Finite speed axially symmetric Navier-Stokes flows passing a cone

Let $D$ be the exterior of a cone inside a ball, with its altitude angle at most $\pi/6$ in $\mathbb{R}^3$, which touches the $x_3$ axis at the origin. For any initial value $v_0 = v_{0,r}e_{r} + v_{0,\theta} e_{\theta} + v_{0,3} e_{3}$ in a $C^2(\overline{D})$ class, which has the usual even-odd-odd symmetry in the $x_3$ variable and has the partial smallness only in the swirl direction: $| r v_{0, \theta} | \leq \frac{1}{100}$, the axially symmetric Navier-Stokes equations (ASNS) with Navier-Hodge-Lions slip boundary condition has a finite-energy solution that stays bounded for all time. In particular, no finite-time blowup of the fluid velocity occurs. Compared with standard smallness assumptions on the initial velocity, no size restriction is made on the components $v_{0,r}$ and $v_{0,3}$. In a broad sense, this result appears to solve $2/3$ of the regularity problem of ASNS in such domains in the class of solutions with the above symmetry. Equivalently, this result is connected to the general open question which asks that if an absolute smallness of one component of the initial velocity implies the global smoothness, see e.g. page 873 in \cite{CZZ17}. Our result seems to give a positive answer in a special setting. As a byproduct, we also construct an unbounded solution of the forced Navier Stokes equation in a special cusp domain that has finite energy. The forcing term, with the scaling factor of $-1$, is in the standard regularity class. This result confirms the intuition that if the channel of a fluid is very thin, arbitrarily high speed in the classical sense can be attained under a mildly singular force which is physically reasonable in view that Newtonian gravity and Coulomb force have scaling factor $-2$.Comment: 85 pages. A blow up solution in a special cusp domain, two references and a few sentences adde

An Improved Genetic Algorithms for Multi-objective Hybrid Flow-shop Scheduling Problem

To deal with the multi-objective hybrid flow Shop Scheduling Problem (HFSP), an improved genetic algorithms based on parallel sequential moving and variable mutation rate is proposed. Compared with the traditional GA, the algorithm proposed in this paper uses the two-point mutation rule based on VMR to find the global optimum which can make the algorithm jump out of the local optimum as far as possible, once it falls into the local optimum quickly. Decoding rules based on parallel sequential movement ensures that the artifact can start processing in time, so that the buffer between stages in the flow-shop is as little as possible, and the production cycle is shortened. Finally, a program was developed with the actual data of a workshop to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above

Increased recruitment of endogenous stem cells and chondrogenic differentiation by a composite scaffold containing bone marrow homing peptide for cartilage regeneration

Even small cartilage defects could finally degenerate to osteoarthritis if left untreated, owing to the poor self-healing ability of articular cartilage. Stem cell transplantation has been well implemented as a common approach in cartilage tissue engineering but has technical complexity and safety concerns. The stem cell homing-based technique emerged as an alternative promising therapy for cartilage repair to overcome traditional limitations. In this study, we constructed a composite hydrogel scaffold by combining an oriented acellular cartilage matrix (ACM) with a bone marrow homing peptide (BMHP)-functionalized self-assembling peptide (SAP). We hypothesized that increased recruitment of endogenous stem cells by the composite scaffold could enhance cartilage regeneration. Methods: To test our hypothesis, in vitro proliferation, attachment and chondrogenic differentiation of rabbit mesenchymal stem cells (MSCs) were tested to confirm the bioactivities of the functionalized peptide hydrogel. The composite scaffold was then implanted into full-thickness cartilage defects on rabbit knee joints for cartilage repair, in comparison with microfracture or other sample groups. Stem cell recruitment was monitored by dual labeling with CD29 and CD90 under confocal microcopy at 1 week after implantation, followed by chondrogenic differentiation examined by qRT-PCR. Repaired tissue of the cartilage defects was evaluated by histological and immunohistochemistry staining, microcomputed tomography (micro-CT) and magnetic resonance imaging (MRI) at 3 and 6 months post-surgery. Macroscopic and histological scoring was done to evaluate the optimal in vivo repair outcomes of this composite scaffold. Results: The functionalized SAP hydrogels could stimulate rabbit MSC proliferation, attachment and chondrogenic differentiation during in vitro culture. At 7 days after implantation, increased recruitment of MSCs based on CD29(+)/CD90(+) double-positive cells was found in vivo in the composite hydrogel scaffold, as well as upregulation of cartilage-associated genes (aggrecan, Sox9 and type II collagen). After 3 and 6 months post-surgery, the articular cartilage defect in the composite scaffold-treated group was fully covered with cartilage-like tissue with a smooth surface, which was similar to the surrounding native cartilage, according to the results of histological and immunohistochemistry staining, micro-CT and MRI analysis. Macroscopic and histological scoring confirmed that the quality of cartilage repair was significantly improved with implantation of the composite scaffold at each timepoint, in comparison with microfracture or other sample groups. Conclusion: Our findings demonstrated that the composite scaffold could enhance endogenous stem cell homing and chondrogenic differentiation and significantly improve the therapeutic outcome of chondral defects. The present study provides a promising approach for in vivo cartilage repair without cell transplantation. Optimization of this strategy may offer great potential and benefits for clinical application in the future

A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15-1.73 GHz

We conducted a search for narrowband radio signals over four observing sessions in 2020-2023 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,680 stars and planetary systems in the ~9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. In this work, we also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense RFI are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ~15 times smaller at ~6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake Figure of Merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is detectable in our search (EIRP > 1e13 W). For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5e16 W) is at most 3e-4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al. (2023).Comment: 22 pages, 9 figures, submitted to AJ, revise

Classification in the presence of heavy label noise: A Markov chain sampling framework

Heavy label noise is often present in many practical scenarios where observed labels of instances are corrupted. Classification with heavy label noise has great significance and attracts a lot of attention, since label noise may lead to many potential negative consequences. Many state-of-the-art approaches assume that label noise is class-dependent, and thus cannot be generalized to situations without this assumption. In this thesis, we propose a Markov chain sampling framework, MCS, to conquer the limitations of the existing methods in the binary classification problem. The main idea is to utilize the predictions of a sequence of classifiers in an ensemble way to detect mislabeled instances, the sequence of classifiers is trained on different subsets of the training data by sampling the states of a carefully designed Markov chain with random walk. Our proposed MCS framework is general and can entertain a wide spectrum of classification algorithms. We theoretically prove the correctness and effectiveness of the MCS framework. We further present experimental results showing the effectiveness and efficiency of the proposed framework and derivative algorithms

A Tabu-Genetic Hybrid Search Algorithm for Job-shop Scheduling Problem

To deal with the job-shop scheduling problem (JSP), a tabu-genetic hybrid search algorithm is proposed. The algorithm generates several initial solutions distributed in the whole solution space for tabu search by genetic algorithm, which avoids the over-dependence on the initial solution of tabu search algorithm. With the mechanism mentioned above, the algorithm proposed has both global search performance of genetic algorithm and local search performance of labu search algorithm. Finally, a program was developed with the achral data of FT (10x 10). to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above

An Improved Genetic Algorithms for Multi-objective Hybrid Flow-shop Scheduling Problem

To deal with the multi-objective hybrid flow Shop Scheduling Problem (HFSP), an improved genetic algorithms based on parallel sequential moving and variable mutation rate is proposed. Compared with the traditional GA, the algorithm proposed in this paper uses the two-point mutation rule based on VMR to find the global optimum which can make the algorithm jump out of the local optimum as far as possible, once it falls into the local optimum quickly. Decoding rules based on parallel sequential movement ensures that the artifact can start processing in time, so that the buffer between stages in the flow-shop is as little as possible, and the production cycle is shortened. Finally, a program was developed with the actual data of a workshop to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above

An integrated federated learning algorithm for short-term load forecasting

Accurate power load forecasting plays an integral role in power systems. To achieve high prediction accuracy, models need to extract effective features from raw data, and the training of models needs a large amount of data. However, data sharing will require the disclosure of the private data of the participants. To address this issue, we combined variational mode decomposition (VMD), the federated k-means clustering algorithm (FK), and SecureBoost into a single algorithm, called VMD-FK-SecureBoost. First, we used VMD to decompose the original data into several sub-sequences. This enabled us to extract the implied features to separately predict each sub-sequence to improve the prediction accuracy. Second, we use FK to recombine the sub-sequences into several clusters with common characteristics. Finally, with SecureBoost, we use clustering results to realize federated learning with privacy protection. We calculated the prediction values by accumulating the prediction results of the sub-sequences. The results for the examples in the US and Australia showed that the prediction performance of VMD-FK-SecureBoost was better than those of XGBoost and SecureBoost. Particularly, the MAPEs of one-step-ahead forecasting in the Texas and Newcastle CBD from our proposed method are 0.209% and 2.127% respectively, which are the lowest of all the algorithms

Predicting the Evolution Trend of Water and Land Resource Carrying Capacity Based on CA–Markov Model in an Arid Region of Northwest China

The evolution of water and land resource carrying capacity significantly impacts optimal water and land resource allocation and regional sustainable development in arid regions. This study proposes a model that combines cellular automaton (CA) and Markov; this model aids in predicting spatial changes in water and land resource availability. In this study, taking the Jingdian Irrigation District in China’s northwest arid region as an example, we used long-series monitoring data and a Landsat dataset to create a raster-weighted fusion of 18 indicators and quantitatively analyzed the carrying status of water and land resources from 1994 to 2018. The CA–Markov model was used to simulate the carrying status of water and land resources in 2018 and to perform accuracy correction. The validated CA–Markov model was used to predict water and land resource carrying status in 2026 and 2034. The results show (1) from 1994 to 2018, the area of “good carrying” zone increased by 10.42%, the area of “safe carrying” zone increased by 7%, and spatially rose in an arc from the town to the surrounding regions. The area of “critical carrying” zone remains almost unchanged. The area of “slight carrying” zone decreased by 5.18% and the area of “severe carrying” zone decreased by 11.99%. (2) Comparing the actual and predicted carrying state of water and land resources in 2018, it was found that the simulation accuracy of “good carrying”, “safe carrying”, “critical carrying”, “slight carrying”, and “severe carrying” reached 98.71%, 92.07%, 95.34%, 94.05%, and 93.73%, respectively. This indicates that the simulation results have high reliability and applicability. (3) The future medium and long-term carrying status of water and land resources are healthy, but this trend is gradually slowing. The “slight carrying” and “severe carrying” zones show the gradual spatial transition from land desertification to soil salinization

Chronic Corticosterone Exposure Suppresses Copper Transport through GR-Mediated Intestinal CTR1 Pathway in Mice

Numerous studies have discovered that chronic stress induces metabolic disorders by affecting iron and zinc metabolism, but the relationship between chronic stress and copper metabolism remains unclear. Here, we explore the influence of chronic corticosterone (CORT) exposure on copper metabolism and its regulatory mechanism in mice. Mice were treated with 100 μg/mL CORT in drinking water for a 4-week trial. We found that CORT treatment resulted in a significant decrease in plasma copper level, plasma ceruloplasmin activity, plasma and liver Cu/Zn-SOD activity, hepatic copper content, and liver metallothionein content in mice. CORT treatment led to the reduction in duodenal expression of copper transporter 1 (CTR1), duodenal cytochrome b (DCYTB), and ATPase copper-transporting alpha (ATP7A) at the mRNA and protein level in mice. CORT treatment activated nuclear glucocorticoid receptor (GR) and down-regulated CRT1 expression in Caco-2 cells, whereas these phenotypes were reversible by an antagonist of GR, RU486. Chromatin immunoprecipitation analysis revealed that GR bound to the Ctr1 promoter in Caco-2 cells. Transient transfection assays in Caco-2 cells demonstrated that the Ctr1 promoter was responsive to the CORT-activated glucocorticoid receptor, whereas mutation/deletion of the glucocorticoid receptor element (GRE) markedly impaired activation of the Ctr1 promoter. In addition, CORT-induced downregulation of Ctr1 promoter activity was markedly attenuated in Caco-2 cells when RU486 was added. These findings present a novel molecular target for CORT that down-regulates intestinal CTR1 expression via GR-mediated trans-repression in mice