丹尼尔斯大厦 加拿大多伦多

正多伦多大学丹尼尔斯建筑、景观和设计学院的设计采用了分期的方法,包括修复和扩建以前称为知识学院的标志性建筑,将现有建筑物改造成与教学和需求更相关的框架体系。这个十九世纪遗址的总体规划是通过对预期用途模式和场地生态的分析而制定的,目的是重新定位校园西南角与安大略湖的轴线,并为学院创造新的特性。丹尼尔斯建筑、景观和设计学院需要一个可持续发展的新型工     

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating seismic reliability analysis, on a transportation network case study. Two distinct deep neural network surrogates are constructed and studied: (1) a classifier surrogate that speeds up the connectivity determination of networks and (2) an end‐to‐end surrogate that replaces modules such as roadway status realization, connectivity determination, and connectivity averaging. Numerical results from k‐terminal connectivity analysis of a California transportation network subject to a probabilistic earthquake event demonstrate the effectiveness of the proposed surrogates in accelerating reliability analysis while achieving accuracies of at least 99%.     

Elliptical slot based polarization insensitive compact and flexible chipless RFID tag

A miniaturized, polarization insensitive, and fully passive chipless radio frequency identification tag is proposed in this research article. The realized tag is based on slotted elliptical structures in a nested loop fashion with identical lengths and widths of slot resonators. Alteration of data sequence is accomplished by addition and elimination of nested resonators in the geometric structure. The tag is capable to encode 10 bits of data and covers spectral range from 3.6 to 15.6 GHz. The formulated structure demonstrates polarization insensitive characteristic. The data encoding structure is analyzed and optimized for different substrates that are, Rogers RT/duroid/5880, Rogers RT/duroid/5870, and Taconic TLX‐0 over the miniaturized footprint of 22.8 × 16 mm². The presented tag is robust, novel, compact, and flexible exhibiting a stable response to impinging electromagnetic waves at various angles of incidence.     

A new predictor parameter for production rate of ornamental stones

Bulletin of Engineering Geology and the Environment - In this study, a new predictor parameter (NPP), which is based on the product of uniaxial compressive strength (UCS) and Mohs hardness (MH),...     

Potent Antidiabetic Activity and Metabolite Profiling of Melicope Lunu‐ankenda Leaves

Diabetes mellitus is normally characterized by chronic hyperglycemia associated with disturbances in the fat, carbohydrate, and protein metabolism. There is an increasing trend of using natural products instead of synthetic agents as alternative therapy for disorders due to their fewer side effects. In this study, antidiabetic and antioxidant activities of different Melicope lunu‐ankenda (ML) ethanolic extracts were evaluated using inhibition of α‐glucosidase and 2,2‐diphenyl‐l‐picrylhydrazyl (DPPH) radicals scavenging activity, respectively; whereas, proton nuclear magnetic resonance (¹H NMR) and ultra‐high performance liquid chromatography‐tandem mass spectrometric (UHPLC‐MS/MS) techniques were used for metabolite profiling of ML leaf extracts at different concentrations of ethanol and water. Sixty percent of ethanolic ML extract showed highest inhibitory effect against α‐glucosidase enzyme (IC₅₀ of 37 μg/mL) and DPPH scavenging activity (IC₅₀ of 48 μg/mL). Antidiabetic effect of ML extracts was also evaluated in vivo and it was found that the high doses (400 mg/Kg BW) of ML extract exhibited high suppression in fasting blood glucose level by 62.75%. The metabolites responsible for variation among ML samples with variable ethanolic levels have been evaluated successfully using ¹H‐NMR–based metabolomics. The principal component analysis (PCA) and partial least squares(PLS) analysis scores depicted clear and distinct separations into 4 clusters representing the 4 ethanolic concentrations by PC1 and PC2, with an eigenvalue of 69.9%. Various ¹H‐NMR chemical shifts related to the metabolites responsible for sample difference were also ascribed. The main bioactive compounds identified attributing toward the separation included: isorhamnetin, skimmianine, scopoletin, and melicarpinone. Hence, ML may be used as promising medicinal plant for the development of new functional foods, new generation antidiabetic drugs, as a single entity phytomedicine or in combinational therapy.     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

Workload Distribution in Fault Simulation

Simulation at the gate level is computationally very expensive.Parallel processing is one technique to reduce simulation time.Possessing knowledge of the distribution of computational activity insimulation can aid in parallelizing it efficiently. We present a newcharacterization of the distribution of the computational workload infault simulation. An empirical analysis shows that the workloaddistribution is circuit specific, and is largely independent of thevector set being simulated. An inexpensive method to predict theworkload distribution is also discussed.