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Bolted connection is one of the most widely used connection forms in various mechanical structures. However, the discretization of preload during tightening will be aggravated by the presence of geometric error caused by manufacturing. In this work, the geometric error transmitted by all parts of the bolted joint is equated to the existence of a certain wedge angle between the nut and the bearing surface of the connected part, that is, the nut and the bearing surface are in non-parallel contact. A torque-preload mathematical equation is theoretically established to account for non-parallel contact. On this basis, the relation between torque and preload deviation is considered to analyze the formation mechanism of preload deviation according to non-parallel contact. Subsequently, the influence of non-parallel contact on tightening performance was quantitatively analyzed by experiments and finite element. The analysis results showed that the tightening process under non-parallel contact can be divided into two stages: nonlinear increase stage and linear increase stage. Among them, the deviation mainly appears in the first stage. Tightening performance analysis herein is helpful to reduce the influence of geometric error on the preload control and further improve its reliability.

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2’s (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth’s energy balance.

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (−1.39 × 10–3/year). Compared to the general deep-learning model, our method reduces the global trend’s previously overestimated magnitude by 7% per year. China has experienced the most significant decline (−5.07 × 10–3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10–4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

A 1200-V rated CoolSiC Trench MOSFET structure has excellent overall electrical characteristics due to its unique asymmetric structure. However, it is sensitive to space heavy ion radiation. In this study, heavy ion irradiation experiments were conducted on 1.2 kV CoolSiC Trench MOSFET devices to calibrate the simulation structure model and critical failure conditions. The 2-D numerical simulator SILVACO TCAD was used to investigate the single event burnout (SEB) hardening method for the 1.2 kV-rated CoolSiC Trench MOSFET under high linear energy transfer (LET) values. The simulation results show that the improved CoolSiC Trench MOSFET structure can effectively enhance the SEB capability of the device without sacrificing its excellent overall electrical characteristics compared to the traditional CoolSiC Trench MOSFET. At 360 V voltage and 0.5 pC/ $\mu $ m LET value, the highest temperature is reduced from 3097 to 1502 K, and the lowest burnout threshold is increased by 200 V in the improved structure while maintaining almost unchanged characteristics at other locations.

AbstractIn the case of a new nuclear reactor, existing evaluation experience is limited; thus, accidents and troubles may occur as a result of such lack of experience. To deal with such situations, it is desirable to use a virtual nuclear plant to reproduce behaviors under various conditions and identify unknown anomalies from the behaviors. Then, when an abnormal situation occurs, one can quickly determine the cause of the abnormality to operate plant equipment and return the plant to a stable condition as quickly as possible. Two types of deep neural network (DNN) systems have been constructed to support the identification of unknown anomalies and the determination of their causes. One is a surrogate system that can estimate physical quantities of a nuclear power plant in a computational time of several orders less than a physical simulation model. The other is an abnormal situation identification system that can estimate the state of the disturbance causing an anomaly from physical quantities of a nuclear power plant. Both systems are trained and tested using data obtained from the analytical code for incore and plant dynamics (ACCORD), which reproduces the steady and dynamic behavior of the actual High Temperature Engineering Test Reactor (HTTR) under various scenarios. The DNN models are built by adjusting the main hyperparameters. Through these procedures, these systems are shown to be able to perform with a high degree of accuracy.

The Bolted joint loosening is challenging to describe with an accurate mathematical model. Therefore, a prediction model of bolted joint loosening was proposed based on a deep learning network. Firstly, the loosening experiments of the bolted joint were carried out with an orthogonal test. And the nonlinear and uncertain characteristics of bolted joint loosening process were further analyzed. Furthermore, a prediction model of bolted joint loosening based on a deep learning network was established. The proposed model was trained and verified using the experimental data. The results showed that compared with the traditional mathematical and regression models, the model could not only obtain the change of the mean value of preload but also describe the confidence interval of the change of preload in the sense of probability synchronously. The experiment data and prediction data are in good agreement, which can be used to validate the rationality of the model.

Epigenetic modifications of chromatin, including histone acetylation, and tumor angiogenesis play pivotal roles in creating an immunosuppressive tumor microenvironment. In the randomized phase 2 CAPability-01 trial, we investigated the potential efficacy of combining the programmed cell death protein-1 (PD-1) monoclonal antibody sintilimab with the histone deacetylase inhibitor (HDACi) chidamide with or without the anti-vascular endothelial growth factor (VEGF) monoclonal antibody bevacizumab in patients with unresectable chemotherapy-refractory locally advanced or metastatic microsatellite stable/proficient mismatch repair (MSS/pMMR) colorectal cancer. Forty-eight patients were randomly assigned to either the doublet arm (sintilimab and chidamide, n= 23) or the triplet arm (sintilimab, chidamide and bevacizumab, n= 25). The primary endpoint of progression-free survival (PFS) rate at 18 weeks (18wPFS rate) was met with a rate of 43.8% (21 of 48) for the entire study population. Secondary endpoint results include a median PFS of 3.7 months, an overall response rate of 29.2% (14 of 48), a disease control rate of 56.3% (27 of 48) and a median duration of response of 12.0 months. The secondary endpoint of median overall survival time was not mature. The triplet arm exhibited significantly improved outcomes compared to the doublet arm, with a greater 18wPFS rate (64.0% versus 21.7%, P= 0.003), higher overall response rate (44.0% versus 13.0%, P= 0.027) and longer median PFS rate (7.3 months versus 1.5 months, P= 0.006). The most common treatment-emergent adverse events observed in both the triplet and doublet arms included proteinuria, thrombocytopenia, neutropenia, anemia, leukopenia and diarrhea. There were two treatment-related fatalities (hepatic failure and pneumonitis). Analysis of bulk RNA sequencing data from the patients suggested that the triplet combination enhanced CD8+T cell infiltration, resulting in a more immunologically active tumor microenvironment. Our study suggests that the combination of a PD-1 antibody, an HDACi, and a VEGF antibody could be a promising treatment regimen for patients with MSS/pMMR advanced colorectal cancer. ClinicalTrials.gov registration: NCT04724239.

To evaluate the effect of acupuncture on anisometropic amblyopia (AA) and explore its potential mechanism for children with AA.

Wide applications of diazonium salts (DS) are limited due to their nature of poor thermal stability and explosive hazards. In this work, strong acid cation-exchange resin (Amberlyst-15), as an acid and anion provider, was used to synthesize the corresponding diazonium salt of weakly basic p-nitroaniline (p-NA). The thermal stability of raw materials (p-nitroaniline, Amberlyst 15) and cation-exchange resin-supported DS (CER-supported DS) was characterized by DSC and ARC, respectively. The synthesis conditions were optimized, and the exothermicity of the diazotization reaction was characterized by reaction calorimetry. Diazonium salts of p-NA were demonstrated to be obtained and chemisorbed on the cation-exchange resin surface. The optimal yield of CER-supported DS reached up to 80%. The initial decomposition temperature and decomposition energy of the CER-supported DS were about 55 °C and 183 J/g, respectively. The decomposition process of the CER-supported DS followed a single n-order reaction model, and the main decomposition product was p-nitrophenol. The merits of this synthesis method include being free of environmentally harmful inorganic acids and high-cost of tert-butyl nitrites and having good storage stability.

Hydrofoils, as the actuators in hydrofoil attitude control systems (HACSs), are prone to faults and failures resulting in fatal accidents. In this article, a fault detection and estimation (FDE) strategy for such failures is presented based on a developed baseline model. The actuator fault is first parameterized by rewriting the dynamic model of the HACS. To address non-Gaussian factors in the joint estimation of the unscented Kalman filter (UKF), the Gaussian mixture model (GMM) is then integrated with the conventional UKF to approximate the complex distributions. Considering the problem of singular or ill-conditioned covariance matrices in the GMM, a covariance regularization approach based on the posterior estimation with the inverse-Wishart distribution is explored. Actuator anomalies can be detected via a scale metric utilizing the covariance matrices, together with the peak signal-to-noise ratio (PSNR) index, and accurate fault estimation is implemented by isolating fault information from the augmented state even in the presence of non-Gaussian disturbances. The effectiveness of the proposed strategy is validated by the experiment carried out on a hardware-in-loop (HIL) platform. The RMSE results for fault estimation obtained through the method proposed in this article exhibit the reductions of 19.23% and 31.77% for the constant-value fault scenario, 5.03% and 16.31% for the time-varying fault mode, when compared with the results obtained using the joint UKF and the Bayesian-based adaptive Kalman filter with Gaussian-inverse-Wishart mixture (BAKF-GIWM) techniques. These findings underscore the superior efficacy and performance of the proposed method.

A series of nano-WC surface modified AlSi10Mg powders with different WC contentswere synthesized by the electrostatic assembly method. After the selective laser melting (SLM) processing, various Al−W intermetallicswere in-situ formed in the SLM nano-WC/AlSi10Mg composites. A well-coherent Al/Al5W interface with a low lattice mismatch of 4.7% was confirmed with an orientation relationship of (002)α(Al)//(104)Al5W. The nano-WC particles and Al−W intermetallics triggeredthe columnar-to-equiaxed transition, leading to a fine equiaxed microstructure. As a result, the SLMed 3 wt.% nano-WC/AlSi10Mg composites showed remarkable mechanical properties, with an ultimate tensile strength of (464.1±8.68) MPa and elongation of (5.6±0.95)%. Additionally, the SLMed 3 wt.% WC/AlSi10Mg samples also achieved the lowest average coefficient of friction (0.429) and wear rate of 3.842×10−5mm3/(N·m) compared to other WC/AlSi10Mg composites. The remarkable mechanical and wear properties are ascribed to fine-grain and second-phase strengthening.

Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicability of cAOD for climate research. Here, we propose a spatiotemporal coaction deep-learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001–2021. In contrast to conventional deep-learning models, the SCAM considers the impacts of spatiotemporal feature interactions and can simultaneously describe linear and nonlinear relationships for retrievals. Based on these unique characteristics, the SCAM considerably improved global daily cAOD accuracies and coverages (R = 0.82, root-mean-square error [RMSE] = 0.04). Compared to official products from the multiangle imaging spectroradiometer (MISR), the moderate resolution imaging spectroradiometer (MODIS), and the polarization and directionality of Earth’s reflectances (POLDER) instrument, as well as the physical-deep learning (Phy-DL) derived cAOD, the SCAM cAOD improved the monthly R from 0.44 to 0.88 and more accurately captured over the desert regions. Based on the SCAM cAOD, daily dust cases decreased over the Sahara, Thar Desert, Gobi Desert, and Middle East during 2001–2021 (>3 × 10–3/year). The SCAM-retrieved cAOD can contribute considerably to resolving the climate change uncertainty related to coarse-mode aerosols. Our proposed method is highly valuable for reducing uncertainties regarding coarse aerosols and climate interactions.

The effects of different annealing processes on the phase precipitation behavior and corrosion properties of copper-bearing 430 ferritic stainless steel were systematically investigated. The shape, quantity and distribution of copper-rich precipitates by different annealing processes were characterized by scanning electron microscopy, transmission electron microscopy, backscatter electron and backscatter diffraction. The pitting resistance behavior in simulated physiological saline environments (0.9 wt.% NaCl) was investigated using electrochemical workstation and X-ray photoelectron spectroscopy. The results showed that the copper-rich phase prepared by repeated rolling and annealing gradually changed from long needle-like to short thick rod-like and granular, whose distribution tended to be uniform and diffusive, and the number of copper-rich phases increased. After solution/antibacterial annealing process, the size and density of the copper-rich phase increase, resulting in a discontinuity of the passivation film on the stainless steel, which reduces the pitting resistance to some extent. The refinement mechanism revealed that pre-deformation brings about a modification in both precipitation mechanism and growth kinetics of epsilon copper.

We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution prediction of $\mathbb {P}(\mathbf {y}|\mathbf {X}=x)$P(y|X=x) in regression tasks. This conditional distribution's quantiles of probability levels spreading the interval (0,1) are boosted by additive models which are designed by us with intuitions and interpretability. We seek an adaptive balance between the structural integrity and the flexibility for $\mathbb {P}(\mathbf {y}|\mathbf {X}=x)$P(y|X=x), while Gaussian assumption results in a lack of flexibility for real data and highly flexible approaches (e.g., estimating the quantiles separately without a distribution structure) inevitably have drawbacks and may not lead to good generalization. This ensemble multi-quantiles approach called EMQ proposed by us is totally data-driven, and can gradually depart from Gaussian and discover the optimal conditional distribution in the boosting. On extensive regression tasks from UCI datasets, we show that EMQ achieves state-of-the-art performance comparing to many recent uncertainty quantification methods. Visualization results further illustrate the necessity and the merits of such an ensemble model.

In this article, a sliding mode control design method is developed for a class of fully nonlinear systems in generalized regular form, where both input distribution uncertainty and system uncertainties are considered. Based on the generalized regular form, a novel nonlinear sliding surface is designed and uniform ultimate stability of the corresponding sliding mode dynamics is analyzed. Then, under the assumption that the uncertainties are bounded by known nonlinear functions of the system states, a sliding mode controller is formulated to ensure that the dynamical system reaches the sliding surface in finite time even in the presence of the system uncertainties and input distribution uncertainties. Further, for the case of system uncertainty with unknown bound in parameterized form, an adaptive sliding mode controller is developed to drive the dynamical system to the sliding surface and maintain a sliding motion thereafter. The developed sliding mode controller is applied to a high incidence research model aircraft model. Simulation results demonstrate that the developed methods are effective.

Increasing the proportion of clean energy is an inevitable trend of high-quality energy development. While enjoying the benefits of clean energy, traditional fossil energy enterprises face great challenges. In this study, coal power and photovoltaic power are selected as examples for analysis. Considering coal consumption, carbon emission, unit ramp conditions and photovoltaic abandonment, a comprehensive income model containing thermal power and photovoltaic power generation is proposed. The objective function of maximizing earnings is established and solved by genetic algorithm. The results show that the overall benefit will increase if the proportion of photovoltaic does not increase blindly. The unreasonable ratio of thermal and photovoltaic power and the imbalance of supply and consumption will lead to the decline of economic benefits and the increase of coal consumption and carbon emission. In the case of a high proportion of clean energy penetration, on the one hand, traditional power plants should be responsible for ensuring the instability of electricity output when the photovoltaic is weak, on the other hand, they need to give up some of the supply proportion when clean power is at a high output state. Therefore, while increasing the proportion of clean energy, it is necessary to improve the speed of load adjustment, the depth of peak regulation and the efficiency of low load work in traditional power plants to ensure the healthy development of new energy.

Lung cancer is one of the most malignant cancers in China with a rising incidence rate. Despite the fact that surgical treatment is the only possible cure for lung cancer, its long-term efficacy is compromised by the high level of postoperative local recurrence rate. Minimal residual disease is the leading cause of tumor recurrence, yet the suggested combination of clinical, radiological and serological (carcinoembryonic antigen) tests fails to reveal the underlying residual tissue in all stage I-III lung cancer cases, which makes tumor recurrence surveillance timely. Through monitoring circulating tumor DNA, the minimal residual disease level can be accessed and provide guidance for more precise postoperative personalized treatment, and its scientific feasibility can revolutionize lung cancer therapy. In the present review we summarized the progress of circulating tumor DNA in lung cancer minimal residual disease detection and discussed its application value in guiding precise treatment of lung cancer.

The successful bid for the 2022 Winter Olympics has provided a strong impetus for the development of China's ski industry. Ski areas have sprung up throughout the country, even in the low latitudes south of 30°N. However, ski tourism is extremely susceptible to weather and climate conditions. In the context of global warming, it has become important to assess the climate reliability of ski areas. Therefore, this study demonstrates a novel approach to assessing the ski tourism sector's climate risks, which can be easily applied in other markets catering to the same industry. Using the random forest regression model based on climate projections and survey data, we projected the ski season start dates, end dates and season lengths of 694 existing ski areas in China under three emission scenarios (RCP2.6, RCP4.5 and RCP8.5). Climate projections, including air temperature, snowfall, rainfall, wind speed and air humidity, were the ensemble means from five climate models. Results indicate that ski areas in China may have later start dates, earlier end dates and shorter ski seasons before 2099. By the 2090s, under RCP2.6, RCP4.5 and RCP8.5, 20% (139), 28% (195) and 35% (245) of ski areas are projected to be at high climate risk (ski season less than 60 d), respectively, while 28% (197), 23% (157) and 8% (56) of ski areas are at low climate risk (ski season with at least 100 d), respectively. The climate risks are ranked from the highest to lowest in East, Central, Southwest, North, Northwest and Northeast China. Furthermore, the ski tourism sector in the latitudes south of 40°N is exposed to much higher climate risks than in areas north of 40°N. Therefore, climatic reliability should be carefully considered before establishing or expanding ski areas to avoid unnecessary resource waste and ecological damage, as well as to promote sustainable development in mountain areas.

Fine-mode aerosol optical depth (fAOD) is related to anthropogenic aerosols, which play an essential role in global climate change and the atmospheric environment. However, knowledge of the global fAOD in the 1990s is very limited. Compared with general satellite-based fAOD retrieval methods, the spectral deconvolution algorithm (SDA) provides an entirely different way to calculate fAOD. In this study, we derived three new SDA-based global daily fAOD ( $0.55\mu \text{m}$ ) datasets from 1995 to 2003 with 1 $^{\circ }\,\,\times1^{\circ }$ spatial resolution using the second Along-Track Scanning Radiometer (ATSR-2) aerosol product data. In global-scale validation, the application of SDA to Swansea University (SU)–ATSR aerosol products for fAOD retrievals showed the best agreement with AErosol RObotic NETwork (AERONET) fAOD, with a correlation coefficient of 0.72, a root-mean-square error (RMSE) of 0.12, and $\sim $ 65.36% of retrievals inside of the estimated error envelope (EE) (±[0.05 + 15%]). Regionally, SDA improves retrieval performance in Southeast Asia and USA when using the SU and ATSR-dual view (ADV) aerosol products. However, the application of SDA to Oxford-RAL Aerosol and Cloud (ORAC)-based aerosol data considerably worsened its accuracy compared with the official ORAC fAOD. With the improved SU–SDA fAOD, the long-term trend showed a considerable decrease in fAOD in Central South America from 1995 to 2003. However, significantly increasing trends were observed in India and China. As SDA uses spectral AOD only, this study demonstrates that it is a promising method for optical satellite spatial fAOD retrieval.

ObjectiveMethionine metabolism is involved in a myriad of cellular functions, including methylation reactions and redox maintenance. Nevertheless, it remains unclear whether methionine metabolism, RNA methylation and antitumour immunity are molecularly intertwined.DesignThe antitumour immunity effect of methionine-restricted diet (MRD) feeding was assessed in murine models. The mechanisms of methionine and YTH domain-containing family protein 1 (YTHDF1) in tumour immune escape were determined in vitro and in vivo. The synergistic effects of MRD or YTHDF1 depletion with PD-1 blockade were also investigated.ResultsWe found that dietary methionine restriction reduced tumour growth and enhanced antitumour immunity by increasing the number and cytotoxicity of tumour-infiltrating CD8+T cells in different mouse models. Mechanistically, the S-adenosylmethionine derived from methionine metabolism promoted the N6-methyladenosine (m6A) methylation and translation of immune checkpoints, including PD-L1 and V-domain Ig suppressor of T cell activation (VISTA), in tumour cells. Furthermore, MRD or m6A-specific binding protein YTHDF1 depletion inhibited tumour growth by restoring the infiltration of CD8+T cells, and synergised with PD-1 blockade for better tumour control. Clinically, YTHDF1 expression correlated with poor prognosis and immunotherapy outcomes for cancer patients.ConclusionsMethionine and YTHDF1 play a critical role in anticancer immunity through regulating the functions of T cells. Targeting methionine metabolism or YTHDF1 could be a potential new strategy for cancer immunotherapy.

In order to reach the ambitious net-zero emission target by 2050, various technological solutions need to be developed to ensure efficient utilisation of energy. Commercial and residential buildings are a big source of greenhouse gas emissions, where efficient utilisation of energy can play a major role towards decarbonisation of the buildings sector.  Heat pumps have recently emerged as an effective solution for space heating applications in buildings. Energy-efficient operation of heat pumps will make a significant contribution toward making buildings energy-efficient. In this context, heat pump control systems have a major role. Some of the existing literature on the heat pump control systems assume that various system states are available to measure. This may not always be true and/or economical to measure all the states. Moreover, the system is subject to various disturbances which cannot be directly measured. To reduce the number of sensors in heat pump control systems, an adaptive observer is developed in this paper to estimate inaccessible system states and disturbances simultaneously. An advantage of the proposed approach is that it does not require any bound on the disturbance itself, however, only assumes that the rate of change of disturbance is bounded. This is always the case in practice. In the developed method, adaptive control techniques and variable structure control techniques are combined to implement the proposed observer. In order to estimate the unknown disturbance, an augmented systems model is considered. Globally uniformly ultimately bounded property of the error dynamical systems is established by suitably designing the adaptive laws. The developed method is applied to a model of the heat dynamics of a house floor heating system connected to a ground source-based heat pump. Different disturbance signals formats and amplitudes are considered to show the effectiveness of the proposed technique. Simulation results are given to demonstrate the suitability of the proposed method.

The mechanical performances such as tensile strength and blast property of metal lined SiC/SiC composite cladding tubes were investigated. Nb or Ta was selected as liner material, and the SiC/SiC composite layer was fabricated by winding and different precursor impregnation and pyrolysis (PIP) processes. The tensile strengths of different tube samples were measured at room temperature (RT) and 1200 °C, respectively. The blast property was investigated through the maximum water pressure of tubes. And the fracture microstructures were observed by SEM. The highest tensile strength at RT was 150.7 MPa. The blast strength was enhanced with the PIP process increasing from 1 to 4 cycles and the tube of 4 PIP cycles had the highest water pressure of 34.7 MPa. Compared with the metal tubes, the multi-layer structure improved tensile and blast properties significantly. The different processes such as PIP cycles and pyrolytic carbon (PyC) coating were important factors to enhance the mechanical performances of SiC/SiC-based tubes. However, the retention rate of tensile strength was only 18.5% at 1200 °C.

Threaded fasteners are one of the most commonly used connection methods for mechanical structures. Its primary function is to generate appropriate clamping forces and fasten the connected parts. An inappropriate preload can cause loosening, fatigue fracture, and other problems. This will affect the safety and reliability of mechanical equipment. The precise control of the preload has become a critical issue in mechanical assembly processes. Over the past few decades, various tightening measures and methods have been proposed to address this issue. However, many problems continue to exist with practical applications that have not been reviewed comprehensively and systematically. First, various control methods were summarized systematically, and their advantages and disadvantages in engineering applications were analyzed. Torque control is the most widely used tightening method owing to its simple operation and low cost. Therefore, the research on the torque control method was summarized systematically from three aspects: the torque–preload correlation formula, effective friction radius, and friction characteristics during tightening. In addition, the special circumstances that may increase preload uncertainty were discussed. Finally, based on a summary of the current research status, the prospects for future research were discussed. This study would aid researchers in extensively understanding the problems in preload control.

AbstractWhen total volume control is applied to toxicity in nuclear waste management it becomes a limiting factor for the permittable total operating capacity of nuclear reactors. An alternative conceptual scenario is proposed, aimed at toxicity reduction through partitioning and transmutation. Specifically, the electricity generation capacity could be increased by transmutation of 90Sr and 137Cs. Simultaneously, the cooling time before disposal is reduced to 50 years from the 300 years required by existing scenarios, such as the accelerator-driven system. Finally, the scenario is also found to be feasible in terms of energy balance and cost, using an Li(d,xn) reaction neutron source with a deuteron accelerator for transmutation.