A generalized Udwadia-Kalaba control design for uncertain belt conveyor systems with inequality constraints.

Uncertainty can lead to jitter or overshoot in mechanical systems, necessitating the design of multiple constraints to stabilize them. This paper proposes a control structure based on the generalized Udwadia-Kalaba equation to address these constraints simultaneously. An uncertain dynamical model is developed, incorporating both equality and inequality constraints. By integrating diffeomorphism theory, a robust control strategy is designed to ensure compliance with these constraints. Utilizing the Lyapunov approach, the uniform boundedness and uniform ultimate boundedness of the dynamical system are demonstrated. Finally, the feasibility of the proposed control method is validated through its application to a belt conveyor system.

Kirigami-Triggered Spoof Plasmonic Interconnects for Radiofrequency Elastronics.

The flexible and conformal interconnects for electronic systems as a potential signal transmission device have great prospects in body-worn or wearable applications. High-efficiency wave propagation and conformal structure deformation around human body at radio communication are still confronted with huge challenges due to the lack of methods to control the wave propagation and achieve the deformable structure simultaneously. Here, inspired by the kirigami technology, a new paradigm to construct spoof plasmonic interconnects (SPIs) that support radiofrequency (RF) surface plasmonic transmission is proposed, together with high elasticity, strong robustness, and multifunction performance. Leveraging the strong field-confinement characteristic of spoof surface plasmons polaritons, the Type-I SPI opens its high-efficiency transmission band after stretching from a simply connected metallic surface. Meanwhile, the broadband transmission of the kirigami-based SPI exhibits strong robustness and excellent stability undergoing complex deformations, i.e., bending, twisting, and stretching. In addition, the prepared Type-II SPI consisting of 2 different subunit cells can achieve band-stop transmission characteristics, with its center frequency dynamically tunable by stretching the buckled structure. Experimental measurements verify the on-off switching performance in kirigami interconnects triggered by stretching. Overcoming the mechanical limitation of rigid structure with kirigami technology, the designer SPIs exhibit high stretchability through out-of-plane structure deformation. Such kirigami-based interconnects can improve the elastic functionality of wearable RF electronics and offer high compatibility to large body motion in future body network systems.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

[Research progress of traditional Chinese medicines as quorum sensing inhibitors in collaboration with antibiotics to inhibit drug-resistant bacteria].

Quorum sensing system regulates the expression of genes related to bacterial growth, metabolism and other behaviors by sensing bacterial density, and controls the unified action of the entire bacterial population. This mechanism can ensure the normal secretion of bacterial metabolites and the stability of the biofilm microenvironment, providing protection for the formation of biofilms and the normal growth and reproduction of bacteria. Traditional Chinese medicine, capable of quorum sensing inhibition, can inhibit the formation of bacterial biofilms, reduce bacterial resistance, and enhance the anti-infection ability of antibiotics when combined with antibiotics. In recent years, the combination of traditional Chinese and Western medicine in the treatment of drug-resistant bacterial infections has become a research hotspot. Starting with the associations between quorum sensing, biofilm and drug-resistant bacteria, this paper reviews the relevant studies about the combined application of traditional Chinese medicines as quorum sensing inhibitors with antibiotics in the treatment of drug-resistant bacteria. This review is expected to provide ideas for the development of new clinical treatment methods and novel anti-infection drugs.

Structural transition and migration of incoherent twin boundary in diamond.

Grain boundaries (GBs), with their diversity in both structure and structural transitions, play an essential role in tailoring the properties of polycrystalline materials1-5. As a unique GB subset, {112} incoherent twin boundaries (ITBs) are ubiquitous in nanotwinned, face-centred cubic materials6-9. Although multiple ITB configurations and transitions have been reported7,10, their transition mechanisms and impacts on mechanical properties remain largely unexplored, especially in regard to covalent materials. Here we report atomic observations of six ITB configurations and structural transitions in diamond at room temperature, showing a dislocation-mediated mechanism different from metallic systems11,12. The dominant ITBs are asymmetric and less mobile, contributing strongly to continuous hardening in nanotwinned diamond13. The potential driving forces of ITB activities are discussed. Our findings shed new light on GB behaviour in diamond and covalent materials, pointing to a new strategy for development of high-performance, nanotwinned materials.

Stability of Quadruple Hydrogen Bonds in an Ionic Liquid Environment.

Hydrogen bonds (H-bonds) are highly sensitive to the surrounding environments owing to their dipolar nature, with polar solvents kown to significantly weaken H-bonds. Herein, the stability of the H-bonding motif ureidopyrimidinone (UPy) is investigated, embedded into a highly polar polymeric ionic liquid (PIL) consisting of pendant pyrrolidinium bis(trifluoromethylsulfonyl)imide (IL) moieties, to study the influence of such ionic environments on the UPy H-bonds. The content of the surrounding IL is changed by addition of an additional low molecular weight IL to further boost the IL content around the UPy moieties in molar ratios of UPy/IL ranging from 1/4 up to 1/113, thereby promoting the polar microenvironment around the UPy-H-bonds. Variable-temperature solid-state MAS NMR spectroscopy and FT-IR spectroscopy demonstrate that the UPy H-bonds are largely present as (UPy-) dimers, but sensitive to elevated temperatures (>70 °C). Subsequent rheology and DSC studies reveal that the ILs only solvate the polymeric chains but do not interfere with the UPy-dimer H-bonds, thus accounting for their high stability and applicability in many material systems.

Effects of different extreme cold exposure on heart rate variability.

Frequent extreme cold events in recent years have brought serious threats to outdoor workers and rescuers. Changes in ambient temperature are associated with altered cardiac autonomic function. The study aims to investigate heart rate variability (HRV) and its relationship to other physiological parameters under extreme cold exposures. Twelve males underwent a 30-min preconditioning phase in a neutral environment followed by a 30-min cold exposure (-5, -10, -15, and -20 °C). Time-domain indexes(meanRR, SDNN, RMSSD, and pNN50), frequency domain indexes [Log(HF), Log(LF), and low frequency/high frequency (LF/HF)], parasympathetic nervous system (PNS), and sympathetic nervous system (SNS) were analysed. Results showed all HRV indexes of four cold exposures were significant. The decrease in temperature was accompanied by progressive PNS activation with SNS retraction. SDNN was the most sensitive HRV index and had good linear relationships with blood pressure, pulse, and hand temperature. The results are significant for formulating safety protection strategies for workers in extremely cold environments.Practitioner Summary: This study investigated heart rate variability (HRV) in 12 males during a 30-min cold exposure (-5, -10, -15, and -20 °C). Results showed all HRV indexes of four cold exposures were significant. The decrease in temperature was accompanied by progressive PNS activation with SNS retraction. SDNN was the most sensitive HRV index and had good linear relationships with blood pressure, pulse, and hand temperature.

Active role of amino acid metabolism in early diagnosis and treatment of diabetic kidney disease.

Diabetic Kidney Disease (DKD) is one of the significant microvascular consequences of type 2 diabetes mellitus with a complex etiology and protracted course. In the early stages of DKD, the majority of patients experience an insidious onset and few overt clinical symptoms and indicators, but they are prone to develop end-stage renal disease in the later stage, which is life-threatening. The abnormal amino acid metabolism is tightly associated with the development of DKD, which involves several pathological processes such as oxidative stress, inflammatory response, and immune response and is also closely related to autophagy, mitochondrial dysfunction, and iron death. With a focus on taurine, branched-chain amino acids (BCAAs) and glutamine, we explored the biological effects of various amino acid mechanisms linked to DKD, the impact of amino acid metabolism in the early diagnosis of DKD, and the role of amino acid metabolism in treating DKD, to offer fresh objectives and guidelines for later early detection and DKD therapy.

Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space.

BACKGROUND AND OBJECTIVES: Compared with traditional RGB images, medical hyperspectral imagery (HSI) has numerous continuous narrow spectral bands, which can provide rich information for cancer diagnosis. However, the abundant spectral bands also contain a large amount of redundancy information and increase computational complexity. Thus, dimensionality reduction (DR) is essential in HSI analysis. All vector-based DR methods ignore the cubic nature of HSI resulting from vectorization. To overcome the disadvantage of vector-based DR methods, tensor-based techniques have been developed by employing multi-linear algebra. METHODS: To fully exploit the structure features of medical HSI and enhance computational efficiency, a novel method called unsupervised dimensionality reduction via tensor-based low-rank collaborative graph embedding (TLCGE) is proposed. TLCGE introduces entropy rate superpixel (ERS) segmentation algorithm to generate superpixels. Then, a low-rank collaborative graph weight matrix is constructed on each superpixel, greatly improving the efficiency and robustness of the proposed method. After that, TLCGE reduces dimensions in tensor space to well preserve intrinsic structure of HSI. RESULTS: The proposed TLCGE is tested on cholangiocarcinoma microscopic hyperspectral data sets. To further demonstrate the effectiveness of the proposed algorithm, other machine learning DR methods are used for comparison. Experimental results on cholangiocarcinoma microscopic hyperspectral data sets validate the effectiveness of the proposed TLCGE. CONCLUSIONS: The proposed TLCGE is a tensor-based DR method, which can maintain the intrinsic 3-D data structure of medical HSI. By imposing the low-rank and sparse constraints on the objective function, the proposed TLCGE can fully explore the local and global structures within each superpixel. The computational efficiency of the proposed TLCGE is better than other tensor-based DR methods, which can be used as a preprocessing step in real medical HSI classification or segmentation.

Orthogonal light-triggered multiple effects based on photochromic nanoparticles for DNA cleavage and beyond.

Efficient and spatiotemporally controllable cleavage of deoxyribonucleic acid (DNA) is of great significance for both disease treatment (e.g. tumour, bacterial infection, etc) and molecular biology applications (e.g. gene editing). The recently developed light-induced cleavage strategy based on catalytic nanoparticles has been regarded as a promising strategy for DNA controllable cleavage. Although the regulation based on orthogonal light in biomedical applications holds more significant advantages than that based on single light, nanoparticle-mediated DNA cleavage based on orthogonal light has yet to be reported. In this article, for the first time, we demonstrated an orthogonal light-regulated nanosystem for efficient and spatiotemporal DNA cleavage. In this strategy, tungsten oxide (WO3) nanoparticles with photochromic properties were used as nano-antennae to convert the photoenergy from the orthogonal visible light (405 nm) and near-infrared light (808 nm) into chemical energy for DNA cleavage. We verified that only the orthogonal light can trigger high cleavage efficiency on different types of DNA. Moreover, such an orthogonal light-response nano-system can not only induce significant apoptosis of tumour cells, but also effectively eliminate bacterial biofilms.

Prediction of human thermal comfort preference based on supervised learning.

Human thermal comfort is relevant to human life comfort and plays a pivotal role in occupational health and thermal safety. To ensure that intelligent temperature-controlled equipment can deliver a sense of cosiness to people while improving its energy efficiency, we designed a smart decision-making system that sets the thermal comfort adjustment preference as a label, reflecting both the human body's thermal feeling and its acceptance of the thermal environment. By training a series of supervised learning models underpinned by environmental and human features, the most appropriate adjustment mode in the current environment was predicted. To bring this design into reality, we tried six supervised learning models, and then, by comparison and evaluation, we identified that the Deep Forest's performance was the best. The model takes into account objective environmental factors and human body parameters. In this way, it can achieve high accuracy in application and good simulation and prediction results. The results can provide feasible references for feature selection and model selection in further research with the aim of testing thermal comfort adjustment preference. The model can provide recommendations for the thermal comfort preference in a specific place at a particular time, as well as guidance on human thermal comfort preference and thermal safety precautions in specific occupational groups.

Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer.

Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm-1, 1004 cm-1, 1449 cm-1, 1089-1128 cm-1, 1311-1367 cm-1 and 1585-1665 cm-1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm-1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.

High-order robust control and Stackelberg game-based optimization for uncertain fuzzy PMSM system with inequality constraints.

There exist the uncertainties and the inequality constraints in permanent magnet synchronous motor (PMSM) system. In order to meet the safety control requirements in industrial applications, the state transformation is used to meet the inequality constraints for limiting the outputs within desired bounds. Then, fuzzy set theory, which is different from fuzzy logic, is used to describe uncertainty, and the fuzzy PMSM dynamical model is established. Based on that, a robust control with high-order term is proposed to compensate for the time-varying uncertainty. Furthermore, for improving the system performance and decreasing the control cost, the Stackelberg game is introduced into the optimization scheme design, in which the leader plays a more important role than follower. These characteristic corresponds to the influence of the two tunable control parameters on the system. Thus, the optimal parameters are obtained by the rules of Stackelberg game. Finally, experimental results show the effectiveness of the above theories.

Synthesis and Characterization of Quadrupolar-Hydrogen-Bonded Polymeric Ionic Liquids for Potential Self-Healing Electrolytes.

Within the era of battery technology, the urgent demand for improved and safer electrolytes is immanent. In this work, novel electrolytes, based on pyrrolidinium-bistrifluoromethanesulfonyl-imide polymeric ionic liquids (POILs), equipped with quadrupolar hydrogen-bonding moieties of ureido-pyrimidinone (UPy) to mediate self-healing properties were synthesized. Reversible addition-fragmentation chain-transfer (RAFT) polymerization was employed using S,S-dibenzyl trithiocarbonate as the chain transfer agent to produce precise POILs with a defined amount of UPy and POIL-moieties. Kinetic studies revealed an excellent control over molecular weight and polydispersity in all polymerizations, with a preferable incorporation of UPy monomers in the copolymerizations together with the ionic monomers. Thermogravimetric analysis proved an excellent thermal stability of the polymeric ionic liquids up to 360 °C. By combining the results from differential scanning calorimetry (DSC), broadband dielectric spectroscopy (BDS), and rheology, a decoupled conductivity of the POILs from glass transition was revealed. While the molecular weight was found to exert the main influence on ionic conductivity, the ultimate strength and the self-healing efficiency (of up to 88%) were also affected, as quantified by tensile tests for both pristine and self-healed samples, evidencing a rational design of self-healing electrolytes bearing both hydrogen bonding moieties and low-molecular-weight polymeric ionic liquids.

[Research on batch-to-batch quality control of compound Danshen extract based on physical characterization and multivariate statistical analysis].

Physical attributes of Chinese herbal extracts are determined by their chemical components, and the physical and chemical attributes jointly affect the preparation process performance and the final product quality. Therefore, in order to improve the quality control of Chinese herbal extracts, we should comprehensively study the batch-to-batch consistency of physical and chemical attributes as well as the correlations between them. This paper first explored the physical attributes affecting the preparation process performance of the compound Danshen extract and developed a method for characterizing the texture attributes. With such main chemical components as water, phenolic acids, saponins, and saccharides and texture, rheology, and other physical attributes taken into consideration, the batch-to-batch quality fluctuation of products from different production lines and time was analyzed by principal components analysis(PCA). Finally, the correlation and partial least squares(PLS) analysis was conducted, and the regression equation was established. The fitting result of the PLS model for dynamic viscosity was satisfying(R~2Y=0.857, Q~2=0.793), suggesting that the chemical components could be adjusted by the component transfer rate in the extraction process, the impurity removal rate in the alcohol precipitation process, and the water retention rate of the concentration process to meet the control of the extract dynamic viscosity. This study clarified the correlations between physical and chemical attributes of the compound Danshen extract and established a method for controlling its physical attributes based on process regulation, which would provide reference for improving the quality control of Chinese herbal extracts.

Optimal Design of High-Order Control for Fuzzy Dynamical Systems Based on the Cooperative Game Theory.

In this article, we propose a high-order robust control for fuzzy dynamical systems. The time varying but bounded uncertainty in this system is described by the fuzzy set theory. The control is deterministic and is not based on IF-THEN fuzzy rules. By the Lyapunov approach, we prove that the control is able to guarantee uniform boundedness and uniform ultimate boundedness. In addition, the tunable parameters in the high-order control are regarded as two players in a cooperative game. Two cost functions are also proposed based on the two players. These two cost functions are related to system performance and control cost. Then, the optimal design problem is solved by finding the Pareto-optimality parameters. Numerical simulations are performed for verification.

Control Design With Optimization for Fuzzy Steering-by-Wire System Based on Nash Game Theory.

In this article, we apply a high-order control to a dynamical system with uncertainty. There are two characteristics. First, the uncertain part, which is time-varying but bounded, is described in a fuzzy aspect. Specifically, the uncertainty lies within a fuzzy set and the bound is regarded as a fuzzy number. Second, the systems are uniformly bounded and uniformly ultimately bounded with a deterministic controller based on the Lyapunov theory. To obtain better system performance and lower control input, we apply the noncooperative game theory to optimize the parameters by establishing a Nash game. Then, the D-operation is proposed for the uncertainty related to fuzzy numbers. Finally, we perform the numerical simulations of the steering-by-wire system for verification.

Comparing C2=O and C2=S Barbiturates: Different Hydrogen-Bonding Patterns of Thiobarbiturates in Solution and the Solid State.

Carbonyl-centered hydrogen bonds with various strength and geometries are often exploited in materials to embed dynamic and adaptive properties, with the use of thiocarbonyl groups as hydrogen-bonding acceptors remaining only scarcely investigated. We herein report a comparative study of C2=O and C2=S barbiturates in view of their differing hydrogen bonds, using the 5,5-disubstituted barbiturate B and the thiobarbiturate TB as model compounds. Owing to the different hydrogen-bonding strength and geometries of C2=O vs. C2=S, we postulate the formation of different hydrogen-bonding patterns in C2=S in comparison to the C2=O in conventional barbiturates. To study differences in their association in solution, we conducted concentration- and temperature-dependent NMR experiments to compare their association constants, Gibbs free energy of association ∆Gassn., and the coalescence behavior of the N-H‧‧‧S=C bonded assemblies. In Langmuir films, the introduction of C2=S suppressed 2D crystallization when comparing B and TB using Brewster angle microscopy, also revealing a significant deviation in morphology. When embedded into a hydrophobic polymer such as polyisobutylene, a largely different rheological behavior was observed for the barbiturate-bearing PB compared to the thiobarbiturate-bearing PTB polymers, indicative of a stronger hydrogen bonding in the thioanalogue PTB. We therefore prove that H-bonds, when affixed to a polymer, here the thiobarbiturate moieties in PTB, can reinforce the nonpolar PIB matrix even better, thus indicating the formation of stronger H-bonds among the thiobarbiturates in polymers in contrast to the effects observed in solution.

A 3D-2D Multibranch Feature Fusion and Dense Attention Network for Hyperspectral Image Classification.

In recent years, hyperspectral image classification (HSI) has attracted considerable attention. Various methods based on convolution neural networks have achieved outstanding classification results. However, most of them exited the defects of underutilization of spectral-spatial features, redundant information, and convergence difficulty. To address these problems, a novel 3D-2D multibranch feature fusion and dense attention network are proposed for HSI classification. Specifically, the 3D multibranch feature fusion module integrates multiple receptive fields in spatial and spectral dimensions to obtain shallow features. Then, a 2D densely connected attention module consists of densely connected layers and spatial-channel attention block. The former is used to alleviate the gradient vanishing and enhance the feature reuse during the training process. The latter emphasizes meaningful features and suppresses the interfering information along the two principal dimensions: channel and spatial axes. The experimental results on four benchmark hyperspectral images datasets demonstrate that the model can effectively improve the classification performance with great robustness.

Calibration and location analysis of a heterogeneous binocular stereo vision system.

In the dairy farming industry, we can obtain the temperature, color, and location information of dairy cows by patrol inspection robot so as to monitor the health status and abnormal behaviors of dairy cows. We build and calibrate a heterogeneous binocular stereo vision (HBSV) system comprising a high-definition color camera and infrared thermal camera and mount it on a patrol inspection robot. First, based on the traditional chessboard, an easy-to-make calibration board for the HBSV system is designed. Second, an accurate locating and sorting algorithm for the calibration points of the calibration board is designed. Then, the cameras are calibrated and the HBSV system is stereo-calibrated. Finally, target locating is achieved based on the above calibration results and Yolo target detection technology. In this paper, several experiments are carried out from many aspects. The target locating average error of HBSV system is 3.11%, which satisfies the needs of the dairy farming environment. The video's FPS captured by using HBSV is 7.3, which is 78% higher than that by using binocular stereo vision system and infrared thermal camera. The results show that the HBSV system has application value to a certain degree.