Integrated Production and Maintenance Planning for Flow Line Systems

This study extends the investigation of the capacitated lot-sizing problem to the production and maintenance planning in unreliable flow line systems. An integrated modelling framework is proposed with the aim of seeking a cost-optimal plan for both production and maintenance. In the model, preventive maintenance is scheduled to avoid unplanned failures, and corrective maintenance is carried out in any machine in which an unplanned failure occurs. A regression-based approximation approach was introduced to calculate the production time under random failures. Then, the integrated planning model can be solved by any commercial optimization software. The numerical example demonstrates that the integrated model guarantees the effectiveness of the production and maintenance plan. It also showed that the buffer capacity has significant effects on the capacity utilization

Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization

Zero-shot skeleton-based action recognition aims to recognize actions of unseen categories after training on data of seen categories. The key is to build the connection between visual and semantic space from seen to unseen classes. Previous studies have primarily focused on encoding sequences into a singular feature vector, with subsequent mapping the features to an identical anchor point within the embedded space. Their performance is hindered by 1) the ignorance of the global visual/semantic distribution alignment, which results in a limitation to capture the true interdependence between the two spaces. 2) the negligence of temporal information since the frame-wise features with rich action clues are directly pooled into a single feature vector. We propose a new zero-shot skeleton-based action recognition method via mutual information (MI) estimation and maximization. Specifically, 1) we maximize the MI between visual and semantic space for distribution alignment; 2) we leverage the temporal information for estimating the MI by encouraging MI to increase as more frames are observed. Extensive experiments on three large-scale skeleton action datasets confirm the effectiveness of our method. Code: https://github.com/YujieOuO/SMIE.Comment: Accepted by ACM MM 202

Peg Precipitation Coupled with Chromatography is a New and Sufficient Method for the Purification of Botulinum Neurotoxin Type B

Clostridium botulinum neurotoxins are used to treat a variety of neuro-muscular disorders, as well as in cosmetology. The increased demand requires efficient methods for the production and purification of these toxins. In this study, a new purification process was developed for purifying type B neurotoxin. The kinetics of C.botulinum strain growth and neurotoxin production were determined for maximum yield of toxin. The neurotoxin was purified by polyethylene glycol (PEG) precipitation and chromatography. Based on design of full factorial experiment, 20% (w/v) PEG-6000, 4°C, pH 5.0 and 0.3 M NaCl were optimal conditions to obtain a high recovery rate of 87% for the type B neurotoxin complex, as indicated by a purification factor of 61.5 fold. Furthermore, residual bacterial cells, impurity proteins and some nucleic acids were removed by PEG precipitation. The following purification of neurotoxin was accomplished by two chromatography techniques using Sephacryl™ S-100 and phenyl HP columns. The neurotoxin was recovered with an overall yield of 21.5% and the purification factor increased to 216.7 fold. In addition, a mouse bioassay determined the purified neurotoxin complex possessed a specific toxicity (LD50) of 4.095 ng/kg

Status and trend of development of the higher foreign language education research—based on the analysis of citespace visualization

This paper presents a scient metric analysis of status and trend of development of the higher foreign language education using Citespace based on the 2313 research articles from the Web of Science core collection between 2000 and 2023. Based on previous studies, this paper proposes assumptions and data verification. According to the keywords, citations and relevant information in the selected literatures, the research carried out clustering analysis, timeline view and citation bursts analysis. It is found that there is a phased focus in the study of higher foreign language education and no obvious evolutionary trend was found in the data results for the new picture of interdisciplinary has not yet been formed. Besides, the impact of innovative technologies will have more and more influence on higher foreign language education

The efficacy and safety of tyrosine kinase 2 inhibitor deucravacitinib in the treatment of plaque psoriasis: a systematic review and meta-analysis of randomized controlled trials

BackgroundOrally effective therapeutics for plaque psoriasis with improved response rates, lower toxicity and costs are needed in clinical practices. This study aims to assess the efficacy and safety of the recently approved TYK2 inhibitor deucravacitinib in adults with moderate to severe plaque psoriasis through meta-analysis.MethodsA systematic search was performed for eligible studies using electronic databases, including PubMed, Embase, Cochrane Library, Clinical Trials, the EU Clinical Trials Register, and the International Clinical Trials Registry Platform (ICTRP). Randomized controlled trials (RCTs) comparing the efficacy and safety of deucravacitinib vs. placebo or active comparators in adult patients with plaque psoriasis were included. The effectiveness of deucravacitinib was evaluated using a 75% improvement in Psoriasis Area and Severity Index (PASI 75) from baseline and the proportion of patients achieving the static Physician’s Global Assessment (sPGA) response. The secondary endpoint was the proportion of patients achieving PASI 90, PASI 100, ssPGA 0/1, and Dermatology Life Quality Index 0/1 (DLQI). The incidence of adverse events (AEs), serious AEs (SAEs), and AE-related treatment discontinuation were statistically analyzed to determine the safety of deucravacitinib.ResultsThe systematic review and meta-analysis included five RCTs involving 2,198 patients with moderate to severe plaque psoriasis. Results showed that deucravacitinib was superior to placebo as well as active comparator apremilast in multiple key endpoints, including PASI 75, sPGA 0/1, PASI 90, PASI 100, DLQI 0/1 at week 16. Moreover, a durable response was seen in the two 52-week studies. Safety assessment showed that deucravacitinib was generally well tolerated, and the incidence of AEs, SAEs, and AE-related treatment discontinuation was low and balanced across groups.ConclusionDeucravacitinib demonstrated superior efficacy to apremilast in adult patients with moderate to severe plaque psoriasis with an acceptable safety profile and has the potential to be used as the first-line oral therapy for plaque psoriasis

A PLCβ/PI3Kγ-GSK3 Signaling Pathway Regulates Cofilin Phosphatase Slingshot2 and Neutrophil Polarization and Chemotaxis

Neutrophils, in response to a chemoattractant gradient, undergo dynamic F actin remodeling, a process important for their directional migration or chemotaxis. However, signaling mechanisms for chemoattractants to regulate the process are incompletely understood. Here, we characterized chemoattractant-activated signaling mechanisms that regulate cofilin dephosphorylation and actin cytoskeleton reorganization and are critical for neutrophil polarization and chemotaxis. In neutrophils, chemoattractants induced phosphorylation and inhibition of GSK3 via both PLCβ-PKC and PI3Kγ-AKT pathways, leading to the attenuation of GSK3-mediated phosphorylation and inhibition of the cofilin phosphatase slingshot2 and an increase in dephosphorylated, active cofilin. The relative contribution of this GSK3-mediated pathway to neutrophil chemotaxis regulation depended on neutrophil polarity preset by integrin-induced polarization of PIP5K1C. Therefore, our study characterizes a signaling mechanism for chemoattractant-induced actin cytoskeleton remodeling and elucidates its context-dependent role in regulating neutrophil polarization and chemotaxis

Extensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression

In recent decades, the collection of visual art paintings is large, digitized, and available for public uses that are rapidly growing. The development of multi-media systems is needed due to the huge amount of digitized artwork collections for retrieving and archiving this large-scale data. This multimedia system benefits from high-level tasks and has an essential step for measuring the similarity of visual between the artistic items. For modeling the similarities between the artworks or paintings, it is essential to extract useful features of visual paintings and propose the best approach for learning these similarity metrics. The infield of visual arts education, knowing the similarities and features, makes education more attractive by enhancing cognitive development in students. In this paper, the detailed visual features are listed, and the similarity measurement between the paintings is optimized by the Sparse Metric Learning-based Kernel Regression (KR-SML). A classification model is developed using hybrid SVM-ANN for semantic-level understanding to predict painting’s genre, artist, and style. Furthermore, the Human-Computer Interaction (HCI) based formulation model is built to analyze the proposed technique. The simulation results show that the proposed model is better in terms of performance than other existing techniques

How to coadd images: II. Anti-aliasing and PSF deconvolution

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables PSF deconvolution, resulting in enhanced restoration of extended sources, the highest PSNR, and reduced ringing artefacts. To test our method, we conducted numerical simulations that replicated observation runs of the CSST/VST telescope and compared our results to those obtained using previous algorithms. The simulation showed that our method outperforms previous approaches in several ways, such as restoring the profile of extended sources and minimizing ringing artefacts. Additionally, because our method relies on the inherent advantages of least squares fitting, it is more versatile and does not depend on the local uniformity hypothesis for the PSF. However, the new method consumes much more computation than the other approaches.Comment: 16 pages, 5 figures, 2 tables, accepted for publishing on RA