Hubungan IL-10 Dengan Serum Kreatinin Dan Terjadinya Komplikasi Pada Preeklampsia Perawatan Konservatif

Tujuan: mengetahui hubungan penurunan IL-10 dengan terjadinya peningkatan serum kreatinin (SK) dan terjadinya komplikasi preeklampsia.Bahan dan Metode: penelitian ini merupakan analitik observasional yang dilakukan pada 30 wanita preeklampsia berat tipe dini yang dilakukan perawatan konservatif. Dilakukan pemeriksaan IL-10 pada serum darah dengan metode ELISA sebelum perawatan konservatif dan kemudian dinilai luaran maternal (SK dan komplikasi preeklampsia)Hasil: didapatkan rerata kadar IL-10 pada preeklampsia tipe dini 0,71 ± 0,66 pg/mL, rerata luaran SK 0,83 ± 0,29, terjadinya komplikasi 9 kasus (edema paru, impending eklampsia, sindroma HELLP). Tidak didapatkan hubungan antara IL-10 dengan peningkatan serum kreatinin (p = 0,483) dan komplikasi preeklampsia (p = 0,828).Simpulan: IL-10 bukan merupakan faktor prediktif untuk luaran maternal pada preeklampsia perawatan konservatif

Greenhouse Gas Implications of Fleet Electrification Based on Big Data-Informed Individual Travel Patterns

Environmental implications of fleet electrification highly depend on the adoption and utilization of electric vehicles at the individual level. Past research has been constrained by using aggregated data to assume all vehicles with the same travel pattern as the aggregated average. This neglects the inherent heterogeneity of individual travel behaviors and may lead to unrealistic estimation of environmental impacts of fleet electrification. Using “big data” mining techniques, this research examines real-time vehicle trajectory data for 10,375 taxis in Beijing in one week to characterize the travel patterns of individual taxis. We then evaluate the impact of adopting plug-in hybrid electric vehicles (PHEV) in the taxi fleet on life cycle greenhouse gas emissions based on the characterized individual travel patterns. The results indicate that 1) the largest gasoline displacement (1.1 million gallons per year) can be achieved by adopting PHEVs with modest electric range (approximately 80 miles) with current battery cost, limited public charging infrastructure, and no government subsidy; 2) reducing battery cost has the largest impact on increasing the electrification rate of vehicle mileage traveled (VMT), thus increasing gasoline displacement, followed by diversified charging opportunities; 3) government subsidies can be more effective to increase the VMT electrification rate and gasoline displacement if targeted to PHEVs with modest electric ranges (80 to 120 miles); and 4) while taxi fleet electrification can increase greenhouse gas emissions by up to 115 kiloton CO₂-eq per year with the current grid in Beijing, emission reduction of up to 36.5 kiloton CO₂-eq per year can be achieved if the fuel cycle emission factor of electricity can be reduced to 168.7 g/km. Although the results are based on a specific public fleet, this study demonstrates the benefit of using large-scale individual-based trajectory data (big data) to better understand environmental implications of fleet electrification and inform better decision making

CO₂ Emissions Embodied in Interprovincial Electricity Transmissions in China

Existing studies on the evaluation of CO₂ emissions due to electricity consumption in China are inaccurate and incomplete. This study uses a network approach to calculate CO₂ emissions of purchased electricity in Chinese provinces. The CO₂ emission factors of purchased electricity range from 265 g/kWh in Sichuan to 947 g/kWh in Inner Mongolia. We find that emission factors of purchased electricity in many provinces are quite different from the emission factors of electricity generation. This indicates the importance of the network approach in accurately reflecting embodied emissions. We also observe substantial variations of emissions factors of purchased electricity within subnational grids: the provincial emission factors deviate from the corresponding subnational-grid averages from −58% to 44%. This implies that using subnational-grid averages as required by Chinese government agencies can be quite inaccurate for reporting indirect CO₂ emissions of enterprises’ purchased electricity. The network approach can improve the accuracy of the quantification of embodied emissions in purchased electricity and emission flows embodied in electricity transmission

Betweenness-Based Method to Identify Critical Transmission Sectors for Supply Chain Environmental Pressure Mitigation

To develop industry-specific policies for mitigating environmental pressures, previous studies primarily focus on identifying sectors that directly generate large amounts of environmental pressures (a.k.a. production-based method) or indirectly drive large amounts of environmental pressures through supply chains (e.g., consumption-based method). In addition to those sectors as important environmental pressure producers or drivers, there exist sectors that are also important to environmental pressure mitigation as transmission centers. Economy-wide environmental pressure mitigation might be achieved by improving production efficiency of these key transmission sectors, that is, using less upstream inputs to produce unitary output. We develop a betweenness-based method to measure the importance of transmission sectors, borrowing the betweenness concept from network analysis. We quantify the betweenness of sectors by examining supply chain paths extracted from structural path analysis that pass through a particular sector. We take China as an example and find that those critical transmission sectors identified by betweenness-based method are not always identifiable by existing methods. This indicates that betweenness-based method can provide additional insights that cannot be obtained with existing methods on the roles individual sectors play in generating economy-wide environmental pressures. Betweenness-based method proposed here can therefore complement existing methods for guiding sector-level environmental pressure mitigation strategies

CO₂ Emissions Embodied in Interprovincial Electricity Transmissions in China

Existing studies on the evaluation of CO₂ emissions due to electricity consumption in China are inaccurate and incomplete. This study uses a network approach to calculate CO₂ emissions of purchased electricity in Chinese provinces. The CO₂ emission factors of purchased electricity range from 265 g/kWh in Sichuan to 947 g/kWh in Inner Mongolia. We find that emission factors of purchased electricity in many provinces are quite different from the emission factors of electricity generation. This indicates the importance of the network approach in accurately reflecting embodied emissions. We also observe substantial variations of emissions factors of purchased electricity within subnational grids: the provincial emission factors deviate from the corresponding subnational-grid averages from −58% to 44%. This implies that using subnational-grid averages as required by Chinese government agencies can be quite inaccurate for reporting indirect CO₂ emissions of enterprises’ purchased electricity. The network approach can improve the accuracy of the quantification of embodied emissions in purchased electricity and emission flows embodied in electricity transmission

Estimating Missing Unit Process Data in Life Cycle Assessment Using a Similarity-Based Approach

In life cycle assessment (LCA), collecting unit process data from the empirical sources (i.e., meter readings, operation logs/journals) is often costly and time-consuming. We propose a new computational approach to estimate missing unit process data solely relying on limited known data based on a similarity-based link prediction method. The intuition is that similar processes in a unit process network tend to have similar material/energy inputs and waste/emission outputs. We use the ecoinvent 3.1 unit process data sets to test our method in four steps: (1) dividing the data sets into a training set and a test set; (2) randomly removing certain numbers of data in the test set indicated as missing; (3) using similarity-weighted means of various numbers of most similar processes in the training set to estimate the missing data in the test set; and (4) comparing estimated data with the original values to determine the performance of the estimation. The results show that missing data can be accurately estimated when less than 5% data are missing in one process. The estimation performance decreases as the percentage of missing data increases. This study provides a new approach to compile unit process data and demonstrates a promising potential of using computational approaches for LCA data compilation

Estimating Missing Unit Process Data in Life Cycle Assessment Using a Similarity-Based Approach

In life cycle assessment (LCA), collecting unit process data from the empirical sources (i.e., meter readings, operation logs/journals) is often costly and time-consuming. We propose a new computational approach to estimate missing unit process data solely relying on limited known data based on a similarity-based link prediction method. The intuition is that similar processes in a unit process network tend to have similar material/energy inputs and waste/emission outputs. We use the ecoinvent 3.1 unit process data sets to test our method in four steps: (1) dividing the data sets into a training set and a test set; (2) randomly removing certain numbers of data in the test set indicated as missing; (3) using similarity-weighted means of various numbers of most similar processes in the training set to estimate the missing data in the test set; and (4) comparing estimated data with the original values to determine the performance of the estimation. The results show that missing data can be accurately estimated when less than 5% data are missing in one process. The estimation performance decreases as the percentage of missing data increases. This study provides a new approach to compile unit process data and demonstrates a promising potential of using computational approaches for LCA data compilation

Estimating Missing Unit Process Data in Life Cycle Assessment Using a Similarity-Based Approach

In life cycle assessment (LCA), collecting unit process data from the empirical sources (i.e., meter readings, operation logs/journals) is often costly and time-consuming. We propose a new computational approach to estimate missing unit process data solely relying on limited known data based on a similarity-based link prediction method. The intuition is that similar processes in a unit process network tend to have similar material/energy inputs and waste/emission outputs. We use the ecoinvent 3.1 unit process data sets to test our method in four steps: (1) dividing the data sets into a training set and a test set; (2) randomly removing certain numbers of data in the test set indicated as missing; (3) using similarity-weighted means of various numbers of most similar processes in the training set to estimate the missing data in the test set; and (4) comparing estimated data with the original values to determine the performance of the estimation. The results show that missing data can be accurately estimated when less than 5% data are missing in one process. The estimation performance decreases as the percentage of missing data increases. This study provides a new approach to compile unit process data and demonstrates a promising potential of using computational approaches for LCA data compilation

Alignment Control of Carbon Nanotube Forest from Random to Nearly Perfectly Aligned by Utilizing the Crowding Effect

Alignment represents an important structural parameter of carbon nanotubes (CNTs) owing to their exceptionally high aspect ratio, one-dimensional property. In this paper, we demonstrate a general approach to control the alignment of few-walled CNT forests from nearly random to nearly ideally aligned by tailoring the density of active catalysts at the catalyst formation stage, which can be experimentally achieved by controlling the CNT forest mass density. Experimentally, we found that the catalyst density and the degree of alignment were inseparably linked because of a crowding effect from neighboring CNTs, that is, the increasing confinement of CNTs with increased density. Therefore, the CNT density governed the degree of alignment, which increased monotonically with the density. This relationship, in turn, allowed the precise control of the alignment through control of the mass density. To understand this behavior further, we developed a simple, first-order model based on the flexural modulus of the CNTs that could quantitatively describe the relationship between the degree of alignment (HOF) and carbon nanotube spacing (crowding effect) of any type of CNTs

Red-Light-Controllable Liquid-Crystal Soft Actuators via Low-Power Excited Upconversion Based on Triplet–Triplet Annihilation

A red-light-controllable soft actuator has been achieved, driven by low-power excited triplet–triplet annihilation-based upconversion luminescence (TTA-UCL). First, a red-to-blue TTA-based upconversion system with a high absolute quantum yield of 9.3 ± 0.5% was prepared by utilizing platinum­(II) tetra­phenyl­tetra­benzo­porphyrin (PtTPBP) as the sensitizer and 9,10-bis­(di­phenyl­phosphoryl)­anthracene (BDPPA) as the annihilator. In order to be employed as a highly effective phototrigger of photodeformable cross-linked liquid-crystal polymers (CLCPs), the PtTPBP&BDPPA system was incorporated into a rubbery polyurethane film and then assembled with an azotolane-containing CLCP film. The generating assembly film bent toward the light source when irradiated with a 635 nm laser at low power density of 200 mW cm^–2 because the TTA-UCL was effectively utilized by the azotolane moieties in the CLCP film, inducing their <i>trans</i>–<i>cis</i> photo­isomerization and an alignment change of the mesogens via an emission–reabsorption process. It is the first example of a soft actuator in which the TTA-UCL is trapped and utilized to create photomechanical effect. Such advantages of using this novel red-light-controllable soft actuator in potential biological applications have also been demonstrated as negligible thermal effect and its excellent penetration ability into tissues. This work not only provides a novel photo­manipulated soft actuation material system based on the TTA-UCL technology but also introduces a new technological application of the TTA-based upconversion system in photonic devices