A Cost Model for 3D Woven Preforms

Lack of cost information is a barrier to acceptance of 3D woven preforms as reinforcements for composite materials, compared with 2D preforms. A parametric, resource-based technical cost model (TCM) was developed for 3D woven preforms based on a novel relationship equating manufacturing time and 3D preform complexity. Manufacturing time, and therefore cost, was found to scale with complexity for seventeen bespoke manufactured 3D preforms. Two sub-models were derived for a Weavebird loom and a Jacquard loom. For each loom, there was a strong correlation between preform complexity and manufacturing time. For a large, highly complex preform, the Jacquard loom is more efficient, so preform cost will be much lower than for the Weavebird. Provided production is continuous, learning, either by human agency or an autonomous loom control algorithm, can reduce preform cost for one or both looms to a commercially acceptable level. The TCM cost model framework could incorporate appropriate learning curves with digital twin/multi-variate analysis so that cost per preform of bespoke 3D woven fabrics for customised products with low production rates may be predicted with greater accuracy. A more accurate model could highlight resources such as tooling, labour and material for targeted cost reduction.

Cranial materials from the Oglakhty burial ground

In this article means of cranial measurements and indexes of the Tashtyk sample from the Oglakhty burial ground obtained as a result of analysis and integration of the measurements of G. Debets, V. Alexeev and I. Gokhman are presented. Also we updated the means of the pooled Tashtyk sample. It was demonstrated that the Oglakhty cranial sample cover the whole spectrum of variability of the Tashtyk population. The data on 37 male and 35 female Early Iron Age series of the Tashtyk culture, Early Tes tombs, Tes flat-grave burial grounds, Podgornovo, Bidzha, and Saragashen stages of the Tagar culture, were subjected to canonical variate analysis. The results of the analysis reveals that Tashtyk male and female series are very similar to the Early Tes samples mainly due to higher cranial index in both male and female samples and smaller nose protrusion angle in male sample. Describing the variety of options for postmortem trepanations of the Tashtyk skulls, we argue that the trepanation process was not a ritual in itself, but was a routine procedure aimed at extracting the brain.

Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method

Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics T2 and Q. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the k-nearest neighbor (kNN) technique is applied to design the statistics nearest neighbor distance DT2 and DQ as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVAkNN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.

Predictive factors of COVID-19 pneumonia: a retrospective cross-sectional survey in a Japanese university hospital

BackgroundThe predictive factors of coronavirus disease 2019 (COVID-19) pneumonia, including clinical parameters with symptoms, vital signs, and laboratory data, have not been compared directly between cases with and without complicated pneumonia. AimsWe aimed to identify predictive factors for COVID-19 patients with complicated pneumonia, and determine which COVID-19 patients should undergo computed tomography (CT). MethodsThis retrospective cross-sectional survey was conducted at the Juntendo University Nerima Hospital in Tokyo, Japan. We recruited patients diagnosed with COVID-19 between 1 January and 31 December 2020 and all patients underwent blood tests and CT. Clinical information, including vital signs, symptoms, laboratory results, and CT findings, were extracted from medical charts. Factors potentially predicting COVID-19 pneumonia were analysed using Student’s t -test or the chi-squared test, and variables with a p- value of < 0.05 in the bi-variate analysis were entered into multivariate logistic regression models. ResultsAmong 221 included patients (119 males [53.8%]; mean age, 54.59 ± 18.61 years), 160 (72.4%) had pneumonia. The significant factors in the multi-variate analysis were the lactate dehydrogenase (odds ratio [OR], 3.41; 95% confidence interval [CI], 1.47–7.95; p < 0.01) and C-reactive protein (OR, 3.94; 95% CI, 1.05–14.80; p = 0.04) levels. No significant differences were observed in vital signs and the symptoms. ConclusionLDH and CRP level of > 220 IU/L and > 3.0 mg/dL, respectively, are independent risk factors for COVID-19 pneumonia. The present results are extremely useful for deciding whether to perform CT among COVID-19 patients.

Application of Canonical Variate Analysis to Compare Different Groups of Food Industry Companies in Terms of Financial Liquidity and Profitability

Financial liquidity and profitability are two critical phenomena present in the financial economy of a company, whose relations depend on each other and may course in different directions. At the same time, they are an example of the complexity of the problem, which demands a proper approach, allowing one to reconcile two opposing objectives of any enterprise, i.e., maximizing the benefits for the owners and minimizing the risk of losing financial liquidity. Until now, the relationship between liquidity and profitability has not been examined explicitly, using multidimensional methods in particular. Nevertheless, the links between profitability and financial liquidity maintenance ensure the sustainable development of enterprises in different branches. This paper formulates two aims: scientific and practical. The scientific one concerns adopting the canonical variate analysis method to visualize the differences and relationships between food industry companies regarding financial liquidity and profitability. The practical one relates to indicating the relationship between financial liquidity and profitability in different groups of food industry companies. To study the relationships between the selected groups of enterprises and describe them, the liquidity and profitability ratios were utilized, involving canonical variate analysis based on transformation by linear combination and singular value decomposition. The analysis found that the most important feature highlighting the group of the examined entities regarding financial liquidity was the cash conversion cycle. The research results showed the existence of multidirectional relationships between liquidity and profitability. The research indicates that they depend on indicators describing financial dependencies and the industries in which they operate. This led to a much deeper and broader interpretation of the assessment of the financial situation of companies to support their sustainable development.

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

Background Testing a hypothesis for ‘factors-outcome effect’ is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. Methods The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. Results The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. Conclusion ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.