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The evolution of flexible Zn‐ion batteries (FZIBs) significantly hinges on the development of gel electrolytes, characterized by their mechanical properties, ionic conductivity, and environmentally friendly production processes. The prevailing challenge in this domain has been devising a gel electrolyte that encapsulates all these critical attributes effectively for practical application. This study presents a novel zinc ion gel (Zn‐gel) electrolyte developed for FZIBs, synthesized via ethanol vapor‐induced assembly of cellulose molecules. This innovative process fosters significant hydrogen bonding and ion‐complexation with Zn2+ ions, resulting in a gel with exceptional mechanical strength (0.88 MPa), high ion transference (over 0.7), and impressive ionic conductivity (8.39 mS cm−1). The Zn‐gel enables a FZIB to achieve a reversible capacity of 207.3 mAh g−1 and over 93% Coulombic efficiency after 500 cycles, devoid of liquid electrolyte. Highlighting a promising route for high‐performance, eco‐friendly gel electrolytes, this research advances flexible electronics and portable device applications, demonstrating the profound potential of bio‐based polymers in enhancing energy storage technology.

Fish protein hydrolysates (FPH) were applied in a 8-week feeding experiment with juvenile common carp (Cyprinus carpio) (initial body weight 8.75±0.05 g), to preliminarily investigate their effects on growth performance and feed utilization and potential influence on intestinal antioxidant status, expressions of protein synthesis gene and microflora. Common carp were fed with 0%, 0.5%, 1%, 1.5%, and 2% FPH (named as FPH-0, FPH-0.5, FPH-1, FPH-1.5, and FPH-2) respectively, substituting 0%, 13.33%, 28.33%, 41.66%, and 55.00% of dietary fish meal. Each diet was randomly fed to triplicate groups of 25 fish per aquarium. Feeding experimental results revealed that final body weight, weight gain rate and protein efficiency ratio in the FPH-0.5 treatment were significant higher than those in other treatments. The FPH-0.5 treatment had highest crude protein level when compared to the FPH-1.5 and FPH-2 treatments (P

The application of live-streaming in e-commerce has garnered substantial attention in recent years, transforming marketing tactics and customer involvement. Grounded on signal theory and presence theory, this study aims to uncover the mechanism of trust-building in live-streaming commerce that concentrates primarily on live-streaming commerce features. This study, which was exploratory in nature, gathered a dataset of 339 responses from China through an online survey. This dataset was then subjected to exploratory factor analysis, confirmatory factor analysis, and covariance-based structural equation modeling to identify and elucidate the relationship between the independent and dependent variables. The study found that the consumer’s sense of presence and authenticity in live streaming significantly impacts the consumer’s trust. Visual appeal and interactivity influence the consumer’s presence and authenticity; however, only interactivity can directly influence consumer trust; visual appeal can only influence consumer trust indirectly through presence and authenticity. This study expands the existing body of research regarding consumer trust in live-streaming commerce. Additionally, this study provides evidence that the consumer’s perception of presence and authenticity play mediating roles in the relationship between live-streaming commerce features and consumer trust.

The 2022′s Youth Forum on Resources Chemicals and Materials was held on November 12–13, 2022, in Shenyang, Liaoning Province. Panel discussions focus on the cutting-edge researches on “Fine chemicals and advanced alloy materials” and “Utilization of fossil and renewable carbon resources”. This perspective summarizes the major directions of scientific research and technical developments aligned in the discussions. Fine chemical industry tends to pursue green and low-carbon products, intelligent product design, and start manufacturing. In recent years, great efforts have been made for transformation of cellulose into advanced electronic as well as life-service bio-materials and to the high-selectivity extraction of bio-base aromatic chemicals from lignin. Concerning high-end alloy materials, regulating deformation mechanism of crystal to construct bimodal microstructure seems highly prospective in harmonizing precipitate hardening effect and plastic deformation capacity. As we know, utilization of fossil carbon resources constitutes the major anthropogenic carbon emissions, and the related innovations thus should be, for possibly a long period, on increasing energy production efficiency and low-carbon cascaded conversion of fossil fuels, especially of coal.

Abstract Background Irinotecan (CPT-11) is a classic chemotherapeutic agent that plays an important role in the clinical treatment of metastatic colon cancer and other malignant tumors. We previously designed a series of novel irinotecan derivatives. In this study, we select one representative, ZBH-01, to investigate its sophisticated antitumor mechanism in colon tumor cells. Methods The cytotoxic activity of ZBH-01 on colon cancer cells was evaluate by MTT or Cell Counting Kit-8 (CCK8) assay, 3D and xenograft model. The inhibitory effect of ZBH-01 on TOP1 was detected by DNA relaxation assay and Immuno Complex of Ezyme (ICE) bioassay. The molecular mechanism of ZBH-01 was explored by Next-Generation Sequencing (NGS), bioinformatics analyses, flow cytometry, qRT-PCR, and western blot etc. Results ZBH-01 can induce obvious DNA damage and has superior antitumor activity against colon cancer cells compared to CPT-11 and SN38 (7-Ethyl-10-hydroxy camptothecin, the in vivo active form of CPT-11) both in vivo and in vitro. Its inhibitory effect on topoisomerase I (TOP1) was also comparable with these two control drugs. There are a much larger number of 842 downregulated and 927 upregulated mRNAs in ZBH-01 treatment group than that in the controls. The most significantly enriched KEGG pathways for these dysregulated mRNAs were DNA replication, the p53 signaling pathway, and the cell cycle. After constructing a protein–protein interaction (PPI) network and screening out a prominent cluster, 14 involved in the cell cycle process was identified. Consistently, ZBH-01 induced G0/G1 phase arrest in colon cancer cells, while CPT-11/SN38 caused S phase arrest. The initiation of apoptosis by ZBH-01 was also superior to CPT-11/SN38, followed by the increased expression of Bax, active caspase 3, and cleaved-PARP, and decreased expression of Bcl-2. Additionally, CCNA2 (cyclin A2), CDK2 (cyclin-dependent kinase 2), and MYBL2 (MYB proto-oncogene like 2) might be involved in the G0/G1 cell cycle arrest induced by ZBH-01. Conclusions ZBH-01 can be an antitumor candidate drug for preclinical study in the future.

In order to resolve the global crisis of fossil energy shortage and climate warming, the development of efficient energy storage devices is a significant topic at present. Supercapacitors as the novel type of energy storage devices have the unique advantages, including the fast charging/discharging behaviors, high-energy/power density, and stable cycling performance. Compared with traditional supercapacitors, flexible supercapacitors are environmentally friendly, light weight, small size and high safety. Therefore, flexible supercapacitors have a wide application prospect in emerging electronic devices. Due to its flexibility, biocompatibility, and structure designability, cellulose and its gel materials are gradually used as electrodes, separators and electrolytes in flexible supercapacitors. Several construction processes at molecular scale for high-performance cellulose gels are summarized. Meanwhile, this review covers the recent progress of developing the flexible supercapacitors and all-in-one supercapacitors based on cellulose functional gels. We finally discussed the potential challenges and opportunities for cellulose and its derived materials in new-style flexible supercapacitors and other electronic devices.

The conductive polymer poly-3,4-ethylenedioxythiophene (PEDOT), recognized for its superior electrical conductivity and biocompatibility, has become an attractive material for developing wearable technologies and bioelectronics. Nevertheless, the complexities associated with PEDOT’s patterning synthesis on diverse substrates persist despite recent technological progress. In this study, we introduce a novel deep eutectic solvent (DES)-induced vapor phase polymerization technique, facilitating nonrestrictive patterning polymerization of PEDOT across diverse substrates. By controlling the quantity of DES adsorbed per unit area on the substrates, PEDOT can be effectively patternized on cellulose, wood, plastic, glass, and even hydrogels. The resultant patterned PEDOT exhibits numerous benefits, such as an impressive electronic conductivity of 282 S·m−1, a high specific surface area of 5.29 m2·g−1, and an extensive electrochemical stability range from −1.4 to 2.4 V in a phosphate-buffered saline. To underscore the practicality and diverse applications of this DES-induced approach, we present multiple examples emphasizing its integration into self-supporting flexible electrodes, neuroelectrode interfaces, and precision circuit repair methodologies.

The technical bottleneck of carbon materials as counter electrodes (CEs) lies in their limited electrical conductivity, extended ion diffusion paths, poor dispersion, and high contact resistance. Problem-oriented in-situ self-grown N-doped CNTs-coated Ni nanoparticles based on N-doped carbonaceous structures derived from pitaya peel (PC) are adopted to construct Ni-N-C hybrid 3D ionized network sites (Ni@NCNTs/PC-4) as CEs. Structural characterization, micromorphological and chemical composition analyses revealed the 3D network structure of Ni@NCNTs/PC-4 with abundant active sites. They effectively shorten the diffusion distance of I3− ions with a smaller charge transfer resistance (5.21 Ω) than that of PC (12.53 Ω). DSSCs based on Ni@NCNTs/PC-4 display good optoelectronic properties, in which the short-circuit current density (Jsc) is 13.27 mA/cm2, higher than those of Pt (11.66 mA/cm2) and PC (6.99 mA/cm2). The PCE value (5.13%) of DSSCs based on Ni@NCNTs/PC-4 is also higher than that of DSSCs based on PC (2.47%). Overall, this work provides a preliminary research and new ideas for further in-depth study of biomass-derived 3D structured-carbons that contribute to key electrodes in DSSCs.

Heavy metals in flue gas desulfurization (FGD) gypsum from coal-fired power plants are at risk of releaching during the processes of stockpiling and resource utilization. In this study, the effects of organosulfur chelators dithiocarbamate (DTC) and trisodium trithiocyanate-15 (TMT-15) on the solidification characteristics of heavy metals in desulphurized gypsum under different mass fractions, pH values, water contents and reaction times were investigated. The chemical composition and morphology were analyzed by inductively coupled plasma atomic emission spectrometer (ICP-AES) and scanning electron microscope (SEM). The experiments showed that both DTC and TMT-15 were effective at stabilizing the heavy metals in the FGD gypsum, with more than a 50% curing effect for all the heavy metals except Pb. DTC showed a better stabilization for Pb, Hg, Cu, Zn, and Cr, and TMT-15 showed a better curing effect for Cd. The solidified gypsum had good heavy metal stability in low-water-content environments. Increasing the mass fraction, reaction time, and pH decreased the heavy metal leaching, and the mass fraction had the greatest effect on the total heavy metal leaching concentration, followed by the reaction time and pH value.

Network protocol syntax information plays a crucial role in grammar-based fuzzing. Current network protocol syntax extraction methods are less versatile, inefficient, and the extracted information is not comprehensive. This paper proposes a novel method for extracting syntax information, which innovatively extracts network protocol syntax from Wireshark protocol dissector files. The extracted syntax information includes packet types of the protocol, the constituent fields of each packet type, and detailed attributes of each field. Based on this method, an automated system for network protocol syntax information extraction was developed. The experiment was conducted with this system on a variety of protocols including DCCP, DNP3.0, Modbus TCP, and S7COMM. The experimental results show that compared with the current methods, our method has a better performance in terms of efficiency and versatility and at the same time ensures the comprehensiveness and accuracy of the extracted syntax information.

The aim of this investigation is to offer a data-based scheme for predicting electrode wear in resistance spot welding. One of the major factors affecting the mechanical properties of spot welds and the variation in weld quality is electrode wear and alloying. In this study, Rogowski coils and twisted pairs attached to the top and bottom electrodes were used to obtain the welding current and the voltage between the electrodes in the welding process, thereby calculating the dynamic resistance value during the welding process. The electrode tip diameter was obtained from the pressure exerted by the upper and lower electrodes on the carbon paper when the current was cut off and was regarded as an indicator of electrode wear. By continuously welding 0.5 mm thick BH 340 steel plates until the electrode failed, the dynamic resistance signal was recorded in real time. Simultaneously, the electrode diameter after every several welds was also recorded. On this basis, the correlation between electrode tip diameter and dynamic resistance is studied. In order to quantitatively study the mapping relationship between dynamic resistance and electrode wear, 10 characteristic values were extracted from the dynamic resistance, and the stepwise regression method was used to obtain the regression formula between the characteristic values and the electrode tip diameter. Using new data to verify the effectiveness of the regression model, the acquired results display that the maximum error between the predicted value of the electrode tip diameter and the measured value obtained by the regression equation with the interactive quadratic term is 0.3 mm, and the corresponding relative error is 7.69%. When welding with a new pair of electrodes, the maximum absolute error was 0.72 mm, and the relative error of the model prediction is within 20% according to the linear regression model with interaction terms. This indicates that this regression model is barely satisfactory for monitoring electrode condition.

The COVID-19 pandemic has accelerated innovations for supporting learning and teaching online. However, online learning also means a reduction of opportunities in direct communication between teachers and students. Given the inevitable diversity in learning progress and achievements for individual online learners, it is difficult for teachers to give personalized guidance to a large number of students. The personalized guidance may cover many aspects, including recommending tailored exercises to a specific student according to the student′s knowledge gaps on a subject. In this paper, we propose a personalized exercise recommendation method named causal deep learning (CDL) based on the combination of causal inference and deep learning. Deep learning is used to train and generate initial feature representations for the students and the exercises, and intervention algorithms based on causal inference are then applied to further tune these feature representations. Afterwards, deep learning is again used to predict individual students′ score ratings on exercises, from which the Top-N ranked exercises are recommended to similar students who likely need enhancing of skills and understanding of the subject areas indicated by the chosen exercises. Experiments of CDL and four baseline methods on two real-world datasets demonstrate that CDL is superior to the existing methods in terms of capturing students′ knowledge gaps in learning and more accurately recommending appropriate exercises to individual students to help bridge their knowledge gaps.

Abstract Structure design provides an effective solution to develop advanced soft materials with desirable mechanical properties. However, creating multiscale structures in ionogels to obtain strong mechanical properties is challenging. Here, an in situ integration strategy for producing a multiscale‐structured ionogel (M‐gel) via ionothermal‐stimulated silk fiber splitting and moderate molecularization in the cellulose‐ions matrix is reported. The produced M‐gel shows a multiscale structural superiority comprised of microfibers, nanofibrils, and supramolecular networks. When this strategy is used to construct a hexactinellid inspired M‐gel, the resultant biomimetic M‐gel shows excellent mechanical properties including elastic modulus of 31.5 MPa, fracture strength of 6.52 MPa, toughness reaching 1540 kJ m−3, and instantaneous impact resistance of 3.07 kJ m−1, which are comparable to those of most previously reported polymeric gels and even hardwood. This strategy is generalizable to other biopolymers, offering a promising in situ design method for biological ionogels that can be expanded to more demanding load‐bearing materials requiring greater impact resistance.

The water depth bias of LiDAR point cloud data has to be corrected. The previous models, which used the fixed parameters for an entire water area, cannot efficiently correct the bias due to the different water environments. Therefore, this paper develops an adaptive model for the correction of the water depth bias. A coordinate system, in which the water depth is taken as the X-axis and the water depth bias is taken as the Y-axis, is defined. All of the sample points are normalized, and then projected into the defined coordinate system according to their water depths and water depth biases. Second, the scatter points are clustered into several different clusters using the developed subdivision algorithm. With the clusters, an entire water area is subdivided into several sub-regions. Finally, each sub-region is fitted using a model, which is used to correct the water depth bias. Experimental verification and comparison analysis are conducted in three different environments: an indoor tank, an outdoor pond and the Beihai sea. The experimental results demonstrate that the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) from the adaptive model are reduced by approximately 61% and 59%, respectively, relative to those from the traditional models. Therefore, it can be concluded that the proposed model can adapt to changes of the different water environments and achieve a higher accuracy for water depth bias correction than traditional methods do.

As the amount of malware has grown rapidly in recent years, it has become the most dominant attack method in network security. Learning execution behavior, especially Application Programming Interface (API) call sequences, has been shown to be effective for malware detection. However, it is troublesome in practice to adequate mining of API call features. Among the current research methods, most of them only analyze single features or inadequately analyze the features, ignoring the analysis of structural and semantic features, which results in information loss and thus affects the accuracy. In order to deal with the problems mentioned above, we propose a novel method of malware detection based on semantic information of behavioral features. First, we preprocess the sequence of API function calls to reduce redundant information. Then, we obtain a vectorized representation of the API call sequence by word embedding model, and encode the API call name by analyzing it to characterize the API name’s semantic structure information and statistical information. Finally, a malware detector consisting of CNN and bidirectional GRU, which can better understand the local and global features between API calls, is used for detection. We evaluate the proposed model in a publicly available dataset provided by a third party. The experimental results show that the proposed method outperforms the baseline method. With this combined neural network architecture, our proposed model attains detection accuracy of 0.9828 and an F1-Score of 0.9827.

Designing a secure authentication scheme for session initial protocol (SIP) over internet protocol (VoIP) networks remains challenging. In this paper, we revisit the protocol of Zhang, Tang and Zhu (2015) and reveal that the protocol is vulnerable to key-compromise impersonation attacks. We then propose a SIP authenticated key agreement protocol (AKAP) using elliptic curve cryptography (ECC). We demonstrate the correctness of the protocol using Burrows-Abadi-Needham (BAN), and its security using the AVISPA simulation tool. We also evaluate its performance against those of Zhang, Tang and Zhu, and others.

Myxoma is the most common type of benign cardiac tumor in adults, and it has a strong tendency to embolize or metastasize to distant organs. Patients with multiple brain metastases have rarely been seen in clinics; hence, standard treatment protocols for multimyxoma metastasis in the brain have not been established. We present the case of a 47-year-old female who had convulsions in the right hand and repeated seizures. Computed tomography revealed multiple tumor sites in her brain. Craniotomy was conducted to remove the tumor sites. However, recurrent brain tumors and unexpected cerebral infarctions occurred frequently shortly after the treatment because the cardiac myxoma had not been treated due to the patient's personal concerns. The myxoma was resected by gamma knife radiosurgery, and temozolomide was given prior to cardiac surgery. There has been no evidence of tumor recurrence from the 2 years following the surgery until the present. This case highlights the importance of prioritizing cardiac lesions over cerebral lesions; if a cerebral metastasis has been found, it is likely that the cardiac myxoma is already unstable, with high rates of spread and metastasis. Therefore, it is unwise to treat metastasis sites before the cardiac myxoma. Additionally, the case suggests that gamma knife radiosurgery combined with temozolomide is effective as treatment for multiple myxoma metastasis in the brain. Compared with conventional cerebral surgery, gamma knife radiosurgery is safer, causes less bleeding, and requires a shorter time for recovery.

Abstract Poly (3,4‐ethylene dioxythiophene) (PEDOT) is an electrically conductive polymer that shows various promising applications in flexible electronics. However, previous studies have mostly focused on enhancing the conductivity, while ignoring the design and development of porous PEDOT materials. Herein, we report a novel and sustainable strategy of utilizing a deep eutectic solvent of ferric chloride hexahydrate/acetamide to guide the interface‐controlled polymerization of PEDOT at room temperature. The obtained PEDOT material has its unique features of high porosity of 70.61%, high specific surface area of >58 m2/g, and ideal electrical conductivity of 6500 S/m, resulting in a wide voltage window of up to 1.2 V. Notably, this porous PEDOT can be easily formulated into printable electronic ink with controllable rheological properties, process ability, and recyclability, exhibiting the outstanding energy storage behavior in wearable electronics. This study reports an effective, green approach for the development of porous PEDOT materials and printable flexible devices.

PurposeTo identify risk factors associated with short-term postoperative complications in patients with gastrointestinal cancer and develop and validate prediction models to predict the probability of complications.MethodsA total of 335 patients enrolled in the primary cohort of this study were divided into training and validation sets in a chronological order. Using univariate and multivariate logistic regression analyses, the risk factors for postoperative complications were determined, and nomogram prediction models were constructed. The performance of the nomogram was assessed with respect to the receiver operator characteristic and calibration curves.ResultsPatients with complications had a stronger postoperative stress response and a longer duration of daily fluid intake/output ratio >1 after surgery. Logistic analysis revealed that body mass index (BMI), body temperature on POD4 (T.POD4), neutrophil percentage on POD4 (N.POD4), fasting blood glucose on POD4 (FBG.POD4), and the presence of fluid intake/output ratio 1 were risk factors for POD30 complications. The areas under the curve of Nomogram-A for POD7 complications were 0.867 and 0.833 and those of Nomogram-B for POD30 complications were 0.920 and 0.918 in the primary and validation cohorts, respectively. The calibration curves showed good consistency in both cohorts.ConclusionThis study presented two nomogram models to predict short-term postoperative complications in patients with gastrointestinal cancer. The results could help clinicians identify patients at high risk of complications within POD7 or POD30.

Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve accuracy and robustness. However, due to the non-differentiable properties of image transformations, searching algorithms such as reinforcement learning or evolution strategy have to be applied, which are not computationally practical for large-scale problems. In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained. To further improve the accuracy and robustness with adversarial examples, we propose a differentiable adversarial data augmentation method based on spatial transformer networks (STNs). The combined adversarial and random-transformation-based method outperforms the state-of-the-art on multiple DA and DG benchmark datasets. Furthermore, the proposed method shows desirable robustness to corruption, which is also validated on commonly used datasets.

PurposeTo identify and compare the value of functional MRI (fMRI) in predicting the early response of metastatic cervical lymph nodes (LNs) to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) patients.MethodsThis prospective study collected 94 metastatic LNs from 40 consecutive NPC patients treated with IC from January 2021 to May 2021. Conventional diffusion-weighted imaging, diffusion kurtosis imaging, intravoxel incoherent motion, and dynamic contrast-enhanced magnetic resonance imaging were performed before and after IC. The parameter maps apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), mean kurtosis (MK), Dslow, Dfast, perfusion fraction (PF), Ktrans, Ve, and Kep) of the metastatic nodes were calculated by the Functool postprocessing software. All LNs were classified as the responding group (RG) and non-responding group (NRG) according to Response Evaluation Criteria in Solid Tumors 1.1. The fMRI parameters were compared before and after IC and between the RG and the NRG. The significant parameters are fitted by logistic regression analysis to produce new predictive factor (PRE)–predicted probabilities. Logistic regression analysis and receiver operating characteristic (ROC) curves were performed to further identify and compare the efficacy of the parameters.ResultsAfter IC, the mean values of ADC, MD, and Dslow significantly increased, while MK, Dfast, and Ktrans values decreased dramatically, while no significant difference was detected in Ve and Kep. Compared with NRG, PF-pre and Ktrans-pre values in the RG were higher statistically. The areas under the ROC for the pretreatment PF, Ktrans, and PRE were 0.736, 0.722, and 0.810, respectively, with the optimal cutoff value of 222 × 10-4, 934 × 10-3/min, and 0.6624, respectively.ConclusionsThe pretreatment fMRI parameters PF and Ktrans showed promising potential in predicting the response of the metastatic LNs to IC in NPC patients.Clinical Trial RegistrationThis study was approved by the ethics board of the Chinese PLA General Hospital, and registered on 30 January 2021, in the Chinese Clinical Trial Registry; http://www.chictr.org.cn/showproj.aspx?proj=121198, identifier (ChiCTR2100042863).

Objective: The objective of this trial is to evaluate and compare the acute toxicity in patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) treated with docetaxel plus cisplatin, or docetaxel, or cisplatin concurrently with helical tomotherapy during concurrent chemoradiotherapy (CCRT). Methods: In a prospective, single-center, open-label, randomized phase II study, after 2 cycles of induction chemotherapy with docetaxel plus cisplatin regimen, 125 patients with LA-NPC (stage III and IVA, UICC eighth) diagnosed pathologically from June 2017 to November 2019 were randomized into CCRT with docetaxel plus cisplatin group (25 patients), CCRT with docetaxel group (50 patients), and CCRT with cisplatin group (50 patients). The incidence of grade 3 or 4 acute toxicities and clinical efficacy were analyzed among the 3 groups. Results: Safety evaluation was completed in all the 125 patients, during the CCRT period, 66.4% of patients completed 3 cycles of chemotherapy, 24.0% completed 2 cycles of chemotherapy, and 9.6% completed 1 cycle of chemotherapy according to the research plan. The incidence of grade 3 or 4 acute toxicity in CCRT with docetaxel plus cisplatin (DP), docetaxel (D), and cisplatin (P) groups was 88.0%, 72.0%, and 56.0%, respectively. The incidence of grade 3 or 4 acute toxicities in the DP group was significantly higher than that in the D and P groups ( P = .015), no significant difference was detected between the D and P groups ( P = .096). The most common toxicities were mucositis (40.0%), leukopenia (29.6%), neutropenia (26.4%), and pharyngo-esophagitis (12.0%); compared to D and P groups, DP group did not significantly improved the 3-year overall survival (96.0% vs 87.0% and 87.6%), progression-free survival (92.0% vs 79.7% and,76.9%), locoregional failure-free survival (96.0% vs 91.8% and 92.7%), and distant failure-free survival (100% vs 90.0% and 89.0%), there were no significant difference in survival data among the 3 groups (all P > .05). Conclusions: Higher survival benefits did not achieve from intensified CCRT with DP, CCRT with P or D obtained similar short-term survival outcomes with similar acceptable toxicities in LA-NPC patients.

Follicular dendritic cell sarcoma (FDCS) is a rare malignant neoplasm that was classified by the World Health Organization (WHO) under histiocytic and dendritic cell neoplasms in the 2016 revision. Considering the rarity of this tumor, there is no standardized treatment. It is usually treated by complete surgical resection. Adjuvant chemotherapy and radiotherapy are alternative methods. Immune checkpoint inhibitors (ICIs) represented by the programmed death receptor 1/programmed death ligand 1 (PD-1/PD-L1) antibody have achieved significant clinical benefits in a variety of solid tumors. However, reports on the treatment of FDCS with ICIs are rare. FDCS often expresses high levels of PD-L1, which provides a rationale to use immunotherapy in cases of FDCS. Here, we present a 51-year-old Filipino-Chinese man with FDCS who was treated with multimodal treatment, including the PD-1 inhibitor pembrolizumab and achieved a relatively long disease-free survival of 24 months. This case emphasizes that the application of ICIs under the guidance of NGS technology seems to be a meaningful treatment option for patients with FDCS.

The acquisition of genetic- and epigenetic-abnormalities during transformation has been recognized as the two fundamental factors that lead to tumorigenesis and determine the aggressive biology of tumor cells. However, there is a regularity that tumors derived from less-differentiated normal origin cells (NOCs) usually have a higher risk of vascular involvement, lymphatic and distant metastasis, which can be observed in both lymphohematopoietic malignancies and somatic cancers. Obviously, the hypothesis of genetic- and epigenetic-abnormalities is not sufficient to explain how the linear relationship between the cellular origin and the biological behavior of tumors is formed, because the cell origin of tumor is an independent factor related to tumor biology. In a given system, tumors can originate from multiple cell types, and tumor-initiating cells (TICs) can be mapped to different differentiation hierarchies of normal stem cells, suggesting that the heterogeneity of the origin of TICs is not completely chaotic. TIC’s epigenome includes not only genetic- and epigenetic-abnormalities, but also established epigenetic status of genes inherited from NOCs. In reviewing previous studies, we found much evidence supporting that the status of many tumor-related “epigenetic abnormalities” in TICs is consistent with that of the corresponding NOC of the same differentiation hierarchy, suggesting that they may not be true epigenetic abnormalities. So, we speculate that the established statuses of genes that control NOC’s migration, adhesion and colonization capabilities, cell-cycle quiescence, expression of drug transporters, induction of mesenchymal formation, overexpression of telomerase, and preference for glycolysis can be inherited to TICs through epigenetic memory and be manifested as their aggressive biology. TICs of different origins can maintain different degrees of innate stemness from NOC, which may explain why malignancies with stem cell phenotypes are usually more aggressive.

Electronic skin (e-skin), a new generation of flexible electronics, has drawn interest in soft robotics, artificial intelligence, and biomedical devices. However, most existing e-skins involve complex preparation procedures and are characterized by single-sensing capability and insufficient scalability. Here, we report on a one-step strategy in which a thermionic source is used for the in situ molecularization of bacterial cellulose polymeric fibers into molecular chains, controllably constructing an ionogel with a scalable mode for e-skin. The synergistic effect of a molecular-scale hydrogen bond interweaving network and a nanoscale fiber skeleton confers a robust tensile strength (up to 7.8 MPa) and high ionic conductivity (up to 62.58 mS/cm) on the as-developed ionogel. Inspired by the tongue to engineer the perceptual patterns in this ionogel, we present a smart e-skin with the perfect combination of excellent ion transport and discriminability, showing six stimulating responses to pressure, touch, temperature, humidity, magnetic force, and even astringency. This study proposes a simple, efficient, controllable, and sustainable approach toward a low-carbon, versatile, and scalable e-skin design and structure–performance development.

With the continuous development and popularization of the Internet, there has been an increasing number of network security problems appearing. Among them, the rapid growth in the number of malware and the emergence of variants have seriously affected the security of the Internet. Traditional malware detection methods require heavy feature engineering, which seriously affects the efficiency of detection. Existing deep-learning-based malware detection methods have problems such as poor generalization ability and long training time. Therefore, we propose a malware classification method based on transfer learning for multi-channel image vision features and ResNet convolutional neural networks. Firstly, the features of malware samples are extracted and converted into grayscale images of three different types. Then, the grayscale image sizes are processed using the bilinear interpolation algorithm to make them uniform in size. Finally, the three grayscale images are synthesized into three-dimensional RGB images, and the RGB images processed using data enhancement are used for training and classification. For the classification model, we used the previous ImageNet dataset (>10 million) and trained all the parameters of ResNet after loading the weights. For the evaluations, an experiment was conducted using the Microsoft BIG benchmark dataset. The experimental results showed that the accuracy on the Microsoft dataset reached 99.99%. We found that our proposed method can better extract the texture features of malware, effectively improve the accuracy and detection efficiency, and outperform the compared models on all performance metrics.

Anomaly detection in multivariate time series is an important problem with applications in several domains. However, the key limitation of the approaches that have been proposed so far lies in the lack of a highly parallel model that can fuse temporal and spatial features. In this paper, we propose TDRT, a three-dimensional ResNet and transformer-based anomaly detection method. TDRT can automatically learn the multi-dimensional features of temporal–spatial data to improve the accuracy of anomaly detection. Using the TDRT method, we were able to obtain temporal–spatial correlations from multi-dimensional industrial control temporal–spatial data and quickly mine long-term dependencies. We compared the performance of five state-of-the-art algorithms on three datasets (SWaT, WADI, and BATADAL). TDRT achieves an average anomaly detection F1 score higher than 0.98 and a recall of 0.98, significantly outperforming five state-of-the-art anomaly detection methods.

The giant magneto-impedance (GMI) effect of Co83.2Fe5.2Si8.8B2.8 ribbons at frequencies of

Electrode degradation in the continuous resistance spot-welding process of baked hardening (BH) 220 steel was evaluated by an electrode life test, and weldability tests were conducted by geometry feature measurement, mechanical property analysis, and electrode diameter measurement with 88 or 176 weld intervals. The analysis of weld geometry shows that the heat-affected zone (HAZ) width, nugget diameter, and nugget area tend to decrease rapidly, while the nugget height tends to increase with the weld repetitions until the welding heat input becomes too small to form an effective nugget. The maximum displacement and failure energy of the welded joints show a decreasing trend during the welding electrode failure process, while the peak load increases slightly until the 88th weld and then decreases. The cavities and pores in the nugget mainly appear after the 176th spot weld. The electrode diameter increases during welding. The reason for the increase in electrode diameter may be that the contact area between the electrode and the BH 220 steel sheets becomes smaller in the welding process, which causes the continuous sticking phenomenon between the electrode and the BH 220 steel sheets. In the absence of alloying, the edge of the electrode is geometrically deformed, while Cu–Zn–Fe alloying occurs in the area in contact with the BH 220 steel sheet.

For the problem of traditional passive radar target detection, the cross-correlation method is a popular solution. By processing the reference signal and the target echo signal coherently, the method is able to extract the target parameters, such as distance, velocity, and azimuth. However, the estimation performance is limited by the low signal-to-noise ratio. Therefore, a new cross-correlation algorithm for passive radar target detection is proposed in this paper. The echo signal received by the echo antenna and the reference signal received by the reference antenna are processed by differential operations to transform the single signal into multiple signals. Then, the differential reference signal is used to eliminate the signal phase in the differential echo signal. With the two-dimensional Fourier transform, the coherent accumulation of multiple signals is realized based on matched filtering of the single signal. This makes the secondary accumulation of the target signal coherent, while the interference part can only obtain incoherent secondary accumulation. In this way, the signal-to-noise ratio for target detection can be greatly improved. The paper presents the theoretical derivation of the algorithm and the quantitative analysis of its performance. The results of both simulated and measured data show that the detection performance is greatly improved compared with the traditional method.

With the rapid development of Industrial Internet of Things technology, the industrial control system (ICS) faces more and more security threats, which may lead to serious risks and extensive damage. Naturally, it is particularly important to construct efficient, robust, and low-cost protection strategies for ICS. However, how to construct an objective function of optimal security protection strategy considering both the security risk and protection cost, and to find the optimal solution, are all significant challenges. In this paper, we propose an optimal security protection strategy selection model and develop an optimization framework based on Q-Learning particle swarm optimization (QLPSO). The model performs security risk assessment of ICS by introducing the protection strategy into the Bayesian attack graph. The QLPSO adopts the Q-Learning to improve the local optimum, insufficient diversity, and low precision of the PSO algorithm. Simulations are performed on a water distribution ICS, and the results verify the validity and feasibility of our proposed model and the QLPSO algorithm.

Traditional bathymetry LiDAR (light detection and ranging) onboard manned and/or unmanned airborne systems cannot operate in the context of narrow rivers in urban areas with high buildings and in mountainous areas with high peaks. Therefore, this study presents a prototype of a lightweight bathymetry LiDAR onboard an unmanned shipborne vehicle (called “GQ-Cor 19”). The GQ-Cor 19 system primarily includes an emitting optical module, a receiving optical module, control module, detection module, high-speed A/D sampling module, and data processing system. Considering that the “GQ-Cor 19” is extremely close to the water surface, various new technical challenges are encountered, such as significant laser scattering energy from the surface of the water, which saturates signals received by the photomultiplier tube detector. Therefore, this study presents various new technical solutions, including (1) an improved Bresenham algorithm, (2) a small and lightweight receiving optical system with a split-field method, and (3) a data acquisition module with a high-speech A/D collector. Following a series of different experimental verifications, the results demonstrate that the new generation of single-band LiDAR onboard an unmanned shipborne vehicle can swiftly measure the underwater depth, and the maximum measurement depth is more than 25 m. The measurement accuracy is better than 30 cm and the weight is less than 12 kg.

Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack path. In addition, most of the current protection models responding to multi-step attacks have not deeply studied the protection strategy selection method in the case of limited budget. Aiming at the above problems, we propose a protection strategy selection model based on the Genetic Ant Colony Optimization Algorithm. The model firstly evaluates the risk of ICS through the Bayesian attack graph; next, the target attack path is predicted from multiple angles through the maximum probability attack path and the maximum risk attack path; and finally, the Genetic Ant Colony Optimization Algorithm is used to select the most beneficial protection strategy set for the target attack path under limited budget. Compared with the Genetic Algorithm and Ant Colony Optimization Algorithm, the Genetic Ant Colony Optimization Algorithm proposed in this paper can handle the local optimal problem well. Simulation experiments verify the feasibility and effectiveness of our proposed model.

Industrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically construct security strategies before an attack occurs. The characteristics of evolutionary algorithms are very suitable for finding optimal strategies. However, the more common evolutionary algorithms currently used have relatively large limitations in convergence accuracy and convergence speed, such as PSO, DE, GA, etc. Therefore, this paper proposes a hybrid strategy differential evolution algorithm based on reinforcement learning and opposition-based learning to construct the optimal security strategy. It greatly improved the common problems of evolutionary algorithms. This paper first scans the vulnerabilities of the water distribution system and generates an attack graph. Then, in order to solve the balance problem of cost and benefit, a cost–benefit-based objective function is constructed. Finally, the optimal security strategy set is constructed using the algorithm proposed in this paper. Through experiments, it is found that in the problem of security strategy construction, the algorithm in this paper has obvious advantages in convergence speed and convergence accuracy compared with some other intelligent strategy selection algorithms.

With the development of the Industrial Internet of Things (IIoT), industrial wireless sensors need to upload the collected private data to the cloud servers, resulting in a large amount of private data being exposed on the Internet. Private data are vulnerable to hacking. Many complex wireless-sensor-authentication protocols have been proposed. In this paper, we proposed an efficient authentication protocol for IIoT-oriented wireless sensor networks. The protocol introduces the PUF chip, and uses the Bloom filter to save and query the challenge–response pairs generated by the PUF chip. It ensures the security of the physical layer of the device and reduces the computing cost and communication cost of the wireless sensor side. The protocol introduces a pre-authentication mechanism to achieve continuous authentication between the gateway and the cloud server. The overall computational cost of the protocol is reduced. Formal security analysis and informal security analysis proved that our proposed protocol has more security features. We implemented various security primitives using the MIRACL cryptographic library and GMP large number library. Our proposed protocol was compared in-depth with related work. Detailed experiments show that our proposed protocol significantly reduces the computational cost and communication cost on the wireless sensor side and the overall computational cost of the protocol.

When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space–time adaptive processing (STAP) algorithm based on root off-grid sparse Bayesian learning. The proposed algorithm first models the space–time base of the dictionary as an adjustable state. Then, the positions of those dynamic bases are optimized by iterating a maximum expectation algorithm. In this way, the off-grid error in clutter estimation can be eliminated even when the modeling grid is wide. To further improve the accuracy of clutter estimation, the proposed algorithm eliminates the error caused by samples with singular values in the root off-grid sparse Bayes learning by artificially adding pseudorandom noise and using hypothesis testing. The simulation results show that the proposed algorithm achieves better performance than the existing algorithms.

Trust is of vital importance to economic development, social harmony, political stability and cultural succession in the real world, and then understanding how trust emerges and evolves in a selfish population has long been a significant and challenging issue. From the perspective of trustees, we investigate how reputation building and cost for trustees with dynamic adaptive reputation affect the evolution of trust. Trustees can spend specific funds or costs to build or maintain their reputation, which then enhances their own social competitiveness during the trust evolution. Considering the heterogeneity of individual cost, sigmoid function is used to describe the relationship between reputation and cost. It is found that trust and trustworthiness could still emerge and persist even if reputation building is costly. Meanwhile, the moderate level of willingness to build the reputation or the middle reputation cost is more conducive to the evolution of trust, otherwise it could result in the deterioration of trust behaviors among agents. The current results are helpful to illustrate the evolution of trust and cooperation within the networked population.