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In this work, the reactions of nonacarbonyldiiron with 8‐alkynyloxyquinoline ligands (L1‐L3) and their resultant iron carbonyl complexes (1a, 1b, 2, and 3) are described. The complexes including the ligands were fully characterized by using a variety of spectroscopic techniques. The four complexes were also crystallographically determined. In the reactions, the quinoline N atom underwent cyclization to form a quinolinium skeleton while the C‐H bond of the quinoline at position 2 or the substituted methyl group was cleaved to form Fe‐C bond(s). Meanwhile, the alkynyl triple bond was reduced to a single bond (1a) or double bonds (1b, 2, and 3) to further coordinate the iron in σ‐bonds or η‐bonds. Particularly, pathways of hydrogenation (1a and 2) and/or dehydrogenation (1b and 3) were accompanied by the reactions. Moreover, the oxidation states of the iron centers in these complexes were identified through analyses of their bond formation, FTIR and NMR spectroscopic signals (1H NMR and DEPT‐135) in combination with their diamagnetic nature, that is, iron (0) for 2 and iron (I) for the rest of the diiron complexes. [ABSTRACT FROM AUTHOR]

Traffic flow detection plays a significant part in freeway traffic surveillance systems. Currently, effective autonomous traffic analysis is a challenging task due to the complexity of traffic delays, despite the significant investment spent by authorities in monitoring and analysing traffic congestion. This study builds an intelligent analytic method based on machine‐learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom (London, Yorkshire and the Humber, North East, and North West) with a range of relevant factors. While aiming to conduct the study, the dataset 'estimated annual average daily flows (AADFs) Data—major and minor roads' from the UK government was used. Machine‐learning algorithms are used for this research and classification applied consists of Logistic Regression, Decision Trees, Random Forests, K‐Nearest Neighbors, and Gradient Boosting. Each of these algorithms achieves an accuracy of over 93% and the F1 score of over 95%, with Random Forest outperforming the other algorithms. This analytical approach helps to focus attention on critical areas to reduce traffic flows on major and minor roads in the area. In summary, the findings on traffic analysis have been discussed in detail to demonstrate the practical insights of this study. [ABSTRACT FROM AUTHOR]

Air cargo plays an important role in supporting global supply chains; this becomes more vital when facing uncertainties in a crisis such as the COVID-19 pandemic. This motivates our study on air cargo forwarding plans, considering demand uncertainties and economic conditions. Cargos are placed into air containers based on weights and volumes, and then flown from regional collection points into a hub, for consolidation before transporting to onward destinations. Decisions are made in advance by cargo forwarders as to the containers to book, both in regions and in the hub, since airlines offer discounts on containers booked in advance; however, cargo quantities are uncertain when advance bookings are made. We develop a two-stage stochastic programming model, where the first stage determines both the quantities and types of air containers to book; the second stage deals with ordering any extra containers, at higher cost, or returning unused containers, as well as making loading and consolidation plans. The objective is to minimise the total expected costs. We then extend it into a multistage case and design a genetic algorithm as the solution method. Experimental results demonstrate that the proposed approaches provide a cost-efficient plan and responsive to demand as it arises. [ABSTRACT FROM AUTHOR]

Six iron(0) tricarbonyl complexes (1a–f) with a η4-1-azadiene moiety were prepared and their performance in the hydroboration of unsaturated organic compounds was investigated. All the complexes exhibit catalytic activity towards hydroboration of ketones, aldehydes and aldimines with pinacolborane (HBpin) as a hydride source to lead to secondary alcohols, primary alcohols, and secondary amines, respectively, after hydrolysis of the hydroboration products. Of the iron(0) tricarbonyl complexes, complex 1e is the most robust one and was employed throughout the catalytic investigation. Its preference towards the three types of substrates is as follows: aldimines > aldehydes ≫; ketones. In total, 24 substrates were examined for the catalytic hydroboration reactivity and generally, isolation yields ranging from 40% to 95% were achieved. Mechanistic investigation suggests that the catalytic hydroboration of the substrates proceeds via intramolecular hydride transfer without going through an Fe–H intermediate. As indicated by 1H NMR spectroscopic monitoring, the substrates and the borane agent bind to the iron centre and the imine N atom, respectively, which facilitates the hydride transfer by activating the B–H bond and polarizing the double bond of the substrates. [ABSTRACT FROM AUTHOR]

It has been demonstrated the advantage of basic stroke recognition algorithm in the field of human-computer interaction (HCI). However, most traditional techniques heavily rely on the touch-contact operations to obtain character information, which limits the further application in non-contact scenario such as germ infection environment, high/low temperature environment or scene for blind human. This paper proposes a non-contact and automatic basic stroke recognition algorithm for handwritten Chinese characters based on frequency modulated continuous wave (FMCW) radar system. First, the radar system collects intermediate frequency (IF) signal of the eight basic strokes given as follows: (horizontal stroke), (dot stroke), (lift stroke), (left falling stroke), (bend stroke), (right falling stroke), (vertical stroke) and (hook stroke). Second, a range-time sequence (RTS) is obtained from IF signal by the window fast Fourier transform (window-FFT) algorithm, and an azimuth-time sequence (ATS) is obtained from IF signal by the frequency domain Capon (FD-Capon) algorithm. Then, the feature area-framing, binarization and open operation (FA-FBO) algorithm is proposed to enhance the features of the above two sequences. After that, a feature map set containing RTS feature map (RTSFM) and ATS feature map (ATSFM) is obtained. Finally, a novel convolutional neural network (CNN) model is customized to perform the strokes classification task with these feature maps as input. Experimental results demonstrate that the proposed scheme is able to effectively recognize the eight basic strokes and achieve an average classification accuracy of 99.25%. [ABSTRACT FROM AUTHOR]

The unfolding and refolding of the leucine zipper peptide GCN4-pl corresponding to residues 249 to 281 from the yeast transcriptional activator GCN4 indicate the existence of a two-state kinetic and equilibrium model for the peptide. The equilibrium unfolding of the peptide, brought about by guanidine hydrochloride, is observed by stopped-flow circular dichroism.

Visible light communication (VLC) systems can achieve a higher data rate by increasing the number of channels using wavelength division multiplexing (WDM) technology. In this paper, we investigate the maximum number of channels and possible data rate in a WDM-VLC system based on light emitting diode (LED). Channel crosstalk from the spectral overlap of LEDs is analyzed by modeling LED spectra and the formula for crosstalk is derived from VLC link, including optical filter transmittance and detector spectral response. An experimental setup with different wavelength of LEDs is used to confirm the validity of the crosstalk analysis. The number of channels and data rate are determined by the SNR, including signal power, channel crosstalk, and detector noise. The results indicate that for indoor illumination standard with on–off keying modulation and 33 nm channel spacing to achieve a BER of 10^- 6, the maximum and optimal numbers of channels are 12 and 10, respectively. Additionally, the achievable data rate is 5.1 Gb/s which is about 9.3 times than that of the single channel system. [ABSTRACT FROM PUBLISHER]

In container terminal operations, the storage yard plays an important role for a terminal's overall performance because it links the seaside and landside and serves as the buffer area for storing containers. Therefore, storage and stacking logistics has become a field that increasingly attracts attentions in both academic and practical research during the recent years. The purpose of this article is to review and classify the growing literature on storage and stacking logistics, and to identify the research areas that could be further investigated. The literature in this area mainly falls into three categories: storage space allocation problem, design of optimal yard layout and container stacking logistics. This article is among the first known to review the literature focus on this area, it thus provides a new perspective for both managers and researchers on the issue of yard operations management. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM). [ABSTRACT FROM AUTHOR]

Virtually all studies of the protein-folding reaction add either heat, acid, or a chemical denaturant to an aqueous protein solution in order to perturb the protein structure. When chemical denaturants are used, very high concentrations are usually necessary to observe any change in protein structure. In a solution with such high denaturant concentrations, both the structure of the protein and the structure of the solvent around the protein can be altered. X-ray crystallography is the obvious experimental technique to probe both types of changes. In this paper, we report the crystal structures of dihydrofolate reductase with urea and of ribonuclease A with guanidinium chloride. These two classic denaturants have similar effects on the native structure of the protein. The most important change that occurs is a reduction in the overall thermal factor. These structures offer a molecular explanation for the reduction in mobility. Although the reduction is observed only with the native enzyme in the crystal, a similar decrease in mobility has also been observed in the unfolded state in solution (Makhatadze G, Privalov PL. 1992. Protein interactions with urea and guanidinium chloride: A calorimetric study. J Mol Biol 226:491-505). [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets. [ABSTRACT FROM AUTHOR]

Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated gesture recognition system that is based on frequency modulated continuous wave MIMO radar combined with deep learning network for gesture recognition. First, a pre-processing algorithm, which consists of the windowed fast Fourier transform and the intermediate-frequency signal band-pass-filter (IF-BPF), is applied to obtain improved Range Doppler Map. A range FFT based MUSIC (RFBM) two-dimensional (2D) joint super-resolution estimation algorithm is proposed to obtain a Range Azimuth Map to obtain gesture transversal feature. Range Doppler Map and Range Azimuth Map then respectively form a Range Doppler Map Time Sequence (RDMTS) and a Range Azimuth Map Time Sequence (RAMTS) in gesture recording duration. Finally, a Dual stream three-dimensional (3D) Convolution Neural Network combined with Long Short Term Memory (DS-3DCNN-LSTM) network is designed to extract and fuse features from both RDMTS and RAMTS, and then classify gestures with radial and transversal change. The experimental results show that the proposed system could distinguish 10 types of gestures containing transversal and radial motions with an average accuracy of 97.66%. [ABSTRACT FROM AUTHOR]

• The transmission of OFDM signal in integration system of FSO and W-band mm-wave. • The ISFA and the DFT-Spread are used to optimize system performance. • 1Gbaud OFDM signal is transmitted for 100 m SMF, 100 m FSO link and 4 m wireless. In this paper, we propose and experimentally demonstrate a novel multi-access orthogonal frequency division multiplexing (OFDM) signal transmission system for free space optical communication and photon-assisted W-band millimeter-wave (mm-wave) communication. This new structure can combine the advantages of the free space optical communication and the W-band mm-wave wireless communication. Besides, the OFDM signal with high spectral efficiency and anti-interference capability can improve the data transmission rate and flexibility. Furthermore, it can adapt to the requirements of different communication scenarios. Utilizing the intra-symbol frequency-domain averaging (ISFA) technique and the discrete Fourier transform-spread (DFT-Spread) technique, 1Gbaud 16-ary quadrature amplitude modulation (16QAM) OFDM signal is transmitted successfully with the distance of 100 m for single-mode fiber (SMF), 100 m for FSO link and 4 m for wireless. The BER is under the soft-decision Forward Error Correction (SD-FEC) threshold of 2 × 10−2. To our knowledge, this is the first time to realize the transmission of OFDM signal in the integration system of free space optical communication and photon-assisted W-band mm-wave communication. [ABSTRACT FROM AUTHOR]

Non-line-of-sight (NLOS) optical camera communications (OCC) exhibit greater link availability and mobility than line-of-sight links, which are more susceptible to blocking and shadowing. In this work, we propose an NLOS OCC system, where the data signal is mapped into color pulse width modulation (CPWM) symbols prior to transmission using a red-, green-, and blue light-emitting diode. A convolutional-neural-network-based receiver is used to demodulate the CPWM signal. Based on experimental results, the proposed scheme effectively mitigates the effects of diffuse reflection induced intersymbol interference, resulting in an increased data transmission rate to 7.2 kbps over a link span of more than 2 m, which is typical for indoor applications.

Six iron(0) tricarbonyl complexes (1a-f) with a η 4 -1-azadiene moiety were prepared and their performance in the hydroboration of unsaturated organic compounds was investigated. All the complexes exhibit catalytic activity towards hydroboration of ketones, aldehydes and aldimines with pinacolborane (HBpin) as a hydride source to lead to secondary alcohols, primary alcohols, and secondary amines, respectively, after hydrolysis of the hydroboration products. Of the iron(0) tricarbonyl complexes, complex 1e is the most robust one and was employed throughout the catalytic investigation. Its preference towards the three types of substrates is as follows: aldimines > aldehydes ≫ ketones. In total, 24 substrates were examined for the catalytic hydroboration reactivity and generally, isolation yields ranging from 40% to 95% were achieved. Mechanistic investigation suggests that the catalytic hydroboration of the substrates proceeds via intramolecular hydride transfer without going through an Fe-H intermediate. As indicated by 1 H NMR spectroscopic monitoring, the substrates and the borane agent bind to the iron centre and the imine N atom, respectively, which facilitates the hydride transfer by activating the B-H bond and polarizing the double bond of the substrates.