Abstract
Quality inspection is a critical step in ensuring the quality and efficiency of textile production processes. With the increasing complexity and scale of modern textile manufacturing systems, the need for accurate and efficient quality inspection and defect detection techniques has become paramount. This paper compares supervised and unsupervised Machine Learning techniques for defect detection in the context of industrial textile production, in terms of their respective advantages and disadvantages, and their implementation and computational costs. We explore the use of an autoencoder for the detection of defects in textiles. The goal of this preliminary work is to find out if unsupervised methods can successfully train models with good performance without the need for defect labelled data. (c) 2023 The Authors. Published by Elsevier B.V.

Abstract

Abstract
Fishing for deep-sea species occurs on continental slopes, ridges, and seamounts. Fishing operations using fishing gears that contact the bottom (e.g., trawls and bottom longlines) may have significant impacts on Vulnerable Marine Ecosystems (VMEs). VMEs refer to marine ecosystems with a population or community of sensitive taxa or habitats that are likely to experience substantial alteration from short-term to chronic disturbance and that are unlikely to recover during the timeframe in which the disturbance occurs. The VME concept, introduced in the United Nations General Assembly Resolution 61/105, has been worldwide applied to the management of deep-sea fisheries. However, the effective identification and management of VMEs is highly constrained by the scarcity of data on VME indicator taxa. This data deficiency is usually surpassed by the use of VME predictive modelling. Video footage is a non-destructive method commonly used for exploring and investigating areas of seabed and for characterising and identifying habitat types. Remotely Operated Vehicles (ROVs) are one of the tools for seabed mapping. ROVs range in size from small observation-class to large work-class vehicles. Their sizes determine the payload, manoeuvrability, depth rating and ultimately uses of the vehicle. For epifaunal imaging, ROVs can be used in two modes: qualitative inspections and quantitative assessments. This paper presents the development of an innovative system composed of a compact support research vessel and a hybrid autonomous underwater vehicle capable of accurate georeferenced high-resolution imaging and profiling of the seabed for a detailed survey of the seabed for biodiversity studies. The experimental results obtained by the developed system in field work in real VME survey at 600m depth are presented.

Abstract
One of the key tasks in ensuring national security is the ability of the state and society to recognise and effectively assess the conditions for disasters, and to prevent them from threatening the sustainable development of the country. The Kyrgyz Republic is highly vulnerable to the influence of climate change, which in turn affects the frequency and intensity of disasters. The Kyrgyz Republic is exposed to almost all types of geological and man-made hazards, including earthquakes, landslides, debris flows, flash floods, outbursts of mountain lakes, dam failures, avalanches, droughts, extreme temperature, epidemics and releases of hazardous substances. Analysis of information on existing risks and their control systems used to reduce their negative impact makes it possible to assess the degree of probability, the expected consequences of threats, determine the degree of risk, the adaptive potential of communities and select appropriate protective measures. Therefore, this study is conducted to assess the hazard, vulnerability and exposure of Suzak district (Jalal-Abad oblast) in order to quantify the risk of the study area using multi-parameter holistic assessment with field collecting of primary data and utilizing Index-based Risk Assessment approach based on applying INFORM Risk model. Collected data was used to downscale subnational INFORM Risk model for municipal and district level using a multi-layered structure. A risk score is calculated by combining 72 indicators that measure three main dimensions: hazard & exposure, vulnerability, and lack of coping capacity. These findings provide an opportunity to develop a more effective disaster risk management at the local and national levels, by prioritizing relevant actions and investments for municipalities – districts which are demonstrated relatively highest risk scores. Also, the possibility of applying localized risk assessment procedures provides an opportunity to obtain more accurate sub-national (district/oblast based) and national levels with effective assessing dynamics of risk. © Author(s) 2024. CC Atribution 4.0 License

Abstract
This paper analyses how the harmonization between supply and demand uncertainty and supply chain responsiveness (SC fit) impacts business performance. The study analyses data obtained from a sample of 179 manufacturing companies from Portugal. The business performance of companies with different types of SC fit (high-high fit and low-low fit) and misfit (positive and negative) were analyzed and discussed. The results indicate that SC fit is positively related to business performance, economic and productivity, and commercial performance separately. This study advances the literature as the results indicate that SC fit positively affects both commercial and economic, and productivity performance. In contrast, previous empirical studies have mainly addressed the impact only on financial and operational performance.

Abstract
Static source code analysis techniques are gaining relevance in automated assessment of programming assignments as they can provide less rigorous evaluation and more comprehensive and formative feedback. These techniques focus on source code aspects rather than requiring effective code execution. To this end, syntactic and semantic information encoded in textual data is typically represented internally as graphs, after parsing and other preprocessing stages. Static automated assessment techniques, therefore, draw inferences from intermediate representations to determine the correctness of a solution and derive feedback. Consequently, achieving the most effective semantic graph representation of source code for the specific task is critical, impacting both techniques' accuracy, outcome, and execution time. This paper aims to provide a thorough comparison of the most widespread semantic graph representations for the automated assessment of programming assignments, including usage examples, facets, and costs for each of these representations. A benchmark has been conducted to assess their cost using the Abstract Syntax Tree (AST) as a baseline. The results demonstrate that the Code Property Graph (CPG) is the most feature -rich representation, but also the largest and most space -consuming (about 33% more than AST).