The age of fast fashion: how consumer behaviour has changed and how it’s impacting the companies

Consumer trends are constantly evolving, nowadays choosing a garment could have significant environmental and social impacts. Therefore, to understand if consumers make informed choices, fast fashion companies were analyzed in the first part of the thesis. Through the research, I found that the business model of these companies focuses on a quick response to the growth in demand, which constantly requires the production of a new selection of clothes but low prices. Unfortunately to do this, companies produce abroad by exploiting workers and polluting the surrounding environment. From here I tried to analyze if there could be solutions to this situation, one of these is the introduction of sustainability within fast fashion companies. How? through the implementation of CSR and Green standards and labels. With this new information, consumers are able to recognize companies that are committed to being more transparent, so as to guarantee them a more conscious choice. In fact, the sustainable options on the market are various, from vintage to slow fashion. Other consumers, on the other hand, prefer not to give up on trendy products at low prices. This, as we will see, is due to the fact that some are simply not interested in purchasing products with a lower environmental and social impact, but in other cases they are simply not informed enough to understand what other options are available, or they are deceived by misleading words that convince to buy sustainable products, as in the case of the strategies used by H&M

DepthFormer: Multimodal Positional Encodings and Cross-Input Attention for Transformer-Based Segmentation Networks

Most approaches for semantic segmentation use only information from color cameras to parse the scenes, yet recent advancements show that using depth data allows to further improve performances. In this work, we focus on transformer-based deep learning architectures, that have achieved state-of-the-art performances on the segmentation task, and we propose to employ depth information by embedding it in the positional encoding. Effectively, we extend the network to multimodal data without adding any parameters and in a natural way that makes use of the strength of transformers' self-attention modules. We also investigate the idea of performing cross-modality operations inside the attention module, swapping the key inputs between the depth and color branches. Our approach consistently improves performances on the Cityscapes benchmark

SynDrone -- Multi-modal UAV Dataset for Urban Scenarios

The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs) imagery heavily relies on the availability of annotated high-resolution aerial data. However, the scarcity of large-scale real datasets with pixel-level annotations poses a significant challenge to researchers as the limited number of images in existing datasets hinders the effectiveness of deep learning models that require a large amount of training data. In this paper, we propose a multimodal synthetic dataset containing both images and 3D data taken at multiple flying heights to address these limitations. In addition to object-level annotations, the provided data also include pixel-level labeling in 28 classes, enabling exploration of the potential advantages in tasks like semantic segmentation. In total, our dataset contains 72k labeled samples that allow for effective training of deep architectures showing promising results in synthetic-to-real adaptation. The dataset will be made publicly available to support the development of novel computer vision methods targeting UAV applications.Comment: Accepted at ICCV Workshops, downloadable dataset with CC-BY license, 8 pages, 4 figures, 8 table

RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation

Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies. However, existing methods struggle especially when several incremental steps are performed. In this paper, we extend our previous approach (RECALL) and tackle forgetting by exploiting unsupervised web-crawled data to retrieve examples of old classes from online databases. Differently from the original approach that did not perform any evaluation of the web data, here we introduce two novel approaches based on adversarial learning and adaptive thresholding to select from web data only samples strongly resembling the statistics of the no longer available training ones. Furthermore, we improved the pseudo-labeling scheme to achieve a more accurate labeling of web data that also consider classes being learned in the current step. Experimental results show that this enhanced approach achieves remarkable results, especially when multiple incremental learning steps are performed

Comparision of profile macro-estethic perception among orthodontists, dentistry students, orthodontic patients and surgical orthodontic patients

The patient?s needs should guide the orthodontist in choosing the most appropriate therapy. The purpose of the present survey was to compare the esthetic perception of the facial profile by orthodontists (O), dentistry students (DS), orthodontic patient

A distinctive 'microbial signature' in celiac pediatric patients

<p>Abstract</p> <p>Background</p> <p>Celiac Disease (CD) is an autoimmune disorder of the small intestine in which dietary gluten ingestion leads to a chronic enteropathy. Recently, scientific evidence suggested a potential role of gut microbiota in CD. To have a snapshot of dominant duodenal microbiota we analyzed the mucosa-associated microbiota of 20 children with CD, before and after a gluten-free diet (GFD) regimen, and of 10 controls. Total DNA was extracted from duodenal biopsies and amplification products of 16S ribosomal DNA were compared by temporal temperature gradient gel electrophoresis (TTGE). TTGE profiles were analyzed by statistical multivariate analysis.</p> <p>Results</p> <p>The average number of bands in TTGE profiles was significantly higher (<it>P </it>< 0.0001) in active (n.b. 16.7 ± 0.7) and inactive states (n.b. 13.2 ± 0.8) than in controls (n.b. 3.7 ± 1.3). Mean interindividual similarity index was 54.9% ± 14.9% for active disease, 55.6% ± 15.7% for remission state and 21.8% ± 30.16% for controls. Similarity index between celiac children before and after GFD treatment was 63.9% ± 15.8%. Differences in microbiota biodiversity were among active and remission state (<it>P </it>= 0.000224) and amid active CD and controls (<it>P </it>< 0.001). <it>Bacteroides vulgatus </it>and <it>Escherichia coli </it>were detected more often in CD patients than in controls (<it>P </it>< 0.0001).</p> <p>Conclusions</p> <p>Overall, the results highlighted a peculiar microbial TTGE profile and a significant higher biodiversity in CD pediatric patients' duodenal mucosa. The possible pathophysiological role of these microbial differences needs further characterization.</p

Deep learning image reconstruction algorithm. impact on image quality in coronary computed tomography angiography

PurposeTo perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V).Material and methodsFifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient.ResultsDLIR algorithm did not impact vascular attenuation (P &gt;= 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P &lt;= 0.021).DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P &gt;= 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P &lt;= 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001).ConclusionDLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD

Evaluation of the 2021 ESC recommendations for family screening in hereditary transthyretin cardiac amyloidosis

AIMS: The 2021 European Society of Cardiology (ESC) screening recommendations for individuals carrying a pathogenic transthyretin amyloidosis variant (ATTRv) are based on expert opinion. We aimed to (i) determine the penetrance of ATTRv cardiomyopathy (ATTRv-CM) at baseline; (ii) examine the value of serial evaluation; and (iii) establish the yield of first-line diagnostic tests (i.e. electrocardiogram, echocardiogram, and laboratory tests) as per 2021 ESC position statement.METHODS AND RESULTS: We included 159 relatives (median age 55.6 [43.2-65.9] years, 52% male) at risk for ATTRv-CM from 10 centres. The primary endpoint, ATTRv-CM diagnosis, was defined as the presence of (i) cardiac tracer uptake in bone scintigraphy; or (ii) transthyretin-positive cardiac biopsy. The secondary endpoint was a composite of heart failure (New York Heart Association class ≥II) and pacemaker-requiring conduction disorders. At baseline, 40/159 (25%) relatives were diagnosed with ATTRv-CM. Of those, 20 (50%) met the secondary endpoint. Indication to screen (≤10 years prior to predicted disease onset and absence of extracardiac amyloidosis) had an excellent negative predictive value (97%). Other pre-screening predictors for ATTRv-CM were infrequently identified variants and male sex. Importantly, 13% of relatives with ATTRv-CM did not show any signs of cardiac involvement on first-line diagnostic tests. The yield of serial evaluation (n = 41 relatives; follow-up 3.1 [2.2-5.2] years) at 3-year interval was 9.4%.CONCLUSIONS: Screening according to the 2021 ESC position statement performs well in daily clinical practice. Clinicians should adhere to repeating bone scintigraphy after 3 years, as progressing to ATTRv-CM without signs of ATTRv-CM on first-line diagnostic tests or symptoms is common.</p

Tolerability and efficacy of vortioxetine versus SSRIs in elderly with major depression. Study protocol of the VESPA study: a pragmatic, multicentre, open-label, parallel-group, superiority, randomized trial

Depression is a highly prevalent condition in the elderly, with a vast impact on quality of life, life expectancy, and medical outcomes. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed agents in this condition and, although generally safe, tolerability issues cannot be overlooked. Vortioxetine is an antidepressant with a novel mechanism of action. Based on studies to date, it may have a promising tolerability profile in the elderly, as it does not adversely affect psychomotor or cognitive performance and does not alter cardiovascular and endocrine parameters. The present study aims to assess the tolerability profile of vortioxetine in comparison with the SSRIs considered as a single group in elderly participants with depression. The rate of participants withdrawing from treatment due to adverse events after 6 months of follow up will be the primary outcome

Expansion cone for the 3-inch PMTs of the KM3NeT optical modules

[EN] Detection of high-energy neutrinos from distant astrophysical sources will open a new window on the Universe. The detection principle exploits the measurement of Cherenkov light emitted by charged particles resulting from neutrino interactions in the matter containing the telescope. A novel multi-PMT digital optical module (DOM) was developed to contain 31 3-inch photomultiplier tubes (PMTs). In order to maximize the detector sensitivity, each PMT will be surrounded by an expansion cone which collects photons that would otherwise miss the photocathode. Results for various angles of incidence with respect to the PMT surface indicate an increase in collection efficiency by 30% on average for angles up to 45 degrees with respect to the perpendicular. Ray-tracing calculations could reproduce the measurements, allowing to estimate an increase in the overall photocathode sensitivity, integrated over all angles of incidence, by 27% (for a single PMT). Prototype DOMs, being built by the KM3NeT consortium, will be equipped with these expansion cones.This work is supported through the EU, FP6 Contract no. 011937, FP7 grant agreement no. 212252, and the Dutch Ministry of Education, Culture and Science.Adrián Martínez, S.; Ageron, M.; Aguilar, JA.; Aharonian, F.; Aiello, S.; Albert, A.; Alexandri, M.... (2013). Expansion cone for the 3-inch PMTs of the KM3NeT optical modules. Journal of Instrumentation. 8(3):1-19. https://doi.org/10.1088/1748-0221/8/03/T03006S1198