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This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.

The authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.

The results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.

The originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

This paper aims to put forward a kind of new method of intelligent trust engineering metrics for application of mobile ad hoc network (MANET).

The new method calculates the communication trust by using the number of data packets among the nodes of MANET, predicts intelligently the trust and calculates the comprehensive trust based on the historical trust; then calculates the energy trust based on the residual energy of the nodes of MANET, calculates the direct trust based on the communication trust and energy trust. The new method calculates the recommendation trust based on the recommendation reliability; adopts the adaptive weighting to calculate the integrated direct trust by considering the direct trust with recommendation trust.

Based on the integrated direct trust and the factor of trust propagation distance, the indirect trust among the nodes of MANET is calculated. The above process can be optimized based on the dynamic machine learning presented in this study. The advantage of the new method is its intelligent ability to discover malicious nodes.

The advantage of the new method is its intelligent ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and proceeds to protect the network. The authors have done the experiments based on the tool kits such as NS3, QualNet, OMNET++ and MATLAB. The experimental results show that this study’s approach can effectively avoid the attacks of malicious nodes, and more conformable to the actual engineering application of MANET.

This paper aims to understand the scholarly contributions to mobile advertising by analyzing the publishing trend from 2001 to 2022 from the documents indexed in the Scopus database.

A total of 348 documents were selected for analysis published between 2001 and 2022. The garnered data was examined using a bibliometric domain mapping analysis technique using computer-aided software R and VOSviewer and manually exploring the articles.

The results of this study discover the most prolific authors in the mobile advertising domain and other seminal works carried out by productive researchers in the field of mobile advertising. The journals in which most instrumental research studies have been published are also identified. Moreover, the co-citation, bibliometric coupling and co-occurrence analysis of literature are also carried out to draw themes concerning mobile advertising research that have been identified and categorized.

This research analyzed a singular, exclusive database, “Scopus,” which limited the sectoral scope of publications. Since the present research uses bibliometric analysis, these studies cannot conduct sentiment analysis of the chosen studies.

Marketing professionals looking after technological advancements may use this study to understand the broad scope of mobile advertising applicability across diverse domains and discuss the trade-offs that may address significant bottlenecks in mobile advertising applications.

To the best of the authors’ knowledge, this paper is one of the latest attempts in recent times to understand the research work in mobile advertising using a bibliometric domain analysis approach.

As the tourism industry emerges from full or partial closure caused by the COVID-19 crisis, it is imperative to understand the internal conditions that assisted organizations to maintain positive employee attitudes despite the adverse effects of unpopular cost–retrenchment strategies. Therefore, this study aims to understand the impacts of transformational leadership (TFL), human resource management (HRM) crisis cost–retrenchment and ethical climate (EC) on employee job outcomes affected by COVID-19 pandemic.

Mid-level managers of service organizations from a travel destination heavily reliant on the tourism participated in an online self-administered survey one month after the state eased its COVID-19 travel restrictions. Partial least square structural equation modeling (PLS-SEM) examined how TFL and EC influenced cost–retrenchment crisis–management HRM, satisfaction and trust in the organization, followed by PLS multi-group analysis (PLS-MGA) to understand differences between hospitality and non-hospitality employees.

Results revealed an overall positive effect of TFL that diminished the negative affect of HRM cost-retrenchment on employee satisfaction. PLS-MGA showed a significant positive role of other-focused EC on employee outcomes, especially for hospitality organizations, whereas self-focused EC had a negative impact for non-hospitality firms.

This study contributes to contingency theory of leadership by demonstrating that TFL in combination with EC mitigates or overpowers the negative effects of cost–retrenchment crisis management strategies on employees. The study advances knowledge of self-focused and other-focused moral reasoning climate impacts under COVID-19 conditions for hospitality organizations. The industry comparison results highlight the important positive characteristics of hospitality crisis management.

There has been a burgeoning interest in exploring the impact of uncertainty factors on share returns. However, studies on the influence of global financial uncertainties on emerging market sectoral indices are scarce. Thus, there is a need to have a thorough investigation of the connection between global financial uncertainties and emerging market sectoral indices. To fill this gap, using the theoretical framework of international portfolio diversification (IPD) and utilizing data from 2008 to 2021, this study examines the spillover connection between global uncertainty indices (GUIs) and leading sectoral indices of 28 emerging markets.

The authors employ the quantile spillover-based connectedness approach and minimum connectedness portfolio approach to explore the dynamic connectedness among sectoral indices and global uncertainty indices (GUIs) as well as portfolio implication.

The study found high connectedness among all indices, especially at higher and lower quantiles. Among GUIs, the authors find that stock market volatility (VIX) and oil volatility index (OVX) are strongly interconnected with all leading emerging markets' sectoral indices. Among sectoral indices, the linkage between the financial (F-Index), information technology (IT-Index), and consumer discretionary (CD-Index) sectors shows moderate interconnectedness. In contrast, the communication services (CS-Index) sector has low interconnectedness with the system. In terms of spillover effects, the authors find EVZ, OVX, and the IT sectors to be net recipients for the entire period. The authors also explored portfolio diversification benefits by employing a minimum connectedness portfolio approach. The cumulative returns' findings show a slight decline in the portfolio's value after 2010; during 2012, the pattern remained stable; from 2014 to 2020, the portfolio performed negatively, that is, underperformance due to different events in that period, including COVID-19. The Consumer Discretionary sector is found to be significant because of having the largest weight, 51%, in the portfolio during the study period.

The study suggests that investors should invest in the communication services sector as it is the least connected. However, the connectedness increases during COVID-19, which implies that it may be difficult for investors to benefit from IPD in a crisis period. Hence, to obtain the benefits from IPD, the evidence suggests that investors need to consider Consumer Discretionary sector while considering assets for investment.

The study's uniqueness is that the authors have investigated spillover between GUIs and 28 emerging markets sectoral indices by employing a quantile spillover-based connectedness approach and minimum connectedness portfolio approach with a special focus on portfolio implication.

Drawing on the work-home resources (W-HR) model, this study examines the ripple effects of proactive helping behavior at work on helpers' family relationship quality at home via positive affect and work-family interpersonal capitalization, and tests the moderating role of independent self-construal in the resource spillover process.

Using an experience sampling methodology, data was collected (N = 382) from multiple sources in five consecutive working days. Multilevel path modeling was used to examine the hypotheses.

The results indicated that proactive helping other at work can generate affective resources for helpers, which in turn triggers them to share daily work experiences and feelings with their spouses at home, and strengthens their family relationship quality. Moreover, the effects of helping others on family relationship quality were more pronounced for helpers with relatively high independent self-construal.

The findings explore the enrichment effects and unintended family-related distal outcomes of helping behaviors for helpers, and contributes to the W-HR model by uncovering an affective-behavioral ripple mechanism linking work and family. Finally, our results identify the boundary condition, that proactive helping behaviors are more rewarding for helpers with higher independent self-construal.

Feature selection is an important step for data pre-processing specially in the case of high dimensional data set. Performance of the data model is reduced if the model is trained with high dimensional data set, and it results in poor classification accuracy. Therefore, before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy.

A novel optimization approach that hybridizes binary particle swarm optimization (BPSO) and differential evolution (DE) for fine tuning of SVM classifier is presented. The name of the implemented classifier is given as DEPSOSVM.

This approach is evaluated using 20 UCI benchmark text data classification data set. Further, the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images. From the results, it can be observed that the proposed DEPSOSVM techniques have significant improvement in performance over other algorithms in the literature for feature selection. The proposed technique shows better classification accuracy as well.

The proposed approach is different from the previous work, as in all the previous work DE/(rand/1) mutation strategy is used whereas in this study DE/(rand/2) is used and the mutation strategy with BPSO is updated. Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function. The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier (DEPSOSVM) to handle the feature selection problems.

In this paper, the purpose of research is to verify good thermal performance of heat pipe receiver.

Mathematical model was set up, numerical calculation method was offered. Calculation results were compared with experimental results, with those of NASA project, and those of the basis heat receiver.

Simulation results show that heat pipe receiver involving heat pipe has the performance of perfect heat transfer and ideal identical temperature, the axial temperature difference of heat pipe is small, PCM canisters situated in different places of heat pipe can melt simultaneously and uniformly. At the same time, normal operation of wick ensures the uniformity of heat pipe circumference temperature, thus heat pipe receiver avoids phenomena of thermal spot. In addition, heat pipe receiver has axial and radial performance of ideal identical temperature, all PCM canisters can freeze simultaneously at the end of eclipse periods, and freeze fully at last, so heat pipe receiver avoids thermal ratcheting.

The research in this paper can be used to design heat pipe receiver.

This research study aims to develop regular cylindrical pore network models (RCPNMs) to calculate topology and geometry properties of explosively created fractures along with their resulting hydraulic permeability. The focus of the investigation is to define a method that generates a valid geometric and topologic representation from a computational modelling point of view for explosion-generated fractures in rocks. In particular, extraction of geometries from experimentally validated Eulerian smoothed particle hydrodynamics (ESPH) approach, to avoid restrictions for image-based computational methods.

Three-dimensional stabilized ESPH solution is required to model explosively created fracture networks, and the accuracy of developed ESPH is qualitatively and quantitatively examined against experimental observations for both peak detonation pressures and crack density estimations. SPH simulation domain is segmented to void and solid spaces using a graphical user interface, and the void space of blasted rocks is represented by a regular lattice of spherical pores connected by cylindrical throats. Results produced by the RCPNMs are compared to three pore network extraction algorithms. Thereby, once the accuracy of RCPNMs is confirmed, the absolute permeability of fracture networks is calculated.

The results obtained with RCPNMs method were compared with three pore network extraction algorithms and computational fluid dynamics method, achieving a more computational efficiency regarding to CPU cost and a better geometry and topology relationship identification, in all the cases studied. Furthermore, a reliable topology data that does not have image-based pore network limitations, and the effect of topological disorder on the computed absolute permeability is minor. However, further research is necessary to improve the interpretation of real pore systems for explosively created fracture networks.

Although only laboratory cylindrical rock specimens were tested in the computational examples, the developed approaches are applicable for field scale and complex pore network grids with arbitrary shapes.

It is often desirable to develop an integrated computational method for hydraulic conductivity of explosively created fracture networks which segmentation of fracture networks is not restricted to X-ray images, particularly when topologic and geometric modellings are the crucial parts. This research study provides insight to the reliable computational methods and pore network extraction algorithm selection processes, as well as defining a practical framework for generating reliable topological and geometrical data in a Eulerian SPH setting.

The purpose of this report is to demonstrate open and distance education (ODE) can support poverty alleviation. Taking the practices of the Open University of China (the OUC) as an example, this paper aims to reveal how open universities make contributions to local residents in rural and remote areas.

Focusing on 25 poverty-stricken counties, the OUC had invested 58 million RMB to its learning centers in these counties from 2017 to 2020. The first one is to improve ICT and educational facilities in these learning centers. The second approach is to cultivate local residents with degree programs through ODE so as to promote local economic development. The third one is to design and develop training programs according to local context to meet the specific needs of local villagers.

After 3 years working, cloud-based classrooms and computer rooms have been set up. Bookstores have been founded and printed books have been donated. Hundreds of thousands of digital micro lectures have been supplied to these learning centers which have been improved and fully played their functions. Nearly 50,000 local residents have been directly benefited. Village leaders have helped lift local residents out of poverty. Poverty-stricken villagers have been financed to study on either undergraduate or diploma programs. Local residents have improved their skills by learning with the training programs offered by the OUC.

ODE is proved to be an effective way to eradicate poverty. Open universities are proved to be able to make contributions to social justice. By fulfilling its commitments to eliminate poverty within the national strategy framework, the OUC has built its brand nationwide.