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

GobhiSet: Dataset of raw, manually, and automatically annotated RGB images across phenology of Brassica oleracea var. Botrytis

: This research introduces an extensive dataset of unprocessed aerial RGB images and orthomosaics of Brassica oleracea crops, captured via a DJI Phantom 4. The dataset, publicly accessible, comprises 244 raw RGB images, acquired over six distinct dates in October and November of 2020 as well as 6 orthomosaics from an experimental farm located in Portici, Italy. The images, uniformly distributed across crop spaces, have undergone both manual and automatic annotations, to facilitate the detection, segmentation, and growth modelling of crops. Manual annotations were performed using bounding boxes via the Visual Geometry Group Image Annotator (VIA) and exported in the Common Objects in Context (COCO) segmentation format. The automated annotations were generated using a framework of Grounding DINO + Segment Anything Model (SAM) facilitated by YOLOv8x-seg pretrained weights obtained after training manually annotated images dated 8 October, 21 October, and 29 October 2020. The automated annotations were archived in Pascal Visual Object Classes (PASCAL VOC) format. Seven classes, designated as Row 1 through Row 7, have been identified for crop labelling. Additional attributes such as individual crop ID and the repetitiveness of individual crop specimens are delineated in the Comma Separated Values (CSV) version of the manual annotation. This dataset not only furnishes annotation information but also assists in the refinement of various machine learning models, thereby contributing significantly to the field of smart agriculture. The transparency and reproducibility of the processes are ensured by making the utilized codes accessible. This research marks a significant stride in leveraging technology for vision-based crop growth monitoring

RafanoSet: Dataset of raw, manually, and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation

The rationale for this data article is to provide resources which could facilitate the studies focussed over weed detection and segmentation in precision farming using computer vision. We have curated Multispectral (MS) images over crop fields of Triticum Aestivum containing heterogenous mix of Raphanus raphanistrum in both uniform and random crop spacing. This dataset is designed to facilitate weed detection and segmentation based on manual and automatically annotated Raphanus raphanistrum, commonly known as wild radish. The dataset is publicly available through the Zenodo data library and provides annotated pixel-level information that is crucial for registration and segmentation purposes. The dataset consists of 85 original MS images captured over 17 scenes covering various spectra including Blue, Green, Red, NIR (Near-Infrared), and RedEdge. Each image has a dimension of 1280 × 960 pixels and serves as the basis for the specific weed detection and segmentation. Manual annotations were performed using Visual Geometry Group Image Annotator (VIA) and the results were saved in Common Objects in Context (COCO) segmentation format. To facilitate this resource-intensive task of annotation, a Grounding DINO + Segment Anything Model (SAM) was trained with this manually annotated data to obtain automated Visual Object Classes Extended Markup Language (PASCAL VOC) annotations for 80 MS images. The dataset emphasizes quality control, validating both the ’manual" and ’automated" repositories by extracting and evaluating binary masks. The codes used for these processes are accessible to ensure transparency and reproducibility. This dataset is the first-of-its kind public resource providing manual and automatically annotated weed information over close-ranged MS images in heterogenous agriculture environment. Researchers and practitioners in the fields of precision agriculture and computer vision can use this dataset to improve MS image registration and segmentation at close range photogrammetry with a focus on wild radish. The dataset not only helps with intrasubject registration to improve segmentation accuracy, but also provides valuable spectral information for training and refining machine learning models

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