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This paper aims at assessing the impact of a number of behavioral interventions on the willingness of informal businesses, in the Egyptian informal sector, to join the formal sector.

This paper uses an experimental methodology to examine the impact of behavioral interventions on the formalization of the Egyptian informal sector. Specifically, it conducts a survey experiment on a total of 240 informal businesses, operating in the Egyptian informal sector. The primary data collected from the survey experiment is then analyzed using a binary logistic regression to assess the impact of the behavioral primes on the probability of joining the formal market.

The empirical findings of the survey experiment indicate that the biggest obstacle facing informal businesses is finding a formal source of finance that could help them in penetrating the market. Providing informal businesses with information on funding opportunities offered by the ministry of micro, small and medium enterprises (MSME) significantly increased the probability of joining the formal sector to benefit from this opportunity.

This paper is the first to apply behavioral primes, in the form of informational cues, to the Egyptian case of informal business owners. Previous research on the use of behavioral nudges and primes has focused mainly on the western economies.

Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.

The proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.

The authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.

This research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.

Diabetes is one of the life-threatening chronic diseases, which is already affecting 422m people globally based on (World Health Organization) WHO report as at 2018. This costs individuals, government and groups a whole lot; right from its diagnosis stage to the treatment stage. The reason for this cost, among others, is that it is a long-term treatment disease. This disease is likely to continue to affect more people because of its long asymptotic phase, which makes its early detection not feasible.

In this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas.

The study result shows that feature selection helps in getting better model, as it prevents overfitting and removes redundant data. Hence, the study result when compared with previous research shows the better result has been achieved, after it was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at diagnosing diabetes disease at its early stage.

This study has not been published anywhere else.

Using a mobile phone is increasingly becoming recognized as very dangerous while driving. With a smartphone, users feel connected and have access to information. The inability to access smartphone has become a phobia, causing anxiety and fear. The present study’s aims are as follows: first, quantify the association between nomophobia and road safety among motorists; second, determine a cut-off value for nomophobia that would identify poor road safety so that interventions can be designed accordingly.

Participants were surveyed online for nomophobia symptoms and a recent history of traffic contraventions. Nomophobia was measured using the nomophobia questionnaire (NMP-Q).

A total of 1731 participants responded to the survey; the mean age was 33 ± 12, and 43% were male. Overall, 483 (28%) [26–30%] participants received a recent traffic contravention. Participants with severe nomophobia showed a statistically significant increased risk for poor road safety odds ratios and a corresponding 95% CI of 4.64 [3.35-6.38] and 4.54 [3.28-6.29] in crude and adjusted models, respectively. Receiver operator characteristic (ROC)-based analyses revealed that NMP-Q scores of = 90 would be effective for identifying at risk drivers with sensitivity, specificity and accuracy of 61%, 75% and 72%, respectively.

Nomophobia symptoms are quite common among adults. Severe nomophobia is associated with poor road safety among motorists. Developing screening and intervention programs aimed at reducing nomophobia may improve road safety among motorists.

This research aims to identify the optimal configuration of investment which leads firms to their best competitive positions, considering the degree of concentration in the market.

The methodology was quantitative and based on secondary data with samples of 124, 106 and 90 firms from competitive environment classified as perfect competition, monopolistic competition and oligopoly, respectively. Proposed models' parameters were estimated by means of genetic algorithms.

Adjustments on firm's investment are contingent on the degree of competition they face. Results are in line with existing academic research affirmation that the purpose of investments is to create and exploit opportunities for positive economic rents and that investments allow firms to protect from rivals' competitive actions and reinforce the need for investment decision makers to consider the environment in which the firm is competing, when defining the amount of investment that must be done to achieve and maintain a favorable competitive advantage position.

This research brings two main original contributions. The first one is the identification of the optimal amount of capital and R&D investments which leads firms to their best competitive positions, contingent to the degree of concentration of the competitive environment in which they operate, and the size of the firm. The second one is related to the use of genetic algorithms to estimate optimization models that considers the three competitive environments studied (perfect competition, monopolistic competition and oligopoly) and the investment variables in the linear and quadratic forms.

The purpose of this study is to shed light on the twin transition in China in the organization of innovation processes in artificial intelligence (AI) and green technology (GT) development and to understand the role of foreign multinationals in Chinese innovation systems.

A qualitative research approach is used by interviewing executives from German multinationals with expertise in AI and GT development and organization of innovation processes in China. In total, 11 semi-structured interviews were conducted with companies, and the data were analysed with a thematic qualitative text analysis.

The findings show that AI applications for GT are primarily developed in cross-company projects that are led by local and regional authorities through the organization of industrial districts and clusters. German multinationals are either being integrated, remaining autonomous or being excluded from these twin transition innovation processes.

This paper aims to fill the gap in the literature by providing one of the first qualitative approach towards twin transition innovation processes in China and exploring the integration of multinational enterprises in cluster organizations. To the best of the author’s knowledge, this is one of the first twin transition studies from this perspective in emerging economies.

This study focuses on the classification of targets with varying shapes using radar cross section (RCS), which is influenced by the target’s shape. This study aims to develop a robust classification method by considering an incident angle with minor random fluctuations and using a physical optics simulation to generate data sets.

The approach involves several supervised machine learning and classification methods, including traditional algorithms and a deep neural network classifier. It uses histogram-based definitions of the RCS for feature extraction, with an emphasis on resilience against noise in the RCS data. Data enrichment techniques are incorporated, including the use of noise-impacted histogram data sets.

The classification algorithms are extensively evaluated, highlighting their efficacy in feature extraction from RCS histograms. Among the studied algorithms, the K-nearest neighbour is found to be the most accurate of the traditional methods, but it is surpassed in accuracy by a deep learning network classifier. The results demonstrate the robustness of the feature extraction from the RCS histograms, motivated by mm-wave radar applications.

This study presents a novel approach to target classification that extends beyond traditional methods by integrating deep neural networks and focusing on histogram-based methodologies. It also incorporates data enrichment techniques to enhance the analysis, providing a comprehensive perspective for target detection using RCS.

During the COVID-19 pandemic, Local Educational Management Units (UGELs), the key government stakeholders in the provision of education services in Peru, implemented responsive interventions. This paper analyses the relationship between UGEL organisational resilience and their responses during this period.

A survey was conducted to measure UGEL management practices, with 251 valid responses from directors and managers. Based on organisational resilience theory, 67 questions were grouped into 13 factors and 3 components: (1) leadership and organisational culture, (2) preparation for change, and (3) networks building on the Organisational Resilience Index (ORI). These factors correlated with the number of interventions and the impact of those interventions implemented by UGELs.

The findings indicated that of all ORI components, leadership and organisational culture ranked the highest. Moreover, the ORI is positively associated with the number of interventions and the perceived impact produced by those interventions. Interestingly, it was found that when the gender variable is included in the correlation between the ORI and the number of interventions, women leading UGELs display a higher number of interventions than their male counterparts; and the coefficient increases even more when women lead a UGEL in a more challenging context (i.e., when the UGEL is located in a low-income area and operates under scarce resources).

This is the first study in Peru which analyses organisational resilience in the education sector, specifically about UGELs during the COVID-19 pandemic. It may help set priorities for institutional strengthening initiatives aimed at improving organisational resilience, which is particularly important in such uncertain and changing contexts.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.

This empirical study explores the profound impact of management functions on the productivity of yard cargo handling equipment within container terminals.

By closely examining crucial management aspects such as planning, organizing, leading, and controlling, a comprehensive managerial behavior framework was developed through focus group studies (FGS) and focal interviews. These qualitative methods were complemented by the distribution of questionnaires to practitioners in Vietnam. To validate the concept of management functions and analyze their influence on effective management practices for equipment efficiency, a structural equation model (SEM) technique was employed using partial least-squares estimation (PLS).

The findings of this study demonstrate that planning (PL), organizing (OR), and controlling (CT) significantly contribute to the productivity of yard cargo handling equipment, while leading (LD) does not exhibit a direct positive impact.

Theoretically, this study contributes by providing clarity to the definition, purpose, and value of management functions in the field of cargo handling equipment management. Furthermore, these research findings offer valuable insights to terminal operators and managers, enabling them to optimize their management strategies and enhance productivity levels, ultimately resulting in improved operational outcomes.