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The purpose of this paper is to investigate the status quo of digital poverty among adolescents in China, analyze the characteristics and the causes, then propose countermeasures to provide reference for alleviating digital poverty among adolescents.

The study developed an initial scale of digital poverty among adolescents and used survey data to revise the scale, on this basis, formed a questionnaire, which was distributed to nationwide adolescents. The study developed its findings from the 837 valid questionnaire respondents.

The digital poverty among adolescents is mainly shown in the poverty of digital ability, digital psychology and digital environment and presents the following characteristics, that is, insufficient information seeking ability and information selection ability needing to be improved; equipped with basic information awareness but lack of information evaluation ability; lack of patience in obtaining information and inclined to the principle of least effort; imperfect knowledge structure and immature psychological emotions and vulnerable to external interference; having a certain relationship with the information environment, but not significantly affected by regional economic differences. Finally, the study puts forward countermeasures to alleviate digital poverty among adolescents.

Understanding of the digital poverty among adolescents will likely demand rethinking into a number of issues ignored by information poverty studies.

Few studies focus on digital poverty among adolescents. This study developed an initial scale of digital poverty among adolescents and revised it by survey data, then conducted an empirical study through questionnaire, which could expand the understanding of information poverty in the field of library and information science.

Firms often encounter complicated external relationships and conflicts in inbound and outbound open innovation (OI). Conflict management significantly affects innovation results. Guided by resource dependence theory (RDT), this study aims to examine the moderating effects of conflict management styles in the relationship between OI and organizational performance (OP).

This study focuses on manufacturing and service firms in China, with the respondents composed of senior managers. Using hierarchical regression analysis, data from 270 firm samples are used to empirically test the hypotheses.

Inbound and outbound OI openness positively affects OP. Cooperative conflict management positively moderates the relationship between inbound OI openness and OP, whereas it negatively moderates the impact of outbound OI openness on OP. By contrast, competitive conflict management positively moderates the relationship between outbound OI openness on OP.

Guided by RDT, this study explores the relationship between OI and OP and the moderating role of conflict management styles. However, it does not measure the level of resource dependence, which is among the future research directions for further validating the results of this study.

This study is among the first to investigate the impact of OI on OP in different conflict management styles. Findings suggest that choosing a suitable conflict management style may strengthen the positive effects of OI on OP.

In recent years, personalized recommendations have facilitated easy access to users' personal information and historical interactions, thereby improving recommendation effectiveness. However, due to privacy risk concerns, it is essential to balance the accuracy of personalized recommendations with privacy protection. Accordingly, this paper aims to propose a neural graph collaborative filtering personalized recommendation framework based on federated transfer learning (FTL-NGCF), which achieves high-quality personalized recommendations with privacy protection.

FTL-NGCF uses a third-party server to coordinate local users to train the graph neural networks (GNN) model. Each user client integrates user–item interactions into the embedding and uploads the model parameters to a server. To prevent attacks during communication and thus promote privacy preservation, the authors introduce homomorphic encryption to ensure secure model aggregation between clients and the server.

Experiments on three real data sets (Gowalla, Yelp2018, Amazon-Book) show that FTL-NGCF improves the recommendation performance in terms of recall and NDCG, based on the increased consideration of privacy protection relative to original federated learning methods.

To the best of the authors’ knowledge, no previous research has considered federated transfer learning framework for GNN-based recommendation. It can be extended to other recommended applications while maintaining privacy protection.

Building supply chain resilience is increasingly recognized as an effective strategy to deal with supply chain challenges, risks and disruptions. Nevertheless, it remains unclear how to build supply chain resilience and whether supply chain resilience could achieve a competitive advantage.

By analyzing the data collected from 216 firms in China, the current study empirically examines how information technology (IT) capability and supply chain collaboration affect different forms of supply chain resilience (external resilience and internal resilience) and examines the performance implications of these two forms of supply chain resilience.

Results show that IT capability is positively related to external resilience, whereas supply chain collaboration is positively related to internal resilience. The combination of IT capability and supply chain collaboration is positively related to external resilience. In addition, internal resilience is positively related to firm performance.

This study used only cross-sectional data from China for hypothesis testing. Future studies could utilise longitudinal data and research other countries/regions.

The findings systematically assess how IT capability and supply chain collaboration contribute to supply chain resilience and firm performance. The results provide a benchmark of supply chain resilience improvement that can be expected from IT capability and supply chain collaboration.

The study findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.

The purpose of this investigation was to study these problems and design regional cathodic protection, using numerical simulation. Regional cathodic protection technology is immature at home and abroad. This is reflected in the fact that in gas stations, there are many underground pipelines, which can lead to serious interference and shielding phenomena, and there are many grounding networks that can cause substantial loss of the cathodic protection current.

Based on the above, in this article, first of all, the mathematical model of the buried pipeline cathodic protection potential distribution was established and the control equations solved using the boundary element method. Second, the cathodic shielding effect in pipeline concentration areas, the effect of instrument equipment grounding systems on cathodic protection and the influence of DC stray current on the interference of pipeline corrosion were studied separately using BEASY software. Finally, the BEASY software was used for a regional cathodic protection design for a real gas station.

It was concluded that impressed current used in combination with sacrificial anodes for regional cathodic protection design is often the most economic and effective approach. However, the output current of the auxiliary anode is large with high energy consumption. In consequence, it may be recommended that the station pipelines should be laid on the ground, rather than under it.

It is considered that the results can guide regional cathodic protection design for real-life installations very well.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues.

In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel.

The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods.

The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.