Synergistic Use of Pyridine and Selenophene in a Diketopyrrolopyrrole‐Based Conjugated Polymer Enhances the Electron Mobility in Organic Transistors

To achieve semiconducting materials with high electron mobility in organic field‐effect transistors (OFETs), low‐lying energy levels (the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO)) and favorable molecular packing and ordering are two crucial factors. Here, it is reported that the incorporation of pyridine and selenophene into the backbone of a diketopyrrolopyrrole (DPP)‐based copolymer produces a high‐electron‐mobility semiconductor, PDPPy‐Se. Compared with analogous polymers based on other DPP derivatives and selenophene, PDPPy‐Se features a lower LUMO that can decrease the electron transfer barrier for more effective electron injection, and simultaneously a lower HOMO that, however, can increase the hole transfer barrier to suppress the hole injection. Combined with thermal annealing at 240 °C for thin film morphology optimization to achieve large‐scale crystallite domains with tight molecular packing for effective charge transport along the conducting channel, OFET devices fabricated with PDPPy‐Se exhibit an n‐type‐dominant performance with an electron mobility (μ_e) as high as 2.22 cm² V^?1 s^?1 and a hole/electron mobility ratio (μ_h/μ_e) of 0.26. Overall, this study demonstrates a simple yet effective approach to boost the electron mobility in organic transistors by synergistic use of pyridine and selenophene in the backbone of a DPP‐based copolymer.     

A novel view on bounding execution demand under mixed-criticality EDF

Real-Time Systems - In this paper, we are concerned with scheduling a mix of high-criticality (HI) and low-criticality (LO) tasks under Earliest Deadline First (EDF) on one processor. To this end,...     

Digital simulation of an automotive multiplexing wiring system

A digital simulator for an automotive multiplexing system has been developed and tested. The basic architecture and logical flow and block diagrams are described. Some results of the simulation are given. The contention scheme was found to work successfully in the simulator, and the outputs were found to be ordered in the same manner as expected by the requirements of the protocol. The network traffic and its effect on the latency index has also been studied. The simulator has provisions to incorporate alternate message retrial schemes and to test alternative strategies of the tasks. It is flexible enough so that the effects of noise and other nonideal phenomena can be incorporated. It is concluded that this simulator is a useful design/development tool due to its flexibility in testing alternative design strategies     

Studies on the effect of filtering, digitization, and computationalgorithm on the ABC-DQ current transformation in PWM inverter drivesystem

In the automotive industry, PWM inverter controlled drive systems find extensive applications in electric vehicles and other motion control systems. A PWM inverter controlled three-phase brushless synchronous motor drive requires a microprocessor based controller to implement the control algorithms. The practical implementation of the control algorithms sometimes calls for the use of lookup tables involving discretized quantities. All these processes and the devices introduce nonidealities and errors in the drive system. Of particular importance in such a system is the ABC-DQ current transformations when vector control is implemented. In automotive applications, the emphasis is to design and realize accurate motion control systems in the most economic manner possible. Hence the effects of filtering, digitization, quantization, and digital computation algorithms and the accompanying inaccuracies introduced by them during the ABC-DQ current transformation in PWM drive system have to be dealt with in a careful manner for an optimum and economic choice of system design to be possible. Some of these aspects are discussed in this paper. The studies lead to a better understanding of the importance of various parameters of power electronics systems and in turn contribute towards the proper selection of parameters and therefore help the design process     

Unconventional Molecular Weight Dependence of Charge Transport in the High Mobility n‐type Semiconducting Polymer P(NDI2OD‐T2)

The charge transport and microstructural properties of five different molecular weight (MW) batches of the naphthalenediimide‐thiophene copolymer P(NDI2OD‐T2) are investigated. In particular, the field‐effect transistor (FET) performance and thin‐film microstructure of samples with MW varying from M_n = 10 to 41 kDa are studied. Unlike conventional semiconducting polymers such as poly(3‐hexylthiophene) where FET mobility dramatically drops with decreasing molecular weight, the FET mobility of P(NDI2OD‐T2)‐based transistors processed from 1,2‐dichlorobenzene is found to increase with decreasing MW. Using a combination of grazing‐incidence wide‐angle X‐ray scattering, near‐edge X‐ray absorption fine‐structure spectroscopy, atomic force microscopy, and resonant soft X‐ray scattering, the increase in FET mobility with decreasing MW is attributed to the pronounced increase in the orientational correlation length (OCL) with decreasing MW. In particular, the OCL is observed to systematically increase from <100 nm for the highest MW samples to ≈1 µm for the lowest MW samples. The improvement in OCL and hence mobility for low MW samples is attributed to the lack of aggregation of low MW chains in solution promoting backbone ordering, with the pre‐aggregation of chains in 1,2‐dichlorobenzene found to suppress longer‐range liquid crystalline order.     

Estimation of dispersion coefficients in packed beds

The design and performance of fixed beds are greatly influenced by fluid dispersion. Unfortunately, the existing design data do not provide an accurate picture of this phenomenon. This paper presents an attempt to characterize the dispersive features of packed beds by obtaining reliable estimates of the associated coefficients in the axial (D_L ) and radial (D_R ) directions. Such an objective is achieved by developing a representative two-dimensional pseudo-continuous dispersed flow model which is subsequently employed to compute the desired coefficients using data obtained from a refined experimental approach. The established values have been correlated to allow such coefficients to be reliably predicted under a variety of physical and operating conditions.     

Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers

Background: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. Method: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers. Results: Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers. Conclusion: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers.     

Model-based fault diagnosis in electric drives using machine learning

Electric motor and power electronics-based inverter are the major components in industrial and automotive electric drives. In this paper, we present a model-based fault diagnostics system developed using a machine learning technology for detecting and locating multiple classes of faults in an electric drive. Power electronics inverter can be considered to be the weakest link in such a system from hardware failure point of view; hence, this work is focused on detecting faults and finding which switches in the inverter cause the faults. A simulation model has been developed based on the theoretical foundations of electric drives to simulate the normal condition, all single-switch and post-short-circuit faults. A machine learning algorithm has been developed to automatically select a set of representative operating points in the (torque, speed) domain, which in turn is sent to the simulated electric drive model to generate signals for the training of a diagnostic neural network, fault diagnostic neural network (FDNN). We validated the capability of the FDNN on data generated by an experimental bench setup. Our research demonstrates that with a robust machine learning approach, a diagnostic system can be trained based on a simulated electric drive model, which can lead to a correct classification of faults over a wide operating domain.