The digital twin approach has gained recognition as a promising solution to the challenges faced by the Architecture, Engineering, Construction, Operations, and Management (AECOM) industries. However, its broader application across some AECOM sectors remains limited. A significant obstacle is that traditional DTs rely on deterministic models, which require deterministic input parameters. This limits their accuracy, as they do not account for the substantial uncertainties that are inherent in AECOM projects. These uncertainties are particularly pronounced in geotechnical design and construction. To address this challenge, we propose a probabilistic digital twin (PDT) framework that extends traditional DT methodologies by incorporating uncertainties and is tailored to the requirements of geotechnical design and construction. The PDT framework provides a structured approach to integrating all sources of uncertainty, including aleatoric, data, model, and prediction uncertainties, and propagates them throughout the entire modeling process. To ensure that site-specific conditions are accurately reflected as additional information is obtained, the PDT leverages Bayesian methods for model updating. The effectiveness of the PDT framework is showcased through an application to a highway foundation construction project, demonstrating its potential to integrate existing probabilistic methods to improve decision-making and project outcomes in the face of significant uncertainties. By embedding these methods within the PDT framework, we lower the barriers to practical implementation, making probabilistic approaches more accessible and applicable in real-world engineering workflows.

Microbial mineral weathering has been predominantly investigated at shallow depths in humid and tropical environments. Much less is understood about its role in the deeper subsurface of arid and semi-arid environments where microbial weathering is limited by the availability of water and energy sources for microbial metabolism. However, the deep subsurface in these climate zones may host a microbial community that thrives on weathering of iron (Fe)-bearing minerals that serve as electron donors or acceptors.

To investigate the role of microorganisms in weathering of Fe-bearing minerals in a dry climate, we recovered a >80 m deep weathering profile in a semi-arid region of the Chilean Coastal Cordillera. The bedrock is rich in Fe-bearing minerals (hornblende, biotite, chlorite, magnetite and hematite) but lacks detectable organic carbon. We evaluated the bioavailability of Fe(III)-bearing minerals that may serve as an electron acceptor for Fe(III)-reducing microorganisms. Using geochemical, mineralogical and cultivation-based methods, we found enhanced Fe bioavailability and more in vitro microbial Fe(III) reduction at increased depth. We obtained an Fe(III)-reducing enrichment culture from the deepest weathered rock found at 77 m depth. This enrichment culture is capable of reducing ferrihydrite (up to 0.6 mM d–1) using lactate or dihydrogen as an electron donor and grows at circumneutral pH. The main organism in the enrichment culture is the spore-forming Desulfotomaculum ruminis (abundance of 98.5%) as revealed by 16S rRNA gene amplicon sequencing.

Our findings provide evidence for a microbial contribution to the weathering of Fe-bearing minerals in semi-arid environments. While microorganisms are probably not contributing to the weathering of Fe(II)-bearing silicate minerals, they are most likely of importance regarding reductive dissolution of secondary weathering products. The Fe(III) reduction quantified in this weathering profile by the in situ microbial community suggests that microorganisms are active weathering agents in semi-arid climates.

To maximize its value, the design, development and implementation of structural health monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-making. We propose a classification of SHM use cases aligning with various dimensions that are closely linked to the respective decision contexts. The types of decisions that have to be supported by the SHM system within these settings are discussed along with the corresponding challenges. We provide an overview of different classes of models that are required for integrating SHM in the decision-making process to support the operation and maintenance of structures and infrastructure systems. Fundamental decision-theoretic principles and state-of-the-art methods for optimizing maintenance and operational decision-making under uncertainty are briefly discussed. Finally, we offer a viewpoint on the appropriate course of action for quantifying, validating, and maximizing the added value generated by SHM. This work aspires to synthesize the different perspectives of the SHM, Prognostic Health Management, and reliability communities, and provide directions to researchers and practitioners working towards more pervasive monitoring-based decision-support.

Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on model parameters. In this context, on-line (recursive) Bayesian filtering algorithms typically fail to properly quantify the full posterior uncertainty of time-invariant model parameters. Off-line (batch) algorithms are—in principle—better suited for the uncertainty quantification task, yet they are computationally prohibitive in sequential settings. In this work, we adapt and investigate selected Bayesian filters for parameter estimation: an on-line particle filter, an on-line iterated batch importance sampling filter, which performs Markov Chain Monte Carlo (MCMC) move steps, and an off-line MCMC-based sequential Monte Carlo filter. A Gaussian mixture model approximates the posterior distribution within the resampling process in all three filters. Two numerical examples provide the basis for a comparative assessment. The first example considers a low-dimensional, nonlinear, non-Gaussian probabilistic fatigue crack growth model that is updated with sequential monitoring measurements. The second high-dimensional, linear, Gaussian example employs a random field to model corrosion deterioration across a beam, which is updated with sequential sensor measurements. The numerical investigations provide insights into the performance of off-line and on-line filters in terms of the accuracy of posterior estimates and the computational cost, when applied to problems of different nature, increasing dimensionality and varying sensor information amount. Importantly, they show that a tailored implementation of the on-line particle filter proves competitive with the computationally demanding MCMC-based filters. Suggestions on the choice of the appropriate method in function of problem characteristics are provided.

Background: Phase 3 COMET trial (NCT02782741) compares avalglucosidase alfa (n=51) with alglucosidase alfa (n=49) in treatment-naïve LOPD. Methods: Primary objective: determine avalglucosidase alfa effect on respiratory muscle function. Secondary/other objectives include: avalglucosidase alfa effect on functional endurance, inspiratory/expiratory muscle strength, lower/upper extremity muscle strength, motor function, health-related quality of life, safety. Results: At Week 49, change (LSmean±SE) from baseline in upright forced vital capacity %predicted was greater with avalglucosidase alfa (2.89%±0.88%) versus alglucosidase alfa (0.46%±0.93%)(absolute difference+2.43%). The primary objective, achieving statistical non-inferiority (p=0.0074), was met. Superiority testing was borderline significant (p=0.0626). Week 49 change from baseline in 6-minute walk test was 30.01-meters greater for avalglucosidase alfa (32.21±9.93m) versus alglucosidase alfa (2.19±10.40m). Positive results for avalglucosidase alfa were seen for all secondary/other efficacy endpoints. Treatment-emergent adverse events (AEs) occurred in 86.3% of avalglucosidase alfa-treated and 91.8% of alglucosidase alfa-treated participants. Five participants withdrew, 4 for AEs, all on alglucosidase alfa. Serious AEs occurred in 8 avalglucosidase alfa-treated and 12 alglucosidase alfa-treated participants. IgG antidrug antibody responses were similar in both. High titers and neutralizing antibodies were more common for alglucosidase alfa. Conclusions: Results demonstrate improvements in clinically meaningful outcome measures and a more favorable safety profile with avalglucosidase alfa versus alglucosidase alfa. Funding: Sanofi Genzyme

This paper examines the velocity distribution function and cyclotron resonance conditions for a beam of electrons moving in a magnetic field which gradually changes with time. A spatial gradient of magnetic field is known to result in an unstable horseshoe distribution of electrons. The field gradient in time adds additional effects due to an induced electric field. The resultant anisotropic velocity distribution function, which we call a Luvdisk distribution, has some distinctive properties when compared to the horseshoe. Fitting the cyclotron resonance condition circle shows that the frequency of the resultant emission is under the local cyclotron frequency. While the spatial gradient results in the emission coming almost perpendicularly to the field, the direction of the radiation under a time-changing field has more variability. The Luvdisk distribution also arises when the magnetic field has a gradient both in space and time. The beam can be unstable if those gradients are added or subtracted from each other (if the gradients are of equal or different sign), which occurs even when the total change of magnetic field is negative. While the frequency of the emission is related to the final magnetic field value, its direction is indicative of the field’s history which produced the instability.

Recent studies by Cloninger suggest that the temperament dimension of harm avoidance might be related to serotonergic activity. Since serotonergic mechanisms equally play a major role in sleep regulation, we decided to use Cloninger’s psychobiological model of temperament and character to assess whether there is a link between psychophysiologic insomnia and specific personality traits. Chronic insomnia is a common complaint in modern society, and it is still controversial whether insomniacs share specific personality traits. Thirty-two chronic insomniacs (<50 years) were studied. They underwent polysomnography for two consecutive nights and filled out the 226-item self-questionnaire of Temperament and Character Inventory as well as the Hospital Anxiety and Depression scale. (1) Harm avoidance for all subscores was significantly higher in insomniac patients when compared with controls; (2) self-directedness scores were lower in insomniacs; (3) sleep latency was positively correlated to harm avoidance; (4) HA1 (anticipatory worry) was negatively correlated to REM latency. Temperament and Character Inventory is a useful tool in the investigation of chronic insomnia. Serotonergic mechanisms might explain the high incidence of harm avoidance as personality trait in psychophysiologic insomniac patients. Further studies are needed to see whether harm avoidance could be a psychological vulnerability marker for primary insomnia and be used as predictor of SSRI treatment responders.

The main symptoms of Gilles de la Tourette syndrome (GTS) are motor and vocal tics, but there are also several psychiatric disturbances such as obsessive compulsive symptoms (OCD) and mutilations. The etiology and pathophysiology of this basal ganglia disorder are unknown. Though many patients benefit from therapy with dopamine antagonists, the effect is often insufficient. Therapeutic efficacy of the opiate antagonist naltrexone has been described in recent years. Ten patients (three f, seven m) with GTS were invesitgated in a seven-week, open dose-response trial, using naltrexone in doses ranging from 12.5 mg to 200 mg/day. The severity of the GTS symptoms were rated with the Yale Global Tic Severity Scale (YGTSS) and the Tourette Syndrome Global Scale (TSGS); OCD was estimated by the Maudsley Obsessive Compulsive Inventory (MOCI). The course of tics and psychopathology was assessed by three self-report scales, and two scales rated by clinicians (Clinical Global Impression, CGI; Brief Psychiatric Rating Scale, BPRS). Only two patients showed improvement of the GTS. In four patients, no effect was observed, whereas the other four patients exhibited deterioration. One patient finished the complete trial, three patients dropped out because of lack of improvement, and four because of deterioration of the symptoms. Considering the mean values, no improvement of GTS could therefore be observed, and because of the unpredictable effect, the study was stopped after ten patients. Nevertheless, the effects were quite controversial. The opiate system seems to be affected only in some of the GTS patients in a clinically relevant manner, and opiate antogonists can either improve or provoke GTS symptoms. Pathophysiologically different subgroups of GTS may contribute to the controversal results.