Polyimide (PI) is one of the best engineering plastics with excellent thermal, mechanical, dielectric performance and chemical stability. Among various PI products, film is one of the most dominant ...types. If PI films are to be applied to optical devices and display equipment, it is necessary to improve their optical transparency in the ultraviolet–visible region. However, improving the optical transparency of PI films would affect their thermal performance including thermal and dimensional stability. Therefore, how to balance optical and thermal performance to obtain highly transparent PI films with the best overall performance becomes a focus of current research field. In this review, we categorize transparent PI films into highly transparent PI, transparent PI with high glass transition temperature (Tg) and transparent PI with low coefficient of thermal expansion (CTE) based on their optical and thermal properties. Then, we review research progress in the field of polymer molecular design including synthesis strategies and properties in recent 5 years, to inspire research ideas and elaborate on the possible ways to develop high‐performance, colorless and transparent PI materials.
Building high‐performance colorless and transparent polyimide (PI) films at the molecular level requires balancing the light transmittance, thermal stability, dimensional stability and other properties of PIs. The main strategy is to inhibit the formation of charge transfer complexes intermolecularly and intramolecularly, and to improve the rigidity of the molecule. This review focuses on how to achieve this goal.
Summary
Background and Aims
We aimed to define gender‐specific, optimal alanine aminotransferase (ALT) cut‐off values for the prediction of significant liver histological changes (SLHC) in Chinese ...patients with grey zone (GZ) chronic hepatitis B (CHB) and normal ALT.
Methods
In a retrospective study, we included 1101 consecutive patients with GZ CHB and normal ALT assigned to training or internal validation cohorts. We included an independent cohort of 842 patients for external validation. We performed receiver operating characteristic (ROC) curve, smoothed curve fitting, and threshold effect analyses to determine optimal ALT cut‐off values. Area under the curve (AUC) values were calculated to assess their predictive performance.
Results
A proportion of 79.3% of patients with GZ CHB and normal ALT (≤40 U/L) had SLHC. ROC curve analysis initially identified optimal ALT cut‐off values of 29 U/L (male) and 22 U/L (female). After smoothed curve fitting and threshold effect analyses, new optimal cut‐off values were 27 U/L for males and 24 U/L for females. AUCs for these values were 0.836 (male) and 0.833 (female) in the internal validation cohort, and 0.849 (male) and 0.844 (female) in the external validation cohort. The accuracy and discriminative ability of the newly defined ALT cut‐off values were greater than those of the current recommendations.
Conclusion
This study established novel optimal ALT cut‐off values for more precise prediction of SLHC among Chinese patients with GZ CHB and normal ALT levels. This may help identify individuals who will benefit from timely antiviral therapy.
1101 antiviral treatment naïve CHB patients in the grey zone (GZ) with normal ALT 1.60.1%‐79.3% in GZ CHB patient groups with different upper limits of normal (ULNs) for ALT have SLHC 2. The new optimal ALT cut‐offs to predict SLHC were 27U/L (male) and 24U/L (female).
Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical ...phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here, we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information-processing and for exploring the intriguing physics behind this power.
The link between PM2.5 exposure and adverse health outcomes is well documented from studies across the world. However, the reported effect estimates vary across studies, locations and constituents. ...We aimed to conduct a meta-analysis on associations between short-term exposure to PM2.5 constituents and mortality using city-specific estimates, and explore factors that may explain some of the observed heterogeneity.
We systematically reviewed epidemiological studies on particle constituents and mortality using PubMed and Web of Science databases up to July 2015.We included studies that examined the association between short-term exposure to PM2.5 constituents and all-cause, cardiovascular, and respiratory mortality, in the general adult population. Each study was summarized based on pre-specified study key parameters (e.g., location, time period, population, diagnostic classification standard), and we evaluated the risk of bias using the Office of Health Assessment and Translation (OHAT) Method for each included study. We extracted city-specific mortality risk estimates for each constituent and cause of mortality. For multi-city studies, we requested the city-specific risk estimates from the authors unless reported in the article. We performed random effects meta-analyses using city-specific estimates, and examined whether the effects vary across regions and city characteristics (PM2.5 concentration levels, air temperature, elevation, vegetation, size of elderly population, population density, and baseline mortality).
We found a 0.89% (95% CI: 0.68, 1.10%) increase in all-cause, a 0.80% (95% CI: 0.41, 1.20%) increase in cardiovascular, and a 1.10% (95% CI: 0.59, 1.62%) increase in respiratory mortality per 10μg/m3 increase in PM2.5. Accounting for the downward bias induced by studies of single days, the all-cause mortality estimate increased to 1.01% (95% CI: 0.81, 1.20%). We found significant associations between mortality and several PM2.5 constituents. The most consistent and stronger associations were observed for elemental carbon (EC) and potassium (K). For most of the constituents, we observed high variability of effect estimates across cities.
Our meta-analysis suggests that (a) combustion elements such as EC and K have a stronger association with mortality, (b) single lag studies underestimate effects, and (c) estimates of PM2.5 and constituents differ across regions. Accounting for PM mass in constituent's health models may lead to more stable and comparable effect estimates across different studies.
PROSPERO: CRD42017055765.
•A meta-analysis of acute effects of PM2.5 constituents on mortality was conducted.•EC and K had the strongest and most consistent association with mortality.•Single lag studies underestimate effects.•Mortality effects of PM2.5 and constituents differ across regions.
Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using ...Artificial Neural Network in air compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems' electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load management and look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Network and Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. The forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient of determination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and mean absolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield good results for compressors using variable speed drive (average R2 = 0.8 and no naïve forecasting), only the long short-term memory model gives acceptable results for compressors using on/off control (average R2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting naïve forecasting).
•Prediction of air compressors' electrical load using artificial neural network.•Modeling and prediction of maximum electric load demand.•Energy demand management for short-term electrical load of an air compressor.•Using the predicted energy consumption to reduce air compressor demand charges.
Coal pillars are formed by excavation and mining activities in an intact coal seam and play a key role in underground coal mines. Most previous investigations of pillars have mainly focused on ...existing coal pillar failures. However, few scholars have investigated the failure mechanisms of coal pillars during their formation process. This paper focuses on the failure of a 7 m-wide coal pillar that caused the large deformation of a tailgate in the Sijiazhuang coal mine in China. Field tests and numerical modeling were used to study the initiation, propagation, and failure of cracks within this gob-side coal pillar during its formation. Field monitoring revealed that the maximum roof-to-floor and rib-to-rib convergence reached 860 mm and 1460 mm, respectively. The coal pillar became a yield pillar with substantial fractures. A numerical model was built using UDEC Trigon logic and calibrated with laboratory tests and RQD methods. Both the natural roadway deformation and crack distribution in the coal pillar were simulated. A FISH function was used to document the propagation of shear and tensile cracks in pillars with different
W/H
ratios, and a damage parameter was adopted to evaluate the failure of these pillars. The results suggest that the most appropriate pillar width is 10 m. Field trials prove that a 10 m-wide coal pillar combined with optimized support measures can effectively control deformation around the tailgate.
A composite separator is prepared with polyacrylonitrile (PAN) and metal-organic framework (MOF) particles by electrospinning, and its physical properties, battery performances and the mechanism of ...how it improves the performance of high energy density lithium battery are studied. The MOF/PAN separator shows an outstanding thermal stability, an ultra-high electrolyte uptake (794±30%), a high ionic conductivity (2.83 mS cm−1 at 25 °C) and a wide electrochemical window (5.2 V vs. Li+/Li). The Li||separator||Li half-cell can operate stably for 1000 h at 5 mA cm−2. A strong electrostatic interaction between MOF and PF6- in the electrolyte inhibits movement of the anions and enhances the Lithium-ion transference number (tLi+) up to 0.74, which is 64.4% higher than that of PAN separator. After 200 cycles at 0.2 C, the interfacial resistance between separator and electrode only increases from 22.00 to 30.58 Ω. The initial discharge capacity of the NCM811||10% MOF/PAN separator||Li battery system is 166.3 mAh g−1 with 81.3% capacity retention after 250 cycles at 5 C. This research offers an idea to design MOF-based composite separator that makes NCM811||Li battery outstanding in electrochemical stability and high rate performance.
Directional strain sensing is essential for advanced sensor applications in the field of human‐machine interfaces and healthcare. Here, the angle dependent anisotropic strain sensitivity caused by ...charge carriers percolating through cross‐linked gold nanoparticle (GNP) networks is studied and these versatile materials are used for the fabrication of wearable triaxial pulse and gesture sensors. More specifically, the anisotropic response of 1,9‐nonanedithiol cross‐linked GNP films is separated into geometric and piezoresistive contributions by fitting the measured data with an analytic model. Hereby, piezoresistive coefficients of g11 ∼ 32 and g12 ∼ 21 are extracted, indicating a slightly anisotropic response behavior of the GNP‐based material. To use the material for healthcare applications, arrangements of three GNP transducers are patterned lithographically and fully embedded into a highly flexible silicone polymer (Dragon Skin 30). The new encapsulation method ensures good and robust electrical contacts and enables facile handling and protection from external influences. A facile read‐out with wireless data transmission using off‐the‐shelf electrical components underlines the great potential of these devices for applications as skin‐wearable healthcare sensors.
The angle dependent response of strain‐sensitive composites from organically cross‐linked gold nanoparticles is investigated. A weak response anisotropy is observed and further exploited in multiaxial strain sensors. By applying a new protocol for embedding multiple transducers into a silicone matrix, robust triaxial sensors for healthcare applications, suitable for pulse monitoring and for the classification of hand gestures, are fabricated.
Heating, ventilation, and air conditioning systems are the largest consumers of energy in commercial and residential sectors. Minimizing their energy consumption without compromising indoor air ...quality and thermal comfort would result in environmental and financial benefits. However, most buildings still utilize constant air volume systems with on/off control to meet the thermal loads. Previous studies show that CO2-based demand-controlled ventilation is the most widely used strategy to determine the optimal level of supply air volume. Conventional CO2 mass balanced models, however, do not take the thermal comfort level into account. This paper aims to develop and implement a practical and efficient optimization framework to help office building designers and operators enhance thermal comfort and indoor air quality while minimizing energy costs for ventilation using demand-controlled ventilation. The objective function was constructed to consider thermal comfort indices, indoor air quality, and ventilation power demand. Long Short-Term Memory networks in conjunction with a generic algorithm optimization were utilized to generate the search spaces and solve the objective function. The proposed optimization strategy achieved near-optimal results within approximately 1 h of computation time. The findings indicate a reduction of the average ventilation power by 5.6% compared to the current on/off control approach and a slight increase of 0.25% in ventilation power when compared against the minimum ventilation rate recommended by ASHRAE. Additionally, the optimized approach leads to a saving of 26.9 kg per day of greenhouse gas emissions in terms of carbon dioxide equivalent.
•Demand-controlled ventilation may have unexpected effects on thermal comfort of a breathing zone.•ASHRAE recommended minimum ventilation may not satisfy the indoor air quality and thermal comfort requirements.•Using Long Short-Term Memory can create enhanced search spaces for demand-controlled ventilation applications.•Optimizing the demand-controlled ventilation to meet the indoor CO2 concentration recommendation and the thermal comfort recommendation.
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