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In this paper, the remelting process is applied to Laser Directed Energy Deposition (L-DED) to repair single crystal superalloys. The main formation modes of stray grains in single crystal epitaxial growth were analyzed, and the inhibitory effect of remelting process on stray grains in single crystal epitaxial growth using L-DED was studied. Comparative experiments between remelting and non-remelting processes have been conducted. Three process parameters in the remelting process: power, z-axis lift and cladding-remelting interval time were studied. The impact of different remelting process parameters on single crystal epitaxial growth and the formation of stray grains has been studied. Remelting has been shown to effectively prevent the formation of stray grains during the solidification of the melt pool, which are otherwise caused by the presence of unmelted powder during the powder feeding phase of the L-DED process. A cladding layer without stray grains on both sides is obtained. After remelting, the epitaxial growth structure can achieve larger cellular and columnar dendritic zones and smaller primary dendrite arms. A favorable epitaxial growth structure was obtained in this paper, under the optimized remelting process window.

Within this research, a novel method for updating time-varying information of milling thin-walled components based on digital twin model is proposed, which is used to solve the problem of chatter prediction and feature mapping for thin-walled components during milling. According to the characteristics of the workpiece to be machined, the overall workpiece is divided into two substructure parts. Among them, the substructure of constant workpiece is analyzed using the double coordinated free interface method, and the substructure of removed material workpiece is analyzed using the finite element method. Based on interface coordination conditions, the two substructure models are coupled in hybrid coordinate space, which is to obtain the reduced-order model and modal characteristics about overall thin-walled components. Meanwhile, a digital twin model is constructed to characterize the time-varying information of milling thin-walled components, which combines the structural dynamics modification method with data model. This proposed method has better generalization ability and efficiency, which is known by comparing with other existing methods. Also, its normalized relative frequency difference (NRFD) values are all less than 5% and consistently close to 0; its computational efficiency is also improved by 97.06%. A series of milling experiments are carried out to verify the effectiveness of the novel strategy. The results show that the predicted and experimental results are in good agreement with each other, indicating that the currently constructed digital twin model is effective.

Recent research shows a big convergence in model architecture, training objectives, and inference methods across various tasks for different modalities. In this paper, we propose VioLA, a single auto-regressive Transformer decoder-only network that unifies various cross-modal tasks involving speech and text, such as speech-to-text, text-to-text, text-to-speech, and speech-to-speech tasks, as a conditional language model task via multi-task learning framework. To accomplish this, we first convert the speech utterances to discrete tokens (similar to the textual data) using an offline neural codec encoder. In such a way, all these tasks are converted to token-based sequence prediction problems, which can be naturally handled with one conditional language model. We further integrate task IDs (TID), language IDs (LID), and LSTM-based acoustic embedding into the proposed model to enhance the modeling capability of handling different languages and tasks. Experimental results demonstrate that the proposed VioLA model can support both single-modal and cross-modal tasks well, and the decoder-only model achieves a comparable and even better performance than the strong baselines.

Human adoption of artificial intelligence (AI) technique is largely hampered because of the increasing complexity and opacity of AI development. Explainable AI (XAI) techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human understanding. However, the current adoption of XAI technique still lacks “human-centered” guidance for designing proper solutions to meet different stakeholders’ needs in XAI practice. We first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of XAI. In addition, a taxonomy of XAI methods is developed for visual computing with analysis of method properties. Holding clearer human demands and XAI methods in mind, we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders’ human-centered demands in practice. And we check the availability of open-source XAI tools for stakeholders’ use. This survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.

Long-content automatic speech recognition (ASR) has obtained increasing interest in recent years, as it captures the relationship among consecutive historical utterances while decoding the current utterance. In this paper, we propose two novel approaches, which integrate long-content information into the factorized neural transducer (FNT) based architecture in both non-streaming (referred to as LongFNT) and streaming (referred to as SLongFNT) scenarios. We first investigate whether long-content transcriptions can improve the vanilla conformer transducer (C-T) models. Our experiments indicate that the vanilla C-T models do not exhibit improved performance when utilizing long-content transcriptions, possibly due to the predictor network of C-T models not functioning as a pure language model. Instead, FNT shows its potential in utilizing long-content information, where we propose the LongFNT model and explore the impact of long-content information in both text (LongFNT-Text) and speech (LongFNT-Speech). The proposed LongFNT-Text and LongFNT-Speech models further complement each other to achieve better performance, with transcription history proving more valuable to the model. The effectiveness of our LongFNT approach is evaluated on LibriSpeech and GigaSpeech corpora, and obtains relative 19% and 12% word error rate reduction, respectively. Furthermore, we extend the LongFNT model to the streaming scenario, which is named SLongFNT, consisting of SLongFNT-Text and SLongFNT-Speech approaches to utilize long-content text and speech information. Experiments show that the proposed SLongFNT model achieves relative 26% and 17% WER reduction on LibriSpeech and GigaSpeech respectively while keeping a good latency, compared to the FNT baseline. Overall, our proposed LongFNT and SLongFNT highlight the significance of considering long-content speech and transcription knowledge for improving both non-streaming and streaming speech recognition systems.

How to boost speech pre-training with textual data is an unsolved problem due to the fact that speech and text are very different modalities with distinct characteristics. In this paper, we propose a cross-modal Speech and Language Model (SpeechLM) to explicitly align speech and text pre-training with a pre-defined unified discrete representation. Specifically, we introduce two alternative discrete tokenizers to bridge the speech and text modalities, including phoneme-unit and hidden-unit tokenizers, which can be trained using unpaired speech or a small amount of paired speech-text data. Based on the trained tokenizers, we convert the unlabeled speech and text data into tokens of phoneme units or hidden units. The pre-training objective is designed to unify the speech and the text into the same discrete semantic space with a unified Transformer network. We evaluate SpeechLM on various spoken language processing tasks including speech recognition, speech translation, and universal representation evaluation framework SUPERB, demonstrating significant improvements on content-related tasks.

A gearbox is an indispensable component of a mechanical transmission system, and in the event of an anomaly, it can have a serious impact on the stability of the system. Graph convolutional network (GCN) have achieved some results in the study of fault diagnosis, but the way of topology construction from a single metric space is somewhat inexact and one-sided. Meanwhile, GCNs can only aggregate node features in the spatial dimension, which does not guarantee a comprehensive expression of fault information. Therefore, we propose a gearbox fault diagnosis method based on refined topology and spatio-temporal GCN to accurately identify the gearbox health state. First, two topologies are constructed based on the distance metric and the similarity metric, respectively, and the edge structure is pruned to obtain a more lightweight topology based on the analysis of the structural similarity between the two. Then, depending on the number of common neighbors of two nodes, the weights between them are enhanced or weakened to further refine the topological information. Finally, a network model with spatio-temporal feature co-extraction is developed to mine fault features in depth in two dimensions to improve network performance. Case studies, ablation experiments, and robustness validation of the proposed method are carried out, and the results show that the accuracy of the proposed method is as high as 99.41% and 99.93% on the two datasets, respectively, which is much higher than that of other comparison methods, and at the same time, it has good noise robustness.

Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech. However, existing models still face limitations in handling diverse audio-text speech generation tasks involving transforming input speech and processing audio captured in adverse acoustic conditions. This paper introduces SpeechX, a versatile speech generation model capable of zero-shot TTS and various speech transformation tasks, dealing with both clean and noisy signals. SpeechX combines neural codec language modeling with multi-task learning using task-dependent prompting, enabling unified and extensible modeling and providing a consistent way for leveraging textual input in speech enhancement and transformation tasks. Experimental results show SpeechX's efficacy in various tasks, including zero-shot TTS, noise suppression, target speaker extraction, speech removal, and speech editing with or without background noise, achieving comparable or superior performance to specialized models across tasks.

The efficacy of radiotherapy (RT) is usually restricted by the hypoxic microenvironment and the poor radiation attenuation coefficient of tumor tissue. Theranostic probes that simultaneously evaluate the hypoxia degree and sensitize cancer cells toward RT are promising for improving the treatment efficacy and avoiding overtreatment. We rationally designed a metal–organic framework (MOF)-derived multifunctional nanoprobe for hypoxia imaging-guided radiosensitization. Hf-MOF was carbonized to obtain a porous carbonous nanostructure containing ultrasmall HfO2(HfC); then, a fluorophore-labeled HIF-α mRNA antisense sequence was readily adsorbed and quenched by HfC to obtain the nanoprobe (termed HfC-Hy). The antisense sequence could easily hybridize with HIF-α mRNA and recover its fluorescence signal to evaluate the degree of hypoxia, while the HfC nanostructure could deposit more radiation energy in cancer cells for radiosensitization. A series of in vitro and in vivo experiments demonstrated that the nanoprobe could be successfully utilized for imaging the hypoxic degree of cancer cells/tumor tissue and guiding radiosensitization. This work not only developed a highly efficient and safe nanosensitizer but also offered a potential solution for customized clinical RT.

As the next generation of artificial enzymes, nanozymes have attracted increasing attention in biomedical applications due to their multienzyme-like characteristics, multifunctionalities, low cost, and high stability. By taking advantage of their diverse activities, a growing number of nanozyme-mediated therapeutic strategies have been developed for various diseases. Herein, we provide a brief review of the representative studies of nanozymes, especially in orthopaedic diseases over the past decade, which include arthritis, osteoporosis, bone regeneration, bacteria-associated infections, and osteosarcoma. Moreover, the future potential applications and some major challenges are also discussed. This review would not only provide some instructive views of nanozymes but also promote the development of enzyme-mimetic strategies in orthopaedics.

Hydrate-based CO2sequestration is a novel technology to reduce CO2emissions and mitigate global warming. The reduction of the induction time and increase of the formation rate and gas storage capacity are the key points for industrially storing CO2hydrate. In this review, the research progress of various compound additives for promoting the formation of CO2hydrate is reviewed. The promotion mechanisms of different additives and effects of different compound additives on the formation characteristics of CO2hydrate are analyzed and summarized. Additionally, the promotion effects of different compound additives are compared and evaluated, challenges and constraints of the current compound additives discussed, and development trends and directions proposed.

ABSTRACTThis study used the data from the TALIS 2018 to explore how teacher job satisfaction is affected by factors at the teacher-level (e.g., teacher motivation and teachers’ perceived disciplinary climate) and school-level (e.g., school delinquency and violence) simultaneously. The HLM results showed that both the school- and teacher-level factors were significantly related to teacher job satisfaction. These findings were discussed in relation to Chinese educational contexts.

Yaks living on the Qinghai-Tibetan Plateau for a long time have evolved a series of mechanisms to adapt to the unique geographical environment and climate characteristics of the plateau. Compared with other ruminants, yaks have higher energy utilization and metabolic efficiency. This paper presents possible mechanisms responsible for the efficient energy utilization, absorption and metabolism resulting from the unique evolutionary process of yaks. It is hoped that the information discussed in this review will give a better insight into the uniqueness and superiority of yaks in regards to energy metabolism and utilization compared with cattle and open new avenues for the targeted regulation of energy utilization pathways of other ruminants.

A stacked deposition strategy to in situ activation and reconstruction of Ni-based catalysts improves the OER and HER performances.

Recently, the application of high performance concrete (HPC) is popular in inner lining of frozen shaft in water-rich stratum. The circular crack of inner lining concrete easily appears in the early age (0∼240 h) leading to water burst accidents in the service period. The early-age cracking potential of HPC in inner lining is investigated by Temperature Stress Test Machine (TSTM) in the laboratory. The complex boundary, temperature history and variable restraint degree, was performed simultaneously by TSTM in this study. The experimental analysis indicated that (1) The range of restraint degree is almost 40% to 89.3% under steel frame restrained condition which can almost realize actual constraint degree of inner lining; (2) The cracking potential increased slightly for Mixture C60, C70, and increased severely once the Mixture strength exceeded C70; (3) The integrated crack indicators of C60, C70 and C90 repeat such as cracking stress, crack age, cracking temperature and cracking thermal drop were fluctuant slightly with the concrete strength increased; (4) The increasing of concrete strength decreased the cracking potential of inner lining concrete under steel frame restrained condition, though the tensile strength is improved.

The detection of surface damage is an important part of the process before remanufacturing a retired steel shaft (RSS). Traditional damage detection is mainly done manually, which is time-consuming and error-prone. In recent years, computer vision methods have been introduced into the community of surface damage detection. However, some advanced typical object detection methods perform poorly in the detection of surface damage on RSS due to the complex surface background and rich diversity of damage patterns and scales. To address these issues, we propose a Faster R-CNN–based surface damage detection method for RSS. To improve the adaptability of the network, we endow it with a feature pyramid network (FPN) as well as adaptable multiscale information modifications to the region proposal network (RPN). In this paper, a detailed study of an FPN-based feature extraction network and the multiscale object detection network is conducted. Experimental results show that our method improves the mean average precision (mAP) score by 8.9% compared with the original Faster R-CNN for surface damage detection of RSS, and the average detection accuracy for small objects is improved by 18.2%. Compared with the current advanced object detection methods, our method is more advantageous for the detection of multiscale objects.

Modulating tumor-associated macrophages from tumor-promoting M2 to tumoricidal M1 phenotype is promising for anti-tumor immunotherapy but remains challenging, especially in a controllable manner. Herein, we report a radiosensitive nanoregulator (AuDAP) for activatable immunotherapy. AuDAP can simultaneously recognize and bind M2 macrophages with tumor cells by polyvalent interactions. Under low-dose medical X-ray irradiation, the nanoregulator could generate reactive oxygen species (ROS) to activate the nuclear factor kappa-B (NF-κB) signaling pathway to repolarize M2 macrophages into M1 phenotype, thereby activating the immune function of macrophages in situ.A series of in vitroand in vivoexperiments demonstrated the cell communication remodeling and immunotherapy activation effects of AuDAP. As a result, AuDAP + X-ray inhibited the growth of tumors and effectively prevented their metastasis during a long period of observation. This nanoregulator may serve as a promising nanomedicine for precise tumor therapy, and this work offers new insights for activatable immunotherapy.

Mimicking enzyme specificity via construction of on-demand geometric structures on nanozymes is of great interest in recent years. Although building substrate-specific polymers on nanozymes has achieved great success, polymer-blocked active sites would inevitably lead to decreased activity of nanozymes. Here, we have developed three photoactive metal–organic framework (MOF)-based nanozymes (called 2D-TCPP, 3D-TCPP, and AD-TCPP), which have different geometric structures as well as unshielded active sites. Together with their structural variations and excellent photoresponsive oxidase-like activities, these photoactive nanozymes exhibit structure-dependent specificity for three kinds of substrates (typical oxidase substrates, organic pollutants, and antioxidants). Moreover, AD-TCPP and 3D-TCPP show potential applications for environmental protection and bioanalysis, respectively. This work offers a promising approach to the development of nanozymes with enzyme-like specificity.

Dual-gradient drilling technology is being increasingly used in formations with narrow pressure margins. For dual-gradient drilling based on downhole separation, hollow spheres are separated into the annulus at the separator position, resulting in variable mass flow in the wellbore. Thus, existing heat transfer models are no longer suitable for describing wellbore temperature profiles in dual-gradient drilling. This study focused on developing a wellbore heat transfer model that fully considers separated hollow spheres entering the annulus, complex casing programs, and heat sources, for dual-gradient drilling based on downhole separation. The model was solved using an iterative method. Then, the accuracy of the model was verified using temperature data measured from two wells. Finally, the difference in the annular temperature distributions between dual-gradient drilling and conventional single-gradient drilling were investigated, as were the wellbore heat transfer characteristics for dual-gradient drilling. The following major conclusions were drawn: (1) for dual-gradient drilling based on downhole separation, at the separator location, the annular fluid temperature does not decrease, but rather increase in the flow direction because of the inflow of hollow spheres; (2) a clear inflection point exists in the annular fluid temperature curve at the location where the separator would be; (3) the magnitude of the mutation of the temperature curve at the inflection point is considerably affected by the heat capacities of the hollow spheres and the pure drilling fluid; (4) under the same change in separation efficiency, distance between the bit and separator, flow rate, and thermal conductivity of formation, the variation range of the fluid temperature at the bottom hole is greater than that at the wellhead.

Using electrocatalysts is effective in solving the slow reaction kinetics of polysulfides in Li–S batteries, but designing stable electrocatalysts with an integrated adsorption-catalysis-desorption system is challenging. Here, we report a stable metal–semiconductor (Co-ZrO2) heterojunction electrocatalyst fabricated by assembling electron-coupled Co-ZrO2nanodots into macroporous carbon nanofibers. The Co-ZrO2contact causes interfacial electron enrichment and electron transfer from Co to ZrO2, which creates abundant Lewis-acid sites on Co that can adsorb polysulfides. Simultaneously, the enriched interfacial electrons can activate the S–S bond and boost the catalytic conversion of long-chain polysulfides, while the ZrO2with Lewis-base sites facilitate the desorption of short-chain polysulfides from the electrocatalyst. Moreover, the nanodot heterojunctions show great chemical stability and high redox reaction kinetics of polysulfides. Li–S batteries show high discharge capacities of 954.5 mA h·g–1at 0.5 C with a retention of 84.9% over 200 cycles, and 710.2 mA hg–1at 1 C with a retention of 98.6% over 200 cycles. This study provides an effective strategy for developing active and durable electrocatalysts for Li–S batteries.

This study proposes an approach to combat construction waste in the architecture, construction, and engineering (ACE) industry by developing a disassemblable brick partition wall. Brick reuse is severely restricted by the presence of mortar; innovative approaches need to be explored. An existing strategy, utilizing mortarless interlocking, relies on non-standardized bricks. It is worth noting that these methods are not specifically created for disassembly, despite the fact that they theoretically could be. A relatively innovative technique for tightening and stabilizing brick units emerged in recent years, involving the utilization of metal components. Despite its potential, there are limited case studies of this approach. By drawing on two typical examples of pros and cons, MIFA 1862 and the UMAR Unit, we propose a new strategy and examine it from multiple perspectives. The findings of the analysis demonstrate how adaptable and versatile the proposed system is, allowing it to be modified into a variety of sizes and forms. Additionally, the system has proven to have considerable advantages in terms of construction speed, and energy efficiency throughout the structure’s service time and in future use phases.

Currently, alkaline earth metal vanadates as the anode materials for lithium-ion batteries (LIBs) are gradually sparking attention due to their low cost, environmental friendliness, high theoretical capacity and safety. However, their practical application is still hampered by the fast capacity degradation resulting from the prolonged cycling condition and intrinsic low electrical conductivity. Herein, single-crystal Fe-doped CaV2O6is prepared viathe low-temperature combustion synthesis route. The Fe-doping accelerates the generation of low-valence-state vanadium in CaV2O6–2%Fe, leading to the production of oxygen vacancies for maintaining electroneutrality. The versatile design of CaV2O6–2%Fe improves the electron conductivity and promotes the Li-ion diffusion rate based on the generated dual defects of interstitial atoms of Fe and oxygen vacancies accordingly. As expected, the CaV2O6–2%Fe electrode exhibits superior rate capacity of 96.6 mA h g−1at 5 A g−1and a remarkable capacity retention of 125.6 mA h g−1after 1000 cycles at 1 A g−1. Additionally, the partial decomposition of CaV2O6during the initial lithiation stage, and the formation of amorphous Li–V–O in the subsequent cycle can be used to depict the lithium storage mechanism of CaV2O6–2%Fe.

Ion transport efficiency, the key to determining the cycling stability and rate capability of all-solid-state lithium metal batteries (ASSLMBs), is constrained by ionic conductivity and Li+-migration ability across the multicomponent phases and interfaces in ASSLMBs. Here, we report a robust strategy for the large-scale fabrication of a practical solid electrolyte composite with high-throughput linear Li+-transport channels by compositing an all-trans block copolymer PVDF-b-PTFE matrix with ferroelectric BaTiO3–TiO2nanofiber films. The electrolyte shows a sustainable electromechanical-coupled deformability that enables the rapid dissociation of anions with Li+to create more movable Li+ions and spontaneously transform the battery internal strain into Li+-ion migration kinetic energy. The ceramic framework homogenizes the interfacial potential with electrodes, endowing the electrolyte with a high conductivity of 0.782 mS·cm–1and stable ion transport ability in ASSLMBs at room temperature. The batteries of LiFePO4/Li can stably cycle 1000 times at 0.5 C with a high capacity retention of 96.1%, and Ah-grade pouch or high-voltage Li(Ni0.8Mn0.1Co0.1)O2/Li batteries also exhibit excellent rate capability and cycling performance.

Silicon (Si) is a promising anode material to replace the broadly adopted graphite due to its high capacity and abundant source. However, Si anodes suffer from severe problems of huge volume change (∼300%), and the commonly used binders like poly(vinylidene fluoride) (PVDF) cannot accommodate such changes. Here, we report a tough block copolymer PVDF-b-Teflon (PTFE) binder that can coalesce pulverized Si and thus enhance the stability of Si anodes. The suspension copolymerization of vinylidene fluoride and tetrafluoroethylene produces elastic PVDF-b-PTFE with large breaking elongations of >250% and high viscosity as well as high ionic conductivity and thermal stability. We show that 5 wt % of the binder forms elastic cobweb structures in the electrode matrix that can effectively coalesce Si particles and conductive agents together, enabling long cycling stability (>250 cycles) and high rate performance (1 C) for electrodes at a commercial-level Si loading of 1 mg·cm–2. The findings point out to a promising strategy for developing highly elastic and tenacious binders for electrodes with large volume changes during the electrochemical reactions.

Flexible oxide ceramic films offer prospects for revolutionizing diverse fields such as energy and electronics, but their fabrication methods are typically elaborate and cannot be expanded. Here, we report a scalable strategy to fabricate flexible and robust SiO2nanofiber films with controllable morphology using a sol–gel electrospinning method followed by low-temperature calcination. When applied to composite polymer electrolytes (CPEs) for solid-state batteries by filling polyethylene oxide into porous ceramic films, SiO2nanofibers with large surface areas (51 m2·g–1) demonstrate strong Lewis interfacial interactions and isotropic ionic transfer channels that mitigate polymer crystallinity and Li+-concentration polarization, imparting high conductivity (1.3 × 10–4S·cm–1at 30 °C) and structural stability to the electrolytes. As a result, all-solid-state LiFePO4||Li shows great rate capability and long cycling stability with high discharge capacities of 159 and 132 mA·h·g–1at 0.5C under 60 and 45 °C, respectively, demonstrating broad commercial prospects for the scale production of efficient solid electrolytes.

Natural language processing (NLP) is a subfield of artificial intelligence that focuses on enabling computers to understand and process human languages. In the last five years, we have witnessed the rapid development of NLP in tasks such as machine translation, question-answering, and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data. In this paper, we will review the latest progress in the neural network-based NLP framework (neural NLP) from three perspectives: modeling, learning, and reasoning. In the modeling section, we will describe several fundamental neural network-based modeling paradigms, such as word embedding, sentence embedding, and sequence-to-sequence modeling, which are widely used in modern NLP engines. In the learning section, we will introduce widely used learning methods for NLP models, including supervised, semi-supervised, and unsupervised learning; multitask learning; transfer learning; and active learning. We view reasoning as a new and exciting direction for neural NLP, but it has yet to be well addressed. In the reasoning section, we will review reasoning mechanisms, including the knowledge, existing non-neural inference methods, and new neural inference methods. We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledge-driven neural NLP models to handle complex tasks. At the end of this paper, we will briefly outline our thoughts on the future directions of neural NLP.

The prevention and control of natural gas hydrate (hereinafter, “hydrate” for short) blockage during deepwater gas well production tests is very important in ensuring the test safety. In this paper, the distribution of wellbore temperature and pressure field under different test conditions was analyzed, and the changes of hydrate deposition and blockage degree in the pipe string in the whole process of test under different test systems were evaluated using the hydrate generation–deposition–decomposition calculation method. On this basis, a deepwater gas well production test method based on hydrate prevention and control was proposed. And the following research results were obtained. First, in the process of deepwater gas well tests, the vapor–liquid phase of annular-mist flow pattern with the greatest risk of hydrate blockage is often formed in the wellbore. Therefore, it is more reasonable to take measures to prevent hydrate blockage in the process of tests than to prevent the formation of hydrate. Second, when the conventional four-point test method is used, it is required to set low gas production measurement points with lower flowing temperature. In the wellbore with high temperature and low temperature, however, hydrate tends to form and deposit easily, and a long period of test will increase the risk of test string blockage. Third, the mixed-sequence test system suitable for deepwater gas well tests can change wellbore temperature by adjusting the sequence of measuring points without changing the production rate and duration, so as to decompose hydrate sediment layers and reduce the maximum blockage degree of test string in the process of tests. Fourth, a three-point or two-point test method is recommended for deepwater gas wells without sand production, stress sensitivity, retrograde condensation and water production. Superior to the conventional four-point test method, three-point and two-point test methods can effectively reduce the risk of hydrate deposition and blockage in the test string, and it can shorten the testing time and reduce the test cost on the premise of ensuring the accuracy of the productivity equation. In conclusion, the research results are of help to the field test construction of deepwater gas wells.

The remaining useful life (RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety (safety awareness and safety improvement). These studies incorporated many different models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and difficulty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model (SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation (sequential Monte Carlo). Being effective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM (condition based maintenance), PHM (prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.

Ca-bentonite was modified firstly by Na2CO3and then by octadecyl trimethyl ammonium chloride to prepare organically modified bentonite, and Ca-bentonite and organically modified bentonite were characterized by infrared spectroscopy and X-ray diffraction, respectively. The factors affecting the absorption of copper ions on organically modified bentonite were investigated by absorption experiments, and the absorption mechanism was discussed. The results showed that the absorption performance of organically modified bentonite for copper ions was improved. The 100 mL copper ion solution at a concentration of 10 mg L–1was absorbed by organically modified bentonite, under the condition of initial solution pH of 4.00, absorbent dosage of 3.0 g L–1and absorption time of 30 min, a removal rate of 98.40% was achieved at an absorption capacity of 3.28 mg g–1. Quasi-second-order kinetic equation can well describe the reaction process of organically modified bentonite absorbing copper ions.

This work aims to determine the material qualification of 13Cr-L80 casing for sour conditions which is of great importance to check the sulfide stress cracking susceptibility, confirm the range of applicable conditions and recommend anti-corrosion materials. First, the casing material was tested to judge whether its properties were normal. Secondly, the testing matrix was defined to determine actual casing exposure conditions. Thirdly, slow strain rate testing was used to screen the conditions. Fourthly, thirty-day tests of uniaxial tensile specimens were conducted to check for SSC susceptibility. The casing material satisfied API 5CT standards and could be used for slow strain rate testing and sulfide stress cracking testing. The testing matrix is intended to distinguish the combined effect of temperatures (41, 75, 140 °F), chloride levels (1000, 13 000 mg × L−1), pH (3.5, 5.5) and partial pressures of H2S (3.5 psi) in shut-in and flowing conditions. The slow strain rate testing showed a reduction of signs of embrittlement with increased temperature, suggesting that the mechanism is supported by sulfide stress cracking. The 13Cr-L80 passed field-specific qualification tests for sulfide stress cracking resistance in sour wells at maximum H2S partial pressures of 3.5 psi in the casing-tubing annulus. The API 5CT 13Cr-L80 casing application is in compliance with ISO15156/NACE MR0175 standards as long as the aggressiveness of the field conditions does not go beyond the envelope conditions tested (3.5 psi of H2S, pH 3.5, temperature of 41 or 75 °F).

We have computed the free energy per unit area (i.e., interfacial tension) between a solid surface and two coexisting polymer solutions, where there is no specific interaction between the particles and either polymer, via self-consistent field calculations. Several different systems have been studied, including those where the two polymers differ in molecular weight (Mw) by a factor of ∼2 or where the polymers have the same Mw, but one set of chains is branched with the other linear. In the absence of any enthalpic contribution resulting from adsorption on the solid particle surface, the differences in the free energy per unit area resulting from the polymer-depleted regions around the particles in the two coexisting phases are found to be ∼1 μN m–1. Although this value may seem rather small, this difference is more than capable of inducing the partitioning of particles of 100 nm in size (or larger) into the phase with the lower interfacial free energy at the solid surface. By examining the density profile variation of the polymers close to the surface, we can also infer information about the wettability and contact angle (θ) of solid particles at the interface between the two coexisting phases. This leads to the conclusion that for all systems of this type, when the incompatibility between the two polymers is sufficiently large, θ will be close to 90°.

The lifetime evolution of mechanical equipment with complicated structure and the harsh operating environment cannot be accurately expressed due to the dynamics of the failure mechanism. However, the performance monitoring of equipment, with the information characterizing the failure process from the sensed data, can be used to assess the failure time and then the online remaining useful life. Because of the existence of nonlinearity and non-Gaussian for most real systems, for online assessment, unscented Kalman filter combined with particle filter is studied, instead of the standard particle filter with importance sampling, which is modified to update the states iteratively. Meanwhile, Markov chain Monte Carlo is performed after resampling to improve the prediction accuracy. In the modeling, state–space model is developed to quantify the relationship between the information from online observation and underlying degradation, and the unscented particle filter is investigated to realize the assessment of remaining useful life. In particular, the sufficient statistic method is presented to obtain a joint recursive estimation on both the system state and model parameters for those state–space model with unknown time-invariant ones. At the end of this article, the acoustic emission signals of a milling cutter are illustrated as a case study for cutter online remaining useful life estimate. The milling cutter example demonstrates the effectiveness of the proposed method for online estimate and provides useful insights regarding the necessity of online updating and the assessment.