Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches

The adoption of deep neural networks (DNNs) in safety-critical contexts is often prevented by the lack of effective means to explain their results, especially when they are erroneous. In our previous work, we proposed a white-box approach (HUDD) and a black-box approach (SAFE) to automatically characterize DNN failures. They both identify clusters of similar images from a potentially large set of images leading to DNN failures. However, the analysis pipelines for HUDD and SAFE were instantiated in specific ways according to common practices, deferring the analysis of other pipelines to future work. In this paper, we report on an empirical evaluation of 99 different pipelines for root cause analysis of DNN failures. They combine transfer learning, autoencoders, heatmaps of neuron relevance, dimensionality reduction techniques, and different clustering algorithms. Our results show that the best pipeline combines transfer learning, DBSCAN, and UMAP. It leads to clusters almost exclusively capturing images of the same failure scenario, thus facilitating root cause analysis. Further, it generates distinct clusters for each root cause of failure, thus enabling engineers to detect all the unsafe scenarios. Interestingly, these results hold even for failure scenarios that are only observed in a small percentage of the failing images.Comment: 16 Tables, 15 Figure

How do food safety regulations influence market price? A theoretical analysis

Purpose This study is in line with the debate concerning the compatibility between the qualitative and quantitative food production objectives. The purpose of this paper is to identify the causal relationship that may exist between public food safety regulations (specifically, the maximum authorised levels of chemical or microbiological contaminants), and the expected price in the spot markets (wholesale markets, for example). Design/methodology/approach The authors propose a theoretical industrial economic model that identifies the causal link which may exist between public food safety regulations (e.g. the maximum authorised levels of chemical or microbiological contaminants), the expected price in domestic markets, and the rate of exclusion of local producers. This general model allows one to characterize the price formation process in markets subject to maximum residue level constraints by focusing on the role of the official inspection systems established by public authorities. Findings The authors show how strengthening official controls does not systematically impact negatively on producers’ participation and does not always decrease supply. Moreover, the authors show that reinforcing the maximum permitted contamination thresholds is not always sufficient for ensuring consumer health. Originality/value The originality of the model is that it shows how all variables (economic and sanitary variables) interact in the formation of agricultural prices and determine the final size of the productive system (number of active producers). The characterisation of the market price as a function of producers’ investment efforts and of the level of official control reliability allows one to determine both the total supply and the proportion of this supply that is contaminated (i.e. does not comply with the maximum threshold of contamination)

Epidemiological and Clinical Analysis of Intentional Injuries: A Comprehensive Study in Laghouat Province, Algeria

Introduction:Traumas resulting from intentional assaults, termed as intentional injuries (II), pose a significant challenge to public health. Addressing this challenge requires a meticulous approach from healthcare professionals to provide essential psychological support during evidence collection. This study aims to investigate the correlation between the severity of intentional injuries and variables such as gender, age, and occupation of the affected individuals.Materials and Methods:This research takes the form of a prospective monocentric cross-sectional study conducted over a six-month period, from September 2022 to March 2023. The study was carried out at the forensic medicine department of Ahmed Benadjaila Hospital and at the medical-surgical emergency department of the Laghouat Mixed HospitalResults:The primary objective was to assess the existence of a causal relationship between the severity of intentional injuries and factors such as gender, age, and occupation. The results indicate that 89% of the sampled patients were assaulted by male individuals. The mean age of the patients was 29.89 years, with a range from 4 to 73 years, and a majority of individuals were without a profession (46% of the population). Inferential analysis revealed that gender was a significant risk factor in the severity of intentional injuries, while neither age nor occupation were identified as risk factors in this study.Conclusion:Intentional injuries pose a major public health concern with potentially severe consequences for victims. It is imperative to continue in-depth investigations and studies to develop tailored preventive and safety measures. These findings underscore the importance of devising targeted strategies to mitigate the impact of intentional injuries, highlighting the necessity of a multidisciplinary approach to address this issue comprehensively

Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering

Deep neural networks (DNNs) have demonstrated superior performance over classical machine learning to support many features in safety-critical systems. Although DNNs are now widely used in such systems (e.g., self driving cars), there is limited progress regarding automated support for functional safety analysis in DNN-based systems. For example, the identification of root causes of errors, to enable both risk analysis and DNN retraining, remains an open problem. In this paper, we propose SAFE, a black-box approach to automatically characterize the root causes of DNN errors. SAFE relies on a transfer learning model pre-trained on ImageNet to extract the features from error-inducing images. It then applies a density-based clustering algorithm to detect arbitrary shaped clusters of images modeling plausible causes of error. Last, clusters are used to effectively retrain and improve the DNN. The black-box nature of SAFE is motivated by our objective not to require changes or even access to the DNN internals to facilitate adoption. Experimental results show the superior ability of SAFE in identifying different root causes of DNN errors based on case studies in the automotive domain. It also yields significant improvements in DNN accuracy after retraining, while saving significant execution time and memory when compared to alternatives

Influence of the carburization time on the structural and mechanical properties of XC20 steel

This study focuses on the effect of carburization time on the structural and mechanical properties of low carbon XC20 mild steel (C. Wt.% <0.25). The XC20 steel was carburized with activated carbon with a carbon potential Cp1 = 1.1%, at 910 C at different carburization times of 2, 4 and 6 h. The results obtained show that XC20 steel (non-carburized) has a ferrite-pearlitic structure with a hardness and a Young's modulus of the order of (150 HV, 26 KN/mm2). After carburization, the structure of the carburized layer is transformed in martensite (Fe γ) in which cementite (Fe3C) is imbricated. The depth of the carburized layer and the amount of carbon on the surface gradually increase with increasing carburization time. In addition, the carburized XC20 steel becomes hard and brittle where the hardness and Young's modulus have been increased for a high holding time until reaching maximum values (845 HV, 48 KN mm-2) after 6 h of carburization . However, the toughness of XC20 steel has been reduced from 163 to 40 J cm-2

A step-by-step synthesis of triazole-benzimidazole-chalcone hybrids: Anticancer activity in human cells+

Novel series of triazole-benzimidazole-chalcone hybrid compounds have been synthesized via click chemistry, between different azide derivatives and substituted benzimidazole terminal alkynes bearing a chalcone moiety. The starting alkynes are prepared via base-catalysed nitrogen alkylation of pre-synthetized benzimidazole-chalcone substrates. All the intermediates as well as the final products are fully characterized by 1D and 2D NMR and mass spectrometry techniques. HMBC correlations permits the identification of a unique 1,4-disubstitued triazole-benzimidazole-chalcone isomer. Evaluation of the anti-proliferative potential in breast and prostate cancer cell lines showed that the presence of chloro substituents at the chalcone ring of the triazole-benzimidazole-chalcone skeleton enhanced the cytotoxic effects. The benzyl group linked to the 1,2,3-triazole moiety provides more antiproliferative potential.publishe

Exploring the potential of a Ephedra alata leaf extract: Phytochemical analysis, antioxidant activity, antibacterial properties, and green synthesis of ZnO nanoparticles for photocatalytic degradation of methylene blue

Ephedra alata leaf extracts have therapeutic properties and contain various natural compounds known as phytochemicals. This study assessed the phytochemical content and antioxidant effects of a Ephedra alata leaf extract, as well as zinc oxide (ZnO) nanoparticle production. The extract contained phenolic acids, including vanillic acid, chlorogenic acid, gallic acid, p-coumaric acid, vanillin and rutin. Its total phenolic content and total flavonoid content were 48.7 ± 0.9 mg.g-1 and 1.7 ± 0.4 mg.g-1, respectively. The extract displayed a DPPH inhibition rate of 70.5%, total antioxidant activity of 49.5 ± 3.4 mg.g-1, and significant antimicrobial activity toward Gram-positive and negative bacteria. The synthesized ZnO nanoparticles had spherical shape, crystallite size of 25 nm, particle size between 5 and 30 nm, and bandgap energy of 3.3 eV. In specific conditions (90 min contact time, pH 7, and 25°C), these nanoparticles efficiently photodegraded 87% of methylene blue, suggesting potential applications for sustainable water treatment and pollution control

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century