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The utilization of sheet structure composites as a viable conductive filler has been implemented in polymer-based electromagnetic shielding materials. However, the development of an innovative sheet structure to enhance electromagnetic shielding performance remains a significant challenge. Herein, we propose a novel design incorporating silver-modified nanosheet self-assembled hollow spheres to optimize their performance. The unique microporous structure of the hollow composite, combined with the self-assembled surface nanosheets, facilitates multiple reflections of electromagnetic waves, thereby enhancing the dissipation of electromagnetic energy. The contribution of absorbing and reflecting electromagnetic waves in hollow nanostructures could be attributed to both the inner and outer surfaces. When multiple reflection attenuation is implemented, the self-assembled stack structure of nanosheets outside the composite material significantly enhances the occurrence of multiple reflections, thereby effectively improving its shielding performance. The structure also facilitates multiple reflections of incoming electromagnetic waves at the internal and external interfaces of the material, thereby enhancing the shielding efficiency. Simultaneously, the incorporation of silver particles can enhance conductivity and further augment the shielding properties. Finally, the optimized Ag/NiSi-Ni nanocomposites can demonstrate superior initial permeability (2.1 × 10−6 H m−1), saturation magnetization (13.2 emu g−1), and conductivity (1.2 × 10−3 Ω•m). This work could offer insights for structural design of conductive fillers with improved electromagnetic shielding performance.

Effective construction project planning relies on addressing constraints related to materials, labor, equipment, and others. Planning meetings are typical venues for stakeholders to identify, communicate, and remove constraints. However, a critical gap exists in lacking an automated approach to identify, classify, analyze, and track constraint discussions during onsite planning meetings. Therefore, this research aims to 1. develop a natural language processing model to classify constraints in meeting discussions; 2. uncover the discussion patterns of managers and foremen regarding various constraints; and 3. extract the root causes for constraints, evaluate their impacts, and prepare managers to develop practical solutions for constraint removal. This research collected meeting transcripts from 94 onsite planning meetings of a building project, spanning 263,836 words. Next, this research leveraged a general pretrained transformer (GPT) to segment discussion dialogs into topics. A Bidirectional Encoder Representations from Transformers (BERT)-based model was developed to categorize constraint types for each topic. The constraint patterns among meeting attendees were assessed. Furthermore, a GPT-based tool was devised to track root causes, impacts, and solutions for various constraints. Test results revealed an 8.8% improvement in constraint classification accuracy compared with the traditional classification model. An occupational characteristic in constraint discussion was observed in that the management team tended to balance their focus on various constraints, while foremen concentrated on more practical issues. This research contributes to the body of knowledge by leveraging language models to analyze construction planning meetings. The findings facilitate project managers in establishing constraint logs for diagnosing and prognosticating planning issues.

This paper proposes a method and an algorithm called Q-sorting for reinforcement learning (RL) problems with multiple cumulative constraints. The primary contribution is a mechanism for dynamically determining the focus of optimization among multiple cumulative constraints and the objective. Executed actions are picked through a procedure with two steps: first filter out actions potentially breaking the constraints, and second sort the remaining ones according to the Q values of the focus in descending order. The algorithm was originally developed upon the classic tabular value representation and episodic setting of RL, but the idea can be extended and applied to other methods with function approximation and discounted setting. Numerical experiments are carried out on the adapted Gridworld and the motor speed synchronization problem, both with one and two cumulative constraints. Simulation results validate the effectiveness of the proposed Q-sorting in that cumulative constraints are honored both during and after the learning process. The advantages of Q-sorting are further emphasized through comparison with the method of lumped performances (LP), which takes constraints into account through weighting parameters. Q-sorting outperforms LP in both ease of use (unnecessity of trial and error to determine values of the weighting parameters) and performance consistency (6.1920 vs. 54.2635 rad/s for the standard deviation of the cumulative performance index over 10 repeated simulation runs). It has great potential for practical engineering use.

To alleviate the problems of environmental pollution and energy crisis, aggressive development of clean and alternative energy technologies, in particular, water splitting, metal–air batteries, and fuel cells involving two key half reactions comprising hydrogen evolution reaction (HER) and oxygen reduction (ORR), is crucial. In this work, an innovative hybrid comprising heterogeneous Cu/Co bimetallic nanoparticles homogeneously dispersed on a nitrogen-doped carbon layer (Cu/Co/NC) was constructed as a bifunctional electrocatalyst toward HER and ORR via a hydrothermal reaction along with post-solid-phase sintering technique. Thanks to the interfacial coupling and electronic synergism between the Cu and Co bimetallic nanoparticles, the Cu/Co/NC catalyst showed improved catalytic ORR activity with a half-wave potential of 0.865 V and an excellent stability of more than 30 h, even compared to 20 wt% Pt/C. The Cu/Co/NC catalyst also exhibited excellent HER catalytic performance with an overpotential of below 149 mV at 10 mA/cm2 and long-term operation for over 30 h.

Radiation pneumonitis (RP) is a prevalent and fatal complication of thoracic radiotherapy due to the lack of effective treatment options. RP primarily arises from mitochondrial injury in lung epithelial cells. The mitochondrial-derived peptide MOTS-c has demonstrated protective effects against various diseases by mitigating mitochondrial injury. C57BL/6 mice were exposed to 20 Gy of lung irradiation (IR) and received daily intraperitoneal injections of MOTS-c for 2 weeks. MOTS-c significantly ameliorated lung tissue damage, inflammation, and oxidative stress caused by radiation. Meanwhile, MOTS-c reversed the apoptosis and mitochondrial damage of alveolar epithelial cells in RP mice. Furthermore, MOTS-c significantly inhibited oxidative stress and mitochondrial damage in MLE-12 cells and primary mouse lung epithelial cells. Mechanistically, MOTS-c increased the nuclear factor erythroid 2-related factor (Nrf2) level and promoted its nuclear translocation. Notably, Nrf2 deficiency abolished the protective function of MOTS-c in mice with RP. In conclusion, MOTS-c alleviates RP by protecting mitochondrial function through an Nrf2-dependent mechanism, indicating that MOTS-c may be a novel potential protective agent against RP.

This study investigated the potential mechanism of action of tea polyphenols (TPs), one of the major active ingredients in tea, to enhance heat resistance in Drosophila and the attenuating effect of heat treatment of TPs on their efficacy. The results showed that TPs were able to prolong the average survival time of Drosophila under high-temperature stress (p < 0.05), but the effect of TPs in prolonging the survival time of Drosophila melanogaster was significantly reduced (p < 0.05) with increasing TP heat-treatment time until it disappeared. The composition of TPs changed after heat treatment. It was also shown that the weakening of the effect of TPs in improving the heat tolerance of Drosophila was related to the decrease in the content of catechins and phenolic acids in their fractions as well as with the increase in the content of laccase. Transcriptomic analysis showed that the effect of TPs on heat tolerance in Drosophila melanogaster was closely related to the longevity regulation pathway, the neuroactive ligand–receptor interaction signaling pathway, and the drug metabolism–cytochrome P450 pathway. Metabolomics analysis showed that the effect of TP intervention in improving the body’s heat tolerance was mainly related to amino acid metabolism and energy metabolism. However, thermal processing weakened the relevance of these transcriptomes and metabolomes. The present study reveals the mechanism of action by which heat-treated TPs affect the body’s heat tolerance, which is important for the development and utilization of the heat-protection function of tea.

Black tea (Fuyun 6) samples collected from three regions, Youxi, Fu’an, and Datian, were analysed by automatic thermal desorption-gas chromatography–mass spectrometry (ATD-GC–MS) combined with the electronic nose (E-nose) technique to investigate the aroma composition differences between black teas from different regions. The response surface methodology was used to optimize the ATD conditions for extracting the aroma components from the black tea. The results revealed that the optimal conditions for aroma component accumulation from black tea samples included a sample weight of 2.8 g, an adsorption time of 39 min, an adsorption temperature of 75 °C, and a cold trap temperature of −30 °C. The ATD-GC–MS analyses identified a total of 71 aroma components in the black tea samples, of which 31 were utilized to differentiate the origins of the black teas. Additional aroma activity analyses indicated that benzyl alcohol, linalool, hexanal, octanal, and nonanal had odour activity values (OAVs) greater than 10. Additionally, the OAV of decanal exceeded 100, indicating its significant contribution to the aroma profile of Fuyun 6 black tea. The E-nose results demonstrated the ability to differentiate the black tea samples from the three different origins. This study successfully identified the specific aroma substances associated with different tea origins, providing valuable insights into the aroma characteristics of black teas from various regions.

A high-performance, low-voltage, transparent, metal-semiconductor-metal ultraviolet (UV) photodetector (PD) is proposed and experimentally demonstrated, based on gold (Au) asymmetric interdigitated (aIDT) electrodes with thicknesses well below 10 nm. A 7-nm-thick Au film, with a visible transmittance of 80.4% and a sheet resistance of 11.55 Ω/sq, is patterned into aIDT electrodes on a ZnO active layer, whose average visible transmittance is up to 74.3%. Meshing the pads further improves the overall transmittance of the device. Among all fabricated devices, the PD with the aIDT finger width ratio of 1:4 performs the best. Very low dark currents are achieved at 0, 0.5 and 1 V, allowing for high responsivities and specific detectivities to the UV light. It is also a fast device, especially under the biases of 0.5 and 1 V. The comprehensive performances are comparable and even superior to those of the reported devices. The asymmetric Schottky junctions induced by the aIDT electrodes under UV illumination are the main mechanism for the low-voltage operation of our transparent PD, which is promising to be applied widely.

Rape pollen has always been considered as a research hotspot in health foods and pharmaceuticals due to its abundance of natural active ingredients. In this work, a compound with antioxidant activity was directly isolated from the methanol extract of rape pollen using a two-step procedure, under the supervision of online HPLC−1,1-diphenyl-2-picrylhydrazyl (HPLC-DPPH) detection. Firstly, online HPLC−DPPH detection was used to identify the active peaks in the methanol extract of rape pollen, and then the methanol extract was pretreated via medium-pressure liquid chromatography (MPLC) to obtain the target fraction 3 (Fr3). Fr3 was further purified using HPLC to finally obtain the target fraction 3-1, which was identified as kaempferol 3,4′-di-O-β-D-glucopyranoside through NMR and mass spectrometry. To further explore the free radical scavenging activity of this compound, its DPPH scavenging ability was determined, and two proteins related to the antioxidant pathway were used for molecular docking. The results revealed that the chromatographic strategy used in this study was efficient and reliable in separating high−purity antioxidants from rape pollen. A strategy such as this, meanwhile, also holds promise for qualitatively identifying and specifically isolating active compounds from other natural products.

Invasive alien plants have invaded various habitats, posing a threat to biodiversity. Several hypotheses have been proposed to explain the mechanisms of invasion, but few studies have considered the characteristics of the invaded communities and the effects of human interference in the invasion. In this study, we compared the invasibility of three different habitats: abandoned land, eucalyptus plantations, and natural secondary forests. We explored the effects of species diversity, phylogenetic diversity, and disturbance factors on the invasibility of different habitats. The results showed that the invasibility of abandoned land was the highest and the invasibility of the natural secondary forest was the lowest. Phylogenetic indicators affected the invasibility of abandoned land and eucalyptus plantations, and disturbance factors affected the invasibility of all three habitats, while the characteristics of the invaded communities had a weak impact. Our research provided supporting evidence for Darwin’s naturalization hypothesis and his disturbance hypothesis but found no relationship between biotic resistance and invasibility. This study indicated that the differences among habitats should be considered when we prove Darwin’s naturalization hypothesis in nature reserves.

Metal sulfide electrocatalyst is developed as a cost-effective and promising candidate for hydrogen evolution reaction (HER). In this work, we report a novel Mo-doped Cu2S self-supported electrocatalyst grown in situ on three-dimensional copper foam via a facile sulfurization treatment method. Interestingly, Mo-Cu2S nanosheet structure increases the electrochemically active area, and the large fleecy multilayer flower structure assembled by small nanosheet facilitates the flow of electrolyte in and out. More broadly, the introduction of Mo can adjust the electronic structure, significantly increase the volmer step rate, and accelerate the reaction kinetics. As compared to the pure Cu2S self-supported electrocatalyst, the Mo-Cu2S/CF show much better alkaline HER performance with lower overpotential (18 mV at 10 mA cm−2, 322 mV at 100 mA cm−2) and long-term durability. Our work constructs a novel copper based in-situ metal sulfide electrocatalysts and provides a new idea to adjust the morphology and electronic structure by doping for promoting HER performance.

The demand for improving the activity, durability, and recyclability of metal-organic cages (MOCs) that work as photocatalytic molecular devices in a homogeneous system has promoted research to combine them with other solid materials. An M2L4 type photosensitive metal-organic cage MOC-Q2 with light-harvesting ligands and catalytic Pd2+ centers has been synthesized and further heterogenized with graphitic carbon nitride to prepare a robust direct Z-scheme heterojunction photocatalyst for visible-light-driven hydrogen generation. The optimized g-C3N4/MOC-Q2 (0.7 wt%) sample exhibits a high H2 evolution activity of 6423 μmol g−1 h−1 in 5 h, and a total turnover number of 39,695 after 10 h, significantly superior to the bare MOC-Q2 used in the homogeneous solution and the comparison sample Pd/g-C3N4/L-4. The enhanced performances of g-C3N4/MOC-Q2 can be ascribed to its direct Z-scheme heterostructure, which effectively improves the charge separation and transfer efficiency. This work presents a rational approach of designing a binary photocatalytic system through combing micromolecular MOCs with heterogeneous semiconductors for water splitting.

Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep neural network in a supervised fashion usually requires the collection of a great number of examples and the consumption of a significant amount of time. However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging. In this paper, we overcome the problem of a lack of training data by using an unsupervised deep-learning-based method. Specifically, we propose a deep-learning-based method based on the deep image prior (DIP) method, which only requires a noisy image as training data, without any clean data. It infers the natural images with random inputs and the corrupted observation with the help of performing correction via a convolutional network. We improve the original DIP method as follows: Firstly, the original optimization objective function is modified by adding nonlocal regularizers, consisting of a spatial filter and a frequency domain filter, to promote the gradient sparsity of the solution. Secondly, we solve the optimization problem with the alternating direction method of multipliers (ADMM) framework, resulting in two separate optimization problems, including a symmetric U-Net training step and a plug-and-play proximal denoising step. As such, the proposed method exploits the powerful denoising ability of both deep neural networks and nonlocal regularizations. Experiments validate the effectiveness of leveraging a combination of DIP and nonlocal regularizers, and demonstrate the superior performance of the proposed method both quantitatively and visually compared with the original DIP method.

As one of the essential indicators for the development of a city, urban vibrancy plays an important role in evaluating the quality of urban areas and guiding urban construction. The development of spatial big data makes it possible to obtain information on user trajectories and the built environment, providing support for the evaluation of urban vibrancy. However, previous studies focused on the number of regional activities when evaluating urban vibrancy and ignored diversity, which was produced by diverse economic landscapes. In this paper, using mobile phone trajectory data, we propose a method for evaluating urban vibrancy from two dimensions: busyness and diversity, based on the improved PageRank algorithm and an index of entropy. Furthermore, in order to explore the relationship between urban vibrancy and the economic landscape, we construct an economic landscape index system based on multi-source data, including points of interest (POIs), roads, building footprints, house prices, the gross domestic product (GDP), and population data. Then, multiple linear regression is utilized to model the relationship between urban vibrancy and the urban economic landscape. The results show that combining busyness and diversity can better characterize urban vibrancy than any single indicator, and the adjusted R-squared (R2) of the regression with economic landscape reaches 0.59.

Multispeed transmissions are helpful for improvement of the economy and drivability of electric vehicles (EVs). In this paper, we propose a two-speed transmission based on dual planetary gear mechanism, in which shifts are realized by torque transfer between two brakes located on ring gears. To synthesize the dynamic and economic performances of the vehicle, a multiobjective optimization problem is constructed for gear ratio optimization and Pareto-optimal solutions of gear ratio combinations are obtained by Nondominated sorting genetic algorithm-II (NSGA-II). In particular, the minimum electric energy consumption of the EV is calculated with a fast Dynamic Programming (DP) in each iteration. Following this, a constant-output-torque control (COTC) scheme is adopted for the torque phase and inertia phase of gearshift process to ensure constant output torque on the wheel. To enhance transient responses, the feedforward–feedback controller structure is applied and a disturbance observer is integrated to improve robustness. Simulation results demonstrate that the two-speed transmission has much better performance in terms of acceleration time and energy economy compared to the fixed-ratio transmission, and the proposed gearshift control method is able to achieve fast and smooth gear shift robustly while maintaining constant output torque.

Precise motor speed regulation control is essential to achieve a good gear shifting quality of the integrated motor-transmission (IMT) system, in which the relative speed between outgoing shaft and the gearwheel to be engaged can be eliminated directly through regulation of the motor speed. The speed regulation control confronts the difficulty that there exist external disturbances on the motor shaft, like the unknown load torque arised from bearing friction, oil shearing and oil churning, etc. To deal with these disturbances, a robust speed regulation controller combined a nominal proportional control and integral sliding mode control is proposed. The former is designed to achieve a good speed tracking performance and the latter provides functionality of disturbances rejection. The effects of different controller parameters for the robust controller design are assessed via simulations. Moreover, to verify the effectiveness of the combined control scheme in practical engineering use, experiments are carried out on a test bench with a real IMT powertrain system. Results indicate that the proposed approach can attain a rapid and smooth speed regulation process with a simple controller structure and good robustness.

This paper presents a gear shift method for the dual clutch transmission (DCT) with integrated electric motor in pure electric drive mode. In contrast to clutch-to-clutch shift in conventional DCT, a good gear shifting process relies on the coordinated control of the motor and synchronizer in electric drive mode of the hybrid DCT. To shorten the torque interruption time and reduce the wear of the synchronizer during engagement, the key point is to adjust the oncoming gear speed to the output shaft speed rapidly. This study provides a speed regulation control framework based on model predictive control (MPC) and disturbance observer (DO), where the MPC controller is designed to achieve a good tracking performance and the DO is to eliminate effects from exogenous disturbances. Simulation and experimental results demonstrate that the proposed approach can attain a rapid and robust gear shifting performance.

Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq.

To achieve tactile detection on the irregular surface of a robot link, a flexible annular sectorial sensor with a five-layer structure was proposed that could be wrapped on the surface of a truncated cone-shaped link. The sensor was designed for the detection of a contact position when robots collide with other objects during movement. The sensor obtains the coordinates of the contact position by exerting a constant electric field on the upper and lower conductive layers. The mathematical model linking the coordinates of the contact position and the corresponding electric potential on the conductive layer was established, based on the uniqueness of the electric field. The design of the sensor was simulated using COMSOL software, and the detection error of the contact position was discussed. A sensor sample was fabricated and wrapped on the mechanical arm. The results of the simulations and experiments indicated that the flexible sensor performed very well when wrapped on the robot link.

Hierarchical tungsten oxide assemblies such as spindle-like structures, flowers with sharp petals, nanowires and regular hexagonal structures are successfully synthesized via a solvothermal reduction method by simply adjusting the reaction conditions. On the basis of the experimental results, it is determined that the reaction time significantly influences the phase transition, microstructure and photocatalytic activity of the prepared samples. The possible mechanisms for the morphology evolution process have been systematically proposed. Moreover, the as-prepared products exhibit significant morphology-dependent photocatalytic activity. The flower-like W18O49 prepared at 6 h possesses a large specific surface area (150.1 m2∙g−1), improved separation efficiency of electron-hole pairs and decreased electron-transfer resistance according to the photoelectrochemical measurements. As a result, the flower-like W18O49 prepared at 6 h exhibits the highest photocatalytic activity for the degradation of Methyl orange aqueous solution. The radical trap experiments showed that the degradation of MO was driven mainly by the participation of h+ and •O2− radicals.

Updating topographic maps in multi-representation databases is crucial to a number of applications. An efficient way to update topographic maps is to propagate the updates from large-scale maps to small-scale maps. Because objects are often portrayed differently in maps of different scales, it is a complicated process to produce multi-scale topographic maps that meet specific cartographical criteria. In this study, we propose a new approach to update small-scale maps based on updated large-scale maps. We first group spatially-related objects in multi-scale maps and decompose the large-scale objects into triangles based on constrained Delaunay triangulation. We then operate the triangles and construct small-scale objects by accounting for cartographical generalization rules. In addition, we apply the Tabu Search algorithm to search for the optimal sequences when constructing small-scale objects. A case study was conducted by applying the developed method to update residential areas at varied scales. We found the proposed method could effectively update small-scale maps while maintaining the shapes and positions of large-scale objects. Our developed method allows for parallel processing of update propagation because it operates grouped objects together, thus possesses computational advantages over the sequential updating method in areas with high building densities. Although the method proposed in this study requires further tests, it shows promise with respect to automatic updates of polygon data in the multi-representation databases.