Supply Chain Finance and Financing Constraints on SMEs—An Empirical Analysis of Software Company

With the perspective of small and medium-sized enterprises in China, it has trouble getting financing. Based on cash - cash flow sensitivity model, this paper figures out the existence of financial constraints on SMEs in software industry and supply chain finance’s effect on it. The cash flow sensitivity of cash and supply chain finance’s effectiveness are evaluated using a large sample of listed companies on the SME boardfrom2008 to 2018. Through empirical analysis and robustness checks, it is concluded that SMEs in software industry have financing difficulties and supply chain finance can alleviate this financial dilemma to some extent. Furthermore, the essay analyzes risk points of three different forms of supply chain finance and puts forward some suggestions about risk management for small and medium-sized enterprises, bank and third-party logistics

In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing

Liquid Metal Jet Printing (LMJP) is a revolutionary three-dimensional (3D) printing technique in fast but low-cost additive manufacturing. The driving force is produced by magneto-hydrodynamic property of liquid metal in an alternating magnetic field. Due to its integrated melting and ink-jetting process, it can achieve 10x faster speed at 1/10th of the cost as compared to current metal 3D printing techniques. However, the jetting process is influenced by many uncertain factors, which impose a significant challenge to its process stability and product quality. To address this challenge, we present a closed-loop control framework by seamlessly integrating vision-based technique and neural network tool to inspect droplet behaviours and accordingly stabilize the printing process. This system automatically tunes the drive voltage applied to compensate the uncertain influence based on vision inspection result. To realize this, we first extract multiple features and properties from images to capture the droplet behaviour. Second, we use a neural network together with PID control process to determine how the drive voltage should be adjusted. We test this system on a piezoelectric-based ink-jetting emulator, which has a very similar jetting mechanism to the LMJP. Results show that significantly more stable jetting behavior can be obtained in real-time. This system can also be applied to other droplet related applications owing to its universally applicable characteristics

Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation

This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs. Existing works turn to enhance the vanilla vision transformer by introducing explicit grouping recognition, i.e., employing several group tokens/centroids to cluster the image tokens and perform the group-text alignment. Nevertheless, these methods suffer from a granularity inconsistency regarding the usage of group tokens, which are aligned in the all-to-one v.s. one-to-one manners during the training and inference phases, respectively. We argue that this discrepancy arises from the lack of elaborate supervision for each group token. To bridge this granularity gap, this paper explores explicit supervision for the group tokens from the prototypical knowledge. To this end, this paper proposes the non-learnable prototypical regularization (NPR) where non-learnable prototypes are estimated from source features to serve as supervision and enable contrastive matching of the group tokens. This regularization encourages the group tokens to segment objects with less redundancy and capture more comprehensive semantic regions, leading to increased compactness and richness. Based on NPR, we propose the prototypical guidance segmentation network (PGSeg) that incorporates multi-modal regularization by leveraging prototypical sources from both images and texts at different levels, progressively enhancing the segmentation capability with diverse prototypical patterns. Experimental results show that our proposed method achieves state-of-the-art performance on several benchmark datasets. The source code is available at https://github.com/Ferenas/PGSeg.Comment: 14 pages, Accept in NeurIPS 202

Association between TSH suppression therapy and type 2 deiodinase gene polymorphism in differentiated thyroid carcinoma

Introduction: Oral levothyroxine (L-T4) suppression of thyroid-stimulating hormone (TSH) levels is the most commonly used clinical approach to manage and treat patients after thyroid cancer surgery. This study aimed to investigate the association between TSH suppression therapy and type 2 deiodinase gene (DIO2) polymorphism in differentiated thyroid carcinoma (DTC). Material and methods: A total of 240 patients with DTC who received total thyroidectomy (TT; 120) and hemithyroidectomy (HT; 120) were enrolled in this study. The serum TSH, free triiodothyronine (FT3), and free thyroxine (FT4) levels were detected using an automatic serum immune analyser and electrochemiluminescence immunoassay. Based on the results of DIO2 gene detection, 3 genotypes of Thr92Ala were detected. Results: The serum TSH levels were inhibited after oral L-T4 treatment, but the proportion of patients who reached the TSH suppression standard in the hemithyroidectomy group was higher than in the total thyroidectomy group. After TSH suppression treatment, serum FT4 levels were increased in both total thyroidectomy and hemithyroidectomy. The difference in serum TSH, FT3, and FT4 levels was associated with different genotypes, and patients with high cytosine cytosine (CC) genotypes may have difficulty meeting the TSH suppression criteria. Conclusions: Patients who underwent total thyroidectomy exhibited higher postoperative serum FT4 levels than patients in the hemithyroidectomy group after TSH suppression therapy. The Thr92Ala polymorphism of type 2 deiodinase (D2) was associated with TSH suppression therapy

E2F1 Suppresses Oxidative Metabolism and Endothelial Differentiation of Bone Marrow Progenitor Cells

RATIONALE: The majority of current cardiovascular cell therapy trials use bone marrow progenitor cells (BM PCs) and achieve only modest efficacy; the limited potential of these cells to differentiate into endothelial-lineage cells is one of the major barriers to the success of this promising therapy. We have previously reported that the E2F transcription factor 1 (E2F1) is a repressor of revascularization after ischemic injury. OBJECTIVE: We sought to define the role of E2F1 in the regulation of BM PC function. METHODS AND RESULTS: Ablation of E2F1 (E2F1 deficient) in mouse BM PCs increases oxidative metabolism and reduces lactate production, resulting in enhanced endothelial differentiation. The metabolic switch in E2F1-deficient BM PCs is mediated by a reduction in the expression of pyruvate dehydrogenase kinase 4 and pyruvate dehydrogenase kinase 2; overexpression of pyruvate dehydrogenase kinase 4 reverses the enhancement of oxidative metabolism and endothelial differentiation. Deletion of E2F1 in the BM increases the amount of PC-derived endothelial cells in the ischemic myocardium, enhances vascular growth, reduces infarct size, and improves cardiac function after myocardial infarction. CONCLUSION: Our results suggest a novel mechanism by which E2F1 mediates the metabolic control of BM PC differentiation, and strategies that inhibit E2F1 or enhance oxidative metabolism in BM PCs may improve the effectiveness of cell therapy

Sharing Economy in Local Energy Markets

With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of 'access over ownership', the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.</p

Monitoring of the central blood pressure waveform via a conformal ultrasonic device.

Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 μm) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments