Modelling and Optimizing an Open-Pit Truck Scheduling Problem

This paper addresses a special truck scheduling problem in the open-pit mine with different transport revenue consideration. A mixed integer programming model is formulated to define the problem clearly and a few valid inequalities are deduced to strengthen the model. Some properties and two upper bounds of the problem are proposed. Based on these inequalities, properties, and upper bounds, a heuristic solution approach with two improvement strategies is proposed to resolve the problem and the numerical experiment demonstrates that the proposed solution approach is effective and efficient

Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by both simulations with real datasets and system implementation in a real testbed. Evaluation results show that the performance of the proposed algorithm with distributed inference framework is promising, by reaching the accuracies of attribute recognition and person identification up to 92.9% and 96.6% respectively, and significantly reducing the inference delay by at least 40.6% compared with existing methods

Global, regional, and national burden of chronic kidney disease attributable to high fasting plasma glucose from 1990 to 2019: a systematic analysis from the global burden of disease study 2019

PurposeGiven the rising prevalence of high fasting plasma glucose (HFPG) over the past three decades, it is crucial to assess its global, national, and regional impact on chronic kidney disease (CKD). This study aims to investigate the burden of CKD attributed to HFPG and its distribution across various levels.Methods and materialsThe data for this research was sourced from the Global Burden of Diseases Study 2019. To estimate the burden of CKD attributed to HFPG, we utilized DisMod-MR 2.1, a Bayesian meta-regression tool. The burden was measured using age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALYs) rate. Correlation analysis was performed using the Spearman rank order correlation method. Temporal trends were analyzed by estimating the estimated annual percentage change (EAPC).ResultsGlobally in 2019, there were a total of 487.97 thousand deaths and 13,093.42 thousand DALYs attributed to CKD attributed to HFPG, which represent a substantial increase of 153.8% and 120%, respectively, compared to 1990. Over the period from 1990 to 2019, the burden of CKD attributable to HFPG increased across all regions, with the highest increases observed in regions with high socio-demographic index (SDI) and middle SDI. Regions with lower SDI exhibited higher ASMR and age-standardized DALYs (ASDR) compared to developed nations at the regional level. Additionally, the EAPC values, which indicate the rate of increase, were significantly higher in these regions compared to developed nations. Notably, high-income North America, belonging to the high SDI regions, experienced the greatest increase in both ASMR and ASDR over the past three decades. Furthermore, throughout the years from 1990 to 2019, males bore a greater burden of CKD attributable to HFPG.ConclusionWith an increasing population and changing dietary patterns, the burden of CKD attributed to HFPG is expected to worsen. From 1990 to 2019, males and developing regions have experienced a more significant burden. Notably, the EAPC values for both ASMR and ASDR were higher in males and regions with lower SDI (excluding high-income North America). This emphasizes the pressing requirement for effective interventions to reduce the burden of CKD attributable to HFPG

JAK3 restrains inflammatory responses and protects against periodontal disease through Wnt3a signaling

Homeostasis between pro- and anti- inflammatory responses induced by bacteria is critical for the maintenance of health. In the oral cavity, proinflammatory mechanisms induced by pathogenic bacteria are well-established; however, the anti-inflammatory responses that act to restrain innate responses remain poorly characterized. Here, we demonstrate that infection with the periodontal pathogen P. gingivalis enhances the activity of JAK3 in innate immune cells, and subsequently phospho-inactivates Nedd4-2, a ubiquitin E3 ligase. In turn, Wnt3 ubiquitination is decreased, while total protein levels are enhanced, leading to a reduction in proinflammatory cytokine levels. In contrast, JAK3 inhibition or Wnt3a robustly enhances NF-κB activity and the production of proinflammatory cytokines in P. gingivalis-stimulated innate immune cells. Moreover, using gain- and loss-of-function approaches, we demonstrate that downstream molecules of Wnt3a signaling, including Dvl3 and β-catenin, are responsible for the negative regulatory role of Wnt3a. In addition, using an in vivo P. gingivalis-mediated periodontal disease model, we show that JAK3 inhibition enhances infiltration of inflammatory cells, reduces expression of Wnt3a and Dvl3 in P. gingivalis-infected gingival tissues, and increases disease severity. Together, our results reveal a new anti-inflammatory role for JAK3 in innate immune cells and show that the underlying signaling pathway involves Nedd4-2-mediated Wnt3a ubiquitination

Redox signaling by glutathione peroxidase 2 links vascular modulation to metabolic plasticity of breast cancer

In search of redox mechanisms in breast cancer, we uncovered a striking role for glutathione peroxidase 2 (GPx2) in oncogenic signaling and patient survival. GPx2 loss stimulates malignant progression due to reactive oxygen species/hypoxia inducible factor-α (HIF1α)/VEGFA (vascular endothelial growth factor A) signaling, causing poor perfusion and hypoxia, which were reversed by GPx2 reexpression or HIF1α inhibition. Ingenuity Pathway Analysis revealed a link between GPx2 loss, tumor angiogenesis, metabolic modulation, and HIF1α signaling. Single-cell RNA analysis and bioenergetic profiling revealed that GPx2 loss stimulated the Warburg effect in most tumor cell subpopulations, except for one cluster, which was capable of oxidative phosphorylation and glycolysis, as confirmed by coexpression of phosphorylated-AMPK and GLUT1. These findings underscore a unique role for redox signaling by GPx2 dysregulation in breast cancer, underlying tumor heterogeneity, leading to metabolic plasticity and malignant progression

A Hybrid IP/GA Approach to the Parallel Production Lines Scheduling Problem

A special parallel production lines scheduling problem is studied in this paper. Considering the time window and technical constraints, a mixed integer linear programming (MILP) model is formulated for the problem. A few valid inequalities are deduced and a hybrid mixed integer linear programming/constraint programming (MILP/CP) decomposition strategy is introduced. Based on them, a hybrid integer programming/genetic algorithm (IP/GA) approach is proposed to solve the problem. At last, the numerical experiments demonstrate that the proposed solution approach is effective and efficient

A Hybrid Algorithm for the Permutation Flowshop Scheduling Problem without Intermediate Buffers

This paper deals with the permutation flowshop scheduling problem without intermediate buffers and presents a hybrid algorithm based on the scatter search and the variable neighborhood search. In the hybrid algorithm, the solutions with good quality and diversity are maintained by a reference set of scatter search, and the search at each generation starts from a solution generated from the reference set so as to improve the search diversity while guaranteeing the quality of the initial solution. In addition, a variable neighbourhood based on the notion of job-block is developed, and the neighbourhood size can adaptively change according to the construction of the job-block. Such a dynamic strategy can help to obtain a balance between search depth and diversity. Extensive experiments on benchmark problems are carried out and the results show that the proposed hybrid algorithm is powerful and competitive with the other powerful algorithms in the literature

Response of Community Structure and Activity of Methanotroph to Different CH4/O2 Ratios

AbstractBiological methane oxidation is affected by many environmental factors. In this study, 13C-labeling coupling with PLFAs detection was used to investigate the community structure of methanotrophs and the methane oxidation rate under different CH4/O2 ratios (1:1, 1:3 and 1:5 respectively). Results showed that variation of PLFAs concentration (ΔPLFAs) and carbon isotope richness (Δ13CPLFAs) of the soil incubated under the above CH4/O2 ratios (CH4 concentration: 15.6% 6.3% and 3.9% respectively) were both significant, and were positively related to the methane concentration. ΔPLFAs and Δ13CPLFAs of 16:1ω7c, 16:1ω5c and 16:0 were the most significant for all the three different incubations. The rates of oxygen consuming/methane consuming under the three incubation conditions were 1.88, 1.73 and 1.62 respectively, showing different assimilation occured. Methane oxidation rate per methanotrophic biomass calculated from 16:1ω7c, 16:1ω5c, and 16:0 were 1.11×10-7, 1.99×10-7, 1.53×10-7 nmol·h-1·cell-1 respectively

The research progress on the role of FMRP in the pathogenesis of tumors

Fragile X mental retardation protein (FMRP) is a selective RNA-binding protein that is highly expressed in neurons and influences cytoskeletal remodeling, cell-cell signal transduction and interactions. Patients with fragile X syndrome display FMRP deficiency and a lower risk of cancers. However, the specific function of FMRP and the underlying mechanisms are not clear. Evidence indicates the involvements of FMRP in multiple processes of malignant development, including tumor cell migration, invasion, apoptosis, proliferation and metastasis. In addition, FMRP is associated with the occurrence, development and prognosis of tumors. In this paper, we review the progress on the role of FMRP in the pathogenesis of tumors, exploring more potential biomarkers of tumors and new therapeutic targets