Poverty Traps

The understanding of why some countries fail to develop is one of the most intriguing and productive challenges for the modern theory of economic growth. Although many questions remains unanswered, the theory has gone a long way to explain and reveal many of the reasons underlying the persistence of poverty traps. Multiple causes, endogeneity and country-specific differences make it difficult to explain within a unified framework why some economies, caught in a vicious cycle, suffer from persistent underdevelopment

Migration flows, structural change, and growth convergence: A panel data analysis of Italian regions

The aim of this paper is to measure the impact of migration flows on growth via their effect on structural change. To this extent we build an empirical growth model in which migration flows and intersectoral wage differentials can affect the speed of labour readjustment between sectors and, ultimately, total factor productivity and growth. By employing Italian regional data stemming over more than four decades we measure the effects of interregional migration on regional growth and convergence. The results confirm that migration in general, and in particular the content of human capital of moving workers, is a relevant factor in determining the speed of technological change and growth.Migration; Structural change; Growth and convergence.

Acoustic impact of a wave energy converter in Mediterranean shallow waters

In this study, underwater noise from a full-scale wave energy converter system (ISWEC), installed on the coast of Pantelleria Island (central Mediterranean Sea), was characterized. The noise was measured using an autonomous acoustic recorder anchored to the sea bottom 40 m from the ISWEC hull. Acoustic monitoring continued for 15 months, starting 7 months before (PRE), 2 months during (INST) and 6 months after the ISWEC installation (POST). The levels of noise, assessed with power spectrum density and octave and third-octave band sound pressure levels (BSPLs), were higher during the POST period than during the PRE period at lower frequencies up to 4 kHz and increased with wave height. During the ISWEC activation for energy production (POST_ON) in the wave height range 1–2.9 m, the BSPLs increased much more at lower frequencies up to 4 kHz (the median BSPLs at 63 Hz for the PRE, POST, and POST_ON conditions were 73, 106, and 126 dB re 1μPa, respectively). Considering the biophonies that make up the soundscape of the area, we examined the possible masking of fish choruses due to ISWEC noise and highlighted that at a distance of 1000 m, the 800 Hz peak frequency was 10 dB above the ISWEC signal. Within this distance from ISWEC, a possible masking effect is supposed

Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment

This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing Triticum aestivum crop and Raphanus raphanistrum weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a [email protected] of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances

Identifying Critical Issues in Smart City Big Data Project Implementation

Many cities across the globe are adopting smart city initiatives, as smart city holds the promise of better quality of life and equity for city\u27s residents, more efficient use of city\u27s infrastructure, and more effective city planning. Big data analytics is the backbone of smart city and the drive engine to achieve smart city\u27s promises. However, statistics indicate that more than 50% of big data projects fail; they either never finish or do not offer the expected value. Resulting in severe consequences as such projects tends to be expensive and require allocating the organization\u27s best resources while doing the project. This is even more crucial in the case of smart city, as cities usually have limited budget and resources. This paper conducted literature review and perspectives analysis to identify challenges, which can cause big data projects to fail, with focus on smart city related big data projects. The goal is to offer a list of challenges, that a project manager can consider as an initial list of risks for the upcoming project, and evaluate the city\u27s readiness against each of them