Preliminary tests on a wireless sensor network for pervasive dust monitoring in construction sites

One of the critical aspects in health and safety is the control of fine particle emissions from demolition and construction activities. Such exposure is very often the cause of professional illnesses causing a relevant economic burden for welfare and insurance institutions, besides harming workers. Hence this paper performs a feasibility study of a realtime control system of fine particle concentration on construction sites. It was conceived as a ZigbeeTM based wireless, pervasive and non-invasive system, which is easy to deploy over the site and relatively cheap. Dust sensors were interfaced with the system and calibrated in the laboratory. The prototype is described in detail and tested under controlled and real conditions, in order to determine its potential for application. The prototype was shown to be an excellent tool to support health and safety inspectors, to provide in real-time a broad map of dust concentration over the whole extension of the site, provided that calibration coefficients are worked out for the various types of dust which can be encountered on the site

Training of YOLO Neural Network for the Detection of Fire Emergency Assets

Building assets surveys are cost and time demanding and the majority of current methods still rely on manual procedures. New technologies could be used to support this task. The exploitation of Artificial Intelligence (AI) for the automatic interpretation of data is spreading throughout various application fields. However, a challenge with AI is the very large number of training images required for robustly detect and classify each object class. This paper details the procedure and parameters used for the training of a custom YOLO neural network for the recognition of fire emergency assets. The minimum number of pictures for obtaining good recognition performances and the image augmentation process have been investigated. In the end, it was found that fire extinguishers and emergency signs are reasonably detected and their position inside the pictures accurately evaluated. The use case proposed in this paper for the use of custom YOLO is the retrieval of as-is information for existing buildings. The trained neural networks are part of a system that makes use of Augmented Reality devices for capturing pictures and for visualizing the results directly on site

Development of a Digital Twin Model for Real-Time Assessment of Collisione Hazards

The AEC industry is nowadays one of the most hazardous industries in the world. The construction sector employees about 7% of the world’s work force but is responsible for 30-40% of fatalities. As statistics demonstrate, interferences between workers-on-foot and moving vehicles have caused several injuries and fatalities over the years. Despite safety organizational measures, passive safety devices imposed by regulations and efforts from training procedures, scarce improvements have been recorded. Recent research studies propose technology driven approaches as the key solutions to integrate standard health and safety management practices. This is motivated by the evidence that the dynamics of complex systems can hardly be predicted; rather a proactive approach to health and safety is more effective. Current technologies installed on construction equipment can usually react according to a strict logic, such as sending proximity alerts when workers and equipment are too close. Nevertheless, these approaches barely do make informed decisions in real-time, e.g. including the level of reactiveness of the endangered worker. In similar circumstances a digital twin of the construction site, updated by real-time data from sensors and enriched by artificial intelligence, can pro-actively support activities, forecasting dangerous scenarios on the base of several factors. In this paper a laboratory mock-up has been assumed as the test case, supported by a game engine, which is able to replicates the job site for the execution of bored piles. In such a scenario populated by an avatar of a sensor-equipped worker and a virtual driller, a Bayesian network, implemented within the game engine and fed in runtime by sensor data, works out collision probability in real-time in order to send warnings and avoid fatal accidents

Trapping redox partnerships in oxidant-sensitive proteins with a small, thiol-reactive cross-linker

A broad range of redox-regulated proteins undergo reversible disulfide bond formation on oxidation-prone cysteine residues. Heightened reactivity of the thiol groups in these cysteines also increases susceptibility to modification by organic electrophiles, a property that can be exploited in the study of redox networks. Here, we explored whether divinyl sulfone (DVSF), a thiol-reactive bifunctional electrophile, cross-links oxidant-sensitive proteins to their putative redox partners in cells. To test this idea, previously identified oxidant targets involved in oxidant defense (namely, peroxiredoxins, methionine sulfoxide reductases, sulfiredoxin, and glutathione peroxidases), metabolism, and proteostasis were monitored for cross-link formation following treatment of Saccharomyces cerevisiae with DVSF. Several proteins screened, including multiple oxidant defense proteins, underwent intermolecular and/or intramolecular cross-linking in response to DVSF. Specific redox-active cysteines within a subset of DVSF targets were found to influence cross-linking; in addition, DVSF-mediated cross-linking of its targets was impaired in cells first exposed to oxidants. Since cross-linking appeared to involve redox-active cysteines in these proteins, we examined whether potential redox partners became cross-linked to them upon DVSF treatment. Specifically, we found that several substrates of thioredoxins were cross-linked to the cytosolic thioredoxin Trx2 in cells treated with DVSF. However, other DVSF targets, like the peroxiredoxin Ahp1, principally formed intra-protein cross-links upon DVSF treatment. Moreover, additional protein targets, including several known to undergo S-glutathionylation, were conjugated via DVSF to glutathione. Our results indicate that DVSF is of potential use as a chemical tool for irreversibly trapping and discovering thiol-based redox partnerships within cells

Serum Potassium and Risk of Death or Kidney Replacement Therapy in Older People With CKD Stages 4-5: Eight-Year Follow-up

Rationale & Objective: Hypokalemia may accelerate kidney function decline. Both hypo- and hyperkalemia can cause sudden cardiac death. However, little is known about the relationship between serum potassium and death or the occurrence of kidney failure requiring replacement therapy (KRT). We investigated this relationship in older people with chronic kidney disease (CKD) stage 4-5. Study Design: Prospective observational cohort study. Setting & Participants: We followed 1,714 patients (≥65 years old) from the European Quality (EQUAL) study for 8 years from their first estimated glomerular filtration rate (eGFR) < 20 mL/min/1.73 m2 measurement. Exposure: Serum potassium was measured every 3 to 6 months and categorized as ≤3.5, >3.5-≤4.0, >4.0-≤4.5, >4.5-≤5.0 (reference), >5.0-≤5.5, >5.5-≤6.0, and >6.0 mmol/L. Outcome: The combined outcome death before KRT or start of KRT. Analytical Approach: The association between categorical and continuous time-varying potassium and death or KRT start was examined using Cox proportional hazards and restricted cubic spline analyses, adjusted for age, sex, diabetes, cardiovascular disease, renin-angiotensin-aldosterone system (RAAS) inhibition, eGFR, and subjective global assessment (SGA). Results: At baseline, 66% of participants were men, 42% had diabetes, 47% cardiovascular disease, and 54% used RAAS inhibitors. Their mean age was 76 ± 7 (SD) years, mean eGFR was 17 ± 5 (SD) mL/min/1.73 m2, and mean SGA was 6.0 ± 1.0 (SD). Over 8 years, 414 (24%) died before starting KRT, and 595 (35%) started KRT. Adjusted hazard ratios for death or KRT according to the potassium categories were 1.6 (95% CI, 1.1-2.3), 1.4 (95% CI, 1.1-1.7), 1.1 (95% CI, 1.0-1.4), 1 (reference), 1.1 (95% CI, 0.9-1.4), 1.8 (95% CI, 1.4-2.3), and 2.2 (95% CI, 1.5-3.3). Hazard ratios were lowest at a potassium of about 4.9 mmol/L. Limitations: Shorter intervals between potassium measurements would have allowed for more precise estimations. Conclusions: We observed a U-shaped relationship between serum potassium and death or KRT start among patients with incident CKD 4-5, with a nadir risk at a potassium level of 4.9 mmol/L. These findings underscore the potential importance of preventing both high and low potassium in patients with CKD 4-5. Plain-Language Summary: Abnormal potassium blood levels may increase the risk of death or kidney function decline, especially in older people with chronic kidney disease (CKD). We studied 1,714 patients aged ≥65 years with advanced CKD from the European Quality (EQUAL) study and followed them for 8 years. We found that both low and high levels of potassium were associated with an increased risk of death or start of kidney replacement therapy, with the lowest risk observed at a potassium level of 4.9 mmol/L. In patients with CKD, the focus is often on preventing high blood potassium. However, this relatively high optimum potassium level stresses the potential importance of also preventing low potassium levels in older patients with advanced CKD

Physical Knowledge in Patterns: Bayesian Network Models for Preliminary Design

Computer applications in design have pursued two main development directions: analytical modelling and information technology. The former line has produced a large number of tools for reality simulation (i.e. finite element models), the latter is producing an equally large amount of advances in conceptual design support (i.e. artificial intelligence tools). Nevertheless we can trace rare interactions between computation models related to those different approaches. This lack of integration is the main reason of the difficulty of CAAD application to the preliminary stage of design, where logical and quantitative reasoning are closely related in a process that we often call'qualitative evaluation'. This paper briefly surveys the current development of qualitative physical models applied in design and propose a general approach for modelling physical behaviour by means of Bayesian network we are employing to develop a tutoring and coaching system for natural ventilation preliminary design of halls, called VENTPad. This tool explores the possibility of modelling the causal mechanism that operate in real systems in order to allow a number of integrated logical and quantitative inference about the fluid-dynamic behaviour of an hall. This application could be an interesting connection tool between logical and analytical procedures in preliminary design aiding, able to help students or unskilled architects, both to guide them through the analysis process of numerical data (i.e. obtained with sophisticate Computational Fluid Dynamics software) or experimental data (i.e. obtained with laboratory test models) and to suggest improvements to the design