Scheduling Performance Evaluation of Logistics Service Supply Chain Based on the Dynamic Index Weight

Scheduling is crucial to the operation of logistics service supply chain (LSSC), so scientific performance evaluation method is required to evaluate the scheduling performance. Different from general project performance evaluation, scheduling activities are usually continuous and multiperiod. Therefore, the weight of scheduling performance evaluation index is not unchanged, but dynamically varied. In this paper, the factors that influence the scheduling performance are analyzed in three levels which are strategic environment, operating process, and scheduling results. Based on these three levels, the scheduling performance evaluation index system of LSSC is established. In all, a new performance evaluation method proposed based on dynamic index weight will have three innovation points. Firstly, a multiphase dynamic interaction method is introduced to improve the quality of quantification. Secondly, due to the large quantity of second-level indexes and the requirements of dynamic weight adjustment, the maximum attribute deviation method is introduced to determine weight of second-level indexes, which can remove the uncertainty of subjective factors. Thirdly, an adjustment coefficient method based on set-valued statistics is introduced to determine the first-level indexes weight. In the end, an application example from a logistics company in China is given to illustrate the effectiveness of the proposed method

A Matlab Toolbox for Feature Importance Ranking

More attention is being paid for feature importance ranking (FIR), in particular when thousands of features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR approaches have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a total of 30 algorithms are collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound images. To each breast mass lesion, 15 features are extracted. To figure out the optimal subset of features for classification, all combinations of features are tested and linear support vector machine is used for the malignancy prediction of lesions annotated in ultrasound images. At last, the effectiveness of FIR is analyzed according to performance comparison. The toolbox is online (https://github.com/NicoYuCN/matFIR). In our future work, more FIR methods, feature selection methods and machine learning classifiers will be integrated

Emerging investigator series:Onsite recycling of saline-alkaline soil washing water by forward osmosis: Techno-economic evaluation and implication

This study investigated the techno-economic feasibility of forward osmosis (FO) for onsite recycling of saline-alkaline soil washing water with an all-purpose liquid fertiliser as a draw solution. Commercially available polyamide thin-film composite and aquaporin FO membranes (denoted HTI and AQP membranes, respectively) were compared under different operating conditions. Results showed that the incorporation of aquaporin vesicles offered the AQP membrane better transport properties (i.e. higher water permeability and lower salt permeability) than the HTI membrane. Thus, the AQP membrane exhibited a much higher water flux and lower reverse solute flux than the HTI membrane in response to either an increase in operating temperature or draw solution concentration. In particular, the water flux of the AQP membrane enhanced from 20.2 to 42.4 L m-2 h-1 with a temperature increase from 25 to 40 °C. Although over 85% water recovery with effective retention of dissolved inorganic salts could be achieved by both FO membranes in concentration of saline-alkaline soil washing water, the AQP membrane was more techno-economically feasible in practice, mainly due to its higher water flux and lower capital and operational expenses. Nevertheless, the economic favourability of the AQP membrane (i.e. the total water cost) over the HTI membrane was largely determined by its membrane element cost. </p

Preventing intimal thickening of vein grafts in vein artery bypass using STAT-3 siRNA

<p>Abstract</p> <p>Background</p> <p>Proliferation and migration of vascular smooth muscle cells (VSMCs) play a key role in neointimal formation which leads to restenosis of vein graft in venous bypass. STAT-3 is a transcription factor associated with cell proliferation. We hypothesized that silencing of STAT-3 by siRNA will inhibit proliferation of VSMCs and attenuate intimal thickening.</p> <p>Methods</p> <p>Rat VSMCs were isolated and cultured in vitro by applying tissue piece inoculation methods. VSMCs were transfected with STAT 3 siRNA using lipofectamine 2000. In vitro proliferation of VSMC was quantified by the MTT assay, while in vivo assessment was performed in a venous transplantation model. In vivo delivery of STAT-3 siRNA plasmid or scramble plasmid was performed by admixing with liposomes 2000 and transfected into the vein graft by bioprotein gel applied onto the adventitia. Rat jugular vein-carotid artery bypass was performed. On day 3 and7 after grafting, the vein grafts were extracted, and analyzed morphologically by haematoxylin eosin (H&E), and assessed by immunohistochemistry for expression of Ki-67 and proliferating cell nuclear antigen (PCNA). Western-blot and reverse transcriptase polymerase chain reaction (RT-PCR) were used to detect the protein and mRNA expression in vivo and in vitro. Cell apoptosis in vein grafts was detected by TUNEL assay.</p> <p>Results</p> <p>MTT assay shows that the proliferation of VSMCs in the STAT-3 siRNA treated group was inhibited. On day 7 after operation, a reduced number of Ki-67 and PCNA positive cells were observed in the neointima of the vein graft in the STAT-3 siRNA treated group as compared to the scramble control. The PCNA index in the control group (31.3 ± 4.7) was higher than that in the STAT-3 siRNA treated group (23.3 ± 2.8) (P < 0.05) on 7d. The neointima in the experimental group(0.45 ± 0.04 μm) was thinner than that in the control group(0.86 ± 0.05 μm) (P < 0.05).Compared with the control group, the protein and mRNA levels in the experimental group in vivo and in vitro decreased significantly. Down regulation of STAT-3 with siRNA resulted in a reduced expression of Bcl-2 and cyclin D1. However, apoptotic cells were not obviously found in all grafts on day 3 and 7 post surgery.</p> <p>Conclusions</p> <p>The STAT-3 siRNA can inhibit the proliferation of VSMCs in vivo and in vitro and attenuate neointimal formation.</p

The Role of Circulating Tight Junction Proteins in Evaluating Blood Brain Barrier Disruption following Intracranial Hemorrhage

Brain injury after intracranial hemorrhage (ICH) results in significant morbidity and mortality. Blood brain barrier (BBB) disruption is a hallmark of ICH-induced brain injury; however, data mirroring BBB disruption in human ICH are scarce. The aim of this study was to assess the significance of circulating biomarkers in evaluating BBB disruption after ICH. Twenty-two patients with ICH were recruited in this study. Concentrations of the tight junction proteins (TJs) Claudin-5 (CLDN5), Occludin (OCLN), and zonula occludens 1 (ZO-1) and vascular endothelial growth factor (VEGF) and matrix metalloproteinase-9 (MMP-9) were measured by using enzyme-linked immunosorbent assay in serum and cerebrospinal fluid (CSF) samples obtained from patients with ICH. The white blood cell (WBC) count in blood and CSF, albumin (ALB) levels in the CSF (ALB CSF ), and the BBB ratio were significantly higher in the ICH than in controls ( &lt; 0.05). Significantly higher levels of CLDN5, OCLN, ZO-1, MMP-9, and VEGF in CSF were observed in the ICH group; these biomarkers were also positively associated with BBB ratio ( &lt; 0.05). Our data revealed that circulating TJs could be considered the potential biomarkers reflecting the integrity of the BBB in ICH

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.Comment: 13 pages, 6 figures, Accepted at Medical Image Analysis Journal (MedIA