Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm

Green logistics is an emerging area in supply chain management, which has been shown to have tremendous impacts in recent years to face the serious climate changes risks. In this paper, the fuel consumption and fuzzy travel time have been delineated in developing and solving the green-fuzzy vehicle routing problem as an extension of the celebrated VRP in which routes are performed to reduce the total expenditure. Different from the existing solution manners, we transform the original fuzzy chance constrained programming model into an equivalent deterministic model, and then revise the original hybrid intelligent algorithm by replacing the embedded fuzzy simulation with analytical function calculation. Finally, a comparative study with the corresponding literature is performed, which shows that the revised algorithm can not only improve the solution accuracy but also shorten the runtime greatly

A Two-Stage Transfer Regression Convolutional Neural Network for Bearing Remaining Useful Life Prediction

Recently, deep learning techniques have been successfully used for bearing remaining useful life (RUL) prediction. However, the degradation pattern of bearings can be much different from each other, which leads to the trained model usually not being able to work well for RUL prediction of a new bearing. As a method that can adapt a model trained on source datasets to a different but relative unlabeled target dataset, transfer learning shows the potential to solve this problem. Therefore, we propose a two-stage transfer regression (TR)-based bearing RUL prediction method. Firstly, the incipient fault point (IFP) is detected by a convolutional neural network (CNN) classifier to identity the start time of degradation stage and label the training samples. Then, a transfer regression CNN with multiloss is constructed for RUL prediction, including regression loss, classification loss, maximum mean discrepancy (MMD) and regularization loss, which can not only extract fault information from fault classification loss for RUL prediction, but also minimize the probability distribution distance, thus helping the method to be trained in a domain-invariant way via the transfer regression algorithm. Finally, real data collected from run-to-failure bearing experiments are analyzed by the TR-based CNN method. The results and comparisons with state-of-the-art methods demonstrate the superiority and reliable performance of the proposed method for bearing RUL prediction

A CMOS Molecular Clock Probing 231.061-GHz Rotational Line of OCS with Sub-PPB Long-Term Stability and 66-MW DC Power

© 2018 IEEE. Recent progress of on-chip spectroscopic systems enables a new set of highly-stable frequency references (i.e. clocks) with low cost, power and volume. It is based on the rotational spectrum of gaseous molecules in sub-THz regime, a physical mechanism alternative to that in traditional atomic clocks. This scheme also enables fast start-up operation and robustness against mechanical vibration and external electromagnetic fields. This paper demonstrates the first chip-scale molecular clock in 65nm CMOS which probes the 231.061GHz spectral line of Carbonyl Sulfide ( 16 O 12 C 32 S). The clock consumes only 66mW DC power and has a measured Allan deviation of 3.8×10- 10 with an averaging time of τ=10 3 s

Antifouling Conductive Composite Membrane with Reversible Wettability for Wastewater Treatment

Membrane fouling severely hinders the sustainable development of membrane separation technology. Membrane wetting property is one of the most important factors dominating the development of membrane fouling. Theoretically, a hydrophilic membrane is expected to be more resistant to fouling during filtration, while a hydrophobic membrane with low surface energy is more advantageous during membrane cleaning. However, conventional membrane materials do not possess the capability to change their wettability on demand. In this study, a stainless steel mesh–sulfosuccinate-doped polypyrrole composite membrane (SSM/PPY(AOT)) was prepared. By applying a negative or positive potential, the surface wettability of the membrane can be switched between hydrophilic and relatively hydrophobic states. Systematic characterizations and a series of filtration experiments were carried out. In the reduction state, the sulfonic acid groups of AOT were more exposed to the membrane surface, rendering the surface more hydrophilic. The fouling filtration experiments verified that the membrane is more resistant to fouling in the hydrophilic state during filtration and easier to clean in the hydrophobic state during membrane cleaning. Furthermore, Ca2+ and Mg2+ could complex with foulants, aggravating membrane fouling. Overall, this study demonstrates the importance of wettability switching in membrane filtration and suggests promising applications of the SSM/PPY(AOT) membrane

A Two-Stage Transfer Regression Convolutional Neural Network for Bearing Remaining Useful Life Prediction

Recently, deep learning techniques have been successfully used for bearing remaining useful life (RUL) prediction. However, the degradation pattern of bearings can be much different from each other, which leads to the trained model usually not being able to work well for RUL prediction of a new bearing. As a method that can adapt a model trained on source datasets to a different but relative unlabeled target dataset, transfer learning shows the potential to solve this problem. Therefore, we propose a two-stage transfer regression (TR)-based bearing RUL prediction method. Firstly, the incipient fault point (IFP) is detected by a convolutional neural network (CNN) classifier to identity the start time of degradation stage and label the training samples. Then, a transfer regression CNN with multiloss is constructed for RUL prediction, including regression loss, classification loss, maximum mean discrepancy (MMD) and regularization loss, which can not only extract fault information from fault classification loss for RUL prediction, but also minimize the probability distribution distance, thus helping the method to be trained in a domain-invariant way via the transfer regression algorithm. Finally, real data collected from run-to-failure bearing experiments are analyzed by the TR-based CNN method. The results and comparisons with state-of-the-art methods demonstrate the superiority and reliable performance of the proposed method for bearing RUL prediction

A Terahertz Molecular Clock on CMOS Using High-Harmonic-Order Interrogation of Rotational Transition for Medium-/Long-Term Stability Enhancement

© 1966-2012 IEEE. Chip-scale molecular clocks (CSMCs) perform frequency stabilization by referencing to the rotational spectra of polar gaseous molecules. With, potentially, the 'atomic' clock grade stability, cm3-level volume, and < 100-mW dc power, CSMCs are highly attractive for the synchronization of the high-speed radio access network (RAN), precise positioning, and distributed array sensing. However, the medium-/long-term stability of CSMCs is hindered by the transmission baseline tilting due to the uneven frequency response of the spectroscopic system and the molecular cell. To enhance the medium-/long-term stability, this article presents a CSMC architecture locking to the high-odd-order dispersion curve of the 231.061-GHz rotational spectral line of carbonyl sulfide (OCS) molecules, which is selected as the clock reference. A monolithic THz transceiver generates a high-precision, wavelength-modulated probing signal. Then, the wave-molecule interaction inside the molecular cell translates the frequency error between the probing signal and the spectral line center to the periodic intensity fluctuation. Finally, the CSMC locks to the third-order dispersion curve after a phase-sensitive lock-in detection. In addition, a pair of slot array couplers is employed as an effective chip-to-molecular cell interface. It leads to not only a higher SNR but also a significantly simplified CSMC package. Implemented on a 65-nm CMOS process, the high-order CSMC presents a measured Allan deviation of 4.3× 10-11 under an averaging time of τ=103 s while consuming 70.4-mW dc power