Influence of cenosphere on CAI strength of glass epoxy laminate

Under controlled lab settings, two distinct laminates, one containing cenosphere and the other with neat resin, were evaluated for impact using a Fractovis impact machine, compression testing, and compression after impact tests (CAI) with a Tinus Olsen UTM. The GFRP laminates were made by hand lay-up method with 16 layers of glass fiber in 4.7±0.2 mm thickness and combined with epoxy resin reinforced Cenospheres at concentrations of 1, 3 and 5 wt. %, according to ASTM specifications. The dominant failure mode controlling the specimen's compression ultimate load resistance, and other failure modes of impacted specimens such like fiber pull-out and debonding, were found to be the effects of delamination using coupled acoustic emission (AE) monitoring and compression tests. On specimens with a 3 wt. % filler additive, there was a noticeable increase in strength. Both impacted and non-impacted samples exhibited significant compression ultimate load resistances, with the 3 wt. % filler impregnated specimen having the maximum.

The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission

Quality of care data has gained transparency captured through various measurements and reporting. Readmission measure is especially related to unfavorable patient outcomes that directly bends the curve of healthcare cost. Under the Hospital Readmission Reduction Program, payments to hospitals were reduced for those with excessive 30-day rehospitalization rates. These penalties have intensified efforts from hospital stakeholders to implement strategies to reduce readmission rates. One of the key strategies is the deployment of predictive analytics stratified by patient population. The recent research in readmission model is focused on making its prediction more accurate. As cost-saving improvements through artificial intelligent-based health solutions are expected, the broad economic impact of such digital tool remains unknown. Meanwhile, reducing readmission rate is associated with increased operating expenses due to targeted interventions. The increase in operating margin can surpass native readmission cost. In this paper, we propose a quantized evaluation metric to provide a methodological mean in assessing whether a predictive model represents cost-effective way of delivering healthcare. Herein, we evaluate the impact machine learning has had on transitional care and readmission with proposed metric. The final model was estimated to produce net healthcare savings at over $1 million given a 50% rate of successfully preventing a readmission.

A Hybrid Capsule Network with Attention and BiLSTM for Opinion Mining in Text

Examining the product review or review on web service facilitate to increase the product quality or web service. By means, comments from online shopping sites such as Amazon, Flipkart, EBay etc will not only assist the users to purchase the product however besides be able to guide the producer/supplier to identify the advantages and disadvantages of the goods. Mining online shopping websites with their information will becomes a major important task. Web Mining plays a major important role to mine the details of online websites efficiently. Web mining is the process of data mining that learns without human intervention and mine information obtained from the documents in web and also services. Sentiment categorization and web mining has become a truly significant task recently, with profound business and research impact. Machine learning algorithms and soon after deep learning methods have been the market leaders in sentiment analysis. The advent of capsule networks has been a landmark event in deep learning. It has been truly proficient in image processing. In case of text classification, standalone capsule networks are not optimally suitable. Here, a hybrid BiLSTM-Capsule framework is introduced for sentiment analysis of web texts of reviews from various datasets. In the model beginning, there is a bidirectional LSTM layer after which is an attention layer and a final capsule layer. This review analysis will helps to improve the products from amazon, increase the movie quality. The analysis of outcome depending on MR, IMDB, SST and Amazon datasets indicated the introduced framework performs better than some benchmark deep learning models. Significantly, the BiLSTM-Capsule can put its words in sentimental trend showing the capsules’ attributes without utilizing the linguistic knowledge.

Machine Learning Approach Identified Multi-Platform Factors for Caries Prediction in Child-Mother Dyads

Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated (Prevotella histicola, Streptococcus mutans, and Rothia muciloginosa) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors.

Insertion of a Viscoelastic Layer to Reduce the Propagation of Energy by Vertical Impacts of Slamming in Planing Hull Vessels

For the design of vessels built by GFRP laminates, an insert with a viscoelastic layer is proposed to reduce the spread of damage produced by the vertical impact of the ship's bottom with the sea or slamming phenomenon. Using vertical drops-weight impact machine that reproduce the energy inferred to the panel during navigation, the propagation of the damage of OoA cured prepreg panels is studied comparing it with modified panels with insertion of viscoelastic layer. The use of acceleration data reading allows the benefits of viscoelastic modification during impact to be quantified through the developed formulation. The force, displacement and energy returned by the panel after impact have also been quantified, which does not become intralaminar and interlaminar damage. It is shown that under 40 joules of impact, the viscoelastic sheet has its best ability to return energy and above 130 joules it loses its capacity.

DESIGNING ANNULAR ELASTIC VALVE THAT CONTROLS AIR EXHAUST FROM WORKING CHAMBER OF PNEUMATIC IMPACT MACHINE

The paper considers one of the promising shut-off and control elements of the air distribution system of pneumatic impact machines - an annular elastic valve (CUV). This element allows you to reduce the energy consumption of pneumatic devices. Unfavorable combinations of factors that hinder the movement of the valve necessary for sealing the working chamber are considered. The paper presents a method for calculating the elastic valve that controls the release of energy from the return chamber of the pneumatic impact machine, which allows determining the main geometric dimensions of the valve device at the design stage, which provides a stable self-oscillating cycle of the pneumatic impact machine with the specified energy characteristics. The developed technique was used in the modernization of the air distribution system of the ring impact machine (KUM), designed for immersion of rod elements in the ground. Tests of the resulting sample in production conditions confirmed the increase in energy performance compared to previously produced machines of a similar type.

On the Nonlinear Vibration Analysis of a Hand-Held Impact Machine

Prolonged exposure of the human arm to vibrations from hand-held impact (HIM) tools can be hazardous as such, it is important that the level of vibration suppression in HIMs is improved. This paper sought to address this issue by studying a model of the hand-arm system (HAS) coupled to a HIM which is also coupled to a nonlinear tuned vibration absorber inerter (NVAI). The HAS is modelled as a 2-DOF system coupled to the HIM at a single point. The HIM is modelled as an oscillator with linear damping, and both linear and nonlinear stiffnesses. The nonlinear stiffness of the HIM is introduced to represent the nonlinearities introduced by the vibro-impact dynamics of the HIM. After obtaining the equations of motion for the system, an analytical solution is obtained using the harmonic balance method. The analytical solution is validated using direct numerical integration and the results show very good agreement. The performance of the NVAI is compared to those of the classical nonlinear and linear vibration absorbers. Parametric study is carried out to examine the role of key design parameters, such as the damping of the absorber, nonlinear stiffness of the HIM and inertance of the NVAI, on the performance of the NVAI.

Rut Depth Evaluation of a Triple-Bogie System for Forwarders—Field Trials with TLS Data Support

In 2019, the machine manufacturer HSM presented a forwarder prototype for timber hauling in cut-to-length processes fitted with a new 10-wheel triple-bogie (TB) setup approach aimed at promoting sustainable forest management by reducing the ecological impact of forest operations, especially under soft-soil working conditions. The purpose of our study was to assess the resulting soil-protection effect emerging from additional wheel-contact surface area. For this, the rut development under known cumulative weight, related to the soil conditions of shear strength and moisture content, was recorded for later comparison. Additional terrestrial laser scanning (TLS) was used to generate a multi-temporal digital terrain model (DTM) in order to enhance the data sample, assess data quality, and facilitate visualization of the impact of local disturbance factors. In all TB configurations, a rut depth of 10 cm (5.8–7.2 cm) was not exceeded after the hauling of a reference amount of 90 m3 of timber (average soil shear strength reference of 67 kPa, volumetric water content (VMC) 43%). Compared to a reference dataset, all observed configurations ranked in the lowest-impact machine categories on related soil stability classes, and the configuration without bogie tracks revealed the highest machine weight to weight distribution trade-off potential.

The Use of Viscoelastic Layers in GFRP, a New Vision of Planning Hull

The planning hull, are types of boats very used in our days, and require special maintenance and repair. It is proposed the insertion of a viscoelastic layer inside the laminated panels of the ships built with GFRP, to protect them from the impacts of slamming. Unmodified and modified laminate panels were manufactured to perform result comparisons. With the reproduction of the phenomenon of fatigue in the laboratory, testing them in the impact machine, the use of strain gauges and by characterization with fluorescent penetrating inks to observe the evolution of micro cracks. It is verified that the proposed modification is the future of shipbuilding of this type of vessel.