Learning to predict ice accretion on electric power lines

Ice accretion on power transmission and distribution lines is one of the major causes of power grid outages in northern regions. While such icing events are rare, they are very costly. Thus, it would be useful to predict how much ice will accumulate. Many current ice accretion forecasting systems use precipitation-type prediction and physical ice accretion models. These systems are based on expert knowledge and experimentations. An alternative strategy is to learn the patterns of ice accretion based on observations of previous events. This paper presents two different forecasting systems that are obtained by applying the learning algorithm of Support Vector Machines to the outputs of a Numerical Weather Prediction model. The first forecasting system relies on an icing model, just as the previous algorithms do. The second system learns an effective forecasting model directly from meteorological features. We use a rich data set of eight different icing events (from 2002 to 2008) to empirically compare the performance of the various ice accretion forecasting systems. Several experiments are conducted to investigate the effectiveness of the forecasting algorithms. Results indicate that the proposed forecasting system is significantly more accurate than other state-of-the-art algorithms.     

Formulation of apple juice beverages containing whey protein isolate or whey protein hydrolysate based on sensory and physicochemical analysis

Whey protein isolate (WPI) or its bioactive hydrolysate (WPH) was mixed with apple juice along with sweetener, obtaining a series of beverages with various pH values. Sedimentation of WPI‐apple juice and WPH‐apple juice beverages was inhibited at pH values of 3.15 and 3.47, respectively. The higher the whey protein content, the more undesirable was the taste of samples. A clearer appearance with smaller particle size was obtained with WPH‐apple juice formulations compared to WPI‐apple juice formulations at pH values closer to the pI of the whey proteins. Intrinsic viscosity measurements revealed the weaker associations of peptides compared with protein molecules.     

Application of surrogate artificial intelligent models for real‐time flood routing

Developing a robust flood forecasting and warning system (FFWS) is essential in flood‐prone areas. Hydrodynamic models, which are a major part of such systems, usually suffer from computational instabilities and long runtime problems, which are particularly important in real‐time applications. In this study, two artificial intelligence models, namely artificial neural network (ANN) and adaptive neuro‐fuzzy inference system (ANFIS), were used for flood routing in an FFWS in Madarsoo river basin, Iran. For this purpose, different rainfall patterns were transformed to run‐off hydrographs using the Hydrologic Engineering Center (HEC)‐1 hydrological model and routed along the river using HEC river analysis system RAS hydrodynamic model. Then, the simulated hydrographs with different lag times were used as inputs for training of ANN and ANFIS models to simulate flood hydrograph at the basin outlet. Results showed that the simulations obtained from ANN and ANFIS coincided with the results simulated by the HEC‐RAS, and application of such models is strongly suggested as a backup tool for flood routing in FFWSs.     

Application of Codes Performance‐Based Design Procedures for a Tall Building Using Recorded and Spectrally Modified Ground Motions

There is a growing tendency toward the performance‐based design of tall buildings, where any assessment using response history analysis requires a set of ground motion (GM) records. This paper considering a tall building as a case study investigates how judgment on the seismic safety of the structure is affected by the use of recorded or spectrally matched GMs. Three model structures are developed: (a) using conventional design procedure of Chapter 12 of ASCE 7‐16; (b) adopting linear analysis requirements of Chapter 16 of ASCE 7‐16; (c) designing for service‐level design earthquake of Los Angles Tall Building Structural Design Council (LATBSDC) procedure. It is shown that all of the structures give acceptable performance when subjected to simulated GMs, although this is not the case for amplitude‐scaled GMs based on ASCE 7‐16 and LATBSDC. Finally, to have an objective assessment of performance, independent of GM types, incremental dynamic analysis is employed to derive fragility and mean annual rate of exceeding (MAR). Results show that for anticipated drifts at Maximum Considered Earthquake (MCE) level, the structures provide acceptable MAR at the fundamental period. However, for the higher modes including the second and third periods, MAR values become acceptable only at drifts as large as 0.085.     

The application of complex quantized feedback in integrated wireless receivers

Integrated wireless receiver architectures, such as direct-conversion receivers, offer many advantages over the conventional heterodyne receivers including smaller size, lower cost, and reduced power consumption. However, the design of monolithic receivers, using direct-conversion, involves many challenges including dealing with low-frequency disturbances, namely, dc-offset and 1/f noise (especially in CMOS implementations), in-phase (I) and quadrature (Q) amplitude and phase mismatch, local oscillator (LO) leakage, and even-order distortions. A cost-effective method to minimize the low-frequency disturbances is to use ac-coupling in the baseband signal path. However, it results in baseline wander effects, especially in spectrally efficient modulation schemes such as quadrature amplitude modulation (QAM) where the baseband signal spectrum contains a significant amount of energy near dc. A system solution to mitigate the effects of low-frequency disturbance is presented in this paper. The quantized feedback (QFB) technique is used in conjunction with ac-coupling to minimize the baseline wander effects. A cross-coupled (CC) QFB extension to compensate for the receiver local oscillator phase error as well as the IQ mismatch is also described. Simulation results are presented to demonstrate the effectiveness of this complex QFB technique.     

A class of multistep methods based on a super-future points technique for solving IVPs

In this article, the details of new methods based on a super-future points technique, for improving stability regions compared with those of the extended BDF (EBDF) and modified EBDF (MEBDF), have been presented, for solving initial value problems (IVPs). Numerical results related to five test problems show that our new methods have good performance in saving CPU time compared with the corresponding MEBDF method.     

Palladium(II)‐Catalyzed Decarboxylative Heck Arylations of Acyclic Electron‐Rich Olefins with Internal Selectivity

Despite the recent emergence of decarboxylative C C bond forming reactions, methodologies providing internally arylated electron‐rich olefins are still lacking. We herein report on palladium(II)‐catalyzed decarboxylative Heck arylations of linear electron‐rich olefins with excellent selectivity for the internal position. The method allows a variety of electron‐rich linear olefins to undergo arylation with ortho‐functionalized aromatic carboxylic acids, including heterocycles. The reaction mechanism has been explored with ESI‐MS studies to confirm previous findings, and to reveal the formation of a highly stable palladium complex as a result of the Heck product reacting with the catalyst.

     

Neural network modeling of biotreatment of triphenylmethane dye solution by a green macroalgae

The potential of a green macroalgae Cladophora species was investigated as a viable biomaterial for biotreatment of Malachite Green (MG) solution. The effects of operational parameters such as temperature, pH, initial dye concentration, reaction time and amount of algae on biological decolorization efficiency were studied. Biotreatment of MG solution by live and dead algae was compared. The reusability and efficiency of the live algae in long-term repetitive operations were also examined. COD and FT-IR analysis revealed the ability of algal species in biological degradation of the dye. An artificial neural network (ANN) model was developed to predict the biotreatment of MG solution. The findings indicated that the ANN provided reasonable predictive performance (R² = 0.987). The influence of each parameter on the variable studied was assessed, and reaction time and initial pH were found to be the most significant factors, followed by temperature, initial dye concentration and amount of algae. Simulations based on the developed ANN model can estimate the behavior of the biological biotreatment process under different conditions.