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The linearly constrained matrix rank minimization problem is widely applicable in many fields such as control, signal processing and system identification. The tightest convex relaxation of this problem is the linearly constrained nuclear norm minimization. Although the latter can be cast as a semidefinite programming problem, such an approach is computationally expensive to solve when the matrices are large. In this paper, we propose fixed point and Bregman iterative algorithms for solving the nuclear norm minimization problem and prove convergence of the first of these algorithms. By using a homotopy approach together with an approximate singular value decomposition procedure, we get a very fast, robust and powerful algorithm, which we call FPCA (Fixed Point Continuation with Approximate SVD), that can solve very large matrix rank minimization problems (the code can be downloaded from http://www.columbia.edu/~sm2756/FPCA.htm for non-commercial use). Our numerical results on randomly generated and real matrix completion problems demonstrate that this algorithm is much faster and provides much better recoverability than semidefinite programming solvers such as SDPT3. For example, our algorithm can recover 1000 × 1000 matrices of rank 50 with a relative error of 10 −5 in about 3 min by sampling only 20% of the elements. We know of no other method that achieves as good recoverability. Numerical experiments on online recommendation, DNA microarray data set and image inpainting problems demonstrate the effectiveness of our algorithms.

The coherent transmission technology using digital signal processing and advanced modulation formats, is bringing networks closer to the theoretical capacity limit of optical fibres, the Shannon limit. The in-phase/quadrature electro-optic modulator that encodes information on both the amplitude and the phase of light, is one of the underpinning devices for the coherent transmission technology. Ideally, such modulator should feature a low loss, low drive voltage, large bandwidth, low chirp and compact footprint. However, these requirements have been only met on separate occasions. Here, we demonstrate integrated thin-film lithium niobate in-phase/quadrature modulators that fulfil these requirements simultaneously. The presented devices exhibit greatly improved overall performance (half-wave voltage, bandwidth and optical loss) over traditional lithium niobate counterparts, and support modulation data rate up to 320 Gbit s −1 . Our devices pave new routes for future high-speed, energy-efficient, and cost-effective communication networks. In-phase/quadrature (IQ) electro-optic modulators are underpinning devices for coherent transmission technology. Here the authors present IQ modulators in the lithium-niobate-on-insulator platform, which provide improved overall performance and advanced modulation formats for future coherent transmission systems.

In order to solve the problems of inefficient allocation of teaching resources and inaccurate recommendation of learning paths in higher education, this paper proposes a smart education optimization model (SEOM) by combining the improved random forest algorithm (RFA) based on adaptive enhancement mechanism and the Graph Neural Network (GNN) algorithm. The public data and information such as the national higher education intelligent education platform are collected, and SEOM is trained and verified. The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching. The Root Mean Square Error (RMSE) value in cross-validation is between 0.2 and 0.5, and the Mean Absolute Error (MAE) value is between 0.1 and 0.5. SEOM shows strong stability when dealing with multidimensional educational resources and complex teaching modes. The accuracy rate remains at 85-97%, indicating its reliability in personalized learning path recommendation. Further analysis shows that the chi-square freedom ratio is between 1.0 and 2.5, the fitting index and the adjusted fitting index are both above 0.85, and the comparative fitting index is close to 0.95, which shows that SEOM has high accuracy and rationality in capturing the dependence of knowledge points in different teaching modes. The Root Mean Square Residual (RMR) and Root Mean Square Error of Approximation (RMSEA) are both below 0.05, which indicates that SEOM has small residual and strong scene adaptability. In addition, in the abnormal network environment, the resource allocation efficiency of SEOM is above 60%, and the Shapley value is between 0.1 and 0.4, which shows that SEOM can adapt to the change of network environment and the resource allocation effect is still obvious. Generally speaking, SEOM can optimize the allocation of educational resources and recommend learning paths in a complex environment, and effectively improve the intelligence and efficiency of teaching decision-making, especially for university administrators and educational technology developers.

With the rapid development of the Internet of Things (IoT), the scope of personal data sharing has significantly increased, enhancing convenience in daily life and optimizing resource management. However, this also poses challenges related to data privacy breaches and holdership threats. Typically, blockchain technology and cloud storage provide effective solutions. Nevertheless, the centralized storage architecture of traditional cloud servers is susceptible to single points of failure, potentially leading to system outages. To achieve secure data sharing, access control, and verification auditing, we propose a data security sharing scheme based on blockchain technology and attribute-based encryption, applied within the InterPlanetary File System (IPFS). This scheme employs multi-agent systems and attribute-based signcryption algorithms to process data, thereby enhancing privacy protection and verifying data holdership. The encrypted data are then stored in the distributed IPFS, with the returned hash values and access control policies uploaded to smart contracts, facilitating automated fine-grained access control services. Finally, blockchain data auditing is performed to ensure data integrity and accuracy. The results indicate that this scheme is practical and effective compared to existing solutions.

Chromium occurs mostly in tri- and hexavalent states in the environment. Hexavalent chromium [Cr(VI)] compounds are extensively used in diverse industries, and trivalent chromium [Cr(III)] salts are used as micronutrients and dietary supplements. In the present work, we report that they both induce genetic mutations in yeast cells. They both also cause DNA damage in both yeast and Jurkat cells and the effect of Cr(III) is greater than that of Cr(VI). We further show that Cr(III) and Cr(VI) cause DNA damage through different mechanisms. Cr(VI) intercalates DNA and Cr(III) interferes base pair stacking. Based on our results, we conclude that Cr(III) can directly cause genotoxicity in vivo.

Investigating the macro and micro structural damage characteristics of red-bed sandstone under freeze–thaw cycles is crucial for guiding construction in seasonal frozen soil regions. This paper analyzes the macro and micro damage characteristics of red bed sandstone in Urumqi area under the action of freeze–thaw cycle through micro-test and uniaxial compression test.The results show that with an increase in freeze–thaw cycles, the porosity and permeability of red-bed sandstone (RBS) samples gradually increase and eventually stabilize.The phase composition of red-bed sandstone samples can be divided into two categories: rock skeleton and cement. When the number of freeze–thaw cycles is 3 ~ 5 times and 20 ~ 25 times, the rock skeleton material is the main loss material. At this stage, due to the large damage of the rock skeleton, the deterioration of mechanical properties is obvious.According to the downward trend, it can be divided into three stages. When the number of freeze–thaw cycles is 0 ~ 5 times and 20 ~ 25 times, it is the severe failure stage. When the number of freeze–thaw cycles is 5 ~ 20 times, it is the slow failure stage.As the number of freeze–thaw cycles increases, the maximum value of the fractal dimension of the middle section of the sample gradually increases, and as the number of freeze–thaw cycles increases, the fractal dimension of the middle section of the sample changes from uneven to uniform.The research results are of great significance to guide the construction of seasonal frozen soil area.

Respiratory syncytial virus (RSV) hijacks cholesterol or autophagy pathways to facilitate optimal replication. However, our understanding of the associated molecular mechanisms remains limited. Here, we show that RSV infection blocks cholesterol transport from lysosomes to the endoplasmic reticulum by downregulating the activity of lysosomal acid lipase, activates the SREBP2–LDLR axis, and promotes uptake and accumulation of exogenous cholesterol in lysosomes. High cholesterol levels impair the VAP-A-binding activity of ORP1L and promote the recruitment of dynein–dynactin, PLEKHM1, or HOPS VPS39 to Rab7–RILP, thereby facilitating minus-end transport of autophagosomes and autolysosome formation. Acidification inhibition and dysfunction of cholesterol-rich lysosomes impair autophagy flux by inhibiting autolysosome degradation, which promotes the accumulation of RSV fusion protein. RSV-F storage is nearly abolished after cholesterol depletion or knockdown of LDLR. Most importantly, the knockout of LDLR effectively inhibits RSV infection in vivo. These findings elucidate the molecular mechanism of how RSV co-regulates lysosomal cholesterol reprogramming and autophagy and reveal LDLR as a novel target for anti-RSV drug development. RSV hijacks cholesterol or autophagy pathways to facilitate optimal replication. Here, the authors show that RSV-mediated changes in lysosomal cholesterol metabolism promote RSV-F accumulation in lysosomes by blocking autophagy flux, thereby facilitating viral replication.

Modifying the loess foundation effectively solved the deformation and settlement of the building foundation and improved its stability. However, burnt rock-solid waste was often used as filling material and light aggregate, while there were few studies on the engineering mechanical properties of modified soil. This paper proposed a method of burnt rock solid waste-modified loess. Therefore, we conducted compression-consolidation and direct shear tests on burnt rock solid waste-modified loess under different burnt rock contents to explore its improved loess’s deformation and strength characteristics. Then, we used an SEM to investigate the modified loess’s micro-structures under different burnt rock contents. The results showed that as the burnt rock-solid waste particle content continued to increase, the void ratio and compressibility coefficient of the samples with different ranges of burnt rock-solid waste particles gradually decreased with rising vertical pressure, while the compressive modulus increased first, then reduced and then increased with the increase of vertical pressure; the shear strength indexes all showed an increasing trend with the increased content of burnt rock-solid waste particles; when the content of burnt rock-solid waste particles was 50%, the compressibility of mixed soil was the lowest, the shear strength was the largest, and the compaction effect and shear resistance were the best. However, when the content of burnt rock particles was 10–20%, the shear strength of the soil improved significantly within the content range. The mechanism of burnt rock-solid waste to enhance the strength of the loess structure was mainly to reduce the porosity and average area of soil, significantly improve the strength and stability of mixed soil particles, and thus significantly improve the mechanical properties of soil. The results of this research will provide technical support for safe engineering construction and geological disaster prevention and control in loess areas.

Adverse pregnancy outcomes, including gestational diabetes mellitus (GDM), gestational hypertension (GHp), macrosomia, preterm birth, and low birth weight, pose significant risks to maternal and neonatal health. Pre-pregnancy overweight is a modifiable risk factor for these outcomes. However, comprehensive analyses of multiple adverse outcomes and their dose-response relationships with pre-pregnancy body mass index (BMI) remain limited. This retrospective cohort study included 748 women with singleton pregnancies who delivered at Yuyao Maternal and Child Health Hospital from January 1, 2022, to December 31, 2022. Participants were categorized into normal-weight and overweight groups based on pre-pregnancy BMI. Logistic regression models were used to evaluate associations between overweight and adverse pregnancy outcomes, adjusting for confounding variables. Restricted cubic spline (RCS) regression was employed to investigate dose-response relationships between BMI and pregnancy outcomes. Pre-pregnancy overweight was significantly associated with higher risks of GDM (adjusted OR = 3.122, 95% CI [1.754-5.557], < 0.001), GHp (adjusted OR = 2.864, 95% CI [1.566-5.239], = 0.001), and macrosomia (adjusted OR = 2.119, 95% CI [1.076-4.173], = 0.030). No significant associations were observed with preterm birth or low birth weight. RCS analysis showed no evidence of nonlinear relationships, indicating that the risk of adverse outcomes increased linearly with BMI. Pre-pregnancy overweight is a significant modifiable risk factor for adverse maternal and neonatal outcomes, particularly GDM, GHp, and macrosomia. These findings underscore the importance of integrating BMI monitoring and personalized weight management strategies into pre-pregnancy care programs to mitigate risks and improve maternal and neonatal health outcomes.

The study of geothermal genesis mechanism is the basis for the evaluation and effective development of geothermal field resources. On the basis of previous research results, the formation mechanism of geothermal field in Qikexing Township was analysed and the geothermal resources were evaluated by identifying geothermal anomalies in the area. In this paper, two geothermal anomalies were identified based on remote sensing inversion, soil chemical exploration, shallow temperature measurement and the main heat-control fractures, and the distribution characteristics and causes of hot springs were comprehensively studied by means of hydrogeochemistry and isotope analysis. The results show that the sources of geothermal water recharge are atmospheric precipitation and ice melt water in the vicinity. The cations of the geothermal water are mainly Na + , with an anionic ratio of 68.75%, followed by Ca 2+ and K + ; the anions are mainly SO 4 2− , with an anionic ratio of 47.25%, followed by Cl − and HCO 3 − ; and the geothermal water is of the Cl·SO 4 -Na(Ca) type. The geothermal wells are mainly distributed along the active fracture spreading direction in the form of beads, and the maximum depth of thermal storage cycle is 2393 m. The temperature of thermal storage ranges from 49.4 to 93.7 ℃, and the heat source mainly comes from the heat transfer of mantle channel flow, the heat generated by the decay of radioactive elements, the heat generated by magmatic activities and so on. The results of the evaluation of geothermal resources in the study area show that the total geothermal resources are 1.38 × 10 19 J/a, equivalent to 4.71 × 10 8 t/a of standard coal. This study mainly focuses on the distribution law of hydrothermal geothermal resources in Yanqi Qikexing town and the geothermal genesis, which can provide a reference basis for the development and utilisation of geothermal resources in Yanqi County in the later stage.