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Using the theoretical framework of the substantive economy, this study aims to point out the main aspects of the substantive mode of operation that help the integration of disadvantaged people while at the same time shedding light on the barriers that hinder economically efficient functioning in a market economy.

Research focuses on Hungarian rural work integration social cooperatives, which are engaged in producing activity by the employment of disadvantaged people. In the research, mixed methods were applied: results of a questionnaire survey covering 102 cooperatives, as well as 20 semi-structured interviews and experiences from the field. A total of 17 indicators were used to explore the substantive operational features, promoting mechanisms and problems in the following areas: organisational goals and outcomes; integrating roles and functions; productive functions; and the embeddedness of cooperatives.

As for results, substantive operational mechanisms and tools that support the integration of disadvantaged people have been identified such as mentoring, social incentives, the ability to create local value or the expansion of local community services. At the same time, several barriers have been detected that make it difficult to operate economically, such as cooperatives being a stepping stone for workers, excessive product heterogeneity or the lack of vertically structured bridging relationships.

The value of the study is to counterpoint the mechanisms promoting social purposes of work-integration social cooperatives and the obstacles to their long-term sustainability within the framework of the substantive economy, to better understand their functioning and the less quantifiable factors of their performance.

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.

A debate on whether new ventures should be supported with public funding is taking place. Adopting a position on this discussion requires rigorous assessments of implemented programs. However, the few existing efforts have mostly focused on regional cases in developed countries. To fill this gap, this paper aims to measure the effects of a regional acceleration program in a developing country (Medellin, Colombia).

The economic notion of capabilities is used to frame the analysis of firm characteristics and productivity, which are hypothesized to be heterogeneous within the program. To test these relationships, propensity score matching is used in a sample of 60 treatment and 16,994 control firms.

This paper finds that treated firms had higher revenue than propensity score-matched controls on average, confirming a positive impact on growth measures. However, such financial growth is mostly observed in service firms rather than other economic sectors.

Further evaluations, with a longer period and using more outcome variables, are suggested in the context of similar publicly funded programs in developing countries.

These findings tip the balance in favor of the literature suggesting supportive programs for high-growth firms as opposed to everyday entrepreneurship. This is an insight, especially under the context of an emerging economy, which has scarce funding to support entrepreneurship.

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.

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.