Scallops as a new source of food protein: high-intensity ultrasonication improved stability of oil-in-water emulsion stabilised by myofibrillar protein

In this study, the effect of high-intensity ultrasound (HIUS) (200 and 400 W for 0, 5, 10 and 15 min respectively) on conformational changes, physicochemical, rheological and emulsifying properties of scallop (Patinopecten yessoensis) myofibrillar protein (SMP) was investigated. HIUS-treated SMP had lower α-helix content and higher β-sheet content compared with the native SMP. HIUS treatment induced the unfolding of SMP and increased the surface hydrophobicity. The particle size of SMP decreased and the absolute zeta-potential increased after ultrasonication, which in turn increased the solubility of SMP. The conformational changes and the improvement of physicochemical properties of SMP increased the ability for SMP to lower the interfacial tension at the oil–water interface and increased the percentage of adsorbed protein. As a result, the emulsifying properties, rheological properties of SMP and storage stability of emulsions were also improved. In conclusion, HIUS treatment has future potential for improving the emulsifying properties of SMP.     

Improved error performance for generalised spatial modulation with enhanced spectral efficiency

The existing analytical average bit error rate (ABER) expression of conventional generalised spatial modulation (CGSM) does not agree well with the Monte Carlo simulation results in the low signal‐to‐noise ratio (SNR) region. Hence, the first contribution of this paper is to derive a new and easy way to evaluate analytical ABER expression that improves the validation of the simulation results at low SNRs. Secondly, a novel system termed CGSM with enhanced spectral efficiency (CGSM‐ESE) is presented. This system is realised by applying a rotation angle to one of the two active transmit antennas. As a result, the overall spectral efficiency is increased by 1 bit/s/Hz when compared with the equivalent CGSM system. In order to validate the simulation results of CGSM‐ESE, the third contribution is to derive an analytical ABER expression. Finally, to improve the ABER performance of CGSM‐ESE, three link adaptation algorithms are developed. By assuming full knowledge of the channel at the receiver, the proposed algorithms select a subset of channel gain vector (CGV) pairs based on the Euclidean distance between all CGV pairs, CGV splitting, CGV amplitudes, or a combination of these.     

Achieving Rich and Active Alkaline Hydrogen Evolution Heterostructures via Interface Engineering on 2D 1T‐MoS2 Quantum Sheets

Large‐scale production of hydrogen from water‐alkali electrolyzers is impeded by the sluggish kinetics of hydrogen evolution reaction (HER) electrocatalysts. The hybridization of an acid‐active HER catalyst with a cocatalyst at the nanoscale helps boost HER kinetics in alkaline media. Here, it is demonstrated that 1T–MoS₂ nanosheet edges (instead of basal planes) decorated by metal hydroxides form highly active ${\text{edge}}_{{\text{1T‐MoS}}_{\text{2}}}$/ ${\text{edge}}_{\text{Ni}{(\text{OH})}_{\text{2}}}$ heterostructures, which significantly enhance HER performance in alkaline media. Featured with rich ${\text{edge}}_{{\text{1T‐MoS}}_{\text{2}}}$/ ${\text{edge}}_{\text{Ni}{(\text{OH})}_{\text{2}}}$ sites, the fabricated 1T–MoS₂ QS/Ni(OH)₂ hybrid (quantum sized 1T–MoS₂ sheets decorated with Ni(OH)₂ via interface engineering) only requires overpotentials of 57 and 112 mV to drive HER current densities of 10 and 100 mA cm^?2, respectively, and has a low Tafel slope of 30 mV dec^?1 in 1 m KOH. So far, this is the best performance for MoS₂‐based electrocatalysts and the 1T–MoS₂ QS/Ni(OH)₂ hybrid is among the best‐performing non‐Pt alkaline HER electrocatalysts known. The HER process is durable for 100 h at current densities up to 500 mA cm^?2. This work not only provides an active, cost‐effective, and robust alkaline HER electrocatalyst, but also demonstrates a design strategy for preparing high‐performance catalysts based on edge‐rich 2D quantum sheets for other catalytic reactions.     

Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

表面活性剂对低渗透油藏渗吸的影响

采用不同类型的表面活性剂进行自发渗吸实验,并对表面活性剂改善岩石润湿性、降低界面张力的能力进行了分析。实验结果表明,阴离子型表面活性剂改善润湿性的能力好于其他类型的表面活性剂,且在岩心中的自发渗吸效果最好,这是由于阴离子型表面活性剂改善润湿性的机理为离子对形成机理,强于阳离子的吸附机理;接触角是决定渗吸能否发生的决定性因素,只有接触角小于70°时渗吸才能发生;界面张力影响渗吸速度和最终采出程度,对于渗透率为1 mD的岩心,最佳界面张力为10~(-1) mN/m。     

Remote Steering of Self‐Propelling Microcircuits by Modulated Electric Field

The principles and design of “active” self‐propelling particles that can convert energy, move directionally on their own, and perform a certain function is an emerging multidisciplinary research field, with high potential for future technologies. A simple and effective technique is presented for on‐demand steering of self‐propelling microdiodes that move electroosmotically on water surface, while supplied with energy by an external alternating (AC) field. It is demonstrated how one can control remotely the direction of diode locomotion by electronically modifying the applied AC signal. The swimming diodes change their direction of motion when a wave asymmetry (equivalent to a DC offset) is introduced into the signal. The data analysis shows that the ability to control and reverse the direction of motion is a result of the electrostatic torque between the asymmetrically polarized diodes and the ionic charges redistributed in the vessel. This novel principle of electrical signal‐coded steering of active functional devices, such as diodes and microcircuits, can find applications in motile sensors, MEMs, and microrobotics.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Liquid Marble Patchwork on Super-Repellent Surface

Liquid marble (LM) is a droplet that is wrapped by hydrophobic solid particles, which behave as a non-wetting soft solid. Based on these properties, LM can be applied in fluidics and soft device applications. A wide variety of functional particles have been synthesized to form functional LMs. However, the formation of multifunctional LMs by integrating several types of functional particles is challenging. Here, a general strategy for the flexible patterning of functional particles on droplet surfaces in a patchwork-like design is reported. It is shown that LMs can switch their macroscopic behavior between a stable and active state on super-repellent surfaces in situ by jamming/unjamming the surface particles. Active LMs hydrostatically coalesce to form a self-sorted particle pattern on the droplet surface. With the support of LM handling robotics, on-demand cyclic activation–manipulation–coalescence–stabilization protocols by LMs with different sizes and particle types result in the reliable design of multi-faced LMs. Based on this concept, a single bi-functional LM is designed from two mono-functional LMs as an advanced droplet carrier.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.