The technological development and growth of the telecommunications industry have had a great positive impact on the education, health, and economic sectors, among others. However, they have also increased rivalry between companies in the market to keep and acquire new customers. A lower level of market concentration is related to a higher level of competitiveness among companies in the sector that drives a country’s socioeconomic development. To guarantee and improve the level of competition, it is necessary to monitor the concentration level in the telecommunications market to plan and develop appropriate strategies by governments. With this in mind, the present work aims to analyze the concentration prediction in the telecommunications market through recurrent neural networks and the Herfindahl-Hirschman index. The results show a slight gradual increase in competition in terms of traffic and access, while a more stable concentration level is observed in revenues.

The undeniable importance of migrants’ remittances to the welfare of developing countries was again demonstrated during the COVID-19 pandemic. This has therefore led to a significant shift in attention to the relevance of remittances and has likewise spurred research interest in factors that motivate the inflows of remittances. However, in spite of the increasing recognition of the roles of digital technology in the macroeconomic performance of developed and developing economies alike, empirical analysis of its possible impacts on remittance inflows has not been well explored in the literature. Therefore, pooling the annual data of 35 sub-Saharan African (SSA) countries from 2011 to 2020, this study investigates the nexus between digital technology and remittance inflows within the generalized method of moments (GMM) framework. Using two measures of digital technology infrastructure—internet usage and mobile cellular subscription—the study finds a positive relationship between digital technology and remittances inflow. In addition, the findings indicate that the magnitude of the effect is relatively higher for internet usage. The study thus shows that the increased rate of remittance mobilization constitutes a significant pathway through which digital technology impacts the economies of the SSA region. Moreover, it offers further insight on the importance of digital technology in the socioeconomic development of developing countries. From a policy standpoint, governments and policymakers in SSA countries should intensify efforts to promote the diffusion and penetration of digital infrastructure.

Human settlement patterns in the South are clearly inequitable and dysfunctional, with tenure insecurity remaining a significant issue. Consequently, there has been a dramatic increase in housing demand driven by rising household sizes and accelerated urbanization. Local governments have a clear mandate to ensure socio-economic development and promote democracy, which necessitates ongoing consultations and renegotiations with citizens. This paper critically examines the de-densification of informal settlements as a pivotal strategy to enhance the quality of life for citizens, all while maintaining essential social networks. Governments must take decisive action against pandemics by transforming spaces into liveable settlements that improve livelihoods. A qualitative method was employed, analyzing data drawn from interviews to gain insights into individual views, attitudes, and behaviors regarding the improvement of livelihoods in informal settlements. The study utilized a simple random sampling technique, ensuring that every individual in the population selected had an equal opportunity for inclusion. Semi-structured interviews were conducted with twenty community members in Cornubia, alongside discussions with three officials from eThekwini Municipality and KwaZulu Natal (KZN) Provincial Department of Human Settlements. Data was analyzed using thematic analysis, and the findings hold substantial benefits for the most disadvantaged citizens. Therefore, municipalities have an obligation to transform urban areas by reducing inequality, bolstered by national government policy, to achieve a resilient, safe, and accessible urban future. The evidence presented in this paper underscores that local governments, through municipalities, must prioritize de-densifying informal settlements in response to pandemics or hazards. It is vital to leverage community-driven initiatives and reinforce networks within these communities. The paper calls for the establishment of a socially centered government through the District Development Model (DDM), emphasizing socio-economic transformation as a pathway to enhance community quality of life.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).