Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection.

Construction drawings are frequently stored in undigitised formats and consequently, their analysis requires substantial manual effort. This is true for many crucial tasks, including material takeoff where the purpose is to obtain a list of the equipment and respective amounts required for a project. Engineering drawing digitisation has recently attracted increased attention, however construction drawings have received considerably less interest compared to other types. To address these issues, this paper presents a novel framework for the automatic processing of construction drawings. Extensive experiments were performed using two state-of-the-art deep learning models for object detection in challenging high-resolution drawings sourced from industry. The results show a significant reduction in the time required for drawing analysis. Promising performance was achieved for symbol detection across various classes, with a mean average precision of 79% for the YOLO-based method and 83% for the Faster R-CNN-based method. This framework enables the digital transformation of construction drawings, improving tasks such as material takeoff and many others

Deep learning for symbols detection and classification in engineering drawings.

Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance. The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings

Deep learning for text detection and recognition in complex engineering diagrams.

Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper copy, meaning the information contained within the diagrams is not readily accessible to inspect and use for further data analytics. In this paper, we make use of the benefits of recent deep learning advances by selecting models for both text detection and text recognition, and apply them to the digitisation of text from within real world complex engineering diagrams. Results show that 90% of text strings were detected including vertical text strings, however certain non text diagram elements were detected as text. Text strings were obtained by the text recognition method for 86% of detected text instances. The findings show that whilst the chosen Deep Learning methods were able to detect and recognise text which occurred in simple scenarios, more complex representations of text including those text strings located in close proximity to other drawing elements were highlighted as a remaining challenge

European colonization, not Polynesian arrival, impacted population size and genetic diversity in the critically endangered New Zealand Kākāpō.

Island endemic species are often vulnerable to decline and extinction following human settlement, and the genetic study of historical museum specimens can be useful in understanding these processes. The kākāpō (Strigops habroptilus) is a critically endangered New Zealand parrot that was formerly widespread and abundant. It is well established that both Polynesian and European colonization of New Zealand impacted the native avifauna, but the timeframe and severity of impacts have differed depending on species. Here, we investigated the relative importance of the 2 waves of human settlement on kākāpō decline, using microsatellites and mitochondrial DNA (mtDNA) to characterize recent kākāpō genetic and demographic history. We analyzed samples from 49 contemporary individuals and 54 museum specimens dating from 1884 to 1985. Genetic diversity decreased significantly between historical and contemporary kākāpō, with a decline in mean number of microsatellite alleles from 6.15 to 3.08 and in number of mtDNA haplotypes from 17 to 3. Modeling of demographic history indicated a recent population bottleneck linked to the period of European colonization (approximately 5 generations ago) but did not support a major decline linked to Polynesian settlement. Effective population size estimates were also larger for historical than contemporary kākāpō. Our findings inform contemporary kākāpō management by indicating the timeframe and possible cause of the bottleneck, which has implications for the management of extant genetic diversity. We demonstrate the broader utility of a historical perspective in understanding causes of decline and managing extinction risk in contemporary endangered species

Enrichment of wearable sensor data from individuals with lower limb amputation in a free-living setting

Background Objective physical activity monitoring of lower limb amputees can be achieved using wearable sensors, however the literature has shown that simplistic objective monitoring (for instance, measuring step count or energy expenditure) is overwhelmingly favoured (Jamieson et al., 2020). For a more detailed analysis of activity, the implementation of machine learning algorithms to recognize wider varieties of activity is a logical solution. Currently, no research has attempted to perform HAR with an amputee population using an unsupervised learning approach. Aim To provide clinically useful information for healthcare professionals specialising in lower limb amputee rehabilitation by developing a robust unsupervised classification system that can recognize different activities and walking terrains covered by lower limb amputees. Method The following methodology was given ethical approval by the University of Strathclyde's Ethics Committee. A combination of amputee volunteers with no known comorbidities and healthy volunteers with no known gait impairments were recruited for the study. The participants were instructed to utilize an IMU and to record themselves with a chest-mounted camera while going on a walk on a variety of terrains in the local vicinity of their homes. The collected IMU data was used to train an unsupervised classification system to recognize walking activities and terrains, with the annotated data from the camera providing validation to the performance of the classifier. Results 12 participants were recruited for the study. This demographic is comprised of 8 healthy individuals, 3 transtibial amputees and 1 bilaterial amputee. The development of the classification algorithm is an ongoing process. Principal component analysis is applied to reduce the dimensionality of the feature set to 2 dimensions. The K-Means clustering algorithm assigns 20 cluster groups, with the intention that each cluster should be equivalent to a different type of activity or terrain. The resultant clustering model can be seen in figure 1. Mathematically, the equivalency of the cluster and class labels is calculated via normalized mutual information, which is currently at 31.1%. Discussion 99% of the variability in the dataset is explained by the first principal component, which is skewed towards the data from the bilateral amputee, suggesting a separate clustering model should be constructed. Additionally, similarity between labels will be analysed to determine the trade-off in the hierarchical detail in the labels and the cluster model performance. While optimisation of the classifier is still ongoing, the clustering model shows potential to recognize different types of walking terrains for lower limb amputees

Early intervention for stigma towards mental illness? Promoting positive attitudes towards severe mental illness in primary school children

Purpose Stigma towards severe mental illness (SMI) is widespread, exacerbating mental health problems, and impacting on help-seeking and social inclusion. Anti-stigma campaigns are meeting with success, but results are mixed. Earlier intervention to promote positive mental health literacy rather than challenge stigma, may show promise, but little is known about stigma development or interventions in younger children. This study will investigate (i) children’s knowledge, attitudes and behaviour towards SMI and (ii) whether we can positively influence children’s attitudes before stigma develops. Design/methodology/approach A cross sectional study investigated mental health schema in 7-11 year olds. An experimental intervention investigated whether an indirect contact story-based intervention in 7-8 year olds led to more positive mental health schema. Findings: Young children’s schema were initially positive, and influenced by knowledge and contact with mental illness & intergroup anxiety, but were more stigmatising in older girls as intergroup anxiety increased. The indirect contact intervention was effective in promoting positive mental health schema, partially mediated by knowledge. Social Implications: Intervening early to shape concepts of mental illness more positively, as they develop in young children, may represent a more effective strategy than attempting to challenge and change mental health stigma once it has formed in adolescents and adults. Originality/Value: This study is the first to investigate an intervention targeted at the prevention of stigma towards severe mental illness, in young children, at the point that stigma is emerging

Canadian packaged gluten-free foods are less nutritious than their regular gluten-containing counterparts

Background A strict gluten-free (GF) diet is required for the management of celiac disease (CD). The nutritional adequacy of this diet has been questioned due to the elimination of wheat, an important vehicle for micronutrient fortification and source of fibre. While novel and/or reformulated packaged GF products have rapidly entered the marketplace, providing alternatives to wheat-based staples, it is unknown whether these new products are nutritionally comparable. Methods From a database of 3,851 foods collected across 21 grocery stores in Eastern Canada, we compared the nutrient content of 398 unique GF items with 445 gluten-containing (GC) equivalents. Wilcoxon rank tests were conducted on listed nutrient content (g, mg, µg) per 100 g of product and the nutrient contribution of iron, folate and fibre were evaluated using Health Canada’s nutrient claim regulations. Results GF staples (cereals, breads, flours, pastas) contained 1.3 times more fat and less iron (by 55%), folate (by 44%) and protein (by 36%), than GC counterparts (P < 0.0001). On average, GF pastas had only 37% of the fibre in GC pastas (P < 0.0001). Notably, GF and GC flours were equivalent in nutrient content. Despite GF and GC flours having similar nutritional content, the vast majority of the processed GF foods fell short in key nutrients. Discussion Packaged GF foods in Canada are generally less nutritious than their GC counterparts, suggesting that GF diets should not be promoted to those who do not require it. The use of nutrient-dense GF flours in homemade foods may improve nutrient intakes on the GF diet

L1 grammatical attrition in late Spanish-English bilinguals in the UK : Aspectual interpretations of present tense in Spanish

This article sheds light on the linguistic and extralinguistic conditions that determine the likelihood of L1 grammatical attrition in late sequential bilinguals. We explore whether aspectual interpretations associated with the present tense may be a vulnerable area for the native grammar of 30 late Spanish-English bilinguals who have settled in the UK for over 15 years. Attrition of this property in L1 Spanish grammars has been reported by Cuza (2010. On the L1 attrition of the Spanish present tense. Hispania 93, no. 2: 256–272. doi:10.1353/hpn.2010.a382874) for Spanish-English bilinguals in the USA and Canada. Our finding of no attrition for UK-based Spanish bilinguals suggests that in Cuza’s study, attrition may be mediated by dialectal variation in the L1 in the North American context, where Spanish is a widespread and visible community language. Further, we ascribe the absence of attrition to a specific characteristic of the grammatical distinction between the L1 and L2: where the L2 grammar provides options representing only a subset of the options available to the L1 for the corresponding grammatical property, attrition may be disfavoured.</p

Association Between Assisted Reproductive Technology Conception and Autism in California, 1997–2007

Objectives. We assessed the association between assisted reproductive technology (ART) and diagnosed autistic disorder in a population-based sample of California births. Methods. We performed an observational cohort study using linked records from the California Birth Master Files for 1997 through 2007, the California Department of Developmental Services autism caseload for 1997 through 2011, and the Centers for Disease Control and Prevention’s National ART Surveillance System for live births in 1997 through 2007. Participants were all 5 926 251 live births, including 48 865 ART-originated infants and 32 922 cases of autism diagnosed by the Department of Developmental Services. We compared births originated using ART with births originated without ART for incidence of autism.Results. In the full population, the incidence of diagnosed autism was twice as high for ART as non-ART births. The association was diminished by excluding mothers unlikely to use ART; adjustment for demographic and adverse prenatal and perinatal outcomes reduced the association substantially, although statistical significance persisted for mothers aged 20 to 34 years. Conclusions. The association between ART and autism is primarily explained by adverse prenatal and perinatal outcomes and multiple births