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Coenzyme Q 10 (CoQ 10 ) is crucial for human beings, especially in the fields of biology and medicine. The aim of this experiment was to investigate the conditions for increasing CoQ 10 production. At present, microbial fermentation is the main production method of CoQ 10 , and the production process of microbial CoQ 10 metabolism control fermentation is very critical. Metabolic flux is one of the most important determinants of cell physiology in metabolic engineering. Metabolic flux analysis (MFA) is used to estimate the intracellular flux in metabolic networks. In this experiment, Rhodobacter sphaeroides was used as the research object to analyze the effects of aqueous ammonia (NH 3 ·H 2 O) and calcium carbonate (CaCO 3 ) on the metabolic flux of CoQ 10 . When CaCO 3 was used to adjust the pH, the yield of CoQ 10 was 274.43 mg·L −1 (8.71 mg·g −1 DCW), which was higher than that of NH 3 ·H 2 O adjustment. The results indicated that when CaCO 3 was used to adjust pH, more glucose-6-phosphate (G6P) entered the pentose phosphate (HMP) pathway and produced more NADPH, which enhanced the synthesis of CoQ 10 . At the chorismic acid node, more metabolic fluxes were involved in the synthesis of p-hydroxybenzoic acid (pHBA; the synthetic precursor of CoQ 10 ), enhancing the anabolic flow of CoQ 10 . In addition, Ca 2+ produced by the reaction of CaCO 3 with organic acids promotes the synthesis of CoQ 10 . In summary, the use of CaCO 3 adjustment is more favorable for the synthesis of CoQ 10 by R. sphaeroides than NH 3 ·H 2 O adjustment. The migration of metabolic flux caused by the perturbation of culture conditions was analyzed to compare the changes in the distribution of intracellular metabolic fluxes for the synthesis of CoQ 10 . Thus, the main nodes of the metabolic network were identified as G6P and chorismic acid. This provides a theoretical basis for the modification of genes related to the CoQ 10 synthesis pathway.

Variation in air pressure severely affects the insulation of electric vehicle (EV) motors, hence weakening the reliability of EVs for safe operation. Nomex‐polyimide‐Nomex, a typical insulation material for EV motors, was used to investigate the motor insulation performance under different air pressures. The results show that the partial discharge inception voltage is significantly reduced for EV motors operated at lower air pressures, and the probability of partial discharge (PD) occurrence is increased. The macroscopic results reveal that the active area of the PD expands at low pressure, while the non‐corroded ring appears in the centre. Additionally, although the number and amplitude of the PD increase significantly with decrease in air pressure, the active area of the PD expands and electrical stress on the insulation per unit area increases slowly. Therefore, when the pressure decreased from 60 to 40 kPa, the endurance life does not show a significant downward trend. Furthermore, the dielectric constant and loss of the low‐pressure samples significantly change during the ageing process, which further indicates critical degradation of the insulation. The aforementioned investigations reveal that the air pressure at different altitudes has a significant impact on the performance of insulation materials.

Stress grading systems using non‐linear resistive coatings are a key component to suppress surface corona in the end‐windings of rotating machine. Compared to a sinusoidal‐fed motor, the high slew rate of the voltage at the flanks of the repetitive square voltages from the inverter cause large capacitive currents to flow in the main wall insulation. These large currents, if not properly considered in the design phase, lead to severe electrothermal stress of the grading system. Experiments and simulations were conducted on a stress grading system whose structure arises from limitation posed by the motor structure. Measurements performed with different rise times show that the maximum potential along the conductive armour tape (CAT) increases non‐linearly with increasing axial distance, and the potential at the edge of the CAT reached nearly twice the peak‐to‐peak voltage at 500 ns rise time, leading to corona inception. As metal plates are used in the machine to dampen vibrations in the end‐winding, similar plates were also fastened to the stress grading system, worsening the already inadequate corona suppression performance. The stress grading system was therefore modified, avoiding the surface corona while, at the same time, reducing the temperature in the grading system to acceptable levels.

End turn grading with resistive–capacitive coupling experiences severe electrothermal stress when subjected to pulse width modulation (PWM) voltage. In this paper, several experiments and simulations were carried conducted for four types of end turn grading. First of all, the temperature rise in the end turn grading increased with a decrease in rise time. When the rise time was less than 500 ns, the temperature rise at the terminal was higher owing to the increased capacitive current coupled from the main wall insulation. Further, the current in the linear region exhibited minimal variation at different fundamental frequencies resulting in synchronized the temperature rise at the terminal and overlap. Furthermore, the jump voltage was the key factor influencing temperature rise in end turn grading, confirmed by comparing different voltage magnitudes. Finally, the transient behaviour of the maximum field in the stress grading material was determined at rise time. The experimental and simulation results indicate that balancing and interdependently addressing the electrical and thermal stress protection in end turn grading is crucial. The study aims to provide an experimental and theoretical foundation for an insulation system of inverter‐fed rotating machinery operating under PWM voltage.

For the two-mode electron pairing, we propose a local stacking force pairing mechanism driven by strong local fluctuations, with two straight pairing orbits where the tying Cooper pairing \\(C_{-k\\downarrow}C_{k\\uparrow}e^{ik\\cdot r}\\) replaces the itinerant pairing. Based on coherent interaction and action-counteraction principles, the strong local variational theory is constructed, with the energy extremum and gap equations forming self-consistent pairs, involving the local variational parameter \\(\\lambda\\), energy gap \\(\\Delta\\), and the energy cut-off \\(\\hbar \\omega_0\\). As \\(\\hbar \\omega_0(j)\\) approaches its cut-off, \\(\\lambda\\) and \\(\\Delta\\) converge to fixed values. The theory predicts that the coupling strength \\(Vg(0)\\) reduces to \\(\\tilde{V}g(0)=e^{-\\left(1-\\alpha_{1}\\right)^{2} k^{2} / 4 \\lambda^{2}} Vg(0)\\), and the Cooper pair reduces similarly. For weak coupling, \\(\\alpha_1=1\\), and when \\(Vg(0)=0.1\\), \\(\\Delta_{\\mathrm{A \\cdot C}}=108 \\Delta_{\\text{BCS}}\\), but \\(\\Delta_{\\mathrm{A \\cdot C}}\\) decreases to \\(28 \\Delta_{\\text{BCS}}\\) at \\(Vg(0)=0.2\\). For strong coupling, \\(\\alpha_1=0\\), if \\(Vg(0)=1.4\\), \\(\\tilde{V} g(0)\\) reduces to 0.2, and the smaller Cooper pair \\(\\widetilde{C_{k \\uparrow} C_{-k \\downarrow}}\\) reduces to \\(0.14 C_{k \\uparrow} C_{-k \\downarrow}\\). Additionally, \\(\\Delta_{\\mathrm{A \\cdot C}} = 0.5676~\\text{eV} \\gg \\hbar \\omega_{\\text{D}}\\), and the local stacking force is \\(\\widetilde{V}_{\\text{st}}=0.264 ~\\text{eV}\\). With \\(k^2/\\lambda^2 =\\) const, the local strength increases, causing the stacking force to grow significantly. Thus, \\(\\hbar \\omega_0\\) and \\(\\Delta\\) yield a unique solution.

Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force. Under such situation, Federated Learning (FL) appears to facilitate privacy-preserving joint modeling among multiple parties. Although many federated algorithms have been extensively studied, there is still a lack of secure and practical gradient tree boosting models (e.g., XGB) in literature. In this paper, we aim to build large-scale secure XGB under vertically federated learning setting. We guarantee data privacy from three aspects. Specifically, (i) we employ secure multi-party computation techniques to avoid leaking intermediate information during training, (ii) we store the output model in a distributed manner in order to minimize information release, and (iii) we provide a novel algorithm for secure XGB predict with the distributed model. Furthermore, by proposing secure permutation protocols, we can improve the training efficiency and make the framework scale to large dataset. We conduct extensive experiments on both public datasets and real-world datasets, and the results demonstrate that our proposed XGB models provide not only competitive accuracy but also practical performance.

The distributed (federated) LLM is an important method for co-training the domain-specific LLM using siloed data. However, maliciously stealing model parameters and data from the server or client side has become an urgent problem to be solved. In this paper, we propose a secure distributed LLM based on model slicing. In this case, we deploy the Trusted Execution Environment (TEE) on both the client and server side, and put the fine-tuned structure (LoRA or embedding of P-tuning v2) into the TEE. Then, secure communication is executed in the TEE and general environments through lightweight encryption. In order to further reduce the equipment cost as well as increase the model performance and accuracy, we propose a split fine-tuning scheme. In particular, we split the LLM by layers and place the latter layers in a server-side TEE (the client does not need a TEE). We then combine the proposed Sparsification Parameter Fine-tuning (SPF) with the LoRA part to improve the accuracy of the downstream task. Numerous experiments have shown that our method guarantees accuracy while maintaining security.

We present S3ML, a secure serving system for machine learning inference in this paper. S3ML runs machine learning models in Intel SGX enclaves to protect users' privacy. S3ML designs a secure key management service to construct flexible privacy-preserving server clusters and proposes novel SGX-aware load balancing and scaling methods to satisfy users' Service-Level Objectives. We have implemented S3ML based on Kubernetes as a low-overhead, high-available, and scalable system. We demonstrate the system performance and effectiveness of S3ML through extensive experiments on a series of widely-used models.

In recent years, gradient boosted decision tree learning has proven to be an effective method of training robust models. Moreover, collaborative learning among multiple parties has the potential to greatly benefit all parties involved, but organizations have also encountered obstacles in sharing sensitive data due to business, regulatory, and liability concerns. We propose Secure XGBoost, a privacy-preserving system that enables multiparty training and inference of XGBoost models. Secure XGBoost protects the privacy of each party's data as well as the integrity of the computation with the help of hardware enclaves. Crucially, Secure XGBoost augments the security of the enclaves using novel data-oblivious algorithms that prevent access side-channel attacks on enclaves induced via access pattern leakage.

The formation mechanism of aerosol sulfate during wintertime haze events in China is still largely unknown. As companions, SO 2 and transition metals are mainly emitted from coal combustion. Here, we argue that the transition metal-catalyzed oxidation of SO 2 on aerosol surfaces could be the dominant sulfate formation pathway and investigate this hypothesis by integrating chamber experiments, numerical simulations and in-field observations. Our analysis shows that the contribution of the manganese-catalyzed oxidation of SO 2 on aerosol surfaces is approximately one to two orders of magnitude larger than previously known routes, and contributes 69.2% ± 5.0% of the particulate sulfur production during haze events. This formation pathway could explain the missing source of sulfate and improve the understanding of atmospheric chemistry and climate change. Sulfate aerosols are an important component of wintertime haze events in China, but their production mechanisms are not well known. Here, the authors show that transition metal-catalyzed oxidation of SO 2 on aerosol surfaces could be the dominant sulfate formation pathway in Northern China.