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Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing. The INSTANCE released a training set of 100 cases with ground-truth and a validation set with 30 cases without ground-truth labels that were available to the participants. A held-out testing set with 70 cases is utilized for the final evaluation and ranking. The methods from different participants are ranked based on four metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), Relative Volume Difference (RVD) and Normalized Surface Dice (NSD). A total of 13 teams submitted distinct solutions to resolve the challenges, making several baseline models, pre-processing strategies and anisotropic data processing techniques available to future researchers. The winner method achieved an average DSC of 0.6925, demonstrating a significant growth over our proposed baseline method. To the best of our knowledge, the proposed INSTANCE challenge releases the first intracranial hemorrhage segmentation benchmark, and is also the first challenge that intended to resolve the anisotropic problem in 3D medical image segmentation, which provides new alternatives in these research fields., Comment: Summarized paper for the MICCAI INSTANCE 2022 Challenge

Multi-robot cooperative control has gained extensive research interest due to its wide applications in civil, security, and military domains. This paper proposes a cooperative control algorithm for multi-robot systems with general linear dynamics. The algorithm is based on distributed cooperative optimisation and output regulation, and it achieves global optimum by utilising only information shared among neighbouring robots. Technically, a high-level distributed optimisation algorithm for multi-robot systems is presented, which will serve as an optimal reference generator for each individual agent. Then, based on the distributed optimisation algorithm, an output regulation method is utilised to solve the optimal coordination problem for general linear dynamic systems. The convergence of the proposed algorithm is theoretically proved. Both numerical simulations and real-time physical robot experiments are conducted to validate the effectiveness of the proposed cooperative control algorithms., Comment: Accepted by AAMAS'23

The internal gelation process without cooling at room temperature is a significant challenge for the preparation of yttria-stabilized zirconia (YSZ) ceramic microspheres. In this work, a high-throughput and instant-mixing droplet microfluidic system that can prepare monodisperse YSZ ceramic microspheres was constructed. It mixed two stable solutions at room temperature in an active micromixer at a flow rate of mL/min, and then the mixed unstable precursor solution was quickly dispersed into sol droplets through a coaxial microfluidic device, and the droplets solidified into gel microspheres. The washed and dried gel microspheres could be sintered to obtain ceramic microspheres. Compared with no-micromixing and T-junction micromixers, the system had the best mixing performance and Y and Zr elements in the prepared ceramic microspheres were evenly distributed. Therefore, the system overcame the problem of precursor solution solidification and clogging of the device, and provided a new approach for the internal gelation process without cooling at room temperature.

Excellent thermal insulating materials are highly demanded in various applications including buildings, aerospace and sport equipment. However, in practical applications, the performance of thermal insulating materials usually deteriorates under diverse temperature and humidity conditions. Therefore, it is highly essential to construct a bulk material that exhibits outstanding thermal insulation performance under extremely humid and hot environment. In this work, we have conceived a green and effective strategy to fabricate a superhydrophobic and compressible polyvinylidene fluoride/polyimide (PVDF/PI) nanofiber composite aerogel via electrospinning and freeze-drying technique. Interestingly, the PVDF nanofibers and PI nanofibers function as the hydrophobic fibrous framework and mechanical support skeleton, respectively, forming a robust three-dimensional framework with good mechanical flexibility. The PVDF/PI aerogel possesses outstanding superhydrophobic feature (water contact angle of 152°) and low thermal conductivity (31.0 mW m−1 K−1) at room temperature. Significantly, even at 100% relative humidity (80°C), the PVDF/PI aerogel still exhibits a low thermal conductivity of only 48.6 mW m−1 K−1, which outperforms the majority of commercial thermal insulating materials. Therefore, the novel PVDF/PI aerogel is promising as an excellent thermal insulating material for the applications in high-temperature and humid environment.

This paper presents Bayesian methods that support conservative dependability claims for a software-based safety-critical system, particularly when evidence suggests the software's executions are not statistically independent. We formalise informal notions of "doubting" that the software's executions are independent, and incorporate such doubts into dependability assessments. We study the extent to which an assumption of independent executions can undermine conservatism in assessments, and identify conditions under which this impact is, or is not, significant. These techniques - novel extensions of conservative Bayesian inference (CBI) methods - are illustrated in two applications: the assessment of a nuclear power-plant safety protection system and the assessment of autonomous vehicle (AV) safety. Our analyses reveals: 1) the required amount of confidence an assessor should possess before subjecting a system to operational testing. Otherwise, such testing is shown to be futile - no amount of favourable operational testing evidence will increase one's confidence in the system being sufficiently dependable; 2) the independence assumption supports optimistic claims in certain situations, and conservative claims in other situations; 3) in some scenarios, upon observing a system operate without failure, an assessor's confidence in the system being sufficiently dependable is less than it would be had the system exhibited some failures; 4) posterior confidence in a system being sufficiently dependable is very sensitive to failures - each additional failure means significantly more operational testing evidence is required, in order to support a dependability claim., Comment: Under review

Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistinguishable input perturbations may lead to different XAI results. Thus, it is vital to assess how robust DL interpretability is, given an XAI method. In this paper, we identify several challenges that the state-of-the-art is unable to cope with collectively: i) existing metrics are not comprehensive; ii) XAI techniques are highly heterogeneous; iii) misinterpretations are normally rare events. To tackle these challenges, we introduce two black-box evaluation methods, concerning the worst-case interpretation discrepancy and a probabilistic notion of how robust in general, respectively. Genetic Algorithm (GA) with bespoke fitness function is used to solve constrained optimisation for efficient worst-case evaluation. Subset Simulation (SS), dedicated to estimate rare event probabilities, is used for evaluating overall robustness. Experiments show that the accuracy, sensitivity, and efficiency of our methods outperform the state-of-the-arts. Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness., Comment: Accepted by the IEEE/CVF International Conference on Computer Vision 2023 (ICCV'23)

With its growing use in safety/security-critical applications, Deep Learning (DL) has raised increasing concerns regarding its dependability. In particular, DL has a notorious problem of lacking robustness. Despite recent efforts made in detecting Adversarial Examples (AEs) via state-of-the-art attacking and testing methods, they are normally input distribution agnostic and/or disregard the perception quality of AEs. Consequently, the detected AEs are irrelevant inputs in the application context or unnatural/unrealistic that can be easily noticed by humans. This may lead to a limited effect on improving the DL model's dependability, as the testing budget is likely to be wasted on detecting AEs that are encountered very rarely in its real-life operations. In this paper, we propose a new robustness testing approach for detecting AEs that considers both the input distribution and the perceptual quality of inputs. The two considerations are encoded by a novel hierarchical mechanism. First, at the feature level, the input data distribution is extracted and approximated by data compression techniques and probability density estimators. Such quantified feature level distribution, together with indicators that are highly correlated with local robustness, are considered in selecting test seeds. Given a test seed, we then develop a two-step genetic algorithm for local test case generation at the pixel level, in which two fitness functions work alternatively to control the quality of detected AEs. Finally, extensive experiments confirm that our holistic approach considering hierarchical distributions at feature and pixel levels is superior to state-of-the-arts that either disregard any input distribution or only consider a single (non-hierarchical) distribution, in terms of not only the quality of detected AEs but also improving the overall robustness of the DL model under testing., Comment: Under Review

With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. While retailers prefer to acquire a model as accurate as possible among different customers, there are two major challenges. First, different retailers in the retail market do not share their consumer's electricity consumption data as these data are regarded as their assets, which has led to the problem of data island. Second, the electricity load data are highly heterogeneous since different retailers may serve various consumers. To this end, a fully distributed short-term load forecasting framework based on a consensus algorithm and Long Short-Term Memory (LSTM) is proposed, which may protect the customer's privacy and satisfy the accurate load forecasting requirement. Specifically, a fully distributed learning framework is exploited for distributed training, and a consensus technique is applied to meet confidential privacy. Case studies show that the proposed method has comparable performance with centralised methods regarding the accuracy, but the proposed method shows advantages in training speed and data privacy., Comment: 5 pages, 4 figures, 2023 ISGT-NA

Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RASs). A key impediment to its deployment in real-life operations is the spuriously unsafe DRL policies--unexplored states may lead the agent to make wrong decisions that may cause hazards, especially in applications where end-to-end controllers of the RAS were trained by DRL. In this paper, we propose a novel quantitative reliability assessment framework for DRL-controlled RASs, leveraging verification evidence generated from formal reliability analysis of neural networks. A two-level verification framework is introduced to check the safety property with respect to inaccurate observations that are due to, e.g., environmental noises and state changes. Reachability verification tools are leveraged at the local level to generate safety evidence of trajectories, while at the global level, we quantify the overall reliability as an aggregated metric of local safety evidence, according to an operational profile. The effectiveness of the proposed verification framework is demonstrated and validated via experiments on real RASs., Comment: Submitted, under review

The internal gelation process was combined with a co-axial capillary microfluidic device to prepare monodisperse ZrO2 ceramic microspheres. Large sized gel microspheres in the range of hundreds of micrometers could be produced with the capillary microfluidic device with co-axial flow structure and the large sized outlet tubing. After drying and sintering, large sized ZrO2 ceramic microspheres (>200 μm) which had good sphericity and narrow size distribution were prepared.

Numerical simulations and experiments showed that bump inlet had a remarkable effect on boundary layer diversion of supersonic flow. However, the design and analysis of bump in hypersonic flow was still few. In this paper, the mechanism of a supersonic bump inlet is introduced to the design of hypersonic forebody. A hypersonic inlet with an integrated bump/forebody is obtained by the Method Of Characteristics (MOC) based on a chin inlet. Numerical simulations show that the modified inlet achieves diversion of low-speed flow. Besides, the integrated bump/forebody is also beneficial to inlet start. During the starting process, the shape of the separation zone is rebuilt by the modified forebody surface which makes spillage much easier. This new design leads to a reduction of the self-start Mach number by 0.95. Keywords: Hypersonic inlets, Inlet start, Integration design, Method Of Characteristics (MOC), Separation zone

With the rapid development of the theory and application of artificial intelligence, the intuitionistic fuzzy rough sets model is extended in this paper for the increasingly complex data in various problem fields in the real world and the problem of intelligent modeling. First of all, the article reviewed the basic operations of picture fuzzy numbers in picture fuzzy environment, the proposed picture fuzzy relation and picture fuzzy approximation space, operations of picture fuzzy relations is given and the relevant conclusion, through the study of picture fuzzy equivalence relation, the picture fuzzy rough sets model was founded by the method of structural and the model structure and mathematical characteristics were studied with some important conclusions obtained. Secondly, based on the basic requirements of attribute reduction and rule extraction in picture fuzzy environment, the positive domain of picture fuzzy rough sets are further proposed, and the corresponding conclusions are given. Finally, this paper proposes an attribute reduction algorithm based on picture fuzzy environment. Through detailed analysis of examples and running results, it is shown that this algorithm is a search process with tree structure, and got its time complexity. Examples are provided to illustrate the effectiveness and feasibility of the algorithm. The purpose of this paper is to construct approximate reasoning and knowledge discovery mechanism of picture fuzzy information under picrure fuzzy knowledge the paper explores the theory of complex data modeling and attribute reduction, and provides a new idea for the further expansion of fuzzy rough sets theory.

The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications. While it shows great potential to provide transformational capabilities, DL also raises new challenges regarding its reliability in critical functions. In this paper, we present a model-agnostic reliability assessment method for DL classifiers, based on evidence from robustness evaluation and the operational profile (OP) of a given application. We partition the input space into small cells and then "assemble" their robustness (to the ground truth) according to the OP, where estimators on the cells' robustness and OPs are provided. Reliability estimates in terms of the probability of misclassification per input (pmi) can be derived together with confidence levels. A prototype tool is demonstrated with simplified case studies. Model assumptions and extension to real-world applications are also discussed. While our model easily uncovers the inherent difficulties of assessing the DL dependability (e.g. lack of data with ground truth and scalability issues), we provide preliminary/compromised solutions to advance in this research direction., Comment: Accepted by the AISafety'21 Workshop at IJCAI-21. To appear in a volume of CEUR Workshop Proceedings

With the increasing demand for the attitude adjustment and vibration isolation of the precision instruments, this paper proposes a three-degrees-of-freedom hybrid-drive parallel mechanism and carries out the kinematic analysis and dimensional synthesis for it. First, the advantages of the hybrid-drive unit are introduced by comparison with the common driving methods. Then, the degrees of freedom of the hybrid-drive parallel mechanism is analyzed by constrained screw method, and the displacement mapping relationship between the generalized input and the moving platform is obtained by a successive approximation iteration method. Furthermore, the first-order and the second-order influence coefficients of the hybrid-drive mechanism are established on the basis of screw theory and the direct method. Besides, the operational dexterity index, the transfer performance index and the stiffness performance index of the hybrid-drive mechanism are introduced, and each single-objective dimensional synthesis of the hybrid-drive mechanism is carried out by the interior point method. Moreover, the multi-objective dimensional synthesis of the hybrid-drive mechanism is proposed by combining the genetic algorithm and the Pareto-optimal solution. Finally, taking the integer numbers of No.23 Pareto set as the numerical example, the kinematics formulas of the hybrid-drive mechanism are verified by the simulation and the experiment.

Increasingly sophisticated mathematical modelling processes from Machine Learning are being used to analyse complex data. However, the performance and explainability of these models within practical critical systems requires a rigorous and continuous verification of their safe utilisation. Working towards addressing this challenge, this paper presents a principled novel safety argument framework for critical systems that utilise deep neural networks. The approach allows various forms of predictions, e.g., future reliability of passing some demands, or confidence on a required reliability level. It is supported by a Bayesian analysis using operational data and the recent verification and validation techniques for deep learning. The prediction is conservative -- it starts with partial prior knowledge obtained from lifecycle activities and then determines the worst-case prediction. Open challenges are also identified., Comment: Accepted by SafeComp2020

Microgripperis animportant tool in highprecisionmicromanipulationtask, whichdirectly affects the quality and efficiency of micromanipulation.Thispaper presents the design and control of a novel asymmetrical microgripperdriven by a piezoelectric(PZT)actuator.Thedevelopedmicrogripperis designedasanasymmetrical structurewithjust onemovablejaw, soit hasthe advantagesofnodensemodeandfixed locatingdatumcomparedwiththesymmetricalmicrogripperwithtwomovablejaws. The mainbody of microgripperis a compact flexure-based mechanicalstructure witha three-stage amplification mechanism. Basedon the designed structure, large-range, robustandparallelgraspingoperationcanberealized.Thecharacteristicsanalysesofthe developedmicrogripperarecarriedoutbyfiniteelementanalysis(FEA). Aposition-force switching control strategy is utilized to regulate the position and grasping force of movablejaw. Discrete-time slidingmodelcontroller(DSMC) is designedto controlthe positionandgraspingforceofgraspingjaw. Experimentalstudiesareconductedandthe resultsshowthatthemicrogripperexhibitsgoodperformanceandhighprecisiongraspingoperationscanberealizedthroughthedevelopedcontrolstrategy.

Environmentally friendly materials with lightweight, good thermal insulation and fire-resistance are highly required for energy efficient buildings. Here, layered double hydroxide (LDH) - graphene oxide (GO) synergistically enhanced polyimide (PI) aerogels with great thermal insulation, good thermal stability and excellent flame retardancy, have been designed and synthesized through a green environmental freeze-drying method followed by thermal imidization. LDH can be uniformly dispersed in aqueous solutions by electrostatic interactions with GO, thus resulting in uniform dispersion in PI matrix. Due to the physical interaction between the two kinds of nanosheets and PI, the pore size of PI/LDH-GO (PLG) composite aerogel was significantly reduced from 20 μm to 5 μm, leading to ultralow density (52 ± 3.6 mg cm−3), low thermal conductivity (36 ± 1.7 mW m−1 K−1), and high compressive modulus (26 ± 1.8 MPa). More importantly, the addition of two pollution-free fire-retardants (LDH-GO) endows the PLG aerogels excellent fire-resistant performance with the limiting oxygen index reach up to 43 ± 1.2%, reaching the nonflammable level. Therefore, this work paves a new way for anti-flaming and heat insulating materials with great potential for practical applications in energy efficient buildings.

Human milk fat-style structured triacylglycerols were produced from microalgal oil in a continuous microfluidic reactor packed with immobilized lipase for the first time. A remarkably high conversion efficiency was demonstrated in the microreactor with reaction time being reduced by 8 times, Michaelis constant decreased 10 times, the lipase reuse times increased 2.25-fold compared to those in a batch reactor. In addition, the content of palmitic acid at sn-2 position (89.0%) and polyunsaturated fatty acids at sn-1, 3 positions (81.3%) are slightly improved compared to the product in a batch reactor. The increase of melting points (1.7°C) and decrease of crystallizing point (3°C) implied higher quality product was produced using the microfluidic technology. The main cost can be reduced from $212.3 to $14.6 per batch with the microreactor. Overall, the microfluidic bioconversion technology is promising for modified functional lipids production allowing for cost-effective approach to produce high-value microalgal coproducts.

The complete genome of Cordyceps militaris was sequenced using single-molecule real-time (SMRT) sequencing technology at a coverage over 300×. The genome size was 32.57 Mb, and 14 contigs ranging from 0.35 to 4.58 Mb with an N50 of 2.86 Mb were assembled, including 4 contigs with telomeric sequences on both ends and an additional 8 contigs with telomeric sequences on either the 5′ or 3′ end. A methylome database of the genome was constructed using SMRT and m4C and m6A methylated nucleotides, and many unknown modification types were identified. The major m6A methylation motif is GA and GGAG, and the major m4C methylation motif is GC or CG/GC. In the C. militaris genome DNA, there were four types of methylated nucleotides that we confirmed using high-resolution LCMS-IT-TOF. Using PacBio Iso-Seq, a total of 31,133 complete cDNA sequences were obtained in the fruiting body. The conserved domains of the nontranscribed regions of the genome include TATA boxes, which are the initial regions of genome replication. There were 406 structural variants between the HN and CM01 strains, and there were 1,114 structural variants between the HN and ATCC strains.

There has been a growing interest in developing novel thermal insulation materials since the traditional insulation material is unable to manage heat intelligently. Herein, bidirectional anisotropic polyimide/bacterial cellulose (b-PI/BC) aerogels with good structural formability, high mechanical strength, and excellent thermal insulation properties have been prepared via bidirectional freezing technique. Polyimide makes the composite aerogels robust in mechanics, while uniform dispersion of bacterial cellulose in the aerogel can inhibit the shrinkage and retain the structural integrity, leading to higher porosity and lower density, thus reducing heat conduction throughout the whole aerogel. Due to its well-aligned lamellar structure obtained by bidirectional freezing technique, b-PI/BC aerogel exhibits distinct anisotropic thermal insulation behaviors with ultra-low thermal conductivity of 23 mW m−1 K−1 in the radial direction (perpendicular to the lamella) and nearly twice (44 mW m−1 K−1) in the axial direction (parallel to the lamella). The anisotropy of the thermal conductivity can substantially reduce the heat transfer in the radial direction, while helps heat diffusion in-plane to avoid heat localization, which possess remarkable advantages over the random and unidirectional counterparts. Therefore, this anisotropic PI/BC aerogel can serve as a promising heat management material for practical and complex thermal insulation applications.

Desilked silkworm (Bombyx mori L.) pupae oil has been identified as a valuable source of unsaturated fatty acids (UFAs). However, direct consumption is a food safety issue due to the presence of sn-1,3 triacylglycerols (TAGs), including palmitic acid (PA). A simple, selective and safe conversion to structured triacylglycerols (STAGs) with high UFA content is highly desirable. Novel STAGs with low PA and high α-linolenic acid (ALA), oleic acid (OA) and linoleic acid (LA) content were synthesized from silkworm pupae oil using a solvent-free system catalyzed by Lipozyme RM IM. Crystallization and melting profiles were determined using thermograms, and optimal conditions involved an enzyme load of 7%, a silkworm pupae oil TAG to OA molar ratio of 1:5, and a reaction temperature of 60°C. The content of PA and stearic acid decreased from 41.04 to 20.11%, while UFAs increased from 61.12 to 77.88%. The STAGs exhibited lower crystallization (−15.44°C) and melting (−23.47°C) profiles than TAGs (4.64 and −5.58°C, respectively). 1 Practical applications The presented methods using a solvent-free system enable STAGs to be synthesized using a natural substrate (desilked silkworm pupae oil). A simple, selective and safe conversion to STAGs with high UFA content and low PA content is highly desirable. This STAG is rare in nature and chemical synthesis is cumbersome. This STAG could be applied as a clinical treatment for nutritional enhancement with a beneficial effect on human health because of the high content of the ALA. Novel structure triacylglycerols (STAGs) with low palmitic acid and rich in oleic acid and linoleic acid were synthesized from silkworm pupae oil and oleic acid, through solvent-free enzymatic interesterification catalyzed by Lipozyme RM IM. Under the optimal conditions, the content of palmitic acid and stearic acid decreased from 41.04 to 20.11% in the sn-1, 3 positions, and the content of unsaturated fatty acids (including palm stearin, oleic acid, linoleic acid and α-linolenic acid) increased from 61.12 to 77.88%. The STAGs could be added into milk fat substitutes, which may be useful as nutrition enhancers for infants and may decrease obesity.

High-performance thermal insulating materials are greatly needed in many areas. Polyimide aerogels by freeze-drying method have shown great potential as thermal insulating materials. Improving the mechanical performance of polyimide aerogels, meanwhile without deteriorating their thermal insulating performance, is very significative. In this work, a significant homogeneity reinforcement of polyimide aerogels has been achieved by using electrospun polyimide nanofibers as the reinforcing fillers. Short nanofibers could improve strength and toughness of polyimide aerogels by dispersing stress along the pore wall and nanofibers via mechanical interlocking effect due to the good compatibility between them. Consequently, the nanofiber-reinforced polyimide (NRPI) aerogels exhibit better structural formability and excellent mechanical performance, showing compressive modulus of 3.7 MPa with a density of 54.4 mg cm−3, nearly twice that of neat polyimide aerogel. More importantly, owing to the high porosity and intertwined three-dimensional network, NRPI aerogels exhibit better thermal insulating properties compared with commercial insulating materials, especially at high temperatures. Therefore, the nanofiber reinforced polyimide aerogels with good mechanical performance are promising candidates for thermal insulating applications.

Unsaturated free fatty acids (UFFAs), which are rich inα-linolenic and omega-3 fatty acids, were obtained by alkali hydrolysis and urea complexation methods from perilla seed oil and used as the acyl donor to produce structured triacylglycerols (STAGs) catalyzed by Lipozyme RM IM. The results indicated that the content ofα-linolenic acid was increased to 73.16 % after urea complexation methods. The highest incorporation rate ofα-linolenic acid was 58.78 %, which were achieved under the optimum conditions: a molar mass ratio of tripalmitin to UFFAs of 1:12, a reaction time of 48 h and a temperature of 60 °C.

The enzymatic hydrolysis parameters of triacylglycerols (TAGs) in algae oil to get sn-2 glycerel monostearte including substrate amount, lipase amount and reaction time was studied, and the products of fatty acid methyl esters (FAMEs) were detected by gas chromatography (GC). The result indicated that the optimum conditions to hydrolyze TAGs in algae oil were as follows: mass ratio of substrate to lipase of 1:1, 2 mL Tris-HCl buffer, 0.2 mL CaCl2solution, 0.5 mL sodium cholate hydrate solution were added in each 30mg substrate, and reaction time of 1.5 min with 120 rpm at 40 °C, the highest palmitic acid (PA) content accounted for 9.1% of total oil.

In order to satisfy the mass spherical fuel elements (SFE) requirements for the High Temperature gas-cooled Reactor (HTR)–Pebble bed Module (HTR-PM), a modified and optimized fabrication of dense uranium dioxide (UO2) kernel via an acid-deficiency uranium nitrate (ADUN) solution was researched and developed in the institute of nuclear and new energy technology (INET). In this study, external gelation process of uranium (EGU) based on the total gelation of uranium (TGU) in the period of 10 MW HTR (HTR-10) was used to prepare the ammonium diuranate (ADU) compound particles, an intermediate for UO2 kernel. The scale of batch production was improved from 500 g U/batch at that time to current 3 kg U/batch. Improvements in many aspects such as large scale preparation of ADUN, multiple-nozzle casting system and new aging, washing and drying (AWD) system without isopropanol were also presented. Under strict controls and measures, ADU particles with good sphericity and uniform diameter were acquired to ensure the subsequent preparation of dense UO2 kernels for HTR-PM.

Silkworm pupae oil derived from reeling waste is a rich source of α-linolenic acid (ALA), which has multipal applications. ALAs were added in sn-1, 3 positions in a triacylglycerol (TAG) to produce an APA-human milk fat analogues (APA-HMFAs, A: α-linolenic acid, P: palmitic acid). The optimum condition is that tripalmitin to free fatty acids of 1:12 (mole ratio) at 65 °C for 48 h using lipase Lipozyme RM IM. Results show that, the major TAG species that comprised APA-HMFAs were rich in ALA and palmitic acid, which contained 64.52% total unsaturated fatty acids (UFAs) and 97.05% PA at the sn-2 position. The melting point of APA was −27.5 °C which is much lower than tripalmitin (40.5 °C) indicating more plastic character. In addition, the practical application of alkyl caffeates as liposoluble antioxidants in APA was developed. Alkyl caffeate showed a superior IC50 (1.25–1.66 μg/mL) compared to butyl hydroxy anisd (1.67 μg/mL) and L-ascorbic acid-6-palmitate (L-AP) (1.87 μg/mL) in DPPH analysis. The addition of ethyl caffeate to oil achieved a higher UFAs content (73.58%) at high temperatures. Overall, APA was obtained from silkworm pupae oil successfully and the addition of caffeates extended storage ranges for APA-HMFAs.

Novel structured triacylglycerols (STAGs) enriched with unsaturated fatty acids (UFAs) and low palmitic acid (PA) content were firstly synthesized from Schizochytrium sp. oil and oleic acid (OA) via solvent-free acidolysis catalyzed by Lipozyme RM IM. The results indicated that, the PA content decreased from 24.49% to 6.95%, while the UFAs content increased from 70.20% to 90.9% at the sn-1,3 positions in the STAGs under the optimal condition (i.e., lipase load of 7%, molar ratio of microalgae oil TAGs to OA of 1:3, and temperature of 65 °C). The lipase Lipozyme RM IM could be reused 16 times without significant loss of activity. The improved plastic and storage ranges of STAGs are useful for infant formula formulations, by which a possible method is blending of this product and 1,3-dioleoyl-2-palmitoylglycerol enriched fats and minor lipids based on the corresponding chemical compositions of human milk fat.

Propyl caffeate has the highest antioxidant capacity in the caffeate alkyl esters family, but industrial production of propyl caffeate is hindered by low yields using either the chemical or enzymatic catalysis method. To set up a high-yield process for obtaining propyl caffeate, a novel chemoenzymatic synthesis method using lipase-catalyzed transesterification of an intermediate methyl caffeate or ethyl caffeate and 1-propanol in ionic liquid was established. The maximum propyl caffeate yield of 98.5% was obtained using lipase-catalyzed transesterification under the following optimal conditions: Novozym 435 as a biocatalyst, [Bmim][CF3SO3] as a medium, a molar ratio of methyl caffeate to 1-propanol of 1:5, a mass ratio of methyl caffeate to lipase of 1:20, and a reaction temperature of 60°C. The two-step conversion of caffeic acid to propyl caffeate via methyl caffeate is an efficient way to prepare propyl caffeate with an overall yield of 82.7%.