A Hybrid Demon Algorithm for the Two-Dimensional Orthogonal Strip Packing Problem

This paper develops a hybrid demon algorithm for a two-dimensional orthogonal strip packing problem. This algorithm combines a placement procedure based on an improved heuristic, local search, and demon algorithm involved in setting one parameter. The hybrid algorithm is tested on a wide set of benchmark instances taken from the literature and compared with other well-known algorithms. The computation results validate the quality of the solutions and the effectiveness of the proposed algorithm

A robust optimisation model for hybrid remanufacturing and manufacturing systems under uncertain return quality and market demand

In remanufacturing research, most researchers predominantly emphasised on the recovery of whole product (core) rather than at the component level due to its complexity. In contrast, this paper addresses the challenges to focus on remanufacturing through component recovery, so as to solve production planning problems of hybrid remanufacturing and manufacturing systems. To deal with the uncertainties of quality and quantity of product returns, the processing time of remanufacturing, remanufacturing costs, as well as market demands, a robust optimisation model was developed in this research and a case study was used to evaluate its effectiveness and efficiency. To strengthen this research, a sensitivity analysis of the uncertain parameters and the original equipment manufacturer’s (OEM’s) pricing strategy was also conducted. The research finding shows that the market demand volatility leads to a significant increase in the under fulfilment and a reduction in OEM’s profit. On the other hand, recovery cost reduction, as endogenous cost saving, encourages the OEM to produce more remanufactured products with the increase in market demand. Furthermore, the OEM may risk profit loss if they raise the price of new products, and inversely, they could gain more if the price of remanufactured products is raised

A novel data-driven robust framework based on machine learning and knowledge graph for disease classification

Abstract(#br)As Noncommunicable Diseases (NCDs) are affected or controlled by diverse factors such as age, regionalism, timeliness or seasonality, they are always challenging to be treated accurately, which has impacted on daily life and work of patients. Unfortunately, although a number of researchers have already made some achievements (including clinical or even computer-based) on certain diseases, current situation is eager to be improved via computer technologies such as data mining and Deep Learning. In addition, the progress of NCD research has been hampered by privacy of health and medical data. In this paper, a hierarchical idea has been proposed to study the effects of various factors on diseases, and a data-driven framework named d-DC with good extensibility is presented. d-DC is able to classify the disease according to the occupation on the premise where the disease is occurring in a certain region. During collecting data, we used a combination of personal or family medical records and traditional methods to build a data acquisition model. Not only can it realize automatic collection and replenishment of data, but it can also effectively tackle the cold start problem of the model with relatively few data effectively. The diversity of information gathering includes structured data and unstructured data (such as plain texts, images or videos), which contributes to improve the classification accuracy and new knowledge acquisition. Apart from adopting machine learning methods, d-DC has employed knowledge graph (KG) to classify diseases for the first time. The vectorization of medical texts by using knowledge embedding is a novel consideration in the classification of diseases. When results are singular, the medical expert system was proposed to address inconsistencies through knowledge bases or online experts. The results of d-DC are displayed by using a combination of KG and traditional methods, which intuitively provides a reasonable interpretation to the results (highly descriptive). Experiments show that d-DC achieved the improved accuracy than the other previous methods. Especially, a fusion method called RKRE based on both ResNet and the expert system attained an average correct proportion of 86.95%, which is a good feasibility study in the field of disease classification

Accuracy of steps measured by smartphones-based WeRun compared with ActiGraph-GT3X accelerometer in free-living conditions

ObjectivesThe purpose of this study was to evaluate the accuracy and reliability of steps tracked by smartphone-based WeChat app compared with Actigraph-GT3X accelerometer in free-living conditions.DesignA cross-sectional study and repeated measures.MethodsA total of 103 employees in the Pudong New Area of Shanghai, China, participated in this study. The participants wore an ActiGraph-GT3X accelerometer during the period of August to September 2019 (Time 1), December 2019 (Time 2) and September 2020 (Time 3). Each time, they wore the ActiGraph-GT3X accelerometer continuously for 7 days to assess their 7-day step counts. The smartphone-based WeRun step counts were collected in the corresponding period when subjects wore accelerometers. The subjects were invited to complete basic demographic characteristics questionnaires and to perform physical examination to obtain health-related results such as height, body weight, body fat percentage, waist circumference, hip circumference, and blood pressure.ResultsBased on 103 participants' 21 days of data, we found that the Spearman correlation coefficient between them was 0.733 (P < 0.01). The average number of WeRun steps measured by smartphones was 8,975 (4,059) per day, which was higher than those measured by accelerometers (8,462 ± 3,486 per day, P < 0.01). Demographic characteristics and different conditions can affect the consistency of measurements. The consistency was higher in those who were male, older, master's degree and above educated, and traveled by walking. Steps measured by smartphone and accelerometer in working days and August showed stronger correlation than other working conditions and time. Mean absolute percent error (MAPE) for step counts ranged from 0.5 to 15.9%. The test-retest reliability coefficients of WeRun steps ranged from 0.392 to 0.646. A multiple regression analysis adjusted for age, gender, and MVPA/step counts measured during Time 1 showed that body composition (body weight, BMI, body fat percentage, waist circumference, and hip circumference) was correlated with moderate-to-vigorous intensity physical activity, but it was not correlated with WeRun step counts.ConclusionsThe smartphone-based WeChat app can be used to assess physical activity step counts and is a reliable tool for measuring steps in free-living conditions. However, WeRun step counts' utilization is potentially limited in predicting body composition

Modification effect of changes in cardiometabolic traits in association between kidney stones and cardiovascular events

BackgroundsWhether longitudinal changes in metabolic status influence the effect of kidney stones on cardiovascular disease (CVD) remains unclarified. We investigated the modification effect of status changes in metabolic syndrome (MetS) in the association of kidney stones with risk of incident CVD events.MethodsWe performed a prospective association and interaction study in a nationwide cohort including 129,172 participants aged ≥ 40 years without CVDs at baseline and followed up for an average of 3.8 years. Kidney stones information was collected by using a questionnaire and validated by medical records. The repeated biochemical measurements were performed to ascertain the metabolic status at both baseline and follow-up.Results4,017 incident total CVDs, 1,413 coronary heart diseases (CHDs) and 2,682 strokes were documented and ascertained during follow-up. Kidney stones presence was significantly associated with 44%, 70% and 31% higher risk of CVDs, CHDs and stroke, respectively. The stratified analysis showed significant associations were found in the incident and sustained MetS patients, while no significant associations were found in the non-MetS at both baseline and follow-up subjects or the MetS remission ones, especially in women. For the change status of each single component of the MetS, though the trends were not always the same, the associations with CVD were consistently significant in those with sustained metabolic disorders, except for the sustained high blood glucose group, while the associations were consistently significant in those with incident metabolic disorders except for the incident blood pressure group. We also found a significant association of kidney stone and CVD or CHD risk in the remain normal glucose or triglycerides groups; while the associations were consistently significant in those with incident metabolic disorders except for the incident blood pressure group. We also found a significant association of kidney stone and CVD or CHD risk in the remain normal glucose or triglycerides groups.ConclusionsA history of kidney stones in women with newly developed MetS or long-standing MetS associated with increased risk of CVD. The mechanisms link kidney stones and CVD risk in the metabolic and non-metabolic pathways were warranted for further studies

The Relative Body Weight Gain From Early to Middle Life Adulthood Associated With Later Life Risk of Diabetes: A Nationwide Cohort Study

AimTo determine the effect of decade-based body weight gain from 20 to 50 years of age on later life diabetes risk.Methods35,611 non-diabetic participants aged ≥ 50 years from a well-defined nationwide cohort were followed up for average of 3.6 years, with cardiovascular diseases and cancers at baseline were excluded. Body weight at 20, 30, 40, and 50 years was reported. The overall 30 years and each 10-year weight gain were calculated from the early and middle life. Cox regression models were used to estimate risks of incident diabetes.ResultsAfter 127,745.26 person-years of follow-up, 2,789 incident diabetes were identified (incidence rate, 2.18%) in 25,289 women (mean weight gain 20-50 years, 7.60 kg) and 10,322 men (7.93 kg). Each 10-kg weight gain over the 30 years was significantly associated with a 39.7% increased risk of incident diabetes (95% confidence interval [CI], 1.33-1.47); weight gain from 20-30 years showed a more prominent effect on the risk of developing diabetes before 60 years than that of after 60 years (Hazard ratio, HR = 1.084, 95% CI [1.049-1.121], P <0.0001 vs. 1.015 [0.975-1.056], P = 0.4643; PInteraction=0.0293). It showed a stable effect of the three 10-year intervals weight gain on risk of diabetes after 60 years (HR=1.055, 1.038, 1.043, respectively, all P < 0.0036).ConclusionsThe early life weight gain showed a more prominent effect on developing diabetes before 60 years than after 60 years; however, each-decade weight gain from 20 to 50 years showed a similar effect on risk developing diabetes after 60 years

An Automatic Generation and Verification Method of Software Requirements Specification

The generation of standardized requirements specification documents plays a crucial role in software processes. However, the manual composition of software requirements specifications is a laborious and time-consuming task, often leading to errors that deviate from the actual requirements. To address this issue, this paper proposes an automated method for generating requirements specifications utilizing a knowledge graph and graphviz. Furthermore, in order to overcome the limitations of the existing automated requirement generation process, such as inadequate emphasis on data information and evaluation, we enhance the traditional U/C matrix by introducing an S/U/C matrix. This novel matrix represents the outcomes of data/function systematic analysis, and verification is facilitated through the design of inspection rules. Experimental results demonstrate that the requirements specifications generated using this method achieve standardization and adherence to regulations, while the devised S/U/C inspection rules facilitate the updating and iteration of the requirements specifications

BiLSTM-CRF-KG: A Construction Method of Software Requirements Specification Graph

A requirement analysis is the basis and source of software system development, and its accuracy, consistency and completeness are the keys to determining software quality. However, at present, most software requirements specifications are prepared manually, which has some problems, such as inconsistency with business description, low preparation efficiency, being error prone and difficulty communicating effectively with business personnel. Aiming at the above problems, this paper realizes a construction model of the software requirements specification graph BiSLTM-CRF-KG by using natural language processing and knowledge graph technology. Simulation experiments on 150 real software system business requirements description corpora show that the BiSLTM-CRF-KG model can obtain 96.31% functional entity recognition accuracy directly from the original corpus, which is better than the classical BiSLTM-CRF, IDCNN-CRF and CRF++ models, and has good performance on different kinds of data sets