MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification.

Structural and functional brain networks are generated from two scan sequences of magnetic resonance imaging data, which can provide different perspectives for describing pathological changes caused by brain diseases. Recent studies found that fusing these two types of brain networks improves performance in brain disease identification. However, traditional fusion models combine these brain networks at a single granularity, ignoring the natural multi-granularity structure of brain networks that can be divided into the edge, node, and graph levels. To this end, this paper proposes a Multi-modal Multi-granularity Fusion Neural Networks (MMF-NNs) framework for brain networks, which integrates the features of the multi-modal brain network from global (i.e., graph-level) and local (i.e., edge-level and node-level) granularities to take full advantage of the topological information. Specifically, we design an interactive feature learning module at the local granularity to learn feature maps of structural and functional brain networks at the edge-level and the node-level, respectively. In that way, these two types of brain networks are fused during the feature learning process. At the global granularity, a multi-modal decomposition bilinear pooling module is designed to learn the graph-level joint representation of these brain networks. Experiments on real epilepsy datasets demonstrate that MMF-NNs are superior to several state-of-the-art methods in epilepsy identification.

Exclusive catalytic hydrogenation of nitrobenzene toward p-aminophenol over atomically precise Au36(SR)24 clusters.

Despite the advances in devising green methodologies for selective hydrogenation of nitrobenzene toward p-aminophenol, it is still difficult to realize p-aminophenol as the exclusive product in heterogeneous metal catalysis, as the excessive hydrogenation of nitrobenzene usually results in the aniline byproduct. Herein we report that a metal cluster containing 36 gold atoms capped by 24 thiolate ligands provides a unique pathway for nitrobenzene hydrogenation to achieve a p-aminophenol selectivity of ∼100%. The gold cluster can efficiently suppress the over-hydrogenation of amino groups via hydroxyl rearrangement with the aid of water and sequentially the proton transfer promoted by acid toward p-aminophenol. More notably, remarkable catalytic performances can be extended to clusters with similar structures such as Au28(SR)20 and Au44(SR)28, where only an atomic layer change of 2.1 Šthickness in the Au36(SR)24 cluster can tailor the proton affinity for the amino group of the key intermediate phenylhydroxylamine, thereby altering the activity while the p-aminophenol selectivity remained.

Catalytic N-Formylation of CO2 by Atomically Precise Au8Pd1(DPPF)42+ Clusters into a Two-Dimensional Metal-Organic Framework.

By highly efficient ligand-exchange strategy, atomically precise Au8Pd1(PPh3)82+ (PPh3 = triphenylphosphine) cluster can be transformed into a Au8Pd1(DPPF)42+ (DPPF = 1,1'-bis(diphenylphosphino)ferrocene) cluster that can maintain the atomic-packing structure but overcome the lability of Au8Pd1(PPh3)82+. Catalytic evaluation for the CO2 hydrogenation coupled with o-phenylenediamine demonstrates that the Au8Pd1(DPPF)42+ catalyst can remarkably enhance both activity and stability compared to the Au8Pd1(PPh3)82+ catalyst. More notably, the direct construction of a two-dimensional metal-organic framework (2D MOF) can be elaborately accomplished in the formylation process of o-phenylenediamine, CO2 and H2 with zinc nitrate enabled by the Au8Pd1(DPPF)42+ catalyst. The 2D MOF further enables the capture and transformation of CO2 to combine in the organic synthesis with epoxides under mild conditions.This work provides opportunities for creating highly active cluster sites for the chemical recycling of CO2.

DSANet: Dynamic and Structure-Aware GCN for Sparse and Incomplete Point Cloud Learning.

Learning 3-D structures from incomplete point clouds with extreme sparsity and random distributions is a challenge since it is difficult to infer topological connectivity and structural details from fragmentary representations. Missing large portions of informative structures further aggravates this problem. To overcome this, a novel graph convolutional network (GCN) called dynamic and structure-aware NETwork (DSANet) is presented in this article. This framework is formulated based on a pyramidic auto-encoder (AE) architecture to address accurate structure reconstruction on the sparse and incomplete point clouds. A PointNet-like neural network is applied as the encoder to efficiently aggregate the global representations of coarse point clouds. On the decoder side, we design a dynamic graph learning module with a structure-aware attention (SAA) to take advantage of the topology relationships maintained in the dynamic latent graph. Relying on gradually unfolding the extracted representation into a sequence of graphs, DSANet is able to reconstruct complicated point clouds with rich and descriptive details. To associate analogous structure awareness with semantic estimation, we further propose a mechanism, called structure similarity assessment (SSA). This method allows our model to surmise semantic homogeneity in an unsupervised manner. Finally, we optimize the proposed model by minimizing a new distortion-aware objective end-to-end. Extensive qualitative and quantitative experiments demonstrate the impressive performance of our model in reconstructing unbroken 3-D shapes from deficient point clouds and preserving semantic relationships among different regional structures.

Accelerated Fatigue Test for Electric Vehicle Reducer Based on the SVR-FDS Method.

The reducer serves as a pivotal component within the power transmission system of electric vehicles. On one hand, it bears the torque load within the power transmission system. On the other hand, it also endures the vibration load transmitted from other vehicle components. Over extended periods, these dynamic loads can cause fatigue damage to the reducer. Therefore, the reliability and durability of the reducer during use are very important for electric vehicles. In order to save time and economic costs, the durability of the reducer is often evaluated through accelerated fatigue testing. However, traditional approaches to accelerated fatigue tests typically only consider the time-domain characteristics of the load, which limits precision and reliability. In this study, an accelerated fatigue test method for electric vehicle reducers based on the SVR-FDS method is proposed to enhance the testing process and ensure the reliability of the results. By utilizing the support vector regression (SVR) model in conjunction with the fatigue damage spectrum (FDS) approach, this method offers a more accurate and efficient way to evaluate the durability of reducers. It has been proved that this method significantly reduces the testing period while maintaining the necessary level of test reliability. The accelerated fatigue test based on the SVR-FDS method represents a valuable approach for assessing the durability of electric vehicle reducers and offering insights into their long-term performance.

Can Subsurface Oxygen Species in Oxides Participate in Catalytic Reactions? An 17O Solid-State Nuclear Magnetic Resonance Study.

The impacts of subsurface species of catalysts on reaction processes are still under debate, largely due to a lack of characterization methods for distinguishing these species from the surface species and the bulk. By using 17O solid-state nuclear magnetic resonance (NMR) spectroscopy, which can distinguish subsurface oxygen ions in CeO2 (111) nanorods, we explore the effects of subsurface species of oxides in CO oxidation reactions. The intensities of the 17O NMR signals due to surface and subsurface oxygen ions decrease after the introduction of CO into CeO2 nanorods, with a more significant decrease observed for the latter, confirming the participation of subsurface oxygen species. Density functional theory calculations show that the reaction involves subsurface oxygen ions filling the surface oxygen vacancies created by the direct contact of surface oxygen with CO. This new approach can be extended to the study of the role of oxygen species in other catalytic reactions.

Nitrate electroreduction to ammonia catalysed by atomically precise Au28Cu12 clusters.

A novel bimetal cluster [Au28Cu12(SR)24](PPh4)4 (SR = 2,4-dichlorothiophenol) has been successfully synthesized, which can be viewed as a Au4@Au24 core and four trimeric Cu3(SR)6 staples. Compared to monometallic Au28(TBBT)20 and Cu28(CHT)18(PPh3)3 clusters, the [Au28Cu12(C6H4Cl2S)24](PPh4)4 cluster has much higher catalytic efficiency for nitrate electroreduction to ammonia.

Broadband wavelength designable achromatic grating based on a cholesteric liquid crystal template.

Liquid crystal (LC) gratings have played important roles in light field control due to the advantages of being lightweight, low cost, having no moving parts, and low power consumption. However, the chromatic aberration limits the bandwidth of the LC device and affects the efficiency of the grating. To solve the chromatic aberration issue, a broadband wavelength designable achromatic grating is proposed. Different grating structures are integrated into a single-layer templated cholesteric liquid crystal (CLC) device, and the achromatic diffraction wavelength of the grating can be freely designed from the visible spectral region to the infrared range within the Bragg reflection band of the CLCs. The diffraction intensity of different orders can be changed with the electric field applied to meet the need for dynamic modulation. This grating shows suitable potential applications in optical communication and displays.

Diachronic evolvement from tetra-icosahedral to quasi-hexagonal close-packed bimetal clusters.

Here we report a diachronic evolvement from tetra-icosahedral Au30Ag12(C[triple bond, length as m-dash]CR)24 to quasi-hcp (hexagonal close-packed) Au47Ag19(C[triple bond, length as m-dash]CR)32 via a one-step reduction, in which the size/structure conversion of the two clusters is not a typical Oswald growth process, but involves interface shrinking followed by core rearrangement and surface polymerization. Au30Ag12(C[triple bond, length as m-dash]CR)24 has an aesthetic Au18Ag8 kernel that is composed of four interpenetrating Au10Ag3 icosahedra, while Au47Ag19(C[triple bond, length as m-dash]CR)32 has a twisted Au19 core capped by a Au12Ag19 shell that are stacked in a layer-by-layer manner with a quasi-hcp pattern. The discovery of the two clusters not only provides further evidence for icosahedral clusters with longer excited-state lifetime compared to hcp-like clusters, but also discloses a double increase in catalytic reactivity for electrocatalytic oxidation of ethanol over quasi-hcp clusters in comparison with icosahedral clusters. This work provides the rationale for reversing the bottom-up growth process to remake bimetal clusters.

A Survey on Deep Active Learning: Recent Advances and New Frontiers.

Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label newly selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep active learning (DAL), remain scarce. Therefore, we conduct an advanced and comprehensive survey on DAL. We first introduce reviewed paper collection and filtering. Second, we formally define the DAL task and summarize the most influential baselines and widely used datasets. Third, we systematically provide a taxonomy of DAL methods from five perspectives, including annotation types, query strategies, deep model architectures, learning paradigms, and training processes, and objectively analyze their strengths and weaknesses. Then, we comprehensively summarize the main applications of DAL in natural language processing (NLP), computer vision (CV), data mining (DM), and so on. Finally, we discuss challenges and perspectives after a detailed analysis of current studies. This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in DAL. We hope that this survey will spur further progress in this burgeoning field.

IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.

Incremental high average-utility itemset mining: survey and challenges.

The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm.

CSTS: Exploring Class-Specific and Task-Shared Embedding Representation for Few-Shot Learning.

Few-shot learning (FSL) is a challenging yet promising technique that aims to discriminate objects based on a few labeled examples. Learning a high-quality feature representation is key with few-shot data, and many existing models attempt to extract general information from the sample or task levels. However, the common sample-level means of feature representation limits the models generalizability to different tasks, while task-level representation may lose class characteristics due to excessive information aggregation. In this article, we synchronize the class-specific and task-shared information from the class and task levels to obtain a better representation. Structure-based contrastive learning is introduced to obtain class-specific representations by increasing the interclass distance. A hierarchical class structure is constructed by clustering semantically similar classes using the idea of granular computing. When guided by a class structure, it is more difficult to distinguish samples in different classes that have similar characteristics than those with large interclass differences. To this end, structure-guided contrastive learning is introduced to study class-specific information. A hierarchical graph neural network is established to transfer task-shared information from coarse to fine. It hierarchically infers the target sample based on all samples in the task and yields a more general representation for FSL classification. Experiments on four benchmark datasets demonstrate the advantages of our model over several state-of-the-art models.

Increased postoperative complications after laparoscopic gastrectomy in patients with preserved ratio impaired spirometry.

BACKGROUND: Preserved ratio impaired spirometry (PRISm), defined as decreased forced expiratory volume in the first second in the setting of normal ratio, is associated with an increased risk of respiratory disease and systemic comorbidities. Unlike severe obstructive pulmonary disease, little is known about the impact of PRISm on short-term outcomes in patients undergoing laparoscopic gastrectomy (LG) and its association with small airway dysfunction (SAD). METHODS: This study enrolled 830 patients who underwent preoperative spirometry and LG between January 2021 and August 2023. Of these, 228 patients were excluded. Participants were categorized into 3 groups based on their baseline lung function, and postoperative outcomes were subsequently analyzed. Potential associations between postoperative outcomes and various clinical variables were examined using univariate and multivariate analyses. RESULTS: PRISm was identified in 16.6% of the patients, whereas SAD was present in 20.4%. The incidence of postoperative pulmonary complications (PPCs) was notably higher in the SAD group (20.3% vs 9.8%, P = .002) and the PRISm group (28.0% vs 9.8%, P < .001) than the normal group. Among the 3 groups, pneumonia was the most frequently observed PPC. Multivariate analysis revealed that both SAD (odds ratio [OR], 2.34; 95% CI, 1.30-4.22; P = .005) and PRISm (OR, 3.26; 95% CI, 1.80-5.90; P < .001) independently constituted significant risk factors associated with the occurrence of PPCs. Univariate analysis showed that female was a possible risk factor for PPCs in PRISm group. CONCLUSION: Our study showed that PRISm and SAD were associated with the increased PPCs in patients undergoing LG for gastric cancer.

Higher ultra processed foods intake is associated with low muscle mass in young to middle-aged adults: a cross-sectional NHANES study.

Design: Ultra-processed foods (UPFs) have become a pressing global health concern, prompting investigations into their potential association with low muscle mass in adults. Methods: This cross-sectional study analyzed data from 10,255 adults aged 20-59 years who participated in the National Health and Nutritional Examination Survey (NHANES) during cycles spanning from 2011 to 2018. The primary outcome, low muscle mass, was assessed using the Foundation for the National Institutes of Health (FNIH) definition, employing restricted cubic splines and weighted multivariate regression for analysis. Sensitivity analysis incorporated three other prevalent definitions to explore optimal cut points for muscle quality in the context of sarcopenia. Results: The weighted prevalence of low muscle mass was 7.65%. Comparing the percentage of UPFs calories intake between individuals with normal and low muscle mass, the values were found to be similar (55.70 vs. 54.62%). Significantly linear associations were observed between UPFs consumption and low muscle mass (P for non-linear = 0.7915, P for total = 0.0117). Upon full adjustment for potential confounding factors, participants with the highest UPFs intake exhibited a 60% increased risk of low muscle mass (OR = 1.60, 95% CI: 1.13 to 2.26, P for trend = 0.003) and a decrease in ALM/BMI (ß = -0.0176, 95% CI: -0.0274 to -0.0077, P for trend = 0.003). Sensitivity analysis confirmed the consistency of these associations, except for the International Working Group on Sarcopenia (IWGS) definition, where the observed association between the highest quartiles of UPFs (%Kcal) and low muscle mass did not attain statistical significance (OR = 1.35, 95% CI: 0.97 to 1.87, P for trend = 0.082). Conclusion: Our study underscores a significant linear association between higher UPFs consumption and an elevated risk of low muscle mass in adults. These findings emphasize the potential adverse impact of UPFs on muscle health and emphasize the need to address UPFs consumption as a modifiable risk factor in the context of sarcopenia.

Recent Approaches for Cleaving the C─C Bond During Ethanol Electro-Oxidation Reaction.

Direct ethanol fuel cells (DEFCs) play an indispensable role in the cyclic utilization of carbon resources due to its high volumetric energy density, high efficiency, and environmental benign character. However, owing to the chemically stable carbon-carbon (C─C) bond of ethanol, its incomplete electrooxidation at the anode severely inhibits the energy and power density output of DEFCs. The efficiency of C─C bond cleaving on the state-of-the-art Pt or Pd catalysts is reported as low as 7.5%. Recently, tremendous efforts are devoted to this field, and some effective strategies are put forward to facilitate the cleavage of the C─C bond. It is the right time to summarize the major breakthroughs in ethanol electrooxidation reaction. In this review, some optimization strategies including constructing core-shell nanostructure with alloying effect, doping other metal atoms in Pt and Pd catalysts, engineering composite catalyst with interface synergism, introducing cascade catalytic sites, and so on, are systematically summarized. In addition, the catalytic mechanism as well as the correlations between the catalyst structure and catalytic efficiency are further discussed. Finally, the prevailing limitations and feasible improvement directions for ethanol electrooxidation are proposed.

A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability.

The adversarial vulnerability of convolutional neural networks (CNNs) refers to the performance degradation of CNNs under adversarial attacks, leading to incorrect decisions. However, the causes of adversarial vulnerability in CNNs remain unknown. To address this issue, we propose a unique cross-scale analytical approach from a statistical physics perspective. It reveals that the huge amount of nonlinear effects inherent in CNNs is the fundamental cause for the formation and evolution of system vulnerability. Vulnerability is spontaneously formed on the macroscopic level after the symmetry of the system is broken through the nonlinear interaction between microscopic state order parameters. We develop a cascade failure algorithm, visualizing how micro perturbations on neurons' activation can cascade and influence macro decision paths. Our empirical results demonstrate the interplay between microlevel activation maps and macrolevel decision-making and provide a statistical physics perspective to understand the causality behind CNN vulnerability. Our work will help subsequent research to improve the adversarial robustness of CNNs.

Solute-particle separation in microfluidics enhanced by symmetrical convection.

The utilization of microfluidic technology for miniaturized and efficient particle sorting holds significant importance in fields such as biology, chemistry, and healthcare. Passive separation methods, achieved by modifying the geometric shapes of microchannels, enable gentle and straightforward enrichment and separation of particles. Building upon previous discussions regarding the effects of column arrays on fluid flow and particle separation within microchips, we introduced a column array structure into an H-shaped microfluidic chip. It was observed that this structure enhanced mass transfer between two fluids while simultaneously intercepting particles within one fluid, satisfying the requirements for particle interception. This enhancement was primarily achieved by transforming the originally single-mode diffusion-based mass transfer into dual-mode diffusion-convection mass transfer. By further optimizing the column array, it was possible to meet the basic requirements of mass transfer and particle interception with fewer microcolumns, thereby reducing device pressure drop and facilitating the realization of parallel and high-throughput microfluidic devices. These findings have enhanced the potential application of microfluidic systems in clinical and chemical engineering domains.

Programmable Photoswitchable Microcapsules Enable Precise and Tailored Drug Delivery from Microfluidics.

The development of precision personalized medicine poses a significant need for the next generation of advanced diagnostic and therapeutic technologies, and one of the key challenges is the development of highly time-, space-, and dose-controllable drug delivery systems that respond to the complex physiopathology of patient populations. In response to this challenge, an increasing number of stimuli-responsive smart materials are integrated into biomaterial systems for precise targeted drug delivery. Among them, responsive microcapsules prepared by droplet microfluidics have received much attention. In this study, we present a UV-visible light cycling mediated photoswitchable microcapsule (PMC) with dynamic permeability-switching capability for precise and tailored drug release. The PMCs were fabricated using a programmable pulsed aerodynamic printing (PPAP) technique, encapsulating an aqueous core containing magnetic nanoparticles and the drug doxorubicin (DOX) within a poly(lactic-co-glycolic acid) (PLGA) composite shell modified by PEG-b-PSPA. Selective irradiation of PMCs with ultraviolet (UV) or visible light (Vis) allows for high-precision time-, space-, and dose-controlled release of the therapeutic agent. An experimentally validated theoretical model was developed to describe the drug release pattern, holding promise for future customized programmable drug release applications. The therapeutic efficacy and value of patternable cancer cell treatment activated by UV radiation is demonstrated by our experimental results. After in vitro transcatheter arterial chemoembolization (TACE), PMCs can be removed by external magnetic fields to mitigate potential side effects. Our findings demonstrate that PMCs have the potential to integrate embolization, on-demand drug delivery, magnetic actuation, and imaging properties, highlighting their immense potential for tailored drug delivery and embolic therapy.

Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models.

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.