MODELING OF STRAIN EFFECT ON THERMAL AND ELECTRICAL TRANSPORT PROPERTIES OF SI/GE NANOCOMPOSITES AND ITS APPLICATIONS

Nanocomposites are composite materials which incorporate nanosized particles, platelets or fibers. The addition of nanosized phases into the bulk matrix can lead to significantly different material properties compared to their macrocomposite counterparts. Due to their extraordinary properties, nanocomposites promise new applications in many fields such as ultra-high strength and ultra-light automotive parts, non-linear optics, biomedical applications, sensors and actuators, and thermoelectric devices. The design and fabrication of nanocomposite structures, devices and systems can be accelerated by developing accurate and efficient computational tools that can describe the properties and behavior of the nanocomposites. However, the development of such tools is challenging due to the multiscale nature of the materials. In addition, many devices where nanocomposites are employed are multiphysics systems with interactions of the mechanical, thermal and electrical energy domains. In such systems, while mechanical deformation is dependent on the temperature change, the thermal and electrical transport properties are functions of mechanical strain. In this work, we develop theoretical and computational models to address these issues and investigate the strain effect on the thermal and electrical transport properties in Si/Ge nanocomposites. We model strain effect on the phonon thermal conductivities in the Si/Ge nanocomposite materials by combining the strain dependent lattice dynamics and the ballistic phonon Boltzmann transport equation (BTE). The Seebeck coefficient and electrical conductivity of the Si/Ge nanocomposites are calculated by using an analytical model derived from the BTE under the relaxation-time approximation. The effect of strain is incorporated into the analytical model through strain induced energy shift and effective mass variation calculated from the deformation potential theory and a degenerate kp method at the zone-boundary X point. By using the models, strain effect on the thermoelectric figure of merit is investigated for n-type Si/Ge nanocomposite materials. Our calculations reveal that in the 300 − 800 K temperature range, uniaxial tensile strain along \u3c 100 \u3e direction increases dimensionless figure of merit parallel to the tension, and biaxial tensile strain along [100] and [010] directions decreases it at low temperatures and increases it at high temperatures in the tension directions. Shear strain and compressive uniaxial and biaxial strains decrease the figure of merit. At 800K with an electron concentration of 10^19/cm^3, 1% uniaxial tensile strain can increase the figure of merit of Si(0.8)Ge(0.2) nanocomposites by as much as 14%. In light of nanocomposites\u27 high electrical to thermal conductivity ratio, we propose to use Si/Ge nanocomposite materials to improve the performance of micro thermal actuators. The high electrical to thermal conductivity ratio of Si/Ge nanocomposites is utilized to facilitate a rapid temperature change within a short distance, enabling a high temperature increase in a large region of the actuator beams. The total structural thermal expansion and consequently the actuation distance can be increased significantly. A top-down quasicontinuum multiscale model is presented for computational analysis of the nanocomposite based thermal actuators. Numerical results indicate that incorporating Si/Ge nanocomposites in thermal actuators can significantly increase their energy efficiency and mechanical performance. In addition, parametric studies show that the size of the nanocomposite region and atomic percentage of the material components have significant effects on the overall performance of the actuators

Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network

The electricity consumption forecasting is a challenging task, because the predictive accuracy is easily affected by multiple external factors, such as society, economics, environment, as well as the renewable energy, including hydro power, wind power and solar power. Particularly, in the smart grid with large amount of data, how to extract valuable information of those external factors timely is the key to the success of electricity consumption forecasting. A method of probability density forecasting based on Least Absolute Shrinkage and Selection Operator-Quantile Regression Neural Network (LASSO-QRNN) is proposed in this paper. First, important features are extracted from external factors affecting the electricity consumption forecasting by LASSO regression. Then, the LASSO-QRNN model is constructed to predict annual electricity consumption. The results of electricity consumption forecasting under different quantiles in the next several years are evaluated. Besides, we introduce kernel density estimation into our LASSO-QRNN model, which can give a probability distribution instead of a single-valued prediction. The prediction accuracy is evaluated through the empirical analyses from the Guangdong province dataset in China and the California dataset in the United States. The simulation results demonstrate that the proposed method provides better performance for electricity consumption forecasting, in comparison with existing quantile regression neural network (QRNN), back-propagation of errors neural network (BP), radial basis function neural network (RBF), quantile regression (QR) and nonlinear quantile regression (NLQR). LASSO-QRNN can not only better learn the high-dimensional data in electricity consumption forecasting, but also provide more precise results

Surgical treatment of the osteoporotic spine with bone cement-injectable cannulated pedicle screw fixation: technical description and preliminary application in 43 patients

OBJECTIVES: To describe a new approach for the application of polymethylmethacrylate augmentation of bone cement-injectable cannulated pedicle screws. METHODS: Between June 2010 and February 2013, 43 patients with degenerative spinal disease and osteoporosis (T-scor

Roles of immune microenvironment in the female reproductive maintenance and regulation: novel insights into the crosstalk of immune cells

Female fertility decline is an accumulative consequence caused by complex factors, among them, the disruption of the immune profile in female reproduction stands out as a crucial contributor. Presently, the effects of immune microenvironment (IME) on the female reproductive process have attracted increasing attentions for their dynamic but precisive roles. Immunocytes including macrophages, dendritic cells, T cells, B cells and neutrophils, with diverse subpopulations as well as high plasticity functioned dynamically in the process of female reproduction through indirect intercellular communication via specific cytokine release transduced by molecular signal networks or direct cell-cell contact to maintain the stability of the reproductive process have been unveiled. The immune profile of female reproduction in each stage has also been meticulously unveiled. Especially, the application of single-cell sequencing (scRNA-seq) technology in this process reveals the distribution map of immune cells, which gives a novel insight for the homeostasis of IME and provides a research direction for better exploring the role of immune cells in female reproduction. Here, we provide an all-encompassing overview of the latest advancements in immune modulation within the context of the female reproductive process. Our approach involves structuring our summary in accordance with the physiological sequence encompassing gonadogenesis, folliculogenesis within the ovaries, ovulation through the fallopian tubes, and the subsequent stages of embryo implantation and development within the uterus. Our overarching objective is to construct a comprehensive portrayal of the immune microenvironment (IME), thereby accentuating the pivotal role played by immune cells in governing the intricate female reproductive journey. Additionally, we emphasize the pressing need for heightened attention directed towards strategies that focus on immune interventions within the female reproductive process, with the ultimate aim of enhancing female fertility

A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial Informatics, Key words: modularity, medical device customization, multi-robot system, robot learning, visual servoing, robot sewin

Clinical evaluation of a bone cement-injectable cannulated pedicle screw augmented with polymethylmethacrylate: 128 osteoporotic patients with 42 months of follow-up

OBJECTIVES: To evaluate the safety and efficacy of a novel bone cement-injectable cannulated pedicle screw augmented with polymethylmethacrylate in osteoporotic spinal surgery. METHODS: This study included 128 patients with osteoporosis (BMD T-score –3.2±1.9; range, –5.4 to –2.5) who underwent spinal decompression and instrumentation with a polymethylmethacrylate-augmented bone cement-injectable cannulated pedicle screw. Postoperative Visual Analogue Scale scores and the Oswestry Disability Index were compared with preoperative values. Postoperative plain radiographs and computed tomography (CT) scans were performed immediately after surgery; at 1, 3, 6, and 12 months; and annually thereafter. RESULTS: The mean follow-up time was 42.4±13.4 months (range, 23 to 71 months). A total of 418 polymethylmethacrylate-augmented bone cement-injectable cannulated pedicle screws were used. Cement extravasations were detected in 27 bone cement-injectable cannulated pedicle screws (6.46%), mainly in cases of vertebral fracture, without any clinical sequela. The postoperative low back and lower limb Visual Analogue Scale scores were significantly reduced compared with the preoperative scores (o0.01), and similar results were noted for the Oswestry Disability Index score (po0.01). No significant screw migration was noted at the final follow-up relative to immediately after surgery (po0.01). All cases achieved successful bone fusion, and no case required revision. No infection or blood clots occurred after surgery. CONCLUSIONS: The polymethylmethacrylate-augmented bone cement-injectable cannulated pedicle screw is safe and effective for use in osteoporotic patients who require spinal instrumentation

The impact of social support for older adults in nursing homes on successful aging: a moderated mediation model

ObjectiveTo investigate the connection between social support (SS) and successful aging (SA) in older adults residing in nursing homes, examining the mediating role of meaning in life (MIL). Additionally, this study aims to assess whether frailty moderates the mediation model.MethodsA cross-sectional survey approach was employed to recruit older adults from six nursing homes in Sichuan Province between August 2022 and December 2022. Questionnaires, including the General Information Questionnaire, Social Support Rating Scale (SSRS), Meaning in Life Questionnaire (MLQ), Tilburg Frailty Indicator (TFI), and Successful Aging Inventory (SAI), were administered. Data obtained from the completed questionnaires were analyzed using SPSS and its macro program PROCESS.ResultsSS emerged as a noteworthy positive predictor of SA in older adults of nursing homes. MIL was identified as a partial mediator in the link between SS and SA. Furthermore, frailty attenuated the positive predictive impact of MIL on SA and moderated the latter part of the mediation model, wherein SS influences SA through MIL. The influence of MIL on SA was more pronounced in older adults with lower frailty levels in nursing homes, while it was diminished in those with higher levels of frailty.ConclusionApart from ensuring the availability of essential medical resources in long-term care for older adults, workers in nursing homes should also recognize the significance of “spiritual aging” to cultivate a sense of MIL among older adults. Simultaneously, attention must be directed toward screening for frailty indicators in older adults. Psychological care and physical exercise programs should be intensified for older adults with a high level of frailty, aiming to decelerate the progression of frailty in nursing home residents. This approach leverages the mediating role of MIL and the moderating influence of frailty, ultimately enhancing SA and promoting healthy aging in older adults within nursing home settings