Kinetic Controlled Chirality Transfer and Induction in 2D Hydrogen-Bonding Assemblies of Glycylglycine on Au(111).

Chirality transfer is of vital importance that dominates the structure and functionality of biological systems and living matters. External physical stimulations, e.g. polarized light and mechanical forces, can trigger the chirality symmetry breaking, leading to the appearance of the enantiomeric entities created from a chiral self-assembly of achiral molecule. Here, several 2D assemblies with different chirality, synthesized on Au(111) surface by using achiral building blocks - glycylglycine (digly), the simplest polypeptide are reported. By delicately tuning the kinetic factors, i.e., one-step slow/rapid deposition, or stepwise slow deposition with mild annealing, achiral square hydrogen-bond organic frameworks (HOF), homochiral rhombic HOF and racemic rectangular assembly are achieved, respectively. Chirality induction and related symmetry broken in assemblies are introduced by the handedness (H-bond configurations in principle) of the assembled motifs and then amplified to the entire assemblies via the interaction between motifs. The results show that the chirality transfer and induction of biological assemblies can be tuned by altering the kinetic factors instead of applying external forces, which may offer an in-depth understanding and practical approach to peptide chiral assembly on the surfaces and can further facilitate the design of desired complex biomolecular superstructures.

Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation.

To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.

An IoT-Based Life Cycle Assessment Platform of Wind Turbines.

Life cycle assessment (LCA) is conducive to the change in the wind power industry management model and is beneficial to the green design of products. Nowadays, none of the LCA systems are for wind turbines and the concept of Internet of Things (IoT) in LCA is quite a new idea. In this paper, a four-layer LCA platform of wind turbines based on IoT architecture is designed and discussed. In the data transmission layer, intelligent sensing of wind turbines can be achieved and their status and location can be monitored. In the data transmission layer, the LCA platform can be effectively integrated with enterprise information systems through the object name service (ONS) and directory service (DS). In the platform layer, a model based on IMPACT 2002+ is developed, and four management modules are designed. In the application layer, different from other systems, energy payback time (EPBT) is selected as an important evaluation index for wind turbines. Compared with the existing LCA systems, the proposed system is specifically for wind turbines and can collect data in real-time, leading to improved accuracy and response time.

Model predicting discomfort glare caused by LED road lights.

To model discomfort glare from LED road lighting, the effect of four key variables on perceived glare was explored. These variables were: the average glare source luminance (Lg), the background luminance (Lb), the solid angle of the glare source from the perspective of the viewer (ω) and the angle between the glare source and the line of sight (θ). Based on these four variables 72 different light conditions were simulated in a scaled experimental set-up. Participants were requested to judge the perceived discomfort glare of these light conditions using the deBoer rating scale. All four variables and some of their interactions had indeed a significant effect on the deBoer rating. Based on these findings, we developed a model, and tested its general applicability in various verification experiments, including laboratory conditions as well as real road conditions. This verification proved the validity of the model with a correlation between measured and predicted values as high as 0.87 and a residual deviation of about 1 unit on the deBoer rating scale. These results filled the gap in estimating discomfort glare of LED road lighting and clarified similarities of and differences in discomfort glare between LED and traditional light sources.

Succulent-Inspired Implicit Structural Change for Smart "ON/OFF" Switchable and Flexible EMI Shielding Coating.

Modern miniaturized intelligent electronics call for smart switchable and flexible electromagnetic interference (EMI) shielding material for highly precise applications. However, most switchable EMI shielding materials are based on an explicit structural change. Herein, we report a succulent-inspired smart switchable MXene (WR-MXene) coating film realized by inner implicit structural change, which benefits from the insertion of our reversible large-cavity yolk-shell biomicrospheres. The novel switchable yolk-shell biomicrospheres contain a soft N-isopropylacrylamide (PNIPAM) hydrogel core, an "ON/OFF" switchable cavity (over 30% volume fraction), and a porous polydopamine (p-PDA) shell. The yolk-shell biomicrospheres can be obtained by a facile two-step polymerization and a simple drying-dehydration treatment. Because of the "ON/OFF" switchable void space brought by the smart biomicrospheres and conductive framework of MXene, an optimized ultralight and flexible WR-MXene coating film (vWR-coating film) showed both large switchable change (over 60 dB) and extraordinary EMI shielding effectiveness, reaching 95 and over 50 dB in the whole X band (8.2-12.4 GHz). These novel reversible yolk-shell biomicrospheres and the succulent-inspired switchable coating films are promising for smart flexible wearable devices and many advanced multifunctional systems needing dynamic real-time response.

On enabling collaborative non-intrusive load monitoring for sustainable smart cities.

Improving energy efficiency is a crucial aspect of building a sustainable smart city and, more broadly, relevant for improving environmental, economic, and social well-being. Non-intrusive load monitoring (NILM) is a computing technique that estimates energy consumption in real-time and helps raise energy awareness among users to facilitate energy management. Most NILM solutions are still a single machine approach and do not fit well in smart cities. This work proposes a model-agnostic hybrid federated learning framework to collaboratively train NILM models for city-wide energy-saving applications. The framework supports both centralised and decentralised training modes to provide a cluster-based, customisable and optimal learning solution for users. The proposed framework is evaluated on a real-world energy disaggregation dataset. The results show that all NILM models trained in our proposed framework outperform the locally trained ones in accuracy. The results also suggest that the NILM models trained in our framework are resistant to privacy leakage.

Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning.

Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.

[Preparation method of polystyrene core-poly (acrylamide-acrylic acid) shell fluorescent microspheres].

OBJECTIVE: To investigate the preparation method of polystyrene core-poly (acrylamide-acrylic acid) shell fluorescent microspheres. METHODS: The polystyrene core-poly (acrylamide-acrylic acid) shell (P-(St-co-AAM)) fluorescent microspheres were prepared using fluorescent microspheres as the core and acrylamide/acrylic as polymerization monomer. Reaction conditions affecting the morphology of core-shell structure including feeding mode, initiator, cross linker, pH, concentration and swelling were studied. RESULT: Fluorescent microscopy showed that the relatively uniform particle sizes were distributed in a range of 7-8 µm. Fourier transform infra-red spectroscopy (FT-IR) proved the existence of poly (acrylamide-acrylic acid) shell and amide group on the surface. The optimal conditions for seeding polymerization: azobisisobutyronitrile was used as the initiator in the absence of cross linker, after a 40 h swelling treatment by using alcohol with the appropriate reaction temperature (70 degree), reaction time (3 h) and pH(6-7). The average dispersion and stability were 25.14 % and 90.21%, respectively. The fluorescein release percentage was kept stable at approximately 30% after 40 h. CONCLUSION: The fluorescent microspheres prepared by this method have core-shell structure and satisfactory fluorescence properties with good dispersion and stability.

[Clustering analysis applied to near-infrared spectroscopy analysis of Chinese traditional medicine].

The present article discusses the clustering analysis used in the near-infrared (NIR) spectroscopy analysis of Chinese traditional medicines, which provides a new method for the classification of Chinese traditional medicines. Samples selected purposely in the authors' research to measure their absorption spectra in seconds by a multi-channel NIR spectrometer developed in the authors' lab were safrole, eucalypt oil, laurel oil, turpentine, clove oil and three samples of costmary oil from different suppliers. The spectra in the range of 0.70-1.7 microm were measured with air as background and the results indicated that they are quite distinct. Qualitative mathematical model was set up and cluster analysis based on the spectra was carried out through different clustering methods for optimization, and came out the cluster correlation coefficient of 0.9742 in the authors' research. This indicated that cluster analysis of the group of samples is practicable. Also it is reasonable to get the result that the calculated classification of 8 samples was quite accorded with their characteristics, especially the three samples of costmary oil were in the closest classification of the clustering analysis.

Eye movement and pupil size constriction under discomfort glare.

PURPOSE: Involuntary physiological responses offer an alternative means to psychophysical procedures for objectively evaluating discomfort glare. This study examined eye movement and pupil size responses to glare discomfort using new approaches to analysis: relative pupil size and speed of eye movement. METHODS: Participants evaluated glare discomfort using the standard de Boer rating scale under various conditions manipulated to influence glare discomfort. Eye movement was recorded using an electro-oculogram (EOG), and pupil size was recorded using Tobii glasses. Ten young (mean age: 24.5 years old) and 10 senior (mean age: 61 years old) participants were recruited for this experiment. RESULTS: Subjective evaluation of glare discomfort was highly correlated with eye movement (multiple correlation coefficient [R(2)] of >0.94, P < 0.001) and pupil constriction (R(2) = 0.38, P < 0.001). Severe glare discomfort increased the speed of eye movement and caused larger pupil constriction. Larger variations of eye movement were found among seniors. CONCLUSIONS: The two physiological responses studied here to characterize discomfort glare under various lighting conditions had significant correlation with the subjective evaluation. The correlation between discomfort glare and physiological responses suggests an objective way to characterize and evaluate discomfort glare that may overcome the problems of conventional subjective evaluation. It also offers an explanation as to why long-term exposure to discomfort glare leads to visual fatigue and eyestrain.

Transient effects of harsh luminous conditions on the visual performance of aviators in a civil aircraft cockpit.

The aim of this work was to examine how harsh luminous conditions in a cockpit, such as lightning in a thunderstorm or direct sunlight immediately after an aircraft passes through clouds, may affect the visual performance of pilots, and how to improve it. Such lighting conditions can result in the temporary visual impairment of aviators, which may greatly increase the risk of accidents. Tests were carried out in a full-scale simulator cockpit in which two kinds of dynamic lighting scenes, namely pulse changed and step changed lighting, were used to represent harsh luminous conditions. Visual acuity (VA), reaction time (RT) and identification accuracy (IA) were recorded as dependent variables. Data analysis results indicate that standardized VA values decreased significantly in both pulsing and step conditions in comparison with the dark condition. Standardized RT values increased significantly in the step condition; on the contrary, less reaction time was observed in the pulsing condition. Such effects could be reduced by an ambient illumination provided by a fluorescent lamp in both conditions. The results are to be used as a principle for optimizing lighting design with a thunderstorm light.