Monitoring onsite-temperature prediction error for condition monitoring of civil infrastructures

An inverse input–output method is proposed for long-term condition monitoring of civil infrastructures through monitoring the prediction error of air temperature recorded at the site of a structure. It is known that structural natural frequencies are affected by temperature. Hence, the proposed method considers the structural natural frequencies as input and temperature as output to train a machine learning algorithm (MLA). To this end, after signal preprocessing using the variational mode decomposition (VMD), different MLAs are employed, and the error associated with this prediction is regarded as damage–sensitive feature. It is hypothesised and further confirmed through solving numerical and benchmark problems that the prediction error deviates significantly from the upper bond control limit of an R-chart (errors signal) constructed based on the prediction error of temperature as soon as the damage occurs. The frequency–temperature scatter plots indicate a linear dependency between the natural frequencies and temperature. Moreover, the similar slope obtained for the regression line fitted to different frequency–temperature scatter plots indicates high collinearity among pairs of natural frequencies. This observation implies that an interaction term must be considered for such pairs of natural frequencies in the linear regression model. The results of both numerical and experimental studies further confirm that the interaction linear regression model is the most accurate machine learning algorithm for solving the inverse problem of predicting temperature using natural frequencies for condition monitoring of structures. The results of the proposed method are also compared with the direct strategy, whereby its superiority is demonstrated

Broadening the knowledge base of small-scale fisheries through a food systems framework: A case study of the Lake Superior region

Lake Superior is the largest and northernmost of the Great Lakes of North America. It supports a diversity of wildlife and fish species, along with commercial, recreational, and Indigenous fisheries that make vital contributions to nutrition, livelihoods, cultures, and food systems. However, this diversity of social and cultural values is not fully reflected in management practices that tend towards a ‘resourcist’ approach. This chapter seeks to ‘broaden the scope’, proposing a food systems framework as a way of grappling with the wicked problem of Lake Superior fisheries governance. Using a food systems framework, we look at the different values associated with fisheries, including the objective, subjective, and relational contributions they make to Lake Superior food systems. We explore these food-related values attached to fisheries by presenting three illustrative examples: The fisheries of Batchewana First Nation; Eat the Fish, a small business marketing local fish through alternative food networks in Northwestern Ontario; and Bodin’s Fisheries in Wisconsin, a regional fish processor and retail outlet. We conclude by identifying ways of strengthening fisheries contributions to regional food systems and offer a set of transdisciplinary questions on fishery-food system linkages that may assist others in ‘broadening the scope’ of fisheries governance

A case study of state-corporate crime: Crown Resorts

The concept of state-corporate crime has emerged in criminological discourse to explain the nexus of political and economic decision-making by states and corporations, and the ways this cooperation can have socially injurious impacts (Michalowski, R. J., & Kramer, R. C. [2006]. The critique of power. In R. J. Michalowski & R. C. Kramer (Eds.), State-corporate crime. New Brunswick: Rutgers, p. 15). This ‘cooperation’ can include corporations engaging in illegality with the tacit approval of state organisations, states failing to prevent crime, and even states colluding with corporate illegality. In this article, we use state-corporate crime theory to situate the recent wrongdoing at Crown Resorts (henceforth ‘Crown’). We explain how this wrongdoing emerged within a politico-economic environment of neo-liberalism, particularly through the recent deregulation of casinos in New South Wales (NSW). We argue that the organisational decisions within Crown that breached laws and caused harms are best understood as a case of state-corporate crime

Understanding and mitigating the distinctive stresses induced by diverse microplastics on anaerobic hydrogen-producing granular sludge.

This work comparatively studied the different stress responses of anaerobic hydrogen-producing granular sludge (HPG) to several typical MPs in wastewater, i.e., polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC) MPs. A new approach to mitigating the inhibition caused by MPs based on biochar was then proposed. The results displayed that microbe in HPG had diverse tolerances to PE-MPs, PET-MPs and PVC-MPs, with the hydrogen production downgraded to 82.0 ± 3.2 %, 72.3 ± 2.5 % and 66.6 ± 2.3 % (p < 0.05) of control respectively, due to the distinct leachates toxicities and oxidative stress level induced by different MPs. The discrepant mitigation reflected in the hydrogen yields of biochar-based HPGs raised back to 88.7 ± 1.4 %, 85.3 ± 3.8 % and 88.5 ± 3.5 % of control. The MPs induced disintegrated granule morphology, fragile microbial viability and impaired defensive function of extracellular polymeric substances were restored by biochar. The effective mitigation was revealed to be due to the strong adsorption of MPs by biochar, reducing direct contact between microbes and MPs. Biochar addition also enhanced protection for HPG by increasing EPS secretion and weakened the oxidative damage to anaerobes induced by MPs. Biochar manifested the disparate adsorption properties of three MPs. The most superior mitigation in HPG contaminated by PVC-MPs was attributed to the strongest affinity of biochar to PVC-MPs and effective alleviation of PVC leachates toxicity

Environment-Robust WiFi-Based Human Activity Recognition Using Enhanced CSI and Deep Learning

Deep learning has demonstrated its great potential in channel state information (CSI)-based human activity recognition (HAR), and hence has attracted increasing attention in both the industry and academic communities. While promising, most existing high-accuracy methodologies require to retrain their models when applying the previous-trained ones to a new/unseen environment. This issue has limited their practical usabilities. In order to overcome this challenge, this article proposes an innovative scheme, which combines an activity-related feature extraction and enhancement (AFEE) method and matching network (AFEE-MatNet). The proposed scheme is 'one-fits-all,' meaning that the trained model can be directly applied in new/unseen environments without any retraining. We introduce the AFEE method to enhance CSI quality by eliminating noise. Specifically, the approach mitigates environmental noises unrelated to activity while better compressing and preserving the behavior-related information. Moreover, the size of feature signals generated by AFEE are reduced, which in turn significantly shortens the training time. For effective feature extraction, we propose to use the MatNet architecture to learn transferable features shared among source environments. To further improve the recognition performance, we introduce a prediction checking and correction scheme to rectify some classification errors that do not abide by the state transition of human behaviors. Extensive experimental results demonstrate that our proposed AFEE-MatNet significantly outperforms existing state-of-the-art HAR methods, in terms of both recognition accuracy and training time

Application of electrolytic manganese residues in cement products through pozzolanic activity motivation and calcination

Degradation in grade of manganese ore aggravates the complexity of electrolytic manganese residue (EMR). Calcination is one of the most practical pretreatment methods to improve EMR activity and dispose the hazardous elements. In this paper, the evolution of mineral phase, pozzolanic activity, pore structure and harmful components induced by calcining EMR was investigated. The results show that EMR calcined at 800 °C has the strength activity index (SAI) of 84.79 at 28 d, which is attributed to the decomposition of dihydrate gypsum and the formation of activated calcium, silicon and aluminum oxide. The formation of β-type hemihydrate gypsum increases the pozzolan activity, while the latter is limited by the formation of stable Mn-spinel (Mn3O4) and Mn-hercynite (MnFe2O4). In the EMR-doped mortar matrix, the production of a large amount of ettringite due to the existence of gypsum, as well as common C-S-H, portlandite and AFm, which strongly verify the pozzolanic activity of EMR. Leaching results show that Mn2+ and NH4+-N could not be eliminated completely at low temperature (<600 °C), but could be completely stabilized in the alkaline environment provided by the cement. The Mn2+ and NH4+-N levels in mortar are fully below the regulatory standards when calcinated above 800 °C. All heavy metals are fixed in the cement and calcination process, ensuring the cleaner utilization of EMR in building materials