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Although recent advances in laparoscopic technology have popularized laparoscopic pancreatoduodenectomy (LPD), laparoscopic pancreaticojejunostomy anastomosis (PJA) still presents a major technical challenge. From February 2021 to January 2023, 42 patients underwent LPD with modified double U-suture PJA. Data on the demographic characteristics and clinical results of these patients were investigated. The median operation time was 316 min (249–596 min). The median PJA time was 32 min (25–40 min). The median intraoperative blood loss was 150 mL (50–500 mL). The median postoperative stay was 12 days (7–30 days). Complications occurred in 10 (23.8%) patients, including two cases (4.8%) of delayed gastric emptying and nine cases (21.4%) of postoperative pancreatic fistula (POPF). One patient presented delayed gastric emptying and POPF. Eight patients (19.0%) experienced biochemical leakage, and one patient (2.4%) had grade B POPF. Laparoscopic double U-suture PJA is a feasible and safe technique for performing LPD.

This article explores the variational generalized Nash equilibrium seeking for a class of fuzzy aggregative games, where each player's cost function is fuzzy and nonsmooth, and the strategy profile is constrained by a coupling nonlinear inequality, a affine coupling equality with $\alpha$-cuts and heterogeneous local convex sets. Under the differential inclusion framework, a continuous-time distributed algorithm with a derivative feedback term is proposed. Benefiting from this algorithm, each player is assigned several auxiliary variables to share with its neighbors and estimate the aggregate function, so that the important information such as cost functions, constraints, and decisions are privatized. Based on fuzzy optimization and nonsmooth analysis, the asymptotic convergence of the algorithm to generalized Nash equilibrium is rigorously proved, and the convergence is not affected by the initial strategy profile of the players. Moreover, two application instances are used to demonstrate the theoretical results.

The increasing privacy concerns associated with cloud-assisted image retrieval have captured the attention of researchers. However, a significant number of current research endeavors encounter limitations, including suboptimal accuracy, inefficient retrieval, and a lack of effective result verification mechanisms. To address these limitations, we propose an adaptive verifiable privacy-preserving medical image retrieval (AVPMIR) scheme in the outsourced cloud. Specifically, we utilize the convolutional neural network (CNN) ResNet50 model to extract the feature of each medical image within the dataset of the medical institution, aiming to enhance retrieval accuracy. To enhance retrieval efficiency, we build an encryption searchable index based on a mini-batch $k$k-means clustering algorithm. Furthermore, we present an index merging method in which multi-data owners build a different index tree according to different standards. To check the correctness of the returned results from the cloud server, we construct an adaptive verification framework for the obtained results based on chameleon hash and BLS signature. To provide strong security for the medical image datasets, we design an improved logistic chaotic mapping algorithm. The security analysis demonstrates that AVPMIR can defend various threat models. The experiment analysis further indicates that the AVPMIR can improve retrieval efficiency and demonstrate its practicability.

ABSTRACTUnbalanced data in intrusion detection seriously affect the overall performance of intrusion detection methods. This paper proposes an intrusion detection algorithm based on unbalanced learning (ID-UL) for unbalanced learning. It uses two strategies to address the issues brought about by unbalanced data. First, the data grouping strategy based on integrated learning alleviates the extreme data imbalance and avoids the negative impact on imbalanced learning. Meanwhile, to avoid the possible negative impact brought by unbalanced data on the learning procedures of convolutional neural networks, the strategy of assigning weight to the loss function is adopted. Besides, a method for determining the weight value is designed to make the model supervise the learning of attack samples during the training process. Experimental results on the NSL-KDD dataset show that the method in this paper can effectively improve the detection of attack classes, and the overall detection accuracy reaches 81.48%. ID-UL has the same significant effect on other imbalanced datasets.

Video Abstract1_mfibp9gtKaltura

Sparsity has been extensively employed in multimedia sensing and computing in consumer electronics, signal and image processing, depth video codec, adaptive sparse-type equalizer, blind speech separation, and machine learning. Throughout this paper, we propose a novel distributed projection neurodynamic approach for solving the Basis Pursuit (BP) with flexible partition methods in a distributed manner. The proposed neurodynamic approach requires only that the network is undirected and connected, and no node can access the entire matrix simultaneously. First, we equivalently formulate the BP into a standard distributed optimization problem with a flexible partition-by-blocks method to obtain global information, and discuss the equivalence of their optimality conditions. Then, we propose a distributed continuous-time neurodynamic approach on the basis of primal-dual dynamical systems and projection operators, and also study its global convergence property. Finally, numerical experiments on sparse signals and image recovery further verify the effectiveness and superiority of our proposed neurodynamic approach.

Recognizing visual objects from single-trial electroencephalograph (EEG) signals is a promising brain-computer interface technology. However, due to the redundant features from noisy multichannel EEG signals, it is still a challenging task to achieve high precision recognition. Recent deep learning approaches commonly extract spatio-temporal features of EEG signals, which neglect important spectral-temporal features and may degrade the EEG recognition performance. To address the deficiency, we propose a novel channel attention weighting and multilevel adaptive spectral aggregation based dual-branch spatio-temporal-spectral transformer feature fusion network (CAW-MASA-STST) for EEG-based visual recognition. Specially, we first develop a channel attention weighting (CAW) to automatically learn the channel weights of EEG signals. Then, a graph convolution-based MASA is employed to aggregate spectral-temporal features of different sub-bands. Finally, an STST is designed to fuse spatio-temporal and spectral-temporal features, which enhances the comprehensive learning ability by modeling the temporal dependencies of the fused features. Competitive experimental results on two public datasets demonstrate that the proposed method is able to achieve superior recognition performance compared with the state-of-the-art methods, indicating a feasible solution for visual recognition-based BCI technology. The code of our proposed method will be available at https://github.com/ljbuaa/VisualDecoding.

Blockchain is extensively applied to many fields as an immutable distributed ledger. However, the immutability contradicts regulations such as the GDPR ruling “the right to be forgotten” of data. Besides, numerous emerging blockchain-based applications call for elastic data management. To erase some data, redactable blockchains are proposed for breaking the immutability in a controlled way. Unfortunately, the prior solutions may suffer from poor security and centralized control of the redaction privilege. They cannot support dynamic nodes, where the departure of participators will result in a single point of failure. This article proposes a novel dynamic and decentralized attribute-based chameleon hash (DACH) to make blockchain history mutable, achieving a securely and dynamically redactable blockchain (SDR-chain) in a decentralized setting. We first propose the formal definition, security models, and concrete construction of our DACH. Meanwhile, we design a delegation algorithm of DACH to support a dynamically changing committee, where participators can freely and securely leave and join the network. Then, the transactions of the SDR-chain are redacted by computing DACH collisions. The security is analyzed in the random oracle model. Finally, theoretical analysis and experimental evaluation demonstrate that our SDR-chain is superior to the prior solutions in terms of security and functionality.

This article investigates a class of constrained distributed fuzzy convex optimization problems, where the objective function is the sum of a set of local fuzzy convex objective functions, and the constraints include partial order relation and closed convex set constraints. In undirected connected node communication network, each node only knows its own objective function and constraints, and the local objective function and partial order relation functions may be nonsmooth. To solve this problem, a recurrent neural network approach based on differential inclusion framework is proposed. The network model is constructed with the help of the idea of penalty function, and the estimation of penalty parameters in advance is eliminated. Through theoretical analysis, it is proven that the state solution of the network enters the feasible region in finite time and does not escape again, and finally reaches consensus at an optimal solution of the distributed fuzzy optimization problem. Furthermore, the stability and global convergence of the network do not depend on the selection of the initial state. A numerical example and an intelligent ship output power optimization problem are given to illustrate the feasibility and effectiveness of the proposed approach.

Despite the demonstrated importance of DNA G-quadruplexes (G4s) in health and disease, technologies to readily manipulate specific G4 folding for functional analysis and therapeutic purposes are lacking. Here we employ G4-stabilizing protein/ligand in conjunction with CRISPR to selectively facilitate single or multiple targeted G4 folding within specific genomic loci. We demonstrate that fusion of nucleolin with a catalytically inactive Cas9 can specifically stabilize G4s in the promoter of oncogene MYCand muscle-associated gene Itga7as well as telomere G4s, leading to cell proliferation arrest, inhibition of myoblast differentiation and cell senescence, respectively. Furthermore, CRISPR can confer intra-G4 selectivity to G4-binding compounds pyridodicarboxamide and pyridostatin. Compared with traditional G4 ligands, CRISPR-guided biotin-conjugated pyridodicarboxamide enables a more precise investigation into the biological functionality of de novo G4s. Our study provides insights that will enhance understanding of G4 functions and therapeutic interventions.

In recent years, Riemannian geometry, as an import- ant tool for designing and analyzing continuous trajectory flows, has been widely applied to solve nonlinear programming problems due to its ability to inspire the design of a new class of approaches for solving such problems. We first review some properties of invariant Riemannian metrics, then investigate the diagonal metric defined in general on the manifold $\mathbb {R}_{+ +}^{n}, $ some geometric properties are derived, and finally a class of neurodynamic flow approaches are also proposed. The global existence and feasibility results of the produced neurodynamic flow approaches are obtained and the results can be further extended to a more universal smooth function. The asymptotical behaviors of proposed neurodynamic flow approaches are also analyzed and studied, and global convergence of the proposed neurodynamic flow approaches for seeking the minimum point of the convex and quasi-convex minimum problems on the non-negative orthant domain is developed. In addition, the derived convergence results can directly be adapted to the competitive Cohen–Grossberg neural networks as well as to image and signal processing in compressive sensing. The correctness and superiority of our proposed neurodynamic flow approaches are verified by some sparse signal and image reconstruction examples.

Facial expression recognition (FER) in the wild is a challenging task for affective computing in human–machine interaction fields. However, most of the existing methods fail to learn the most prominent regions of facial images by simple cross-entropy loss due to the imbalance problem commonly existing in FER datasets, which limits the robustness and interpretability of the model. In addition, these methods only capture local features of original images with multisize shallow convolution and ignore facial texture characteristics, leading to a suboptimal recognition performance. To address these issues, in this article, we propose a novel FER network, named the attention-rectified and texture-enhanced cross-attention transformer feature fusion network (AR-TE-CATFFNet). Specifically, an attention-rectified convolution block is first designed to assist multiple convolution heads to focus on the critical areas of human faces and improve the model generalization. Second, we investigate a texture enhancement block to capture texture features through local binary pattern and gray-level co-occurrence matrix, which solves the limitation of insufficient texture information. Finally, a cross-attention transformer feature fusion block is employed to deeply integrate red, green, blue (RGB) features and texture features globally, which is beneficial to boost the accuracy of recognition. Competitive experimental results on three public datasets validate the efficacy of the proposed method, indicating that our proposed method achieves superior classification performance of 89.50% on real-world affective faces database (RAF-DB) dataset, 65.66% on AffectNet dataset, and 74.84% on FER2013 dataset against the existing methods.

In this article, a distributed smoothing accelerated projection algorithm (DSAPA) is proposed to address constrained nonsmooth convex optimization problems over undirected multiagent networks in a distributed manner, where the objective function is free of the assumption of the Lipschitz gradient or strong convexity. First, based on a distributed exact penalty method, the original optimization problem is translated to a problem of standard assignment without consensus constraints. Then, a novel DSAPA by combining the smoothing approximation with Nesterov's accelerated schemes, is proposed. In addition, we provide a systematic analysis to derive an upper bound on the convergence rate in terms of the objective function based on penalty function and to choose the optimal step size accordingly. Our results demonstrate that the proposed DSAPA can reach $O({\log (k)}/{k})$ when the optimal step size is chosen. Finally, the effectiveness and correctness of the proposed algorithm are verified by numerical and practical application examples.

Vision-based practical applications, such as consumer photography and automated driving systems, greatly rely on enhancing the visibility of images captured in night-time environments. For this reason, various image enhancement algorithms (EHAs) have been proposed. However, little attention has been given to the quality evaluation of enhanced night-time images. In this paper, we conduct the first dedicated exploration of the subjective and objective quality evaluation of enhanced night-time images. First, we build an enhanced night-time image quality (EHNQ) database, which is the largest of its kind so far. It includes 1,500 enhanced images generated from 100 real night-time images using 15 different EHAs. Subsequently, we perform a subjective quality evaluation and obtain subjective quality scores on the EHNQ database. Thereafter, we present an objective blind quality index for enhanced night-time images (BEHN). Enhanced night-time images usually suffer from inappropriate brightness and contrast, deformed structure, and unnatural colorfulness. In BEHN, we capture perceptual features that are highly relevant to these three types of corruptions, and we design an ensemble training strategy to map the extracted features into the quality score. Finally, we conduct extensive experiments on EHNQ and EAQA databases. The experimental and analysis results validate the performance of the proposed BEHN compared with the state-of-the-art approaches. Our EHNQ database is publicly available for download at https://sites.google.com/site/xiangtaooo/.

The globally fixed-time stabilization and $H_{\infty }$ control for the port-Hamiltonian differential algebraic equation (PH-DAE) is addressed by applying the structural properties of the port-Hamiltonian (PH) systems, and the interconnection and damping assignment passivity-based control (IDA-PBC) technique. For fixed-time control design of the differential algebraic equation systems, the main obstacle lies that the algebraic variables are hidden in the system and their dynamics are ambiguous. We propose an available and effective approach to overcome this obstacle. The system variables are decomposed into differential component and the algebraic component, and the explicit representation of the dynamics of algebraic component is obtained. Fist, we investigate the locally fixed-time stabilization control as well as globally attractive for PH-DAE. Second, we focus on solving the globally fixed-time stabilization and $H_{\infty }$ control problems by jointly taking advantage of the locally fixed-time stabilization control for the origin and the globally fixed-time attractivity control for a predeterminate region. Finally, in order to address fixed-time stabilization control and $H_{\infty }$ control for PH-DAE, new controllers are designed. Moreover, the convergence time of systems can be easily estimated after external disturbance vanishes. The efficiency of the theoretical results acquired in present article is verified via two examples with numerical simulation.

The incidence of cutaneous T-cell lymphoma (CTCL) increases with age, and blood involvement portends a worse prognosis. To advance our understanding of the development of CTCL and identify potential therapeutic targets, we performed integrative analyses of paired single-cell RNA and T-cell receptor (TCR) sequencing of peripheral blood CD4+ T cells from patients with CTCL to reveal disease-unifying features. The malignant CD4+ T cells of CTCL showed highly diverse transcriptomic profiles across patients, with most displaying a mature Th2 differentiation and T-cell exhaustion phenotype. TCR-CDR3 peptide prediction analysis suggested limited diversity between CTCL samples, consistent with a role for a common antigenic stimulus. Potential of heat diffusion for affinity-based trajectory embedding transition analysis identified putative precancerous circulating populations characterized by an intermediate stage of gene expression and mutation level between the normal CD4+ T cells and malignant CTCL cells. We further revealed the therapeutic potential of targeting CD82 and JAK that endow the malignant CTCL cells with survival and proliferation advantages.

•Integrated transcriptome analyses identify putative precancerous circulating clonal CD4+T-cell populations in patients with CTCL.•The study results reveal promising therapeutic strategies via targeting of CD82 and JAK kinases.

This article considers constrained nonsmooth generalized convex and strongly convex optimization problems. For such problems, two novel distributed smoothing projection neurodynamic approaches (DSPNAs) are proposed to seek their optimal solutions with faster convergence rates in a distributed manner. First, we equivalently transform the original constrained optimal problem into a standard smoothing distributed problem with only local set constraints based on an exact penalty and smoothing approximation methods. Then, to deal with nonsmooth generally convex optimization, we propose a novel DSPNA based on continuous variant of Nesterov’s acceleration (called DSPNA-N), which has a faster convergence rate $\mathcal {O} ({1}/{t^{2}})$ , and we design a novel DSPNA inspired by the continuous variant of Polyak’s heavy ball method (called DSPNA-P) to address the nonsmooth strongly convex optimal problem with an explicit exponential convergent rate. In addition, the existence, uniqueness, and feasibility of the solution of our proposed DSPNAs are also provided. Finally, numerical results demonstrate the effectiveness of DSPNAs.

In this article, a single-layer projection neural network based on penalty function and differential inclusion is proposed to solve nonsmooth pseudoconvex optimization problems with linear equality and convex inequality constraints, and the bound constraints, such as box and sphere types, in inequality constraints are processed by projection operator. By introducing the Tikhonov-like regularization method, the proposed neural network no longer needs to calculate the exact penalty parameters. Under mild assumptions, by nonsmooth analysis, it is proved that the state solution of the proposed neural network is always bounded and globally exists, and enters the constrained feasible region in a finite time, and never escapes from this region again. Finally, the state solution converges to an optimal solution for the considered optimization problem. Compared with some other existing neural networks based on subgradients, this algorithm eliminates the dependence on the selection of the initial point, which is a neural network model with a simple structure and low calculation load. Three numerical experiments and two application examples are used to illustrate the global convergence and effectiveness of the proposed neural network.

The thermal infrared brightness temperature (BT) of the eastern Tibetan Plateau (TP) was retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) level-1B data. The multiyear averaged BT background field was subtracted from the punctual BT data to yield monthly BT spatial anomaly, and calculated time series of BT for the secondary blocks. Then, the spatial and temporal changes in the BT of the study area before the Menyuan M6.4, Zaduo M6.2, and Jiuzhaigou M7.0 earthquakes were investigated and analyzed based on the tectonic setting. The results show the following. The spatial BT radiation enhancement frequency rose remarkably before strong earthquakes; each of the three earthquakes was preceded by marked spatiotemporal continuous BT anomalies. The tectonic setting significantly influences the BT anomaly feature. The spatial BT anomaly was not notable in the Qaidam and Qilian block before the Menyuan earthquake; the spatial BT anomaly mainly appeared in the Qiangtang and Bayan Har blocks before the Zaduo and Jiuzhaigou earthquakes. The Qiangtang and Bayan Har block’s BT time series curves have similar features. The Qaidam and Qilian block’s BT time series curves have analogous shapes. The three earthquakes may be regarded as one seismic event induced by a stage of tectonic stress enhancement rather than three independent occasions. The spatial BT anomalous behavior before earthquakes is, to a great extent, like the result of the rock stress loading experiment; the rock compression and the lithosphere–atmosphere–ionosphere coupling (LAIC) may be the main reasons for the intensification of the BT radiation.

This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the $L_{1}$ -minimization problem. First, two centralized neurodynamic approaches are designed based on the augmented Lagrange method and the Lagrange method with derivative feedback and projection operator. Then, the optimality and global convergence of them are derived. In addition, considering that the collective neurodynamic approaches have the function of information protection and distributed information processing, first, under mild conditions, we transform the $L_{1}$ -minimization problem into two network optimization problems. Later, two collective neurodynamic approaches based on the above centralized neurodynamic approaches and multiagent consensus theory are proposed to address the obtained network optimization problems. As far as we know, this is the first attempt to use the collective neurodynamic approaches to deal with the $L_{1}$ -minimization problem in a distributed manner. Finally, several comparative experiments on sparse signal and image reconstruction demonstrate that our proposed centralized and collective neurodynamic approaches are efficient and effective.

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this article, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.

High-utility itemsets mining (HUIM) has attracted considerable attention due to its superior performance in discovering the profitable itemsets from databases. Generally, an itemset is discovered as a high-utility itemset (HUI) by HUIM only if its utility (such as profit) is higher than a predefined utility threshold. The previous HUIM methods cannot address the sharp boundary problem and the efficiency problem simultaneously. To tackle the above problems, we take the attempt to combine the fuzzy set theory and the evolutionary algorithms (EAs) in the HUIM method, which is called EA-HUFIM. For the proposed EA-HUFIM, it is challenging to design an appropriate EA and a suitable fitness function. Therefore, we focus on analyzing the characteristics of fuzzy-based HUI (i.e., HUFI) and present a theorem and a lemma based on a new concept, the high database-max utility fuzzy itemsets (HDFIs). The fitness function adopted in the EA-HUFIM method is inspired by the proposed lemma. Besides, we present a general EA framework with considering the elite learning strategy. The top K1-HUFIs and top K1-HDFIs are stored as an elite archive for further offspring production. To improve efficiency, we further employ a fuzzy map and a transaction map to propose a novel preprocessing method, and thus, the computational costs can be reduced. Experiments on several datasets and the results demonstrate that the EA-HUFIM method is effective in quantitative databases.

AbstractCinnamomum migaoplants often face different degrees of drought in karst habitats, which can lead to plants’ death, especially in the seedling stage. Widespread of arbuscular mycorrhizal (AM) fungi in karst soils have the potential to address this drought, which is a threat to C. migaoseedlings. We inoculated C. migaoseedlings with spores from Glomus lamellosumand Glomus etunicatum, two AM fungi widely distributed in karst soils, to observe seedling growth response after simulated drought. Our results showed that 40 g of G. lamellosumand G. etunicatumsignificantly promoted the growth of C. migaoseedlings, 120 days after inoculation. Following a 15-day drought treatment, root colonization of the seedlings with G. lamellosumor G. etunicatumhad lower the accumulation of malondialdehyde (MDA) and increased the accumulation of enzymes and osmotic substances in the seedlings. The relative water content in different organs (roots, stems, and leaves) of the drought-stressed seedlings was higher in plants with G. lamellosumor G. etunicatumthan in plants without AM fungi colonization. Our results showed that inoculation with AM fungi was an effective means to improve the drought resistance of C. migaoseedlings.

Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model; that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications (e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.

In this paper, a leachate anaerobic digestion experiment was carried out in a sequencing batch anaerobic digestion reactor. The anaerobic co-digestion characteristics of leachate and food waste in landfills were studied. To conduct the experiment, 10, 20, 30 and 40 g of TS/L food waste were added to leachate to observe the effect of food waste on the degradation of dissolved organic matter (DOM) in the leachate. The results of this experiment showed that adding food waste into the anaerobic digestion system could significantly increase the gas and methane production per unit organic load, and promote the degradation of DOM in leachate. When the initial load rate of food waste was 30 g TS/L, the gas production was increased by 117.69%, the dissolved organic carbon (DOC) of biogas slurry after anaerobic digestion of leachate and food waste is about 50% of the DOC in the anaerobic mono-digestion leachate. The results show that the combination of leachate and food waste can not only promote the anaerobic digestion of food waste, but also increase the gas and methane production rates; this combination can also promote the degradation of DOM in leachate. The combined anaerobic digestion of leachate and food waste had a synergistic effect.

Background: Extraperitoneal colostomy (EPC) after laparoscopic abdominoperineal resection (APR) remains a challenge for surgeons. This study aims to summarize our laparoscopic EPC method and assess its effects versus a transperitoneal colostomy (TPC) for patients with rectal tumors. Methods: A total of 133 patients with rectal cancer treated with laparoscopic APR between May 2009 and May 2020 were retrospectively reviewed. The clinical data, including demographics, comorbidities, tumor stage, colostomy duration, and complications were compared between the EPC group and the TPC group. Results: The EPC group included 83 patients whose extraperitoneal tunnels were created using a cannula through a trocar port, and the TPC group included 50 patients whose stomata were formed traditionally. There were no differences in colostomy time [(23.1 ± 6) min vs. (21.4 ± 4) min, P= 0.078], number of parastomal dermatitis patients (5 vs. 2, P= 0.916), or number of stomal stenoses (1 vs. 1, P= 0.715) between the EPC and TPC groups. No cases of parastomal hernia developed in the EPC group, whereas 4 patients were diagnosed with a parastomal hernia; the difference between the two groups was statistically significant (P= 0.036). Conclusions: Laparoscopic EPC have a lower incidence of parastomal hernia than TPC. It is easy and inexpensive to create an extraperitoneal tunnel using a cannula through a trocar port.

Sludge-based activated carbons (SACs) prepared from sewage sludge and corn straw with potassium hydroxide activation were modified with six different metal salts, and the H2S removal performance of the modified SACs was studied. The results showed that iron salt modification shows the good effect and can improve the H2S removal performance of the modified SAC, and that the optimum modifier is ferric nitrate. Through an orthogonal experiment, the optimum modification conditions were determined: the ferric nitrate concentration was 1.0 mol/L, the impregnation ratio was 1:4, the carbonization time was 90 min, and the carbonization temperature was 200°C. The modified SAC yielded a breakthrough sulfur capacity and saturation sulfur capacity of 27.21 and 49.17 mg/g, which were as 4.68 and 7.02 times as large as those before modification, respectively. Additionally, the results show that the carbonization temperature was the most important factor affecting the H2S adsorption capacity of the modified SACs. The modification process increased aromatic ring structures and iron oxides (such as Fe2O3); those changes were all conducive to improving the chemical adsorption capacity of the modified SACs.

A magnetic mesoporous expanded perlite-based (EPd-APTES@Fe3O4) composite was designed and synthesized as a novel adsorbent for enrichment of yttrium(III) ions with the maximum adsorption capacity of 383.2 mg/g. FTIR, XPS and the quantum chemical calculation indicated that the yttrium(III) ions were captured on the EPd-APTES@Fe3O4surface mainly by coordination with functional group of -NH2.

Photothermal therapy (PTT) and photodynamic therapy (PDT) have emerged as highly prospective therapeutic modalities in cancer therapy. Notwithstanding, a critical challenge still remains in the exploration of an effective strategy to maximize the synergistic efficacy of PTT and PDT due to low photoconversion efficiency. Herein, inspired by the phospholipid bimolecular structure of the cell membrane, bionic cell membrane polymeric vesicles with photothermal/photodynamic synergy for prostate cancer therapy at one wavelength’s excitation are constructed in one step by the coordination of hexadecyl trimethyl ammonium bromide (CTAB) from the surface of hydrophobic gold nanorods (AuNRs) with indocyanine green (ICG) and polycaprolactone (PCL), achieving their self-assembly in aqueous solutions. Importantly, the aggregation of the assembly improves the stability of the vesicles, realizing the synergistic effect of PTT and PDT for prostate cancer therapy. After being assembled within polymeric vesicles, bifunctional photosensitizer ICG can generate reactive oxygen species (ROS) and photothermal effect under light treatment. Their ROS not only induce PDT efficacy but also destroy the integrity of the lysosomal membrane, promoting the translocation of ICG and another photosensitizer called gold nanorods (AuNRs) into the cytosol. Moreover, their photothermal effects produced by both photosensitizers are able to engender greater damage to the tumor cells because of the close distance with organelles. This structure manifests good cellular uptake, highly effective tumor accumulation, high photothermal conversion efficiency, and excellent properties of enhanced photobleaching resistance, which are beneficial to ICG-based fluorescence tumor imaging. Using the same near-infrared (NIR) wavelength for excitation, the AuNR/ICG vesicles can reduce the side effect rate of photodamage on the skin. In addition, by generating reactive oxygen species (ROS) and double photothermal effect, the vesicles under NIR excitation can promote the apoptosis of PC3 tumor cells. Taken together, the spontaneous self-assembled AuNR/ICG vesicles exhibit huge potential in advanced-stage prostate cancer therapy, especially for the prostate-specific membrane antigen (PSMA)-negative castration-resistant subtype.

Large-scale fabrication of superhydrophobic surfaces with excellent durability by simple techniques has been of considerable interest for its urgent practical application in oil–water separation in recent years. Herein, we proposed a facile vapor–liquid sol–gel approach to fabricating highly durable and robust superhydrophobic polydimethylsiloxane@silica surfaces on the cross-structure polyester textiles. Scanning electron microscopy and Fourier transform infrared spectroscopy demonstrated that the silica generated from the hydrolysis–condensation of tetraethyl orthosilicate (TEOS) gradually aggregated at microscale driven by the extreme nonpolar dihydroxyl-terminated polydimethylsiloxane (PDMS(OH)). This led to construction of hierarchical roughness and micronano structures of the superhydrophobic textile surface. The as-fabricated superhydrophobic textile possessed outstanding durability in deionized water, various solvents, strong acid/base solutions, and boiling/ice water. Remarkably, the polyester textile still retained great water repellency and even after ultrasonic treatment for 18 h, 96 laundering cycles, and 600 abrasion cycles, exhibiting excellent mechanical robustness. Importantly, the superhydrophobic polyester textile was further applied for oil–water separation as absorption materials and/or filter pipes, presenting high separation efficiency and great reusability. Our method to construct superhydrophobic textiles is simple but highly efficient; no special equipment, chemicals, or atmosphere is required. Additionally, no fluorinated slianes and organic solvents are involved, which is very beneficial for environment safety and protection. Our findings conceivably stand out as a new tool to fabricate organic–inorganic superhydrophobic surfaces with strong durability and robustness for practical applications in oil spill accidents and industrial sewage emission.

A Filippov system of Hindmarsh–Rose (HR) neuronal model with threshold policy control is proposed, membrane potential has been taken as the threshold and the corresponding switching function is also established. We first discuss the existence and stability of the equilibria for the two Filippov subsystems based on the 2-D HR model. Subsequently, the sliding dynamics of HR model including the sliding segments, sliding regions, and various equilibria under the Filippov framework are studied. Then, we further consider the equilibria and the sliding bifurcation set of the Filippov system, and find there exist the bistable equilibria and several sliding bifurcation phenomena, such as boundary-node bifurcation, pseudosaddle-node bifurcation, the emergence and disappearance of limit cycles on the sliding line, and so on. Finally, we study the Filippov system of the 3-D HR model, and provide a phase diagram of the system that generates the sliding spiking and the sliding bursting, which lie on the sliding line.

As you open this January issue of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), I hope everyone enjoyed a great holiday season and is excited for the new year of 2019. I am very delighted and honored to report several key metrics of IEEE TNNLS to the community.

This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems &#x0028 MANSs &#x0029. Based on the MANSs, a new distributed differentially private consensus algorithm &#x0028 DPCA &#x0029 is developed. To avoid continuous communication between neighboring agents, a kind of intermittent communication strategy depending on an event-triggered function is established in our DPCA. Based on our algorithm, we carry out the detailed analysis including its convergence, its accuracy, its privacy and the trade-off between the accuracy and the privacy level, respectively. It is found that our algorithm preserves the privacy of initial states of all agents in the whole process of consensus computation. The trade-off motivates us to find the best achievable accuracy of our algorithm under the free parameters and the fixed privacy level. Finally, numerical experiment results testify the validity of our theoretical analysis.

ABSTRACTSeries sludge straw–based activated carbons were prepared by sewage sludge and corn straw with potassium hydroxide (KOH) activation, and the desulfurization performance of activated carbons was studied. To obtain the best desulfurization performance, the optimum ratio between the raw materials and the activator was investigated. The results showed that when the mass ratio of sewage sludge, corn straw, and KOH was 3:7:2, the activated carbon obtained the best breakthrough and saturation sulfur sorption capacities, which were 12.38 and 5.74 times, respectively, those of samples prepared by the nonactivated raw materials. The appropriate KOH could improve the microporosity and alkaline groups, meanwhile reducing the lactone groups, which were all beneficial to desulfurization performance. The chemical adsorption process of desulfurization can be simplified to four main steps, and the main desulfurization products are elemental sulfur and sulfate.Implications: Sewage sludge (SS) and corn straw (CS) both have great production and wide distribution and are readily available in China. Much attention has been paid on how to deal with them effectively. Based on the environment protection idea of waste treatment with waste and resource recycling, low-cost adsorbents were prepared by these processes. The proposed method can be expanded to the municipal solid waste recycling programs and renewable energy plan. Thus, proceeding with the study of preparing activated carbon by SS and straw as a carbon-based dry desulfurization agent could obtain huge social, economic, and environmental benefits.

Sewage sludge and corn straw-derived activated carbon (AC) were prepared by chemical activation with KOH and pyrolyzed in nitrogen atmosphere, then the effects of KOH dosage on the characteristics of AC were investigated. Thermal properties of materials were analyzed, and it was found that KOH facilitates the earlier decomposition of organics and promotes the pyrolysis process of the substances. Parameters such as specific surface area and pore size distribution of AC were studied. AC372 has the highest microporosity of 0.590, AC374 has the smallest average pore size of 3.657 nm, and AC376 has the highest SBETvalue of 689.624 m2/g. These indicate that the right amount of KOH (the mass ratio of sewage sludge, corn straw and KOH was 3:7:2) can promote the formation of micropores in AC, but an excessive KOH would etch or even block the original pore structure. The results of the Boehm titration and infrared spectroscopy analysis showed that the KOH activation did not only increase the number of basic groups on the surface of AC but also reduce the number of acidic groups. KOH, K2CO3, K2O and other substances were detected in pyrolysis products by the XRD analysis. Thus indicates that H2, CO, CO2, K2O and K2CO3may be produced in the process of KOH activation.

Superhydrophobic surfaces with tunable adhesion from lotus-leaf to rose-petal states have generated much attention for their potential applications in self-cleaning, anti-icing, oil–water separation, microdroplet transportation, and microfluidic devices. Herein we report a facile magnetic-field-manipulation strategy to fabricate dual-functional superhydrophobic textiles with asymmetric roll-down/pinned states on the two surfaces of the textile simultaneously. Upon exposure to a static magnetic field, fluoroalkylsilane-modified iron oxide (F-Fe3O4) nanoparticles in polydimethylsiloxane (PDMS) moved along the magnetic field to construct discrepant hierarchical structures and roughnesses on the two sides of the textile. The positive surface (closer to the magnet, or P-surface) showed a water contact angle up to 165°, and the opposite surface (or O-surface) had a water contact angle of 152.5°. The P-surface where water droplets easily slid off with a sliding angle of 7.5° appeared in the “roll-down” state as Cassie mode, while the O-surface was in the “pinned” state as Wenzel mode, where water droplets firmly adhered even at vertical (90°) and inverted (180°) angles. The surface morphology and wetting mode were adjustable by varying the ratios of F-Fe3O4nanoparticles and PDMS. By taking advantage of the asymmetric adhesion behaviors, the as-fabricated superhydrophobic textile was successfully applied in no-loss microdroplet transportation and oil–water separation. Our method is simple and cost-effective. The fabricated textile has the characteristics of superhydrophobicity, magnetic responsiveness, excellent chemical stability, adjustable surface morphology, and controllable adhesion. Our findings conceivably stand out as a new tool to fabricate functional superhydrophobic materials with asymmetric surface properties for various potential applications.

Recently, we demonstrated that treatment with all-trans-retinoic acid (tRA) induced a paradoxical effect on immune activation during the development of autoimmune lupus. Here, we further describe its negative effects on mediating neuroinflammation and neurodegeneration. Female MRL/lpr mice were orally administered tRA or VARA (retinol mixed with 10% tRA) from 6 to 14 weeks of age. Both treatments had a significant effect on brain weight, which correlated with histopathological evidence of focal astrogliosis, meningitis, and ventriculitis. Infiltration of CD138- and Iba1-positve immune cells was observed in the third ventricle and meninges of treated mice that co-labeled with ICAM-1, indicating their inflammatory nature. Increased numbers of circulating plasma cells, autoantibodies, and total IgG were also apparent. IgG and C3 complement deposition in these brain regions were also prominent as was focal astrogliosis surrounding the ventricular lining and meninges. Using Fluoro-Jade staining, we further demonstrate that neuroinflammation was accompanied by neurodegeneration in the cortex of treated mice compared with vehicle controls. These findings indicate that vitamin A exposure exacerbates the immunogenic environment of the brain during the onset of systemic autoimmune disease. Vitamin A may therefore compromise the immuno-privileged nature of the central nervous system under a predisposed immunogenic environment.

Usually, the security of traditional cryptography which works on integer numbers and chaotic cryptosystem which works on real numbers is worthy of study. But the classical chaotic map over the real domain has a disadvantage that the calculation accuracy of the floating point number can be doubled when the map is implemented by computer. This is a serious drawback for practical application. The Logistic map is a classical chaotic system and it has been used as a chaotic cipher in the real number field. This inevitably leads to the degradation of finite precision under computer environment, and it is also very difficult to guarantee security. To solve these drawbacks, we extend the Logistic map to the finite field. In this paper, we consider the Logistic map for the finite field N= 3n, and analyze the period property of sequences generated by the Logistic map over ZN. Moreover, we discuss the control parameters which may influence the behavior of the mapping, and show that the Logistic map over ZNmay be suitable for application by performance analysis. Ultimately, we find that there exists an automorphic map between two Logistic maps with the different control parameters, which makes them suitable for sequence generator in cryptosystem. The automorphic sequence generated algorithm based on the Logistic map over ZNis designed and analyzed in detail. These sequences can be used in the pseudorandom number generator, the chaotic stream cipher, and the chaotic block cipher, etc.