Enhancing cutting tool sustainability based on remaining useful life prediction

As a critical part of machining, cutting tools are of great importance to sustainability enhancement. Normally, they are underused, resulting in huge waste. However, the lack of reliable support leads to a high risk on improving the cutting tool utilization. Aiming at this problem, this paper proposes an approach to enhance the cutting tool sustainability. A non-linear cutting tool remaining useful life prediction model is developed based on tool wear historical data. Probability distribution function and cumulative distribution function are used to quantize the uncertainty of the prediction. Under a constant machining condition, a cutting tool life is extended according to its specific remaining useful life prediction, rather than a unified one. Under various machining conditions, machining parameters are optimized to improve efficiency or capability. Cutting tool sustainability is assessed in economic, environmental and social dimensions. Experimental study verifies that both material removal rate and material removal volume are improved. Carbon emission and cutting tool cost are also reduced. The balance between benefit and risk is achieved by assigning a reasonable confidence level. Cutting tool sustainability can be enhanced by improving cutting tool utilization at controllable risk.©2020 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Static output-feedback stabilization of discrete-time Markovian jump linear systems: a system augmentation approach

This paper studies the static output-feedback (SOF) stabilization problem for discrete-time Markovian jump systems from a novel perspective. The closed-loop system is represented in a system augmentation form, in which input and gain-output matrices are separated. By virtue of the system augmentation, a novel necessary and sufficient condition for the existence of desired controllers is established in terms of a set of nonlinear matrix inequalities, which possess a monotonic structure for a linearized computation, and a convergent iteration algorithm is given to solve such inequalities. In addition, a special property of the feasible solutions enables one to further improve the solvability via a simple D-K type optimization on the initial values. An extension to mode-independent SOF stabilization is provided as well. Compared with some existing approaches to SOF synthesis, the proposed one has several advantages that make it specific for Markovian jump systems. The effectiveness and merit of the theoretical results are shown through some numerical example

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers

The gap in injury mortality rates between urban and rural residents of Hubei province, China

<p>Abstract</p> <p>Background</p> <p>Injury is a growing public health concern in China. Injury death rates are often higher in rural areas than in urban areas in general. The objective of this study is to compare the injury mortality rates in urban and rural residents in Hubei Province in central China by age, sex and mechanism of injury.</p> <p>Methods</p> <p>Using data from the Disease Surveillance Points (DSP) system maintained by the Hubei Province Centers for Disease Control and Prevention (CDC) from 2006 to 2008, injury deaths were classified according to the International Classification of Disease-10^thRevision (ICD-10). Crude and age-adjusted annual mortality rates were calculated for rural and urban residents of Hubei Province.</p> <p>Results</p> <p>The crude and age-adjusted injury death rates were significantly higher for rural residents than for urban residents (crude rate ratio 1.9, 95% confidence interval 1.8-2.0; adjusted rate ratio 2.4, 95% confidence interval 2.3-2.4). The age-adjusted injury death rate for males was 81.6/100,000 in rural areas compared with 37.0/100 000 in urban areas; for females, the respective rates were 57.9/100,000 and 22.4/100 000. Death rates for suicide (32.4 per 100 000 vs 3.9 per 100 000), traffic-related injuries (15.8 per 100 000 vs 9.5 per 100 000), drowning (6.9 per 100 000 vs 2.3 per 100 000) and crushing injuries (2.0 per 100 000 vs 0.7 per 100 000) were significantly higher in rural areas. Overall injury death rates were much higher in persons over 65 years, with significantly higher rates in rural residents compared with urban residents for suicide (279.8 per 100 000 vs 10.7 per 100 000), traffic-related injuries, and drownings in this age group. Death rates for falls, poisoning, and suffocation were similar in the two geographic groups.</p> <p>Conclusions</p> <p>Rates of suicide, traffic-related injury deaths and drownings are demonstrably higher in rural compared with urban locations and should be targeted for injury prevention activity. There is a need for injury prevention policies targeted at elderly residents, especially with regard to suicide prevention in rural areas in Central China.</p

The USGT Method for Suspender Tensioning of Self-Anchored Suspension Bridges

Unlike earth-anchored suspension bridges, self-anchored suspension bridges (SASBs) involve a special construction stage, namely, suspender tensioning, in which the tensioning force and sequence are crucial and complicated. Against this background, an example bridge A, a SASB with a steel-concrete composite beam, is introduced in detail. Using MIDAS finite element software, a suspender tensioning scheme is formulated based on a combination method of the unstrained state method and graded tension method (the USGT method), in which a suspender is tensioned according to its unstrained length. By analyzing the bending moment change of the beam and deflection of the main cable throughout the entire construction process, a “high-to-low” suspender tensioning sequence is proposed that also involves symmetrical tensioning from the main towers to the midspan or the anchor positions. In the optimized construction process, the deviation and stress of the main towers are controlled well, thereby ensuring the safety of the main beam and main towers in the construction process

Fast Multigrid Algorithm for Non-Linear Simulation of Intact and Damaged Ship Motions in Waves

This paper proposes a fast multigrid algorithm to simulate the non-linear motion of ships in both intact and damaged conditions. The simulations of ship motions in waves are known to require much time to calculate due to the strong non-linear interactions between ship and waves. To improve the calculation efficiency while retaining the accuracy, a prediction-correction strategy was designed to accelerate the simulation through three sets of locally refined meshes. The flow field was first estimated in a coarse mesh and then mapped to a locally refine mesh for further higher-fidelity corrections. A partitioned radial basis function (PRBF) method is proposed to interpolate and reconstruct the flow field for the refined mesh. A new two-phase flow solver was developed with a fast multigrid algorithm based on the Reynolds-averaged Navier&ndash;Stokes equations (RANSE). The new solver was applied to study the non-linear behavior of a damaged ship in beam waves and the effect of damaged compartments on ship rolling motion. Validation against the solution with the original method of single set meshes and experimental data indicates that the proposed algorithm yields satisfactory results while saving 30&ndash;40% of the computational time

Fast Multigrid Algorithm for Non-Linear Simulation of Intact and Damaged Ship Motions in Waves

This paper proposes a fast multigrid algorithm to simulate the non-linear motion of ships in both intact and damaged conditions. The simulations of ship motions in waves are known to require much time to calculate due to the strong non-linear interactions between ship and waves. To improve the calculation efficiency while retaining the accuracy, a prediction-correction strategy was designed to accelerate the simulation through three sets of locally refined meshes. The flow field was first estimated in a coarse mesh and then mapped to a locally refine mesh for further higher-fidelity corrections. A partitioned radial basis function (PRBF) method is proposed to interpolate and reconstruct the flow field for the refined mesh. A new two-phase flow solver was developed with a fast multigrid algorithm based on the Reynolds-averaged Navier–Stokes equations (RANSE). The new solver was applied to study the non-linear behavior of a damaged ship in beam waves and the effect of damaged compartments on ship rolling motion. Validation against the solution with the original method of single set meshes and experimental data indicates that the proposed algorithm yields satisfactory results while saving 30–40% of the computational time

Resting energy expenditure based on equation estimation can predict renal outcomes in patients with type 2 diabetes mellitus and biopsy-proven diabetic kidney disease

The aim of this study was to investigate the relationship between resting energy expenditure (REE) based on equation estimation and renal outcomes in patients with diabetes kidney disease (DKD). A total of 124 patients were enrolled from a retrospective cohort of Type 2 Diabetes mellitus (T2DM) patients with biopsy-proven DKD. Renal outcome defined as End-Stage Renal Disease (ESRD). To compare the predictive ability of different REE estimation equations on ESRD. Patients’ REE was assessed according to the estimating equation with the best predictive power, and then the relationship between REE and ESRD risk was fitted using a restricted cubic spline curve (RCS) plot and REE cutoff values were obtained. Grouping using cutoff values, and ultimately evaluate the relationship between REE and the risk of ESRD using a Multivariate Cox regression model. The strongest predictive validity for renal outcomes was the NDCKD-equation. The patients were divided into the higher-REE group (n = 78) and the lower-REE group (n = 46), based on the cutoff value. During the follow-up, 30 of 124 patients (24.2%) proceeded to ESRD. Multivariate Cox regression models showed that the risk of ESRD in patients with lower REE was 6.08 times increased compared with that in those with higher REE (HR = 6.08; 95% CI, 1.28–28.80, p = 0.023). These findings suggested that the lower REE was an independent risk factor for unfavorable renal outcomes in patients with DKD.</p

IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data

In recent years, more and more studies have shown that microRNAs (miRNAs) play a key role in many important biological processes. Dysregulation of miRNAs can lead to a variety of diseases like cancers, thus predicting potential miRNA-disease associations is important for understanding drug development and disease pathogenesis, diagnosis and treatment. It is known that experimental methods to validate miRNA-disease associations typically involve miRNA knockout or knockdown, which is time and labor-intensive. As a result, computational models have been developed to predict unknown miRNA-disease associations from available information related to miRNAs, diseases, genes, and so on. However, their performances are yet to be improved. Noticing that appropriately combining multiple data-source is usually helpful for improving prediction accuracy, we have developed IMDAILM: Inferring miRNA-Disease Association by integrating lncRNA and miRNA data, a low-rank matrix completion model integrating miRNA, long noncoding RNA (lncRNA) and disease information to predict miRNA-disease associations. Specifically, the miRNA-disease association network and the lncRNA-disease association network are fused to form a new heterogeneous network consisting of 3 types of nodes representing miRNAs, lncRNAs and diseases. In addition, a negative sample inference method was proposed to infer unrelated miRNA-disease pairs. Based on both heterogeneous network and negative samples, a low-rank matrix completion model is proposed and solved. In practice, IMDAILM achieved an area under the curve (AUC) of 0.8884 for predicting miRNAs associated with diseases under the 5-fold cross-validation (CV), outperforming a few recent methods. IMDAILM also yielded an AUC of 0.8870 for predicting both lncRNAs and miRNAs associated with diseases. In addition, the 5-fold CV results indicate that IMDAILM is also superior to other methods in predicting miRNAs associated with isolated diseases. Finally, we confirmed a few novel predicted miRNAs associated with specific diseases like lung cancers by literature mining. In summary, the integration of lncRNA information into a matrix completion framework contributes to the prediction of miRNA-disease associations