Robust and Superhydrophobic Polydimethylsiloxane/Ni@Ti3C2Tx Nanocomposite Coatings with Assembled Eyelash-Like Microstructure Array: A New Approach for Effective Passive Anti-Icing and Active Photothermal Deicing.

To solve the problem of ice condensation and adhesion, it is urgent to develop new anti-icing and deicing technologies. This study presented the development of a highly efficient photothermal-enhanced superhydrophobic PDMS/Ni@Ti3C2Tx composite film (m-NMPA) fabricated cost-effectively and straightforwardly. This film was fabricated utilizing PDMS as a hydrophobic agent, adhesive, and surface protector, while Ni@Ti3C2Tx as a magnetic photothermal filler innovatively. Through a simple spraying method, the filler is guided by a strong magnetic field to self-assemble into an eyelash-like microstructure array. The unique structure not only imparts superhydrophobic properties to the surface but also constructs an efficient "light-capturing" architecture. Remarkably, the m-NMPA film demonstrates outstanding superhydrophobic passive anti-icing and efficient photothermal active deicing performance without the use of fluorinated chemicals. The micro-/nanostructure of the film forms a gas layer, significantly delaying the freezing time of water. Particularly under extreme cold conditions (-30 °C), the freezing time is extended by a factor of 7.3 compared to the bare substrate. Furthermore, under sunlight exposure, surface droplets do not freeze. The excellent photothermal performance is attributed to the firm anchoring of nickel particles on the MXene surface, facilitating effective "point-to-face" photothermal synergy. The eyelash-like microarray structure enhances light-capturing capability, resulting in a high light absorption rate of 98%. Furthermore, the microstructure aids in maintaining heat at the uppermost layer of the surface, maximizing the utilization of thermal energy for ice melting and frost thawing. Under solar irradiation, the m-NMPA film can rapidly melt approximately a 4 mm thick ice layer within 558 s and expel the melted water promptly, reducing the risk of secondary icing. Additionally, the ice adhesion force on the surface of the m-NMPA film is remarkably low, with an adhesion strength of approximately 4.7 kPa for a 1 × 1 cm2 ice column. After undergoing rigorous durability tests, including xenon lamp weathering test, pressure resistance test, repeated adhesive tape testing, xenon lamp irradiation, water drop impact testing, and repeated brushing with hydrochloric acid and particles, the film's surface structure and superhydrophobic performance have remained exceptional. The photothermal superhydrophobic passive anti-icing and active deicing technology in this work rely on sustainable solar energy for efficient heat generation. It presents broad prospects for practical applications with advantages such as simple processing method, environmental friendliness, outstanding anti-icing effects, and exceptional durability.

Testing Conditional Independence Between Latent Variables by Independence Residuals.

Conditional independence (CI) testing is an important problem, especially in causal discovery. Most testing methods assume that all variables are fully observable and then test the CI among the observed data. Such an assumption is often untenable beyond applications dealing with, e.g., psychological analysis about the mental health status and medical diagnosing (researchers need to consider the existence of latent variables in these scenarios); and typically adopted latent CI test schemes mainly suffer from robust or efficient issues. Accordingly, this article investigates the problem of testing CI between latent variables. To this end, we offer an auxiliary regression-based CI (AReCI) test by taking the measured variable as the surrogate variable of the latent variables to conduct the regression over the latent variables under the linear causal models, in which each latent variable has some certain measured variables. Specifically, given a pair of latent variables LX and LY , and a corresponding latent variable set LO , [Formula: see text] holds if and only if [Formula: see text] and [Formula: see text] are statistically independent, where A' and A'' are the two disjoint subset of the measured variable for the corresponding latent variables, A'{LO} ∩A''{LO} = ∅ , and ω1 is a parameter vector characterized from the cross covariance between A{LX} and A'{LO} , and ω2 is a parameter vector characterized from the cross covariance between A{LY} and A''{LO} . We theoretically show that the AReCI test is capable of addressing both Gaussian and non-Gaussian data. In addition, we find that the well-known partial correlation test can be seen as a special case of the AReCI test. Finally, we devise a causal discovery method by using the AReCI test as the CI test. The experimental results on synthetic and real-world data illustrate the effectiveness of our method.

Multi-Instance Nonparallel Tube Learning.

In multi-instance nonparallel plane learning (NPL), the training set is comprised of bags of instances and the nonparallel planes are trained to classify the bags. Most of the existing multi-instance NPL methods are proposed based on a twin support vector machine (TWSVM). Similar to TWSVM, they use only a single plane to generalize the data occurrence of one class and do not sufficiently consider the boundary information, which may lead to the limitation of their classification accuracy. In this article, we propose a multi-instance nonparallel tube learning (MINTL) method. Distinguished from the existing multi-instance NPL methods, MINTL embeds the boundary information into the classifier by learning a large-margin-based ϵ -tube for each class, such that the boundary information can be incorporated into refining the classifier and further improving the performance. Specifically, given a K -class multi-instance dataset, MINTL seeks K ϵ -tubes, one for each class. In multi-instance learning, each positive bag contains at least one positive instance. To build up the ϵk -tube of class k , we require that each bag of class k should have at least one instance included in the ϵk -tube. Moreover, except for one instance included in the ϵk -tube, the remaining instances in the positive bag may include positive instances or irrelevant instances, and their labels are unavailable. A large margin constraint is presented to assign the remaining instances either inside the ϵk -tube or outside the ϵk -tube with a large margin. Substantial experiments on real-world datasets have shown that MINTL obtains significantly better classification accuracy than the existing multi-instance NPL methods.

Transferable Time-Series Forecasting Under Causal Conditional Shift.

This paper focuses on the problem of semi-supervised domain adaptation for time-series forecasting, which is underexplored in literature, despite being often encountered in practice. Existing methods on time-series domain adaptation mainly follow the paradigm designed for static data, which cannot handle domain-specific complex conditional dependencies raised by data offset, time lags, and variant data distributions. In order to address these challenges, we analyze variational conditional dependencies in time-series data and find that the causal structures are usually stable among domains, and further raise the causal conditional shift assumption. Enlightened by this assumption, we consider the causal generation process for time-series data and propose an end-to-end model for the semi-supervised domain adaptation problem on time-series forecasting. Our method can not only discover the Granger-Causal structures among cross-domain data but also address the cross-domain time-series forecasting problem with accurate and interpretable predicted results. We further theoretically analyze the superiority of the proposed method, where the generalization error on the target domain is bounded by the empirical risks and by the discrepancy between the causal structures from different domains. Experimental results on both synthetic and real data demonstrate the effectiveness of our method for the semi-supervised domain adaptation method on time-series forecasting.

On the Role of Entropy-Based Loss for Learning Causal Structure With Continuous Optimization.

Causal discovery from observational data is an important but challenging task in many scientific fields. A recent line of work formulates the structure learning problem as a continuous constrained optimization task using an algebraic characterization of directed acyclic graphs (DAGs) and the least-square loss function. Though the least-square loss function is well justified under the standard Gaussian noise assumption, it is limited if the assumption does not hold. In this work, we theoretically show that the violation of the Gaussian noise assumption will hinder the causal direction identification, making the causal orientation fully determined by the causal strength as well as the variances of noises in the linear case and by the strong non-Gaussian noises in the nonlinear case. Consequently, we propose a more general entropy-based loss that is theoretically consistent with the likelihood score under any noise distribution. We run extensive empirical evaluations on both synthetic data and real-world data to validate the effectiveness of the proposed method and show that our method achieves the best in structure Hamming distance, false discovery rate (FDR), and true-positive rate (TPR) matrices.

Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine.

Depression is a common mental disorder that seriously affects patients' social function and daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this study, we used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and measured the whole brain EEG signals and forehead hemodynamic signals from 25 depression patients and 30 healthy subjects during the resting state. On one hand, we explored the EEG brain functional network properties, and found that the clustering coefficient and local efficiency of the delta and theta bands in patients were significantly higher than those in normal subjects. On the other hand, we extracted brain network properties, asymmetry, and brain oxygen entropy as alternative features, used a data-driven automated method to select features, and established a support vector machine model for automatic depression classification. The results showed the classification accuracy was 81.8% when using EEG features alone and increased to 92.7% when using hybrid EEG and fNIRS features. The brain network local efficiency in the delta band, hemispheric asymmetry in the theta band and brain oxygen sample entropy features differed significantly between the two groups (p < 0.05) and showed high depression distinguishing ability indicating that they may be effective biological markers for identifying depression. EEG, fNIRS and machine learning constitute an effective method for classifying depression at the individual level.

Motif Graph Neural Network.

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, the existing motif-based GNNs still often suffer from less discriminative power on high-order structures. To overcome the above limitations, we propose motif GNN (MGNN), a novel framework to better capture high-order structures, hinging on our proposed motif redundancy minimization operator and injective motif combination. First, MGNN produces a set of node representations with respect to each motif. The next phase is our proposed redundancy minimization among motifs which compares the motifs with each other and distills the features unique to each motif. Finally, MGNN performs the updating of node representations by combining multiple representations from different motifs. In particular, to enhance the discriminative power, MGNN uses an injective function to combine the representations with respect to different motifs. We further show that our proposed architecture increases the expressive power of GNNs with a theoretical analysis. We demonstrate that MGNN outperforms state-of-the-art methods on seven public benchmarks on both the node classification and graph classification tasks.

Gradient boosting DD-MLP Net: An ensemble learning model using near-infrared spectroscopy to classify after-stroke dyskinesia degree during exercise.

This study aims to develop an automatic assessment of after-stroke dyskinesias degree by combining machine learning and near-infrared spectroscopy (NIRS). Thirty-five subjects were divided into five stages (healthy, patient: Brunnstrom stages 3, 4, 5, 6). NIRS was used to record the muscular hemodynamic responses from bilateral femoris (biceps brachii) muscles during passive and active upper (lower) limbs circular exercise. We used the D-S evidence theory to conduct feature information fusion and established a Gradient Boosting DD-MLP Net model, combining the dendrite network and multilayer perceptron, to realize automatic dyskinesias degree evaluation. Our model classified the upper limb dyskinesias with high accuracy: 98.91% under the passive mode and 98.69% under the active mode, and classified the lower limb dyskinesias with high accuracy: 99.45% and 99.63% under the passive and active modes, respectively. Our model combined with NIRS has great potential in monitoring the after-stroke dyskinesias degree and guiding rehabilitation training.

Ru(II) Complex Grafted Ti3C2Tx MXene Nano Sheet with Photothermal/Photodynamic Synergistic Antibacterial Activity.

For a long time, the emergence of microbial drug resistance due to the abuse of antibiotics has greatly reduced the therapeutic effect of many existing antibiotics. This makes the development of new antimicrobial materials urgent. Light-assisted antimicrobial therapy is an alternative to antibiotic therapy due to its high antimicrobial efficiency and non-resistance. Here, we develop a nanocomposite material (Ru@MXene) which is based on Ru(bpy)(dcb)2+ connected to MXene nanosheets by ester bonding as a photothermal/photodynamic synergistic antibacterial material. The obtained Ru@MXene nanocomposites exhibit a strengthened antimicrobial capacity compared to Ru or MXene alone, which can be attributed to the higher reactive oxygen species (ROS) yield and the thermal effect. Once exposed to a xenon lamp, Ru@MXene promptly achieved almost 100% bactericidal activity against Escherichia coli (200 µg/mL) and Staphylococcus aureus (100 µg/mL). This is ascribed to its synergistic photothermal therapy (PTT) and photodynamic therapy (PDT) capabilities. Consequently, the innovative Ru@MXene can be a prospective non-drug antimicrobial therapy that avoids antibiotic resistance in practice. Notably, this high-efficiency PTT/PDT synergistic antimicrobial material by bonding Ru complexes to MXene is the first such reported model. However, the toxic effects of Ru@MXene materials need to be studied to evaluate them for further medical applications.

Nonlinear Causal Discovery for High-Dimensional Deterministic Data.

Nonlinear causal discovery with high-dimensional data where each variable is multidimensional plays a significant role in many scientific disciplines, such as social network analysis. Previous work majorly focuses on exploiting asymmetry in the causal and anticausal directions between two high-dimensional variables (a cause-effect pair). Although there exist some works that concentrate on the causal order identification between multiple variables, i.e., more than two high-dimensional variables, they do not validate the consistency of methods through theoretical analysis on multiple-variable data. In particular, based on the asymmetry for the cause-effect pair, if model assumptions for any pair of the data are violated, the asymmetry condition will not hold, resulting in the deduction of incorrect order identification. Thus, in this article, we propose a causal functional model, namely high-dimensional deterministic model (HDDM), to identify the causal orderings among multiple high-dimensional variables. We derive two candidates' selection rules to alleviate the inconvenient effects resulted from the violated-assumption pairs. The corresponding theoretical justification is provided as well. With these theoretical results, we develop a method to infer causal orderings for nonlinear multiple-variable data. Simulations on synthetic data and real-world data are conducted to verify the efficacy of our proposed method. Since we focus on deterministic relations in our method, we also verify the robustness of the noises in simulations.

Microscale Searching Algorithm for Coupling Matrix Optimization of Automated Microwave Filter Tuning.

Automated tuning can significantly improve productivity and save the costs of manual operation in the microwave filter manufacturing industry. This article proposes a mathematical model of scattering data optimization to find the accurate coupling matrix for multiple-version microwave filters, a core step of automated microwave filter tuning. For the large-scale problem of coupling coefficient combination, we propose a decision set decomposition strategy that evenly divides the entire frequency interval into several subintervals according to the correlation between scattering data. With this strategy, we design a microscale (small-size subsets of the decomposed decision set) searching algorithm, which solves each suboptimization problem by searching the decision subset instead of the entire decision set. To verify the validity of the proposed algorithm for multiple-version microwave filters, experiments are conducted on three versions of microwave filters from a real-world production line, including the two-port eighth-order, ninth-order, and tenth-order microwave filters. Experimental results show that the proposed model is feasible within the industrial error for the multiversion microwave filter tuning problem. Besides, the proposed algorithm outperforms the state-of-the-art optimization algorithms in the coupling matrix optimization problem.

Self-Organizing Neural Scheduler for the Flexible Job Shop Problem With Periodic Maintenance and Mandatory Outsourcing Constraints.

Scheduling is significant in improving the production efficiency and reducing delivery delays for manufacturing enterprises. Unlike the flexible job-shop scheduling problem, two special constraints are encountered in real-world power supply manufacturing systems: 1) periodic maintenance and 2) mandatory outsourcing. As the characteristics of these constraints are not considered in existing scheduling algorithms, schedules generated by most existing approaches are not optimal or even conflict with these constraints. In this article, a self-organizing neural scheduler (SoNS) is proposed to overcome this limitation. A long short-term memory encoder is developed to transform the variable-length structural information into fixed-length feature vectors. Moreover, the reinforcement learning model is proposed to automatically select policies for improving candidate schedules. To validate the effectiveness of the proposed algorithm, extensive experiments are conducted on over 300 problem instances. The nonparametric Kruskal-Wallis tests confirm that the proposed algorithm outperforms several state-of-the-art methods in terms of effectiveness and robustness within a limited computational budget. It demonstrates that the proposed SoNS can solve scheduling problems with the periodic maintenance and mandatory outsourcing constraints effectively.

Factorizing time-heterogeneous Markov transition for temporal recommendation.

Temporal recommendation which recommends items to users with consideration of time information has been of wide interest in recent years. But huge event space, highly sparse user activities and time-heterogeneous dependency of temporal behaviors make it really challenging to learn the temporal patterns for high-quality recommendation. In this paper, aiming to handle these challenges, especially the time-heterogeneous characteristic of user's temporal behaviors, we proposed the Neural-based Time-heterogenous Markov Transition (NeuralTMT) model. Firstly, users' temporal behaviors are mathematically simplified as the third-order Markov transition tensors. And then a linear co-factorization model which learns the time-evolving user/item factors from these tensors is proposed. Furthermore, the model is extended to the neural-based learning framework (NeuralTMT), which is more flexible and able to capture time-heterogeneous temporal patterns via nonlinear neural network mappings and attention techniques. Extensive experiments on four datasets demonstrate that NeuralTMT performs significantly better than the state-of-the-art baselines. And the proposed method is fundamentally inspired by factorization techniques, which may also provide some interesting ideas on the connection of tensor factorization and neural-based sequential recommendation methods.

Enantioselective Synthesis of Bispiro[indanedione-oxindole-cyclopropane]s through Organocatalytic [2+1] Cycloaddition.

A series of compounds featuring a novel bispiro[indanedione-oxindole-cyclopropane] moiety have been synthesized through a squaramide-catalyzed [2+1] cycloaddition reaction. The tandem Michael-alkylation reaction of 2-arylidene-1,3-indanediones with 3-bromooxindoles furnished the cycloadducts in high yields with excellent diastereo- and enantioselectivities. The ammonium ylide in the catalytic process, as a key intermediate, was revealed by the high-resolution mass spectrometry study.

Asymmetric Formal (3 + 2) Cyclocondensation of Coumarin-3-Formylpyrazoles as 3-Carbon Partners with 3-Hydroxyoxindoles via Esterification/Michael Addition Sequence.

The first enantioselective formal (3 + 2) cyclocondensation involving α,ß-unsaturated pyrazoleamides as 3-carbon partners was accomplished in a stepwise fashion. The stepwise esterification/Michael addition sequence is promoted by Zn(OTf)2 and quinine-squaramide derivative, respectively. The protocol enables access to spiro-fused pentacyclic spirooxindoles from coumarin-3-formylpyrazoles and 3-hydroxyoxindoles in good to satisfactory overall yields (up to 91%) with excellent dr (all cases >20:1 dr) and high ee values (up to 99%). Mechanistic investigations contributed to shedding light on the enantioselective event of the process.

Synergistic Effect of Multifunctional Layered Double Hydroxide-Based Hybrids and Modified Phosphagen with an Active Amino Group for Enhancing the Smoke Suppression and Flame Retardancy of Epoxy.

To improve the fire hazard of epoxy resin (EP), phosphomolybdate (PMoA), as a classical Keggin cluster, was successfully intercalated into Mg, Al, and Zn layered double hydrotalcite (LDH) by the reconstruction method, and it was denoted as MgAlZn-LDH-PMoA. The structure and morphology of MgAlZn-LDH-PMoA were characterized by X-ray diffraction and Fourier transform infrared spectroscopy. Subsequently, hexa(4-aminophenoxy)cyclotriphosphazene (HACP) was prepared and characterized as a high-performance organic flame retardant, which is rich in flame elements phosphorus and nitrogen. The synergistic effects of MgAlZn-LDH-PMoA and HACP on the fire safety of EP composites loaded with different amounts of flame retardant hybrids were studied in detail. Thermogravimetric analysis showed that the char residue of these EP composites increased significantly. Compared with the EP matrix filled with only MgAlZn-LDH-PMoA or HACP, the incorporation of MgAlZn-LDH-PMoA and HACP had a synergistic effect on promoting char formation of EP composites. Particularly, the char yield of EP7 is as high as 29.0%. Furthermore, the synergistic effects of incorporation of MgAlZn-LDH-PMoA with HACP were investigated using the cone calorimeter combustion tests. The results showed that the total heat release and peak heat release rate of the EP composites remarkably declined by 35.2 and 50.9%, respectively, with a loading of 7 wt % hybrid flame retardant. Moreover, the hybrid flame retardants also showed an obvious inhibitory effect on the total smoke production and the release of toxic CO gas. The detailed analysis of the residual char indicated that the main mechanism for improving the flame retardancy and smoke suppression performance is due to both the catalytic carbonization of MgAlZn-LDH-PMoA and phosphoric acid compounds and physical barrier function of the char layer. In addition, the molybdenum oxides produced from [PMo12O40]3- during combustion can not only increase the yield and compactness of the char layer but also reduce the release of CO through a redox reaction, which has important application value to reduce the fire hazard.

Interpersonal Relationship Stress Brings on Social Networking Sites Addiction Among Chinese Undergraduate Students.

The adverse effects of life stress on social networking sites addiction are increasingly recognized, but so far there is little evidence on how and which specific types of life stress are conducive to the addictive behavior. Interpersonal relationship stress being the main source of stress for undergraduates, the purpose of the current paper is thus to delve into whether perceived stress in interpersonal relationships significantly leads to WeChat addiction and, if so, how this type of stress drives the excessive use of WeChat. The data was collected from self-report questionnaires completed by 463 Chinese undergraduate students and then analyzed with structural equation modeling. The results revealed that the positive association between WeChat users' interpersonal relationship stress and addictive behavior is fully and sequentially mediated by WeChat use intensity and social interaction. More specifically, accumulation of stress in interpersonal relationships gives rise to the intensity of WeChat use, which in turn fuels rising addiction to WeChat both directly and indirectly via social interaction on WeChat. These findings contribute to a more refined understanding of the pathological use of WeChat.

THPs: Topological Hawkes Processes for Learning Causal Structure on Event Sequences.

Learning causal structure among event types on multitype event sequences is an important but challenging task. Existing methods, such as the Multivariate Hawkes processes, mostly assumed that each sequence is independent and identically distributed. However, in many real-world applications, it is commonplace to encounter a topological network behind the event sequences such that an event is excited or inhibited not only by its history but also by its topological neighbors. Consequently, the failure in describing the topological dependency among the event sequences leads to the error detection of the causal structure. By considering the Hawkes processes from the view of temporal convolution, we propose a topological Hawkes process (THP) to draw a connection between the graph convolution in the topology domain and the temporal convolution in time domains. We further propose a causal structure learning method on THP in a likelihood framework. The proposed method is featured with the graph convolution-based likelihood function of THP and a sparse optimization scheme with an Expectation-Maximization of the likelihood function. Theoretical analysis and experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed method.

Flexible thermal conductive Al2O3@siloxane composite with rapid self-healing property based on carboxyl-amine dynamic reversible bonds.

Thermal interface materials (TIMs) are one of the efficacious ways to alleviate the heat accumulation problem of microelectronics devices. However, conventional TIMs based on polydimethylsiloxane (PDMS) always suffer from mechanical damage, leading to shortened service life or loss of thermal conductivity. In this work, we fabricated a high-thermal conductivity and fast self-healable Al2O3@siloxane composite by hydrosilylation reaction. The siloxane matrix consisted of thermosetting silicone rubber matrix (SR) and heat reversibility matrix (SCNR); the SR was synthesized via hydrosilylation between silicon hydrogen bond and vinyl, the SCNR was fabricated by thermal-curing between amino and carboxyl functionalized PDMS. Different sized spherical Al2O3 fillers were introduced into the SR/SCNR matrix system to construct the Al2O3@SR/SCNR composites. By adjusting the ratio of SR/SCNR, the obtained composites can achieve flexibility, self-healing and high filling simultaneously. It is notable that the self-healing efficiency of the composite is high, up to 95.6% within 3 minutes with 6.7 wt% mass ratio of SCNR/SR; these fast self-healing behaviors benefit from the assistance of thermal diffusion by 3D heat conduction pathways on the rearrangement of the dynamic cross-linked network. The resultant composites also exhibited the optimal thermal conductivity of 5.85 W mK-1. This work provides a novel approach for constructing longer service life and high thermal conductivity multifunctional TIM based PDMS.

Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders.

Causal discovery from observational data is a fundamental problem in science. Though the linear non-Gaussian acyclic model (LiNGAM) has shown promising results in various applications, it still faces the following challenges in the data with multiple latent confounders: 1) how to detect the latent confounders and 2) how to uncover the causal relations among observed and latent variables. To address these two challenges, we propose a hybrid causal discovery method for the LiNGAM with multiple latent confounders (MLCLiNGAM). First, we utilize the constraint-based method to learn the causal skeleton. Second, we identify the causal directions, by conducting regression and independence tests on the adjacent pairs in the causal skeleton. Third, we detect the latent confounders with the help of the maximal clique patterns raised by the latent confounders and reconstruct the causal structure with latent variables. Theoretical results show the correctness and efficiency of the algorithms. We conduct extensive experiments on synthetic and real data, which illustrates the efficiency and effectiveness of the proposed algorithms.