Triplet–Triplet Energy Transfer Study in Hydrogen Bonding Systems

The 2,6-diiodoBodipy–styrylBodipy hydrogen bonding system was prepared to study the effect of hydrogen bonding on the triplet–triplet-energy-transfer (TTET) process. 2,6-DiiodoBodipy linked with N-acetyl-2,6-diaminopyridine (D-2) was used as the triplet energy donor, and the styrylBodipy connected with thymine (A-1) was used as triplet energy acceptor, thus the TTET process was established upon photoexcitation. The photophysical processes of the hydrogen bonding system were studied with steady-state UV-vis absorption spectroscopy, fluorescence spectroscopy, fluorescence lifetime measurement and nanosecond time-resolved transient absorption spectroscopies. The TTET of the intramolecular/hydrogen bonding/intermolecular systems were compared through nanosecond transient absorption spectroscopy. The TTET process of the hydrogen bonding system is faster and more efficient (kTTET = 6.9 × 104 s–1, ?TTET = 94.0%) than intermolecular triplet energy transfer (kTTET = 6.0 × 104 s–1, ?TTET = 90.9%), but slower and less efficient than intramolecular triplet energy transfer (kTTET > 108 s–1). These results are valuable for designing self-assembly triplet photosensitizers and for the study of the TTET process of hydrogen bonding systems

Scalable structural index construction for json analytics

JavaScript Object Notation ( JSON) and its variants have gained great popularity in recent years. Unfortunately, the performance of their analytics is often dragged down by the expensive JSON parsing. To address this, recent work has shown that building bitwise indices on JSON data, called structural indices, can greatly accelerate querying. Despite its promise, the existing structural index construction does not scale well as records become larger and more complex, due to its (inherently) sequential construction process and the involvement of costly memory copies that grow as the nesting level increases. To address the above issues, this work introduces Pison – a more memory-efficient structural index constructor with supports of intra-record parallelism. First, Pison features a redesign of the bottleneck step in the existing solution. The new design is not only simpler but more memory-efficient. More importantly, Pison is able to build structural indices for a single bulky record in parallel, enabled by a group of customized parallelization techniques. Finally, Pison is also optimized for better data locality, which is especially critical in the scenario of bulky record processing. Our evaluation using real-world JSON datasets shows that Pison achieves 9.8X speedup (on average) over the existing structural index construction solution for bulky records and 4.6X speedup (on average) of end-to-end performance (indexing plus querying) over a state-of-the-art SIMD-based JSON parser on a 16-core machine

Adaptive Active Anti-vibration Control for a Three-dimensional Helicopter Flexible Slung-load System with Input Saturations and Backlash

This study investigates active anti-vibration control for a three-dimensional helicopter flexible slung-load system (HFSLS) subject to input saturations and backlash. The first target of the study is to establish a model for a three-dimensional HFSLS. The second target is to develop an adaptive control law for a HFSLS by analyzing its ability to compensate for the effects of input saturations, input backlash, and external disturbances, while achieving the goal of vibration reduction. Simulation results of the numerical show that the proposed adaptive active control technology is effective in solving the oscillation suppression problem for the three-dimensional HFSLS with input saturations and backlash.</p

Call Sequence Prediction through Probabilistic Calling Automata

Predicting a sequence of upcoming function calls is important for optimizing programs written in modern managed languages (e.g., Java, Javascript, C#.) Existing function call predictions are mainly built on statistical patterns, suitable for predicting a single call but not a sequence of calls. This paper presents a new way to enable call sequence prediction, which exploits program structures through Probabilistic Calling Automata (PCA), a new program representation that captures both the inherent ensuing relations among function calls, and the probabilistic nature of execution paths. It shows that PCA-based prediction outperforms existing predictions, yielding substantial speedup when being applied to guide Just-In-Time compilation. By enabling accurate, efficient call sequence prediction for the first time, PCA-based predictors open up many new opportunities for dynamic program optimizations

Three-input-three-output air path control system of a heavy-duty diesel engine

In this paper, the control requirement of the air path system of a Heavy Duty (HD) diesel engine which was equipped with a High Pressure (HP) Exhaust Gas Recirculation (EGR), a Variable-Geometry Turbocharger (VGT), and an Electric Turbocharge Assist (ETA) is discussed. A Three-Input-Three-Output (3130) multivariable control structure is proposed. The engine dynamic model required for controller design was obtained using system identification and the controller was tuned by solving an Hoo optimization problem. The engine experimental test results show that this 3130 closed-loop control system has excellent tracking performance, disturbance rejection performance, and gain scheduling capability. The control system has been demonstrated to work with a practical ETA device to make a substantial improvement to engine transient performance

Real-time optimal energy management of electrified engines

© 2016 The electrification of engine components offers significant opportunities for fuel economy improvements, including the use of an electrified turbocharger for engine downsizing and exhaust gas energy recovery. By installing an electrical device on the turbocharger, the excess energy in the air system can be captured, stored, and re-used. This new configuration requires a new control structure to manage the air path dynamics. The selection of optimal setpoints for each operating point is crucial for achieving the full fuel economy benefits. In this paper, a control-oriented model for an electrified turbocharged diesel engine is analysed. Based on this model, a structured approach for selecting control variables is proposed. A model-based multi-input multi-output decoupling controller is designed as the low level controller to track the desired values and to manage internal coupling. An equivalent consumption minimization strategy is employed as the supervisory level controller for real-time energy management. The supervisory level controller and low level controller work together in a cascade which addresses both fuel economy optimization and battery state-of-charge maintenance. The proposed control strategy has been successfully validated on a detailed physical simulation model