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A novel numerical technique is presented with which the temperature profile within a selected transverse plane of an object can be reconstructed provided the boundary data around the transverse plane are known. Numerical simulations of the proposed computed tomography technique are performed to verify its feasibility and accuracy using several heat conduction examples whose exact solutions can be found in literature. Restrictions of and mathematical difficulties encountered in the proposed technique are presented.

Gives a bibliographical review of the finite element methods (FEMs) applied in biomedicine from the theoretical as well as practical points of view. The bibliography at the end of the paper contains 748 references to papers, conference proceedings and theses/dissertations dealing with the finite element analyses and simulations in biomedicine that were published between 1985 and 1999.

It was mid-March 2014, and GE's John F. Welch Technology Centre in Bangalore, India was brimming with activity. GE had developed an advanced, scalable positron emission tomography-computed tomography (PET/CT) scanner as part of its global Healthymagination initiative to provide better healthcare for more people at a lower cost. Munesh Makhija, Managing Director, GE India Technology Centre and Chief Technology Officer (CTO), GE South Asia, was thumbing through a report prepared by the PET/CT product development team and GE's healthcare market research team. In another office, Suresh Kumar R.(Kumar), General Manager of the Essential PET Segment, was putting the finishing touches on a presentation outlining a commercialisation strategy for the new PET/CT product, Discovery IQ (Exhibit 1).

Discovery IQ was a revolutionary product that would be useful for staging, treatment planning and post-treatment planning assessment. Early reviews from nuclear physicians had been positive. However, the product was still too costly for the bottom of the pyramid (BoP) market. Kumar and his team were scheduled to meet with Makhija the following morning to discuss a “go-to-market strategy”. Kumar knew that Makhija would want to talk about their segmentation strategy and the underlying needs of various customer types. He also expected Makhija to focus on return on investment (ROI) projections because diagnostic centres in India first looked at various financial return measures before investing in any new equipment. Kumar wanted to present a commercialisation strategy for Discovery IQ, which required a significant commitment of resources to tackle supply and distribution challenges across tier II and tier III cities^a in India.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

Human space exploration to date has been limited to low Earth orbit and the moon. The International Space Station (ISS) provides a unique opportunity for researchers to prove out the technologies that will enable humans to safely live and work in space for longer periods and venture farther into the solar system. The ability to manufacture parts in-space rather than launch them from earth represents a fundamental shift in the current risk and logistics paradigm for human space exploration. The purpose of this mission is to prove out the fused deposition modeling (FDM) process in the microgravity environment, evaluate microgravity effects on the materials manufactured, and provide the first demonstration of on-demand manufacturing for space exploration.

In 2014, NASA, in cooperation with Made in Space, Inc., launched a 3D printer to the ISS with the goal of evaluating the effect of microgravity on the fused deposition modeling (FDM) process and prove out the technology for use on long duration, long endurance missions where it could leveraged to reduce logistics requirements and enhance crew safety by enabling a rapid response capability. This paper presents the results of testing of the first phase of prints from the technology demonstration mission, where 21 parts where printed on orbit and compared against analogous specimens produced using the printer prior to its launch to ISS.

Mechanical properties, dimensional variations, structural differences and chemical composition for ground and flight specimens are reported. Hypotheses to explain differences observed in ground and flight prints are also developed. Phase II print operations, which took place in June and July of 2016, and ground-based studies using a printer identical to the hardware on ISS, will serve to answer remaining questions about the phase I data set. Based on Phase I analyses, operating the FDM process in microgravity has no substantive effect on the material produced.

Demonstrates that there is no discernable, engineering significant effect on operation of FDM in microgravity. Implication is that material characterization activities for this application can be ground-based.

Summary of results of testing of parts from the first operation of 3D printing in a microgravity environment.

This study aims to find out the participation rate of women in the utilization of screening methods to determine the relationship of sociodemographic health characteristics and breast cancer (BC) awareness with the utilization of screening methods. The authors’ study aims to examine the relationship between women's belief and the utilization of screening methods.

A cross-sectional study was conducted in three health centers from December 13, 2016 to June 12, 2017. A questionnaire was constructed for data collection about sociodemographic characteristics, screening awareness and medical and health background variables. Additionally, BC awareness measure and champion health belief model scales were used to measure women's perceptiveness about BC.

Despite the awareness among 78.9% of women regarding clinical breast examination (CBE) as a screening method, only 9.5% women utilized it for screening. Due to prescription by physicians for diagnosis of BC, 23.6% women had done mammography at least once in their life. Having jobs and a good education significantly influenced the utilization of CBE as a screening method. The logistic regression analysis found that old age, family history of BC, good knowledge about BC, perceived susceptibility, low rate of perceived barriers to mammography and CBE predicted participation in screening.

Enhancing knowledge about BC and screening, emphasizing the susceptibility to BC and the benefits of screening will help in better participation. Importance should be given to illiterate and unemployed women.

Lung cancer is the leading cause of death worldwide. Physical and chemical agents such as tobacco smoke are the leading cause of various lung cancers. The intrinsic heterogeneity of normal lung tissue may be affected in different ways, giving rise to different types of lung cancers classified as either small‐cell lung cancer (SCLC) or non‐small cell lung cancer (NSCLC). Adenocarcinoma, a NSCLC, accounts for 40 percent of all lung cancer cases and the incidence is increasing worldwide, especially among women. The survival rate and prognosis is poorest for adenocarcinoma. Therefore, diagnosis at the earliest stage (Stage I, localized) is critical for increasing survival rates of those suffering from lung cancer. However, many factors affect early diagnosis including the variable natural growth of tumors plus technological and human factors associated with manipulation of tissue samples and interpretation of results. This article reviews potential problems associated with diagnosing lung cancer and considers future directions of diagnostic technology.

The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.

In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.

The proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.

It can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.

It can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.

In this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.

The unstable dynamic propagation of multistage hydrofracturing fractures leads to uneven development of the fracture network and research on the mechanism controlling this phenomenon indicates that the stress shadow effects around the fractures are the main mechanism causing this behaviour. Further studies and simulations of the stress shadow effects are necessary to understand the controlling mechanism and evaluate the fracturing effect.

In the process of stress-dependent unstable dynamic propagation of fractures, there are both continuous stress fields and discontinuous fractures; therefore, in order to study the stress-dependent unstable dynamic propagation of multistage fracture networks, a series of continuum-discontinuum numerical methods and models are reviewed, including the well-developed extended finite element method, displacement discontinuity method, boundary element method and finite element-discrete element method.

The superposition of the surrounding stress field during fracture propagation causes different degrees of stress shadow effects between fractures and the main controlling factors of stress shadow effects are fracture initiation sequence, perforation cluster spacing and well spacing. The perforation cluster spacing varies with the initiation sequence, resulting in different stress shadow effects between fractures; for example, the smaller the perforation cluster spacing and well spacing are, the stronger the stress shadow effects are and the more seriously the fracture propagation inhibition arises. Moreover, as the spacing of perforation clusters and well spacing increases, the stress shadow effects decrease and the fracture propagation follows an almost straight pattern. In addition, the computed results of the dynamic distribution of stress-dependent unstable dynamic propagation of fractures under different stress fields are summarised.

A state-of-art review of stress shadow effects and continuum-discontinuum methods for stress-dependent unstable dynamic propagation of multiple hydraulic fractures are well summarized and analysed. This paper can provide a reference for those engaged in the research of unstable dynamic propagation of multiple hydraulic structures and have a comprehensive grasp of the research in this field.