{1,3-Bis[(diphenyl­phosphanyl-κP)­oxy]propane}dicarbonyl­iron(0)

The structure of the title compound, [Fe(C27H26O2P2)(CO)2], exhibits a distorted tetra­hedral coordination [bond angle range = 96.31 (12)–119.37 (4)°], comprising two P-atom donors from the chelating 1,3-bis­[(diphenyl­phosphan­yl)­oxy]propane ligand [Fe—P = 2.1414 (10) and 2.1462 (10) Å] and two carbonyl ligands [Fe—C = 1.763 (4) and 1.765 (3) Å]

{1,3-Bis[(diphenyl­phosphanyl-κP)­oxy]prop-2-yl-κC 2}iodido(trimethyl­phosphane)cobalt(II)

The title compound, [Co(C27H25O2P2)I(C3H9P)], was synthesized by the addition of 1-iodo­butane to a solution of the parent cobalt complex {1,3-bis­[(diphenyl­phosphan­yl)­oxy]prop-2-yl}bis­(trimethyl­phosphane)cobalt(II). Two five-membered cobaltocycles with considerable ring bending (sum of inter­nal angles = 516.4 and 517.7°) are formed through two P atoms of the PPh2 groups and a metallated Csp 3 atom. The CoII atom is centered in a trigonal-bipyramidal configuration

Reconstruction of cytosolic fumaric acid biosynthetic pathways in Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>Fumaric acid is a commercially important component of foodstuffs, pharmaceuticals and industrial materials, yet the current methods of production are unsustainable and ecologically destructive.</p> <p>Results</p> <p>In this study, the fumarate biosynthetic pathway involving reductive reactions of the tricarboxylic acid cycle was exogenously introduced in <it>S. cerevisiae </it>by a series of simple genetic modifications. First, the <it>Rhizopus oryzae </it>genes for malate dehydrogenase (<it>RoMDH</it>) and fumarase (<it>RoFUM1</it>) were heterologously expressed. Then, expression of the endogenous pyruvate carboxylase (<it>PYC2</it>) was up-regulated. The resultant yeast strain, FMME-001 ↑<it>PYC2 </it>+ ↑<it>RoMDH</it>, was capable of producing significantly higher yields of fumarate in the glucose medium (3.18 ± 0.15 g liter^-1) than the control strain FMME-001 empty vector.</p> <p>Conclusions</p> <p>The results presented here provide a novel strategy for fumarate biosynthesis, which represents an important advancement in producing high yields of fumarate in a sustainable and ecologically-friendly manner.</p

Time-triggered State-machine Reliable Software Architecture for Micro Turbine Engine Control

AbstractTime-triggered (TT) embedded software pattern is well accepted in aerospace industry for its high reliability. Finite-state-machine (FSM) design method is widely used for its high efficiency and predictable behavior. In this paper, the time-triggered and state-machine combination software architecture is implemented for a 25 kg thrust micro turbine engine (MTE) used for unmanned aerial vehicle (UAV) system; also model-based-design development workflow for airworthiness software directive DO-178B is utilized. Experimental results show that time-triggered state-machine software architecture and development method could shorten the system development time, reduce the system test cost and make the turbine engine easily comply with the airworthiness rules

Sensitivity Analysis and Optimization of a Coal-fired Power Plant in Different Modes of Flue Gas Recirculation

AbstractIn a coal-fired power plant with flue gas recirculation, recirculation rate and coal input have a great effect on the performance of the power plant. In this paper, a 600 MW coal-fired boiler is taken as base case, the main parameters of the boiler are calculated at different recirculation rates and coal input conditions, an optimization is carried out and the optimum recirculation rate and coal input are reported. The results show that under optimum recirculation rate and coal input conditions, the net coal consumption rate can be reduced by 3.5g/(kW·h) at 575MW load; while it is 4.36g/(kW·h) and 5.11g/(kW·h), respectively, at 450MW load and 300MW load. Compared to the conventional flue gas recirculation system, the net coal consumption rate can be reduced by 2.31 g/(kW·h), 2.42 g/(kW·h) and 2.41 g/(kW·h), respectively, at 575MW, 450MW and 300MW load

Context-aware and Scale-insensitive Temporal Repetition Counting

Temporal repetition counting aims to estimate the number of cycles of a given repetitive action. Existing deep learning methods assume repetitive actions are performed in a fixed time-scale, which is invalid for the complex repetitive actions in real life. In this paper, we tailor a context-aware and scale-insensitive framework, to tackle the challenges in repetition counting caused by the unknown and diverse cycle-lengths. Our approach combines two key insights: (1) Cycle lengths from different actions are unpredictable that require large-scale searching, but, once a coarse cycle length is determined, the variety between repetitions can be overcome by regression. (2) Determining the cycle length cannot only rely on a short fragment of video but a contextual understanding. The first point is implemented by a coarse-to-fine cycle refinement method. It avoids the heavy computation of exhaustively searching all the cycle lengths in the video, and, instead, it propagates the coarse prediction for further refinement in a hierarchical manner. We secondly propose a bidirectional cycle length estimation method for a context-aware prediction. It is a regression network that takes two consecutive coarse cycles as input, and predicts the locations of the previous and next repetitive cycles. To benefit the training and evaluation of temporal repetition counting area, we construct a new and largest benchmark, which contains 526 videos with diverse repetitive actions. Extensive experiments show that the proposed network trained on a single dataset outperforms state-of-the-art methods on several benchmarks, indicating that the proposed framework is general enough to capture repetition patterns across domains.Comment: Accepted by CVPR202

Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity

Due to the sparsity of user data, sentiment analysis on user reviews in e-commerce platforms often suffers from poor performance, especially when faced with extremely sparse user data or long-tail labels. Recently, the emergence of LLMs has introduced new solutions to such problems by leveraging graph structures to generate supplementary user profiles. However, previous approaches have not fully utilized the graph understanding capabilities of LLMs and have struggled to adapt to complex streaming data environments. In this work, we propose a fine-grained streaming data synthesis framework that categorizes sparse users into three categories: Mid-tail, Long-tail, and Extreme. Specifically, we design LLMs to comprehensively understand three key graph elements in streaming data, including Local-global Graph Understanding, Second-Order Relationship Extraction, and Product Attribute Understanding, which enables the generation of high-quality synthetic data to effectively address sparsity across different categories. Experimental results on three real datasets demonstrate significant performance improvements, with synthesized data contributing to MSE reductions of 45.85%, 3.16%, and 62.21%, respectively