Dielectric elastomer (DE) sensors have been widely used in a wide variety of applications, such as in robotic hands, wearable sensors, rehabilitation devices, etc. A unique dielectric elastomer-based ...multimodal capacitive sensor has been developed to quantify the pressure and the location of any touch simultaneously. This multimodal sensor is a soft, flexible, and stretchable dielectric elastomer (DE) capacitive pressure mat that is composed of a multi-layer soft and stretchy DE sensor. The top layer measures the applied pressure, while the underlying sensor array enables location identification. The sensor is placed on a passive elastomeric substrate in order to increase deformation and optimize the sensor's sensitivity. This DE multimodal capacitive sensor, with pressure and localization capability, paves the way for further development with potential applications in bio-mechatronics technology and other humanoid devices. The sensor design could be useful for robotic and other applications, such as fruit picking or as a bio-instrument for the diabetic insole.
This review intends to introduce the application of lignin-derived catalyst for green organic synthesis over latest two decades and aims to present a renewable alternative for conventional catalyst ...for future industry application. The structure of lignin is initially introduced in this review. Then, various pretreatment and activation technologies of lignin are systematically presented, which includes physical activation for the formation of well-developed porosity and chemical activation to introduce catalytic active sites. Finally, the catalytic performances of various lignin-derived catalysts are rationally assessed and compared with conventional catalysts, which involves lignin-derived solid acids for hydrolysis, hydration, dehydration (trans)esterification, multi-component reaction and condensation, lignin-derived solid base for Knoevenagel reaction, lignin-derived electro-catalysts for electro-oxidation, oxygen reduction reaction, and lignin-derived supported transition metal catalysts for hydrogenation, oxidation, coupling reaction, tandem reaction, condensation reaction, ring-opening reaction, Friedel-Crafts-type reaction, Fischer–Tropsch synthesis, click reaction, Glaser reaction, cycloaddition and (trans)esterification. The above lignin-derived catalysts thus successfully promote the transformations of organic compounds, carbon dioxide, biomass-based cellulose, saccharide and vegetable oil into valuable chemicals and fuels. At the end of this review, some perspectives are given on the current issues and tendency on the lignin-derived catalysts for green chemistry.
This review introduced applications of renewable lignin-derived catalysts on green organic syntheses by efficiently promoting the transformations of various organic compounds, carbon dioxide, biomass-based cellulose, saccharide and vegetable oil into valuable chemicals and fuels. Display omitted
In this paper, we shall formulate and address a problem of covert actuator attacker synthesis for cyber-physical systems that are modeled by discrete-event systems. We assume the actuator attacker ...partially observes the execution of the closed-loop system and is able to modify each control command issued by the supervisor on a specified attackable subset of controllable events. We provide straightforward but in general exponential-time reductions, due to the use of subset construction procedure, from the covert actuator attacker synthesis problems to the Ramadge-Wonham supervisor synthesis problems. It then follows that it is possible to use the many techniques and tools already developed for solving the supervisor synthesis problem to solve the covert actuator attacker synthesis problem for free. In particular, we show that, if the attacker cannot attack unobservable events to the supervisor, then the reductions can be carried out in polynomial time. We also provide a brief discussion on some other conditions under which the exponential blowup in state size can be avoided. Finally, we show how the reduction based synthesis procedure can be extended for the synthesis of successful covert actuator attackers that also eavesdrop the control commands issued by the supervisor.
, the edible ascidian, has been demonstrated to be an important source of bioactive natural metabolites. Here, we reported a novel terpenoid compound named Halorotetin A that was isolated from tunic ...ethanol extract of
by silica gel column chromatography, preparative layer chromatography (PLC), and semipreparative-HPLC.
H and
C NMRs,
H-
H COSY, HSQC, HMBC, NOESY, and HRESIMS profiles revealed that Halorotetin A was a novel terpenoid compound with antitumor potentials. We therefore treated the culture cells with Halorotetin A and found that it significantly inhibited the proliferation of a series of tumor cells by exerting cytotoxicity, especially for the liver carcinoma cell line (HepG-2 cells). Further studies revealed that Halorotetin A affected the expression of several genes associated with the development of hepatocellular carcinoma (HCC), including oncogenes (
and
) and HCC suppressor genes (
and
). In addition, we compared the cytotoxicities of Halorotetin A and doxorubicin on HepG-2 cells. To our surprise, the cytotoxicities of Halorotetin A and doxorubicin on HepG-2 cells were similar at the same concentration and Halorotetin A did not significantly reduce the viability of the normal cells. Thus, our study identified a novel compound that significantly inhibited the proliferation of tumor cells, which provided the basis for the discovery of leading compounds for antitumor drugs.
The automatic extraction of buildings from high-resolution aerial imagery plays a significant role in many urban applications. Recently, the convolution neural network (CNN) has gained much attention ...in remote sensing field and achieved a remarkable performance in building segmentation from visible aerial images. However, most of the existing CNN-based methods still have the problem of tending to produce predictions with poor boundaries. To address this problem, in this article, a novel semantic segmentation neural network named edge-detail-network (E-D-Net) is proposed for building segmentation from visible aerial images. The proposed E-D-Net consists of two subnetworks E-Net and D-Net. On the one hand, E-Net is designed to capture and preserve the edge information of the images. On the other hand, D-Net is designed to refine the results of E-Net and get a prediction with higher detail quality. Furthermore, a novel fusion strategy, which combines the outputs of the two subnetworks is proposed to integrate edge information with fine details. Experimental results on the INRIA aerial image labeling dataset and the ISPRS Vaihingen 2-D semantic labeling dataset demonstrate that, compared with the existing CNN-based model, the proposed E-D-Net provides noticeably more robust and higher building extraction performance, thus making it a useful tool for practical application scenarios.
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•We examined the association of SC/MCCPs with kidney function.•SC/MCCPs positively correlated with male eGFR.•SC/MCCPs were not associated with female eGFR.•SC/MCCPs may be a risk ...factor for early-stage kidney impairment in males.
Chlorinated paraffins (CPs) are contaminants ubiquitously detected in environmental samples, and reports addressing CPs in human samples are expanding. While CP exposure was suggested to impair kidney function by in vivo/in vitro experiments, epidemiological evidence is lacking.
To examine the associations between serum total short-chain CP and medium-chain CP concentrations (∑SCCPs and ∑MCCPs) with human kidney function.
The study samples were obtained from 387 participants living in Jinan, North China. We quantified ∑SCCPs and ∑MCCPs in serum samples and evaluated the kidney function of included subjects by estimated glomerular filtration rate (eGFR). The associations between serum ∑SCCPs, ∑MCCPs and eGFR were estimated using multivariable linear regression and logistic regression. The possible gender-dependent effects were studied by stratified analysis.
After adjusting for age, education, smoking status, drinking status, body mass index (BMI), family history of chronic kidney disease (CKD), fasting serum glucose, systolic blood pressure and diastolic blood pressure, higher concentrations of serum ∑SCCPs and ∑MCCPs were associated with higher male eGFR (β = 3.13 mL/min/1.73 m2 per one ln-unit increase of serum ∑SCCPs, 95%CI: 1.72, 4.54, p = 0.016; β = 3.52 mL/min/1.73 m2 per one ln-unit increase of serum ∑MCCPs, 95%CI: 1.89, 5.17, p = 0.011). Associations between serum ∑SCCPs, ∑MCCPs and female eGFR were null. Comparing higher (above the median serum CP levels) vs. lower exposure groups, serum ∑SCCPs and ∑MCCPs were associated with an elevated risk of glomerular hyperfiltration (GH, eGFR ≥ 135 mL/min/1.73 m2), which was associated with glomerular damage and represented as an early stage of chronic kidney disease (OR = 2.98; 95% CI: 1.24, 4.71 for SCCPs; OR = 3.25; 95% CI: 1.20, 5.29 for MCCPs).
Our study suggests that male serum ∑SCCPs and ∑MCCPs are associated with an increased risk of GH, indicating early-stage kidney impairment.
Addressing the complex issue of multi-attribute decision-making within a probabilistic dual hesitant fuzzy context, where attribute weights are unknown, a novel decision-making method based on ...cumulative prospect theory is proposed, named the probabilistic dual hesitant fuzzy multi-attribute decision-making method based on cumulative prospect theory. Firstly, a decision matrix is formulated, representing probabilistic dual hesitant fuzzy information. Secondly, according to the decision maker’s authentic preference and non-membership information sensitivity, a comprehensive score function suitable for probabilistic dual hesitant fuzzy elements is proposed. The attribute weights are then determined using the entropy method. Next, the value function and decision weight function from the cumulative prospect theory are employed to compute the cumulative prospect value attributed to each available scheme. In addition, a cumulative prospect matrix is constructed, enabling the establishment of scheme rankings based on the comprehensive cumulative prospect value. Finally, the analysis of specific cases and a comparative assessment of methods pertaining to the selection of emergency response schemes collectively demonstrate the rationality and efficacy of the decision-making method presented in this study.
Highlights
This review summarizes the recent progress and application of different metal-organic frameworks (MOFs)-derived non-noble metal materials for zinc-air batteries in the past few years.
This ...work gives extensive insights in understanding the relationship between design strategies and structure-activity relationship.
The challenges and prospects of MOF-derived oxygen electrocatalysts for zinc-air batteries are proposed.
Oxygen electrocatalysts are of great importance for the air electrode in zinc-air batteries (ZABs). Owing to the high specific surface area, controllable pore size and unsaturated metal active sites, metal–organic frameworks (MOFs) derivatives have been widely studied as oxygen electrocatalysts in ZABs. To date, many strategies have been developed to generate efficient oxygen electrocatalysts from MOFs for improving the performance of ZABs. In this review, the latest progress of the MOF-derived non-noble metal–oxygen electrocatalysts in ZABs is reviewed. The performance of these MOF-derived catalysts toward oxygen reduction, and oxygen evolution reactions is discussed based on the categories of metal-free carbon materials, single-atom catalysts, metal cluster/carbon composites and metal compound/carbon composites. Moreover, we provide a comprehensive overview on the design strategies of various MOF-derived non-noble metal–oxygen electrocatalysts and their structure-performance relationship. Finally, the challenges and perspectives are provided for further advancing the MOF-derived oxygen electrocatalysts in ZABs.
Accurate prediction of solar irradiance holds significant value for renewable energy usage and power grid management. However, traditional forecasting methods often overlook the time dependence of ...solar irradiance sequences and the varying importance of different influencing factors. To address this issue, this study proposes a dual-path information fusion and twin attention-driven solar irradiance forecasting model. The proposed framework comprises three components: a residual attention temporal convolution block (RACB), a dual-path information fusion module (DIFM), and a twin self-attention module (TSAM). These components collectively enhance the performance of multi-step solar irradiance forecasting. First, the RACB is designed to enable the network to adaptively learn important features while suppressing irrelevant ones. Second, the DIFM is implemented to reinforce the model’s robustness against input data variations and integrate multi-scale features. Lastly, the TSAM is introduced to extract long-term temporal dependencies from the sequence and facilitate multi-step prediction. In the solar irradiance forecasting experiments, the proposed model is compared with six benchmark models across four datasets. In the one-step predictions, the average performance metrics RMSE, MAE, and MAPE of the four datasets decreased within the ranges of 0.463–2.390 W/m2, 0.439–2.005 W/m2, and 1.3–9.2%, respectively. Additionally, the average R2 value across the four datasets increased by 0.008 to 0.059. The experimental results indicate that the model proposed in this study exhibits enhanced accuracy and robustness in predictive performance, making it a reliable alternative for solar irradiance forecasting.
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