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Background: Cancer patients with financial toxicity experience psychological distress and often miss medical appointments and quit treatments early, which could be a barrier to the effective management of oral chemotherapy drugs at home. This study explores whether financial toxicity predicts symptoms and unplanned healthcare utilization among cancer patients taking oral chemotherapy at home, which will contribute to the safe management of oral chemotherapy. Methods: Data in this study was from a prospective observational study, which was conducted between October 2018 and December 2019. 151 patients completed the Comprehensive Score for Financial Toxicity at discharge and completed the MD Anderson Symptom Inventory and unplanned healthcare utilization questionnaires after finishing one cycle of oral chemotherapy at home. Regression analyses were conducted to explore the associations of financial toxicity with symptoms and unplanned healthcare utilization. Results: Among 151participants, 88.08% reported severe or moderate financial toxicity, 43.05% reported symptom interference, and 31.79% reported unplanned healthcare utilization while taking oral chemotherapy at home. Patients between the age of 45-60y (p = 0.042) have higher financial toxicity, while those living in urban areas (p = 0.016) have lower financial toxicity. Patients with worse financial toxicity suffered increased symptoms of fatigue, emotional distress, disturbed sleep, and lack of appetite. Consequently, their mood and personal relation with other significant suffered. However, no statistical differences in unplanned healthcare utilization were found among patients with different levels of financial toxicity. Conclusion: Middle-aged adults and those living in suburban or rural areas experienced worse financial toxicity than other groups. Patients with worse financial toxicity experienced more severe psychological symptoms (e.g., fatigue, distress, disturbed sleep, and lack of appetite) and affective interference (e.g., mood and relations with others). Identifying at-risk patients is necessary to offer tailored support for psychological symptom management. [ABSTRACT FROM AUTHOR]

Background: Cancer patients with financial toxicity experience psychological distress and often miss medical appointments and quit treatments early, which could be a barrier to the effective management of oral chemotherapy drugs at home. This study explores whether financial toxicity predicts symptoms and unplanned healthcare utilization among cancer patients taking oral chemotherapy at home, which will contribute to the safe management of oral chemotherapy. Methods: Data in this study was from a prospective observational study, which was conducted between October 2018 and December 2019. 151 patients completed the Comprehensive Score for Financial Toxicity at discharge and completed the MD Anderson Symptom Inventory and unplanned healthcare utilization questionnaires after finishing one cycle of oral chemotherapy at home. Regression analyses were conducted to explore the associations of financial toxicity with symptoms and unplanned healthcare utilization. Results: Among 151participants, 88.08% reported severe or moderate financial toxicity, 43.05% reported symptom interference, and 31.79% reported unplanned healthcare utilization while taking oral chemotherapy at home. Patients between the age of 45-60y (p = 0.042) have higher financial toxicity, while those living in urban areas (p = 0.016) have lower financial toxicity. Patients with worse financial toxicity suffered increased symptoms of fatigue, emotional distress, disturbed sleep, and lack of appetite. Consequently, their mood and personal relation with other significant suffered. However, no statistical differences in unplanned healthcare utilization were found among patients with different levels of financial toxicity. Conclusion: Middle-aged adults and those living in suburban or rural areas experienced worse financial toxicity than other groups. Patients with worse financial toxicity experienced more severe psychological symptoms (e.g., fatigue, distress, disturbed sleep, and lack of appetite) and affective interference (e.g., mood and relations with others). Identifying at-risk patients is necessary to offer tailored support for psychological symptom management. [ABSTRACT FROM AUTHOR]

Background: Cancer patients with financial toxicity experience psychological distress and often miss medical appointments and quit treatments early, which could be a barrier to the effective management of oral chemotherapy drugs at home. This study explores whether financial toxicity predicts symptoms and unplanned healthcare utilization among cancer patients taking oral chemotherapy at home, which will contribute to the safe management of oral chemotherapy. Methods: Data in this study was from a prospective observational study, which was conducted between October 2018 and December 2019. 151 patients completed the Comprehensive Score for Financial Toxicity at discharge and completed the MD Anderson Symptom Inventory and unplanned healthcare utilization questionnaires after finishing one cycle of oral chemotherapy at home. Regression analyses were conducted to explore the associations of financial toxicity with symptoms and unplanned healthcare utilization. Results: Among 151participants, 88.08% reported severe or moderate financial toxicity, 43.05% reported symptom interference, and 31.79% reported unplanned healthcare utilization while taking oral chemotherapy at home. Patients between the age of 45-60y (p = 0.042) have higher financial toxicity, while those living in urban areas (p = 0.016) have lower financial toxicity. Patients with worse financial toxicity suffered increased symptoms of fatigue, emotional distress, disturbed sleep, and lack of appetite. Consequently, their mood and personal relation with other significant suffered. However, no statistical differences in unplanned healthcare utilization were found among patients with different levels of financial toxicity. Conclusion: Middle-aged adults and those living in suburban or rural areas experienced worse financial toxicity than other groups. Patients with worse financial toxicity experienced more severe psychological symptoms (e.g., fatigue, distress, disturbed sleep, and lack of appetite) and affective interference (e.g., mood and relations with others). Identifying at-risk patients is necessary to offer tailored support for psychological symptom management. [ABSTRACT FROM AUTHOR]

This paper investigates the economic integration of rural-to-urban migrants in China from a subject-centered perspective, placing migrants' choice of reference group at the centre of the analysis. I empirically demonstrate that the choice of reference group significantly shapes migrants' success frames and their aspirations to attain comparable economic status as urban residents (i.e. aspirations for economic integration). To study the causal effect of reference group choice on economic integration and to address the endogeneity of reference group choice, I implement instrumental variables estimation using exogenous variation in home ties. The results show that choosing urban residents as the reference group shapes both particular frames of success, which entails not only income but also possession of consumer durable goods, and aspirations for economic integration, which together, in turn, stimulate migrants to realise greater economic integration. After disentangling self-motivated work hours from total work hours by fixing the causal pathways of reference group choice, I find evidence that migrants attain a higher degree of economic integration by working longer hours. These findings suggest that migrants' success frames and aspirations for economic integration, embedded in their choice reference group, are essential for understanding what shapes their pathways to economic integration. [ABSTRACT FROM AUTHOR]

The rapid growth of the number of new mobile malware variants has posed a severe threat to user's property and privacy. Recent studies show that deep neural networks can detect malicious traffic with high accuracy. However, a deep neural network works like a black box in the sense that its structure doesn't give any insight on how it works. To overcome this drawback, we propose a method to extract rules from a deep neural network and then use the extracted rules to detect malicious network traffic. Specifically, for a trained deep neural network, we first construct one input-hidden tree per each hidden layer to represent the rules extracted between the input of the neural network and the output of that hidden layer. Then we construct one hidden-output tree to represent the rules extracted between the outputs of all hidden layers and the output of the neural network. Finally, these trees are merged to form one rule tree using the outputs of the hidden layers as a bridge. We have performed extensive experiments to verify the effectiveness of our method in terms of accuracy, precision, recall and F-Measure metrics by comparing it with other state-of-the-art methods. Experimental results show that our method achieves high accuracy using the packet size of only the first nine packets as a feature, which also gives good interpretability on how the deep neural network performs to detect malicious traffic. Besides, we design an online detection system based on FPGA to provide online detection in a high-speed network environment using rule tree, which reduces the difficulty of embedding a deep neural network into FPGA. [ABSTRACT FROM AUTHOR]

Simple Summary: In crustaceans, especially economic shrimp and crabs, autotomy significantly affects the survival rate and growth performance. At present, the molecular mechanism of the limb regeneration process of economic crustaceans is rarely studied. In this study, we used the pressure method to remove the pereopods of Exopalaemon carinicauda and recorded the regeneration process in detail. Moreover, regenerated pereopod tissue was sliced and stained with H.E. Microscopic observations revealed significant changes in the type and number of associated cells including outer epithelial cells, granulocytes, embryonic cells, and so on. This study performed a comparative transcriptome analysis of samples from different stages of limb regeneration and identified multiple differentially expressed genes that may be associated with crustacean growth or molting. The sequence and expression characteristics of the innexin gene were analyzed. Our study explored the morphological and molecular changes during limb regeneration of E. carinicauda, and laid a foundation for further research on molecular regulatory mechanisms. With the increase in breeding density of Exopalaemon carinicauda, appendage breakage may occur, which seriously affects survival and economic benefits. To study the limb regeneration process of E. carinicauda, we induced autotomy of the pereopods. After a period of time, wound swelling disappeared, the pigment gradually accumulated, and a tawny film subsequently formed in the wound. The healing period of the wound occurred 24 h after autotomy, and the blastema formation stage occurred 48 h after autotomy. After 4 days of cutting, the limb buds began to differentiate, grow, and expand rapidly, and this process lasted approximately 15 days. Microscopic observations revealed significant changes in the type and number of associated cells including outer epithelial cells, granulocytes, embryonic cells, columnar epidermal cells, elongated cells, and blastoma cells, during the process from limb fracture to regeneration. A comparative transcriptome analysis identified 1415 genes differentially expressed between the J0h (0 h post autotomy) and J18h (18 h post autotomy), and 3952 and 4366 differentially expressed genes for J0 and J14d (14 days post autotomy) and J18h and J14d, respectively. Some of these genes may be related to muscle growth or molting, as indicated by the presence of troponin C, chitinase, actin, innexin, and cathepsin L. As a functional gene involved in epidermal formation, the mRNA expression level of the innexin inx2 in the pereopod of E. carinicauda changed significantly in the experimental groups (p < 0.05). The results of this study contribute to existing knowledge of regeneration mechanisms in crustaceans. [ABSTRACT FROM AUTHOR]

This article examines the economic integration and reintegration outcomes and processes of temporary and permanent migrants in the case of rural-to-urban migrants in China. Using a life-course perspective, I measure the integration levels of both temporary and permanent migrants in urban destinations and rural hometowns over time. Through this measurement, I investigate the differences between temporary and permanent migrants in terms of economic integration in urban destinations and reintegration in rural hometowns. The findings suggest that temporary migrants experience lower economic integration and reintegration levels than permanent migrants, highlighting the persistent advantages of permanent migrants over time. Additionally, I explore and compare the underlying processes that explain this gap for temporary and permanent migrants in both urban destinations and rural hometowns over time, including the selection process and the heterogeneous integration process. The results indicate that permanent migrants are consistently more educated than temporary migrants, reflecting a consistently positive selection of permanent migrants. Furthermore, temporary migrants rely more on individual traits, such as gender and marital status, than permanent migrants for integration and reintegration. By considering both the integration and reintegration of temporary and permanent migrants, this study provides a holistic and dynamic understanding of their integration outcomes and processes. The underlying mechanisms, the theoretical contributions of these mechanisms, and the implications of these findings to a broader migration context are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, a Topic-based probabilistic model named GIST is proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members’ interest, but also consider some subgroups’ interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members’ choices and subgroups’ choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members’ interest and subgroups’ interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Several methods based on port, payload, and transport layer features have been proposed to detect, identify, and manage Internet traffic. The diminished effectiveness of port-based identification and overheads of deep packet inspection methods motivated us to identify Internet traffic by combining distinctive flow characteristics with the machine learning method. However, the abundant ground truth Internet traffic, which is important for building a supervised classifier, is difficult to be obtained in real conditions. In this study, we propose a semi-supervised learning method that combines further division of recognition space technique with data gravitation theory. The further division of recognition space classifier is a powerful multi-classification tool that can be helpful for multi-application identification. The data gravitation may reveal the underlying data space structure from unlabeled data, and thus, it is integrated into the classification to develop a better classifier. The experimental results on the real Internet application traffic datasets demonstrate the advantages of our proposed work. [ABSTRACT FROM AUTHOR]

Identifying network traffics at their early stages accurately is very important for network management and security. Recent years, more and more studies have devoted to find effective machine learning models to identify traffics with few packets at the early stage. In this paper, we try to build an effective early stage traffic identification model by applying flexible neural trees (FNT). Three network traffic data sets including two open data sets are used for the study. We first extract both packet-level features and statistical features from the first six continuous packets and six noncontinuous packets of each flow. Packet sizes are applied as packet-level features. And for statistical features, average, standard deviation, maximum and minimum are selected. Eight classical classifiers are employed as the comparing methods in the identification experiments. Accuracy, true positive rate (TPR) and false positive rate (FPR) are applied to evaluate the performances of the compared methods. FNT outperforms the other methods for most cases in the identification experiments, and it behaves very well for both TPR and FPR. Furthermore, it can show the selected features in the optimal tree result. Experiment result shows that FNT is effective for early stage traffic identification. [ABSTRACT FROM AUTHOR]

Abstract: Photocatalytic degradation of 2, 4-dichlorophenol (DCP) using granular activated carbon (GAC) supported TiO2 (Ti–GAC, using sol–gel method) is investigated. Operation parameters including annealing temperature, catalyst loading, air flow rate, initial pH and DCP concentration are optimized. The results show that DCP removal increases with the increase of Ti–GAC while decreases with the increase of initial DCP concentration. DCP is removed much faster in neutral conditions than in acidic or alkaline conditions. Comprehensive comparison among adsorption, photolysis and photocatalysis in DCP removal is conducted to reveal the synergistic effect in the photocatalysis process, and the results indicate that the DCP is removed by the joint effect of adsorption and HO∙ oxidation. In addition, the photocatalysis is applied in a continuous form to treat DCP polluted real raw water for drinking water production, and it is found to have potentially advantageous on dissolved pollutants in drinking water, and would have great potential for a wide range of practical applications. [Copyright &y& Elsevier]

• A dual-functional C-type fiber SPR biosensor for simultaneous DNA hybridization and temperature sensing is proposed. • Dual SPR effects were realized on the open microcavity by coating with a gold film and fillingwith thePDMS. • The sensor exhibits high sensitivity, with a response of 0.11 nm/nM for DNA molecules and −1.97 nm/℃ for temperature. • The limit of detection (LOD) for DNA molecules is 7.9 nM. • This compact sensor offers a promising approach for DNA detection with integrated temperature compensation. In this work, an integrated temperature compensated surface plasmon resonance (SPR) biosensor based on C-type special fiber is proposed and its application to DNA hybridization is demonstrated. Dual SPR effects were realized on the open microcavity by coating the C-type fiber with a gold film and filling the open microcavity with the temperature-sensitive material polydimethylsiloxane (PDMS). One effect occurs at the gold-analyte interface, where the presence of target DNA molecules interacting with the probe DNA immobilized on the fiber surface modulates the SPR response. The other effect occurs at the PDMS-gold interface, where temperature changes cause the change of PDMS refractive index (RI), altering the SPR response. The proposed sensor demonstrates exceptional capabilities. It can simultaneously detect DNA hybridization and temperature with high sensitivity. Specifically, the sensor exhibits a response of 0.11 nm/nM for DNA molecules and a significant response of −1.97 nm/°C for temperature. Additionally, it boasts a low limit of detection (LOD) of 7.9 nM, making it suitable for applications where early detection is crucial. Beyond its impressive performance, the sensor offers the advantages of compactness and ease of fabrication. This combination makes it a promising candidate for integration into portable devices, enabling on-site and real-time DNA detection with integrated temperature compensation. [ABSTRACT FROM AUTHOR]

The super junction has been the most important concept for the design of power devices. However, there are still two problems when the conventional super-junction techniques are applied on lateral power devices: a large portion of the drift region is occupied by a p-type region, and the super junction techniques are not suitable for the gallium nitride-based high electron mobility transistor (GaN-HEMT). To solve the problems, a super field plate (SuFP) technique is proposed as a charge balance principle. Our analyses proved that the SuFP can provide a charge balance effect for a lateral double diffused MOS (LDMOS) without occupying the drift region. As a result, the LDMOS with SuFP has a better performance than the LDMOS with other charge balance realization techniques. Moreover, as a kind of field plate, the SuFP is also suitable for GaN-HEMT. Thereby, the proposed SuFP technique overcomes the two problems in conventional super-junction techniques and is significant for lateral power devices. [ABSTRACT FROM AUTHOR]

Objective  Hypoxic–ischemic (HI)-related brain injury, especially HI encephalopathy (HIE) is a leading cause of morbidity and disability in newborns. Long noncoding RNAs (lncRNAs) are implicated in the progress of HI brain damage. However, the mechanisms underlying the regulatory effects of lncRNA H19 on autophagy in HIE remain unknown. This study was designed to identify the potential mechanisms involving lncRNA H19 in HIE. Study Design  We selected three HIE newborns and three healthy newborns for neonatal behavioral neurological assessment and screened the differentially expressed lncRNAs by microarray analysis and detected H19 expression in serum. After that, neonatal HIE rats were established and injected with H19 overexpression lentivirus vector or autophagy activator Rapa. The structure and apoptotic levels of brain tissue were observed, and righting reflex and geotaxis reflex were utilized to evaluate the short-term neurological function of HIE rats. The Morris water maze was performed to measure the long-term neurological functions of HIE rats. The binding relationships among H19/miR-19b/protein kinase B3 (Akt3) were verified. Levels of Akt3- and autophagy-related proteins were measured. Results  H19 was upregulated in HIE newborns and rat models. The areas of cerebral infarction and apoptosis in neonatal HIE rats were increased, and the nerve functions were compromised. The overexpression of H19 alleviated nerve damage of neonatal HIE rats, and reduced autophagy of brain tissue. H19 upregulated Akt3 as a miR-29b sponge. The protective effects of overexpression of H19 on brain tissue and nerve functions of neonatal HIE rats were partially reversed by autophagy activator. Conclusion  H19 improved the brain tissue and alleviated nerve damage of neonatal HIE rats by upregulating the Akt3/mTOR pathway as a miR-29b sponge. Key Points H19 overexpression reduces the nerve damage in neonatal HIE rats. H19 reduces autophagy in neonatal HIE rats by the miR-29b/Akt3/mTOR axis. Autophagy activator reverses the protection of H19 in neonatal HIE. [ABSTRACT FROM AUTHOR]

The imbalanced data classification problem widely exists in many real-world applications. Data resampling is a promising technique to deal with imbalanced data through either oversampling or undersampling. However, the traditional data resampling approaches simply take into account the local neighbor information to generate new instances in linear ways, leading to the generation of incorrect and unnecessary instances. In this study, we propose a new data resampling technique, namely, Gaussian Distribution based Oversampling (GDO), to handle the imbalanced data for classification. In GDO, anchor instances are selected from the minority class instances in a probabilistic way by taking into account the density and distance information carried by the minority instances. Then new minority instances are generated following a Gaussian distribution model. The proposed method is validated in experimental study by comparing with seven imbalanced learning approaches on 40 data sets from the KEEL repository and 10 large data sets from the UCI repository. Experimental results show that our method outperforms the other compared methods in terms of AUC, G-mean and memory usage with an increase in running time. We also apply GDO to deal with two real imbalanced data classification problems: Internet video traffic identification and metastasis detection of esophageal cancer. The experimental results once again validate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

• A facile manufacture strategy constructs the multilayer piezoresistive pressure sensor. • The sensor demonstrates excellent sensing performances, including high sensitivity and fast response-recovery time. • A handwriting recognition intelligent system is designed based on proposed sensor based on different handwriting individuals. • A password unlocking control system is constructed, which achieves dual verification of passwords and behavior patterns. Privacy and security concerns have become a focal point of attention for humans, so that it is crucial to develop a secure and efficient identity recognition smart system. This work proposes a piezoresistive pressure sensor (PPS) based intelligent recognition system for privacy protection, wherein the PPS consists of multiple layers of electrospun fiber membranes with a bionic pine needle shaped structure. The PPS exhibits high sensitivity of 9040.46 kPa−1 at 0–1 kPa, 679.7 kPa−1 at 1–7 kPa, and 50.04 kPa−1 at 7–20 kPa, with a fast response/recovery time of 30 ms and excellent stability. Given these prominent features, the specific application in privacy protection has been construced. PPS was firstly exploited as an intelligent handwriting pad to obtain handwriting signals from different users and combined with convolutional neural network (LeNet-5) and long short-term memory (LSTM) network models to establish a handwriting recognition intelligent system for accurate identification of user identities. Further, a password unlocking control system was succesfully created based on the PPS array and support vector machine (SVM) network model for dual verification of passwords and behavior patterns. This work can easily and effectively detect people's daily behavioral characteristics for identity authentication, and has enormous application potential in fields such as handwriting recognition, security defense, and privacy protection. [ABSTRACT FROM AUTHOR]

Abstract: The combination of Further Division of Partition Space (FDPS) and Flexible Neural Tree (FNT) is proposed to improve the neural network classification performance. FDPS, which divides partition space into many partitions that will attach to different classes automatically, is a novel technique for neural network classification. FNT is a neural network''s structure which uses flexible tree model. The proposed method combines FDPS and FNT to overcome their respective problems by using the other''s merit. In order to evaluate the performance of this method, four well-known data sets are used for classification test. Experiment results have shown that this method has favorable performance. [Copyright &y& Elsevier]

Purpose. To comparatively evaluate the safety and satisfaction of bandage contact lens (BCL) and eye patching in patients after cataract surgery. Methods. Sixteen (32 eyes) patients who planned to undergo bilateral cataract surgery were recruited. The two eyes of each patient were randomly divided into 2 groups. Group A and Group B were instructed to wear BCLs immediately at the end of the surgery until one week and eye patch immediately after surgery until one day, respectively. Visual analog scales of ten specific symptoms, Visual Function Index (VF-14) questionnaire, and best-corrected visual acuity (BCVA) were conducted on the first day before the surgery and Day 1 and Day 7 after surgery. Oculus keratography was conducted on the first day before surgery and on Day 7. Patient satisfaction was determined on Day 1. Moreover, bacterial species in the conjunctival sac, meibomian gland secretions, and BCLs were subsequently identified using 16S rRNA gene sequencing. Results. The patient satisfaction scores of Group A were higher than Group B. Group A were more motivated to choose the same treatment and were more likely to recommend BCLs to others. No statistically significant differences were found in bacterial culture positivity between the groups. The differences in ocular signs and symptoms between the two groups were not statistically significant. There were no significant differences in the BCVA and VF-14 between the groups at any time point. Conclusions. BCLs could be safely and effectively used in patients after cataract surgery. [ABSTRACT FROM AUTHOR]

The privacy of Electronic Health Records (EHRs) is facing a major hurdle with outsourcing private health data in the cloud as there exists danger of leaking health information to unauthorized parties. In fact, EHRs are stored on centralized databases that increases the security risk footprint and requires trust in a single authority which cannot effectively protect data from internal attacks. This research focuses on ensuring the patient privacy and data security while sharing the sensitive data across same or different organisations as well as healthcare providers in a distributed environment. This research develops a privacy-preserving framework viz Healthchain based on Blockchain technology that maintains security, privacy, scalability and integrity of the e-health data. The Blockchain is built on Hyperledger fabric, a permissioned distributed ledger solutions by using Hyperledger composer and stores EHRs by utilizing InterPlanetary File System (IPFS) to build this healthchain framework. Moreover, the data stored in the IPFS is encrypted by using a unique cryptographic public key encryption algorithm to create a robust blockchain solution for electronic health data. The objective of the research is to provide a foundation for developing security solutions against cyber-attacks by exploiting the inherent features of the blockchain, and thus contribute to the robustness of healthcare information sharing environments. Through the results, the proposed model shows that the healthcare records are not traceable to unauthorized access as the model stores only the encrypted hash of the records that proves effectiveness in terms of data security, enhanced data privacy, improved data scalability, interoperability and data integrity while sharing and accessing medical records among stakeholders across the healthchain network. [ABSTRACT FROM AUTHOR]

Resistive random access memories (RRAMs) have attracted tremendous attention due to their miniaturized size, low power consumption, high response speed, and simple fabrication requirements, which are regarded as a promising candidate for future nonvolatile memories. However, the traditional binary storage RRAMs fail to meet the high-density requirement in the area of big data. One of the ways to overcome this obstacle is to realize multilevel state transitions in a unit RRAM. Herein, we propose a facile spin-coating method to develop a multilevel RRAM based on Al/poly(methyl methacrylate) (PMMA)/ZnO quantum dots (QDs)/PMMA/ZnO QDs/PMMA/fluorine-doped tin oxide (FTO) structure, with the significant merits of typical forming-free bipolar resistive switching behavior, low-power operating voltage, stable endurance, and excellent retention. In specific, the ON/OFF ratio of the proposed ZnO QDs-based device can be adjusted by tuning the concentration of the ZnO QDs. Moreover, a multilevel RRAM performance with distinct four-level resistance states is obtained by varying the compliance current, which demonstrates that the proposed device has a specific application in multilevel data storage. The conductive filament model, combining the enhanced internal field and robust formation of the conductive filament, is introduced to explain the mechanism of the resistive switching behavior and multilevel performance. This work paves a new way for its prospect in constructing high-density data storage RRAMs. [ABSTRACT FROM AUTHOR]

Field plate (FP) is a widely used electric field optimization technique in lateral power devices. However, the electric field optimized by 2-D-FP still has limited distribution distance and poor distribution uniformity, which are disadvantages for lateral power devices. To investigate the disadvantages deeply, this paper extracted the charge density (${D}_{\textsf {ch}}$) on 2-D-FP by simulation for the first time. Our investigations found that the disadvantages of 2-D-FP come from the nonuniform distribution of ${D}_{\textsf {ch}}$ on 2-D-FP, which cannot fit the requirement of charge balance. To solve the problem, the method to modulate ${D}_{\textsf {ch}}$ on FP by designing the FP in the width direction is studied. In this way, the 3-D varying density FP (3-D-VDFP) that has relatively uniform ${D}_{\textsf {ch}}$ distribution is proposed. Our studies based on a lateral double-diffused MOS (LDMOS) proved that the 3-D-VDFP alleviates the disadvantages of 2-D-FP effectively and realized the charge balance without introducing p-type region for the first time. The corresponding experimental results show that the LDMOS with 3-D-VDFP has lower specific on-resistance than the buffered step doping super junction LDMOS because the drift region is not wasted at all when the charge balance is realized by 3-D-VDFP. [ABSTRACT FROM AUTHOR]

The hot-carrier lifetime evaluation method of a 90-V-level n-type lateral double-diffused MOS (LDMOS) is investigated in this brief. Our charge pumping experiments demonstrate that the on-resistance degradation of the device is induced by the combination effect of the hot-hole injection at field oxide region and the interface state generation at field oxide and accumulation regions. Thereby, the hot-carrier lifetime of the device was evaluated by the comprehensive model proposed by Moens, which considers the effects of both hot-hole injection and the interface state generation. However, our experiments found that multiple lifetimes were obtained based on the model proposed by Moens and some lifetimes have the risk of overestimation. To solve the problem, an improved hot-carrier lifetime evaluation method based on a novel semiempirical model is proposed. Further experiments and analyses illustrate that the proposed hot-carrier lifetime evaluation method can provide a definite lifetime that has no risk of overestimation for the investigated LDMOS and has improved the lifetime prediction precision compare to the method based on Hu’s model. [ABSTRACT FROM AUTHOR]

Identifying network traffic at their early stages accurately is very important for the application of traffic identification. In recent years, more and more studies have tried to build effective machine learning models to identify traffic with the few packets at the early stage. Packet sizes and statistical features have been proved to be effective features which are widely used in early stage traffic identification. However, an important issue is still unconcerned, that is whether there exists essential effectiveness differences between the two kinds of features. In this paper, we set out to evaluate the effectiveness of statistical features in comparing with packet sizes. We firstly extract the packet sizes and their statistical features of the first six packets on three traffic data sets. Then the mutual information between each feature and the corresponding traffic type label is computed to show the effectiveness of the feature. And then we execute crossover identification experiments with different feature sets using ten well-known machine learning classifiers. Our experimental results show that most classifiers get almost the same performances using packet sizes and statistical features for early stage traffic identification. And most classifiers can achieve high identification accuracies using only two statistical features. [ABSTRACT FROM AUTHOR]

This paper focuses on the social shopping community (SSC), an emerging e-commerce business model of youth entrepreneurship that generates revenues through facilitating online purchase. We develop a model based on the stimulus-organism-response (S-O-R) framework and use it to investigate how website characteristics, which we describe as sociability and self reference, of social shopping environment influence consumers' cognitive and emotional trust in SSCs, as well as how trust in turn affects their purchase intention. The results of our survey of 405 members of Meilishuo, a Chinese SSC, indicate that sociability and self reference features significantly influence cognitive and emotional trust, and in turn the likelihood and magnitude of online purchase. Moreover, the emotional trust plays a greater role in influencing consumers' intention to purchase through the SSC than cognitive trust, and participant involvement moderates the relationship between trust and purchase intention. This work contributes to our understanding of the dynamics that govern consumer behaviour in SSCs and adds to the broader line of research on social commerce. It also provides insights into how young entrepreneurs can tap the economic values of social commerce. [ABSTRACT FROM AUTHOR]

Microstructure is one of the most important research issues in the field of cement hydration. The absence of imaging dynamic three-dimensional microstructure influences the investigation of cement hydration. Furthermore, it is impossible to confirm computer hydration models from real data perspective due to the lack of images of dynamic 3D microstructure. The evolution of the three-dimensional microstructure cannot be observed in situ easily. This article proposes an image registration-based approach to capture dynamic three-dimensional microstructure, whose original images are collected using microtomography. This is the first time that the dynamic 3D microstructure is imaged and analyzed for the hydration of cement. It allows imaging dynamic 3D microstructure for hydrating cement without using any extra equipment. Our research results indicate that the dynamic microstructure is captured easily with low cost and good precision. [ABSTRACT FROM AUTHOR]

Abstract: A laser-diode pumped passively Q-switched new type crystal Nd3+:NaY(WO4)2 (known as Nd:NYW) laser with Cr4+:YAG saturable absorber has been realized. The dependence of pulse repetition rate, pulse energy, pulse width, and peak power on pump power for different small-signal transmission of Cr4+:YAG are measured. The coupled rate equations are used to simulate the Q-switched process of laser, and the numerical solutions agree with the experimental results. [Copyright &y& Elsevier]

An almost colorless La3Ga5SiO14 (LGS) single crystal with 26 mm in diameter and 25 mm in length was grown by the Czochralski technique. The coloration of the crystal depends on the growth atmosphere. The cutoff wavelength of the colorless LGS crystal is shorter than an annealed crystal and the piezoelectric properties of the former are nearly equal to those of the colored one. [Copyright &y& Elsevier]

In the context of natural language processing, accuracy of intention detection is the basis for subsequent research on human-machine speech interaction. However, the problem of ambiguity in word vectors reduces the accuracy of intent detection. Meantime, there is a disconnection between local features and global features as well, resulting in text feature extraction that cannot fully reflect semantic information. These issues are all barriers of intention detection. Therefore, this paper proposes an attention-based convolutional neural network for self-media data learning (called A-CNN) for marketing intention. We cascade the traditional CNN with the self-attention model in the Attention networks to form a new network structure called A-CNN, and put forward a fast feature extraction method based on skip-gram-based learning called FSLText, to represent the high-dimension word vectors in the A-CNN. On the premise of maintaining the advantages of the CNN, A-CNN can not only solve the problem of local and global features disconnection caused by the CNN pooling layer, but also avoid the increase of algorithm complexity. The Self-Attention mechanism in the Attention model can effectively optimize the weight of local features of the information in global features, and retain local features that are more useful for intention detection. A fast feature extraction method which is based on Skip-gram can retain the semantic and word order information of the text. The method is beneficial to the marketing intention detection. According to the experiment, our A-CNN, compared with traditional machine learning methods, can improve 12.32% accuracy. Contrast to the dual-channel CNN, the accuracy rate is improved by 9.68%, and compared with the ATT-CNN, it is improved by 9.97%. On the F1 score, the A-CNN can improve the F1 score by about 9.37% in comparison with the traditional machine learning methods, the accuracy rate is increased by 9.68% compared with the dual-channel CNN, and 9.68% in contrast with ATT-CNN. It illustrates that our A-CNN can effectively address semantic and feature selection for marketing intention detection. [ABSTRACT FROM AUTHOR]

• Sn 3 O 4 /rGO heterostructure is synthesized by a one-step hydrothermal method and features a flower-like microstructure with a large specific surface area. • The Sn 3 O 4 /rGO sensor exhibits a highly improved sensing response to formaldehyde at a relatively lower operating temperature compared with pure Sn 3 O 4 sensor. • The proposed Sn 3 O 4 /rGO sensor shows the linear relationship between sensing response and the formaldehyde concentration in a wide detecting range of 1−1000 ppm, accompanied by a good selectivity, excellent reproducibility and long-term stability. • A specific and exhaustive sensing mechanism is proposed to interpret the enhanced sensing performance of the proposed Sn 3 O 4 /rGO sensor. High-performance formaldehyde (HCHO) sensors are highly desirable for environmental gas monitoring and industrial gas analysis. Herein, an HCHO sensor featuring an excellent sensing response is proposed to offer a broad detecting range and operate at a low temperature, by exploiting a flower-like composite based on a heterostructure consisting of Sn 3 O 4 and reduced graphene oxide (rGO). The Sn 3 O 4 /rGO composites with different rGO contents and the pure Sn 3 O 4 are successfully prepared by a facile one-step hydrothermal method, and their corresponding sensing properties toward HCHO are systematically investigated. The Sn 3 O 4 /rGO sensors exhibit a highly improved sensing response at a relatively low temperature compared with the pure Sn 3 O 4 sensor. In particular, the Sn 3 O 4 /rGO sensor utilizing 0.75 mg of the rGO realizes a much higher sensing response across the wide range of 1−1000 ppm HCHO, and its response is up to 44 when it is exposed to 100 ppm of HCHO at 150 °C. Additionally, the proposed Sn 3 O 4 /rGO sensor shows a good sensing selectivity, excellent reproducibility, and long-term stability. By analyzing the sensing mechanisms of the pure Sn 3 O 4 and Sn 3 O 4 /rGO composite, the enhanced sensing properties for the Sn 3 O 4 /rGO sensor can be ascribed to the heterostructure between Sn 3 O 4 and rGO, which gives rise to a considerable resistance variation when the sensor is exposed to air and HCHO. [ABSTRACT FROM AUTHOR]

In recent years, neural network-based models such as machine learning and deep learning have achieved excellent results in text classification. On the research of marketing intention detection, classification measures are adopted to identify news with marketing intent. However, most of current news appears in the form of dialogs. There are some challenges to find potential relevance between news sentences to determine the latent semantics. In order to address this issue, this paper has proposed a CLSTM-based topic memory network (called CLSTM-TMN for short) for marketing intention detection. A ReLU-Neuro Topic Model (RNTM) is proposed. A hidden layer is constructed to efficiently capture the subject document representation, Potential variables are applied to enhance the granularity of subject model learning. We have changed the structure of current Neural Topic Model (NTM) to add CLSTM classifier. This method is a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN). The CLSTM structure has the ability to find relationships from a sequence of text input, and the ability to extract local and dense features through convolution operations. The effectiveness of the method for marketing intention detection is illustrated in the experiments. Our detection model has a more significant improvement in F1 (7%) than other compared models. • CLSTM-TMN. The CLSTM-TMN model for text classification based on TMN was proposed. CLSTM has the feature of storing historical information. The maximum pooling layer is built in the lower part of CNN to obtain the maximum value of feature map. When LSTM is used to obtain context information semantics, the problem of gradient disappearance can be solved. • RNTM. A novel Neural Topic Model based on NTM is proposed. The modified linear unit layer in the model extracts topics, and the topics and documents are represented as a whole. This method is not easy to cause the loss of key information. The output of some neurons in RNTM is 0, which reduces the interdependence of parameters and the occurrence of overfitting. [ABSTRACT FROM AUTHOR]

The estimation of cement compressive strength is of great significance in the quality inspections, technological designs, and engineering applications for cement. Compared to destructive methods, the nondestructive estimation approaches save the cost in the manpower and material. However, the existing nondestructive methods have the large error because the used influence factors are difficult to control and the used two-dimensional microstructure images can not reflect the specific spatial structure of the entire cement. In this paper, a novel model is proposed to estimate the cement compressive strength using three-dimensional microstructure images and deep belief network. To reduce the computation consumption induced by three-dimensional images with abundant information, this method extracts image features that reflect the cement hydration state to estimate cement compressive strength. Deep belief network is applied to build the estimation model. Its unique training pattern and flexibility of parameters improve the ability to learn nonlinear relationships between microstructure images and cement compressive strength. Furthermore, the training processes are accelerated on the graphics processing units. The experimental results prove that the proposed method estimates cement compressive strength nondestructively and improves the efficiency. [ABSTRACT FROM AUTHOR]

The promoting roles of transcriptional factor six1 have been shown in various tumors, such as breast cancer and colorectal Cancer. However, its roles in hepatocellular carcinoma (HCC) cell stemness and chemotherapeutic sensitivity are never been revealed. In the present study, we showed that six1 expression was negatively correlated the overall survival of HCC patients and significantly increased in HCC tissues. Analysis on normal hepatic cells and HCC cells obtained the consistent result. Functional experiments revealed that six1 knockdown enhanced 5-fluorouracil (5-FU) sensitivity and reduced the stemness of HCC cells. Additionally, six1 knockdown partially reversed 5-FU resistance and attenuated the stemness in 5-FU-resistant HCC cells. Furthermore, we demonstrated that six1 directly bound to sox2 (a stemness master regulator) promoter, enhanced its transcription and expression. Overexpression of sox2 rescued the inhibitory effects of six1 knockdown on the stemness and 5-FU sensitivity of HCC cells. Thus, our work identified a novel six1/sox2 axis in regulating the stemness of HCC cells. [ABSTRACT FROM AUTHOR]