eGovernment projects are capital intensive and have high probability of failure because of the dynamic and technological laden environment in which they operate. The number of skilled labour and technicalities required are often not available in quantity needed to sustain such project. There is always the need to have in place adequate risk assessment framework to guide the execution and monitoring of eGovernment projects. Several studies have been conducted on the critical success factors relating to risk assessment of eGovernment projects to understand the reasons for the high rate of failure. Therefore, there is need to review these articles and categorize them into different research domain in project risk assessment so as to reveal domain with more or less research and those that need to understand the future research directions in risk assessment for eGovernment projects. Using the positivism paradigm, this study utilized the Systematic Literature Review methodology to collect 147 articles from the following academic databases namely IEEE, Preprints, WorldCat Discovery, ArXiv. Ohio-state University databases, Science Direct, Scopus, ACM, NWU digital library, Usenix, Jise database, Sagepub, MDPI Academia published between 2013 to 2023. Different inclusion and exclusion criteria were applied pruning to 48 articles that were used for the study. The results show the classification of articles in risk assessment for eGovernment projects into those that discusses project analysis, review, framework, maturity and model tools, implementation, and integration, applied methodology and evaluation with the percentage of articles published in each domain with the past 10 years. The various critical success factors that should be considered in the development of a robust risk assessment framework were discussed and future research directions in eGovernment risk assessment were given based on the reviews.

The Oued Kert watershed in Morocco is essential for local biodiversity and agriculture, yet it faces significant challenges due to meteorological drought. This research addresses an urgent issue by aiming to understand the impacts of drought on vegetation, which is crucial for food security and water resource management. Despite previous studies on drought, there are significant gaps, including a lack of specific analyses on the seasonal effects of drought on vegetation in this under-researched region, as well as insufficient use of appropriate analytical tools to evaluate these relationships. We utilized the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) to analyze the relationship between precipitation and vegetation health. Our results reveal a very strong correlation between SPI and NDVI in spring (98%) and summer (97%), while correlations in winter and autumn are weaker (66% and 55%). These findings can guide policymakers in developing appropriate strategies and contribute to crop planning and land management. Furthermore, this study could serve as a foundation for awareness and education initiatives on the sustainable management of water and land resources, thereby enhancing the resilience of local ecosystems in the face of environmental challenges.

QR code transforms the way retailers offer their shopping experiences in the current context. In response, various retailers adopted innovative approaches such as QR code-based applications to attract their consumers. A QR code-based virtual supermarket refers to a space where goods or services are traded in a virtual space using a smart app-based QR code. To fully understand the opportunities of this type of supermarket applying QR-code technology, initial research is required to assess consumers’ use intention. This study has examined the antecedents of the adoption of QR code-based virtual supermarket among Vietnam consumers using the expanded Technology Acceptance Model (TAM) and explored the moderating effect of perceived risk on the relationship between attitude and consumers’ intention to use QR code-based virtual supermarket. A questionnaire was used to collect data from a sample of 335 consumers in Vietnam. The findings revealed that the antecedents are effective in predicting consumers’ attitudes and intentions toward QR code-based virtual supermarket adoption. The results showed the negative moderation effects of perceived risk for the effect of attitude on consumers intention. In addition, practical implications are supported for the application of new shopping technology and are likely to stimulate further research in the area of virtual supermarket shopping.

Family violence is the act that causes harm, suffering, or death to members of the family group, especially if they are in a situation of vulnerability due to characteristics associated to age or physical condition. Objective: The social characteristics of aggressors were associate in the risk level of victims of family violence in the city of Arequipa, Peru. Method: The study was descriptive, quantitative, and non-experimental. A total of 205 randomly selected judicial files of aggressors reported for domestic violence were evaluated. The data were secondary, and the chi-square test (association of categorical variables) was used for statistical analysis. Results: A moderate risk level (31.2%) was found, with a tendency to be severe and very severe (49.5%). Likewise, the most observed types of violence are physical and psychological violence (89.3%) and sexual abuse (10.7%). The female aggressor exerts mild violence, while the male aggressor exerts moderate to extreme severe violence, causing more harm to the victim. The profile of the aggressor with low or high education, with high or low incomes, and who occupies a house or only one room can be associated the level of violence that occurs. Conclusion: Men are more likely to attack women, and similarly, female aggressors tend to target men more frequently. Moreover, men exhibit a higher tendency to attack their partners, including wives, cohabitants, and ex-partners, whereas women tend to target a broader range of family members, including parents, children, grandparents, nephews, cousins, as well as in-laws such, brothers-in-law and other relatives.

Malaria is a mosquito-borne infectious disease that affects humans and poses a severe public health problem. Nigeria has the highest number of global cases. Geospatial technology has been widely used to study the risks and factors associated with malaria hazards. The present study is conducted in Ibadan, Oyo State, Nigeria. The objective of this study is to map out areas that are at high risk of the prevalence of malaria by considering a good number of factors as criteria that determine the spread of malaria within Ibadan using open-source and Landsat remote sensing data and further analysis in GIS-based multi-criteria evaluation (MCE). This study considered factors like climate, environmental, socio-economic, and proximity to health centers as criteria for mapping malaria risk. The MCE used a weighted overlay of the factors to produce an element at-risk map, a malaria hazard map, and a vulnerability map. These maps were overlaid to produce the final malaria risk map, which showed that 72% of Ibadan has a risk of malaria prevalence. Identification and delineation of risk areas in Ibadan would help policymakers and decision-makers mitigate the hazards and improve the health status of the state.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.