularis and Trichothyrites padappakkarensis. The presence of microthyriaceous fungi in dominance suggests that the region experienced a warm and humid climate during the course of sediment deposition with thick vegetation providing suitable substrates for the growth and proliferation of fungi. Their presence depicts the prevalence of moist tropical type of vegetation during deposition in the area. The present fungal assemblage is assigned Late Palaeocene-Middle Eocene age.