Development of a comprehensive method to estimate the optical, thermal and electrical performance of a complex PV window for building integration

Increasing concerns over energy consumption and greenhouse gas emissions in buildings have contributed to the emerging of innovative PV glazing technologies to improve the building energy performance. However, some of these glazing systems have complex structures, making it challenging to investigate their optical, thermal and electrical performance for estimating their energy saving potential in buildings. In this research, a validated Computational Fluid Dynamics (CFD) combined with a ray-tracing model has been developed to accurately predict the optical, thermal and electrical performance of complex PV glazing systems under varying incident angles. A ray-tracing model is developed to calculate the light transmittance of the window as well as the solar energy absorbed by each solid-element and PV cells. To estimate the temperature profile (e.g., PV temperature and window temperature) and secondary heat of the window, ray-tracing results of solar flux absorbed by each layer are transferred into a validated CFD model as boundary conditions. Using the CFD combined ray-tracing calculation illustrated above, the Solar Heat Gain Coefficient (SHGC) of the complex PV window can be obtained. Furthermore, a PV modelling algorithm is developed to predict the power output based on the simulated PV temperature. This procedure is implemented to investigate a Crossed Compound Parabolic Concentrator Photovoltaic (CCPC-PV) window, which serves as an example of a complex PV glazing system in this study. The developed optical, thermal and electrical models have been validated through experimental tests. Additionally, new configurations have been designed to explore the impact of the pitch between adjacent optics on the SHGC and power output of the window. The results show that the original window (1.77 mm-pitch) possesses the maximum PV temperature of 64.73 °C and the maximum window inside surface temperature of 61.58 °C under National Fenestration Rating Council (NFRC) standard. Meanwhile the PV efficiency is 15.21 % and the SHGC is 0.463. The SHGC value of this innovative PV window is notably lower than that of a conventional double-glazed window with a SHGC value of 0.813, which reduces the possibility of overheating issues, especially during the summer

Off-diagonal Bethe Ansatz for the D3(1)D^{(1)}_3 model

The exact solutions of the $D^{(1)}_3$ model (or the $so(6)$ quantum spin chain) with either periodic or general integrable open boundary conditions are obtained by using the off-diagonal Bethe Ansatz. From the fusion, the complete operator product identities are obtained, which are sufficient to enable us to determine spectrum of the system. Eigenvalues of the fused transfer matrices are constructed by the $T-Q$ relations for the periodic case and by the inhomogeneous $T-Q$ one for the non-diagonal boundary reflection case. The present method can be generalized to deal with the $D^{(1)}_{n}$ model directly.Comment: 28 pages, no figure, published version. arXiv admin note: text overlap with arXiv:1902.0889

A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes

Quantum size effects on the perpendicular upper critical field in ultra-thin lead films

We report the thickness-dependent (in terms of atomic layers) oscillation behavior of the perpendicular upper critical field $H_{c2\perp}$ in the ultra-thin lead films at the reduced temperature ($t=T/T_c$). Distinct oscillations of the normal-state resistivity as a function of film thickness have also been observed. Compared with the $T_c$ oscillation, the $H_{c2\perp}$ shows a considerable large oscillation amplitude and a $\pi$ phase shift. The oscillatory mean free path caused by quantum size effect plays a role in $H_{c2\perp}$ oscillation.Comment: 4 pages, 4 figure

Rapid assessment of malnutrition based on GLIM diagnosis in Crohn’s disease

Background and aimsMalnutrition is strongly linked to adverse outcomes in patients with Crohn’s disease (CD). In this study, our objective was to validate the Global Leadership Initiative on Malnutrition (GLIM) criteria and develop a fast and accurate diagnostic approach for identifying malnutrition in CD patients.MethodsThis study assessed 177 CD patients from four general hospitals. The efficacy of the GLIM criteria for the diagnosis of CD malnutrition was compared. By analyzing the independent factors, a nomogram model was derived and internally validated to predict the diagnosis of malnutrition in patients with CD. Model performance was assessed using discrimination and calibration, decision curves, and net benefit analyses.ResultsCompared with the SGA criteria, the GLIM criteria was consistent in sensitivity (88.89%) and specificity (78.43%) [AUC = 0.84; 95% Confidence Interval (CI): 0.77–0.89]. The Harvey-Bradshaw index(HBI) score (OR: 1.58; 95% CI: 1.15–2.18), body mass index (OR: 0.41; 95% CI: 0.27–0.64), and mid-upper arm circumference (OR: 0.68; 95% CI: 0.47–0.9) were independent factors associated with malnutrition. The nomogram was developed based on these indicators showing good discrimination in malnutrition diagnosis (AUC = 0.953; 95% CI: 0.922–0.984), with agreement after calibration curve and decision curve analysis.ConclusionThe GLIM criteria are appropriate for diagnosing malnutrition in CD patients. The HBI score may be used to diagnose malnutrition in patients with CD and become a possible selection for the GLIM etiologic criteria of inflammation. The HBM nomogram could be a simple, rapid, and efficient method for diagnosing malnutrition in CD patients