Establishment and Application of Fractal Capillary Tube Bundle Model of Porous Media

In view of the problem of statistical regression constant in the model of capillary tube bundles in the porous media, a capillary bundle percolation model with fractal geometry was reconstructed. The function expressions of the fractal coefficient and Kozeny constant were deduced. The relationship between the macroscopic fractal properties of porous media and the fractal dimension and the micro pore parameters were obtained. Results show: Fractal coefficient is a function of fractal dimension, maximum pore radius and minimum pore radius; The macroscopic physical properties of porous media are a function of the fractal dimension and the radius of the capillary (the maximum capillary radius and the minimum capillary radius). The expression does not contain any empirical or experimental constants. In the fractal capillary percolation model, the relationship between the three kinds of surface volume, skeleton volume and pore volume are the same as the traditional equal diameter straight capillary bundle model. The Kozeny constant can be accurately described by the function expression of the z-h coefficient, which is used for correcting the difference between real and ideal porous media model

The complementary graphene growth and etching revealed by large-scale kinetic Monte Carlo simulation

To fully understand the kinetics of graphene growth, large-scale atomic simulations of graphene islands evolution up to macro sizes (i.e., graphene islands of a few micrometers or with billions of carbon atoms) during growth and etching is essential, but remains a great challenge. In this paper, we developed a low computational cost large-scale kinetic Monte Carlo (KMC) algorithm, which includes all possible events of carbon attachments and detachments on various edge sites of graphene islands. Such a method allows us to simulate the evolution of graphene islands with sizes up to tens of micrometers during either growth or etching with a single CPU core. With this approach and the carefully fitted parameters, we have reproduced the experimentally observed evolution of graphene islands during both growth or etching on Pt(111) surface, and revealed more atomic details of graphene growth and etching. Based on the atomic simulations, we discovered a complementary relationship of graphene growth and etching-the route of graphene island shape evolution during growth is exactly the same as that of the etching of a hole in graphene and that of graphene island etching is exactly same as that of hole growth. The complementary relation brings us a basic principle to understand the growth and etching of graphene, and other 2D materials from atomic scale to macro size and the KMC algorithm is expected to be further developed into a standard simulation package for investigating the growth mechanism of 2D materials on various substrates

The Structural, Electronic, and Optical Properties of Ge/Si Quantum Wells: Lasing at a Wavelength of 1550 nm

The realization of a fully integrated group IV electrically driven laser at room temperature is an essential issue to be solved. We introduced a novel group IV side-emitting laser at a wavelength of 1550 nm based on a 3-layer Ge/Si quantum well (QW). By designing this scheme, we showed that the structural, electronic, and optical properties are excited for lasing at 1550 nm. The preliminary results show that the device can produce a good light spot shape convenient for direct coupling with the waveguide and single-mode light emission. The laser luminous power can reach up to 2.32 mW at a wavelength of 1550 nm with a 300-mA current. Moreover, at room temperature (300 K), the laser can maintain maximum light power and an ideal wavelength (1550 nm). Thus, this study provides a novel approach to reliable, efficient electrically pumped silicon-based lasers

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

The Role of Information Systems in Healthcare

Fundamental changes have been happening in healthcare organizations and delivery in these decades, including more accessible physician information, the low-cost collection and sharing of clinical records, and decision support systems, among others. Emerging information systems and technologies play a signification role in these transformations. To extend the understanding and the implications of information systems on healthcare, my dissertation investigates the influence of information systems on enhancing healthcare operations. The findings reveal the practical value of digitalization in indicating healthcare providers’ cognitive behaviors, responding to healthcare crises, and improving medical performance. The first essay investigates the unrevealed value of a special type of user-generated content in healthcare operations. In today’s social media world, individuals are willing to express themselves on various online platforms. This user-generated content posted online help readers get easy assess to individuals’ features, including but not limited to personality traits. To study the impact of physicians’ personality traits on medicine behaviours and performance, we take a view from the perspective of user generated content posted by their supplier side as well as using physician statements which have been made available in medical review websites. It has been found that a higher openness score leads to lower mortality rates, reduced lab test costs, shorter time usage in hospitals treated by physicians with greater openness scores. Furthermore, taking these personality traits into consideration in an optimization problem of ED scheduling, the estimation of counterfactual analysis shows an average of 11.4%, 18.4%, and 17.8% reduction in in-hospital mortality rates, lab test expenditures, and lengths of stay, respectively. In future operation of healthcare, physicians’ personalities should be taken into account when healthcare resources are insufficient in times of healthcare pandemics like COVID-19, as our study indicates that health service providers personality is an actual influence on clinical quality. In the second essay, we focus on the influences of the most severe healthcare pandemic in these decades, COVID-19, on digital goods consumption and examine whether digital goods consumption is resilient to an individuals physical restriction induced by the pandemic. Leveraging the enforced quarantine policy during the COVID-19 pandemic as a quasi-experiment, we identify the influence of a specific factor, quarantine policy, on mobile app consumption in every Apple app store category in the short and long terms. In the perspective of better responding in the post-pandemic era, the quantitative findings provide managerial implications to the app industry as well as the stock market for accurately understanding the long-term impact of a significant intervention, quarantine, in the pandemic. Moreover, by using the conditional exogenous quarantine policy to instrument app users daily movement patterns, we are able to further investigate the digital resilience of physical mobility in different app categories and quantify the impact of an individuals physical mobility on human behavior in app usage. For results, we find that the reduction in 10% of ones physical mobility (measured in the radius of gyration) leads to a 2.68% increase in general app usage and a 5.44% rise in app usage time dispersion, suggesting practitioners should consider users physical mobility in future mobile app design, pricing, and marketing. In the third essay, we investigate the role of an emerging AI-based clinical treatment method, robot-assisted surgery (RAS), in transforming the healthcare delivery. As an advanced technique to help diminish the human physical and intellectual limitations in surgeries, RAS is expected to but has not been empirically proven to improve clinical performance. In this work, we first investigate the effect of RAS on clinical outcomes, controlling physicians’ self-selection behavior in choosing whether or not to use RAS treatment methods. In particular, we focus on the accessibility of RAS and explore how physician and patient heterogeneity affect the adoption of the RAS method, including learning RAS and using RAS

THE ROLE OF INFORMATION SYSTEMS IN HEALTHCARE

Fundamental changes have been happening in healthcare organizations and delivery in these decades, including more accessible physician information, the low-cost collection and sharing of clinical records, and decision support systems, among others. Emerging information systems and technologies play a signification role in these transformations. To extend the understanding and the implications of information systems on healthcare, my dissertation investigates the influence of information systems on enhancing healthcare operations. The findings reveal the practical value of digitalization in indicating healthcare providers' cognitive behaviors, responding to healthcare crises, and improving medical performance. The first essay investigates the unrevealed value of a special type of user-generated content in healthcare operations. In today's social media world, individuals are willing to express themselves on various online platforms. This user-generated content posted online help readers get easy assess to individuals' features, including but not limited to personality traits. To study the impact of physicians' personality traits on medicine behaviours and performance, we take a view from the perspective of user generated content posted by their supplier side as well as using physician statements which have been made available in medical review websites. It has been found that a higher openness score leads to lower mortality rates, reduced lab test costs, shorter time usage in hospitals treated by physicians with greater openness scores. Furthermore, taking these personality traits into consideration in an optimization problem of ED scheduling, the estimation of counterfactual analysis shows an average of 11.4%, 18.4%, and 17.8% reduction in in-hospital mortality rates, lab test expenditures, and lengths of stay, respectively. In future operation of healthcare, physicians' personalities should be taken into account when healthcare resources are insufficient in times of healthcare pandemics like COVID-19, as our study indicates that health service providers personality is an actual influence on clinical quality. In the second essay, we focus on the influences of the most severe healthcare pandemic in these decades, COVID-19, on digital goods consumption and examine whether digital goods consumption is resilient to an individual’s physical restriction induced by the pandemic. Leveraging the enforced quarantine policy during the COVID-19 pandemic as a quasi-experiment, we identify the influence of a specific factor, quarantine policy, on mobile app consumption in every Apple app store category in the short and long terms. In the perspective of better responding in the post-pandemic era, the quantitative findings provide managerial implications to the app industry as well as the stock market for accurately understanding the long-term impact of a significant intervention, quarantine, in the pandemic. Moreover, by using the conditional exogenous quarantine policy to instrument app users’ daily movement patterns, we are able to further investigate the digital resilience of physical mobility in different app categories and quantify the impact of an individual’s physical mobility on human behavior in app usage. For results, we find that the reduction in 10% of one’s physical mobility (measured in the radius of gyration) leads to a 2.68% increase in general app usage and a 5.44% rise in app usage time dispersion, suggesting practitioners should consider users’ physical mobility in future mobile app design, pricing, and marketing. In the third essay, we investigate the role of an emerging AI-based clinical treatment method, robot-assisted surgery (RAS), in transforming the healthcare delivery. As an advanced technique to help diminish the human physical and intellectual limitations in surgeries, RAS is expected to but has not been empirically proven to improve clinical performance. In this work, we first investigate the effect of RAS on clinical outcomes, controlling physicians' self-selection behavior in choosing whether or not to use RAS treatment methods. In particular, we focus on the accessibility of RAS and explore how physician and patient heterogeneity affect the adoption of the RAS method, including learning RAS and using RAS. Investigating the decision-making process on RAS implementation in both the learning and using stages, we show the synergy of RAS implementation in alleviating healthcare racial disparity. Ultimately, the mechanism analysis will be conducted to reveal the underlying mechanism that induces the enhancement of surgical outcomes. For instance, the estimations tend to reveal that, more than surging clinical performance, RAS tends to increase standardization in time and steps when applying the treatment procedures. </p