Moisture-Driven Degradation Mechanisms in the Viscoelastic Properties of TPU-Based Syntactic Foams

Syntactic foams have found widespread usage in various applications including, marine, aerospace, automotive, pipe insulation, electrical cable sheathing, and shoe insoles. However, syntactic foams are often exposed to moisture when used in these applications that potentially alter their viscoelastic properties, which influences their long-term durability. Despite their significance, previous research has mainly focused on experimental studies concerning mechanical property changes resulting from filler loading and different matrix materials, overlooking the fundamental mechanisms resulting from moisture exposure. The current paper aims to bridge this gap in knowledge by elucidating the impact of long-term moisture exposure on TPU and TPU-based syntactic foam through multi-scale materials characterization approaches. Here, we choose a flexible syntactic foam manufactured using thermoplastic polyurethane elastomer (TPU) reinforced with glass microballoons (GMB) through selective laser sintering. Specifically, the research investigates the influence of moisture exposure time and the volume fraction of GMB on chemical and microphase morphological changes, along with their associated mechanisms. The study further examines how these microphase morphological changes manifest in viscoelastic properties

Physics-Constrained Neural Network for the Analysis and Feature-Based Optimization of Woven Composites

Woven composites are produced by interlacing warp and weft fibers in a pattern or weave style. By changing the pattern or material, the mechanical properties of woven composites can be significantly changed; however, the role of woven composite architecture (pattern, material) on the mechanical properties is not well understood. In this paper, we explore the relationship between woven composite architectures (weave pattern, weave material sequence) and the corresponding modulus through our proposed Physics-Constrained Neural Network (PCNN). Furthermore, we apply statistical learning methods to optimize the woven composite architecture to improve mechanical responses. Our results show that PCNN can effectively predict woven architecture for the desired modulus with much higher accuracy than several baseline models. PCNN can be further combined with feature-based optimization to determine the optimal woven composite architecture at the initial design stage. In addition to relating woven composite architecture to its mechanical responses, our research also provides an in-depth understanding of how architectural features govern mechanical responses. We anticipate our proposed frameworks will primarily facilitate the woven composite analysis and optimization process and be a starting point to introduce Physics knowledge-guided Neural Networks into the complex structural analysis

Autoimmune thyroid disease in pregnancy

Background: Maternal thyroid dysfunction is the common endocrinological disorder during pregnancy. It is associated with adverse maternal and foetal outcomes like pre-eclampsia, GDM, preterm, IUGR and miscarriage. Objective of this study was to study the prevalence of thyroid dysfunction in women with thyroid autoimmunity and its relation with adverse maternal and foetal outcomes.Methods: It was an observational study undertaken at RRMCH from May-2013 to Oct-2013. Pregnant women were screened for thyroid dysfunction. Women with altered thyroid function tests were screened for anti TPO antibodies. Mothers with thyroid dysfunction and anti TPO antibody positive were compared with anti TPO negative mothers.Results: Study group included 1000 pregnant women, 126 women had hypothyroidism. Anti TPO antibodies were positive in 26 women. Prevalence of hypothyroidism and autoimmunity were 7.5% and 12.8% respectively. 46.2% women with hypothyroidism and thyroid autoimmunity had PE, P value <0.01, 7% had GDM with P value <0.603, 15.4% had IUGR with p value of 0.033.7, 7% women had IUD.Conclusions: Hypothyroidism and thyroid autoimmunity are common during pregnancy. They are associated with adverse maternal and foetal outcome. Screening for thyroid dysfunction and early initiation of treatment can prevent adverse maternal and fetal outcome. 

System Architecture for Low-Power Ubiquitously Connected Remote Health Monitoring Applications With Smart Transmission Mechanism

We present a novel smart transmission technique with seamless handoff mechanism to achieve ubiquitous connectivity using multiple on-chip radios targeting remote health monitoring applications. For the first time to the best of our knowledge, a system architecture for low-power ubiquitously connected multiparametric remote health monitoring system is proposed in this paper. The architecture proposed uses a generic adaptive rule engine for classifying the collected multiparametric data from patient and smartly transmit the data when only needed. The on-chip seamless handoff mechanism proposed aids for the ubiquitous connectivity with a very good energy savings by intelligent controlling of the multiple on-chip radios. The performance analysis of the proposed on-chip seamless handoff mechanism along with adaptive rule engine-based smart transmission mechanism achieves on an average of 50.39% of energy saving and 51.01% reduction in duty cycle of transmitter taken over 20 users compared with the continuous transmission. From the hardware complexity analysis made on the proposed seamless handoff controller and adaptive rule engine concludes that they require only 2082 CMOS transistors for real-time implementation

An Elaborate Data Set Characterizing the Mechanical Response of the Foot

Mechanical properties of the foot are responsible for its normal function and play a role in various clinical problems. Specifically, we are interested in quantification of foot mechanical properties to assist the development of computational models for movement analysis and detailed simulations of tissue deformation. Current available data are specific to a foot region and the loading scenarios are limited to a single direction. A data set that incorporates regional response, to quantify individual function of foot components, as well as the overall response, to illustrate their combined operation, does not exist. Furthermore, the combined three-dimensional loading scenarios while measuring the complete three-dimensional deformation response are lacking. When combined with an anatomical image data set, development of anatomically realistic and mechanically validated models becomes possible. Therefore, the goal of this study was to record and disseminate the mechanical response of a foot specimen, supported by imaging data. Robotic testing was conducted at the rear foot, forefoot, metatarsal heads, and the foot as a whole. Complex foot deformations were induced by single mode loading, e.g., compression, and combined loading, e.g., compression and shear. Small and large indenters were used for heel and metatarsal head loading, an elevated platform was utilized to isolate the rear foot and forefoot, and a full platform compressed the whole foot. Three-dimensional tool movements and reaction loads were recorded simultaneously. Computed tomography scans of the same specimen were collected for anatomical reconstruction a priori. The three-dimensional mechanical response of the specimen was nonlinear and viscoelastic. A low stiffness region was observed starting with contact between the tool and foot regions, increasing with loading. Loading and unloading responses portrayed hysteresis. Loading range ensured capturing the toe and linear regions of the load deformation curves for the dominant loading direction, with the rates approximating those of walking. A large data set was successfully obtained to characterize the overall and the regional mechanical responses of an intact foot specimen under single and combined loads. Medical imaging complemented the mechanical testing data to establish the potential relationship between the anatomical architecture and mechanical responses and to further develop foot models that are mechanically realistic and anatomically consistent. This combined data set has been documented and disseminated in the public domain to promote future development in foot biomechanics

The intermediate polar cataclysmic variable GK Persei 120 years after the nova explosion: a first dynamical mass study

We present a dynamical study of the intermediate polar and dwarf nova cataclysmic variable GK Persei (Nova Persei 1901) based on a multisite optical spectroscopy and R-band photometry campaign. The radial velocity curve of the evolved donor star has a semi-amplitude K-2 = 126.4 +/- 0.9 km s(-1) and an orbital period P = 1.996872 +/- 0.000009 d. We refine the projected rotational velocity of the donor star to v(rot) sin i = 52 +/- 2 km s(-1) that, together with K-2, provides a donor star to white dwarf mass ratio q = M-2/M-1 = 0.38 +/- 0.03. We also determine the orbital inclination of the system by modelling the phase-folded ellipsoidal light curve and obtain i = 67 degrees +/- 5 degrees. The resulting dynamical masses are M-1 = 1.03(-0.11)(+0.16) M-circle dot and M-2 = 0.39(-0.06)(+0.07) M-circle dot at 68 per cent confidence level. The white dwarf dynamical mass is compared with estimates obtained by modelling the decline light curve of the 1901 nova event and X-ray spectroscopy. The best matching mass estimates come from the nova light curve models and an X-ray data analysis that uses the ratio between the Alfven radius in quiescence and during dwarf nova outburst

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin