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The study aims to qualitatively analyze how faculty can mobilize the intellectual capital of higher education institutions (HEIs), comprising human, structural and relational capital to enable the education and learning of individuals with intellectual and developmental disabilities.

Drawing upon the extant literature, the researcher conducted a qualitative study through written, in-depth interviews with a sample of 40 academic staff/faculty members having prior experience in teaching individuals with intellectual and developmental disabilities. The data was collected through a set of questions formulated as key questions, to be asked to all participants for their responses.

Results of the analysis demonstrated that intellectual capital’s contribution to higher education of individuals with intellectual and developmental disabilities can be best understood in terms of its three components/dimensions. Accordingly, three main themes, with each comprising two sub-themes were uncovered. The first theme, leveraging human capital comprised: faculty acumen and faculty training as sub-themes; the second theme, resourcing structural capital comprised: tangible and intangible structural capital as sub-themes; and the third theme, nurturing relational capital comprised: in-class engagement and the second is ex-class connection as sub-themes.

The paper collects data from 40 faculty having prior experience in teaching individuals with intellectual and developmental disabilities to explore and reveal a completely new perspective of looking at intellectual capital as a means of providing accessible and inclusive higher education to differently-abled students, making them a part of the mainstream.

This paper aims to investigate the ability of traditional biopolymers, such as funori or the nanoscale form of cellulose nanocrystals, to consolidate fragile paper and preserve it for as long as possible.

Degraded papers dating back two centuries were separated into paper samples for consolidation processes. Funori – a marine spleen – was used as a traditional consolidation material and a mixture with ZnO NPs compared with modern materials, such as cellulose nanocrystals. The samples were aged for 25 years, examinations and analyses were performed using scanning electron microscopy and color change was assessed using the CIELAB system, X-ray diffraction and Fourier-transform infrared spectroscopy.

According to the results, using traditional materials to consolidate damage, such as funori, after aging resulted in glossiness on the surface, a color change and increased water content and oxidation. Furthermore, samples treated with a mixture of ZnO NPs and funori revealed that the mixture improved the sample properties and increased the degree of crystallization. Cellulose nanocrystals improved the surface, filled gaps, formed bridges between the fibers and acted as a protector from aging effects.

This paper highlights the ability of nanomaterials to enhance the properties of materials as additives and treat the paper manuscripts from weaknesses.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This performance autoethnography may or may not be interpreted as a continuation of a conversation regarding the experiences of those with devalued statuses in academic settings. The authors rely on “strange accounting” to consider their experiences in the academy from various standpoints: before and after promotion, before and after leaving academia. While reflecting on our past experiences, we introduce the concept of “everyday precariousness” as a way of explaining the normalization of instability, insecurity, and negative affect that is part of everyday life for those with devalued statuses in academic settings and beyond. Everyday precariousness is an embodied experience for those in vulnerable positions. Normalized exposure to risks, such as discrimination, harassment, bullying, or structural instability, produces an undercurrent of threat that permeates academic culture. Our stories of everyday precariousness span race, ethnicity, class, academic roles, and gender boundaries (among many others). Analyzing these experiences furthers previous work on the uses of strange accounting as well as the dynamics of status silencing. In the final analysis, unresisted and unabated, everyday precariousness and status silencing can lead to institutional failure and resonance disasters.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

This paper aims to propose the mechanism of cross-network effect embedded, which can help cross-border e-commerce (CBEC) platforms strengthen cooperative relationships with sellers more equitably and effectively by using the network structural characteristics of the platforms themselves.

A two-stage evolutionary game model has been used to confirm the influence factors. The mathematical derivation of evolutionary game analysis is combined with the simulation method to examine the role of cross-network effect in cooperation. The evolutionary game model based on the cross-network effect is proposed to achieve better adaptability to the study of cooperation strategy from the two-sided market perspective.

The evolutionary game model captures the interactions of cross-network effect and the influence factors from a dynamic perspective. The cross-network effect has a certain substitution on the revenue-sharing rate of SMEs. CBEC platforms can enhance the connection between consumers and the website by improving the level of construction, which is a good way to attract sellers more cost-effectively and efficiently.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specificCBEC platforms.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specific CBEC platforms.

Investigations that study cooperation strategy from the cross-network effect perspective in CBEC are limited. The research figured out which influence factors are affected by the cross-network effect in cooperation. A two-stage evolutionary game model was proposed to explain the interaction of the factors. The evolutionary game analysis with a simulation method was combined to highlight the role of cross-network effect on cooperation strategy to give a deeper investigation into the sustainable cooperation ofCBEC.

This study examined how economic freedom and its related components, such as open markets, regulatory efficiency, rule of law and the size of government, affect bank risk behavior, focusing on the Japanese context.

The study employs a two-step GMM framework on the annual data of Japanese banks ranging from 2005 to 2020 to empirically test the hypotheses. Furthermore, we also use the ordinary least square method to ensure the robustness of our mainline findings.

The finding suggests that economic freedom increases the banks' risk-taking, thus making them fragile. The results also highlight that out of the four main subcomponents of economic freedom, regulatory efficiency and government size increase bank risk-taking, while the rule of law and open markets decrease banks' risk-taking. Additionally, we examine how the banks' specific characteristics affect the results by creating a subsample based on capitalization and liquidity ratios. Overall, the results are consistent with the baseline findings. Moreover, the results are robust to alternative proxy measures of risk.

The study's findings have several implications for regulators and policymakers. The results suggest that regulators and policymakers should reconsider their strategies for economic freedom to ensure that they promote stability in the banking system and reduce banks' risk-taking inclinations.

Although previous studies have examined the impact of economic freedom on bank stability and risk-taking, this study is the first to do so in the Japanese context, contributing to the literature by providing new insights and empirical evidence.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

This study aims to identify the main challenges to achieving humanitarian logistics in the context of United Nations peace missions in sub-Saharan Africa and to present suggestions for overcoming the logistical gaps encountered.

The methodological approach of the work focuses on the comparative case study of the United Nations Mission in South Sudan, the United Nations Multidimensional Integrated Stabilisation Mission in the Central African Republic and The United Nations Organisation Stabilisation Mission in the Democratic Republic of Congo from 2014 to 2021. The approach combined a systematic literature review with the authors’ empirical experience as participant observers in each mission, combining theory and practice.

As a result, six common challenges were identified for carrying out humanitarian logistics in the three peace missions. Each challenge revealed a logistical gap for which an appropriate solution was suggested based on the best practices found in the case study of each mission.

This paper presents limitations when addressing the logistical analysis based on only three countries under the UN mission as a case study, as well as conceiving that certain flaws in the system, in the observed period, are already in the process of correction with the adoption of the 2016–2021 strategy by the UN Global Logistic Cluster. The authors suggest that further studies can be carried out by expanding the number of cases or using countries where other bodies (AU, NATO or EU) work.

To the best of the authors’ knowledge, this study is the first comparative case study of humanitarian logistics on the three principal missions of the UN conducted by academics and practitioners.