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\"We are now on the brink of a new era in construction - that of autonomous assembly. For some time, the widespread adoption of robotic and digital fabrication technologies has made it possible for architects and academic researchers to design non-standard, highly customised structures. These technologies have largely been limited by scalability, focusing mainly on top-down, bespoke fabrication projects, such as experimental pavilions and structures. Autonomous assembly and bottom-up construction techniques hold the promise of greater scalability, adaptability and potentially evolved design possibilities. By capitalising on the advances made in swarm robotics, the collective construction of the animal/insect kingdom, and advances in physical computational, programmable materials or self-assembly, architects and designers are now able to build from the bottom up. This issue presents future scenarios of autonomous assembly by highlighting the viability of decentralised, collective assembly systems, demonstrating the potential to deliver reconfigurable and adaptive solutions.\"--Back cover.

Background: Insufficient physical activity and unhealthy diets are contributing to the rise in noncommunicable diseases. Preventative mobile health (mHealth) interventions may help reverse this trend, but present bias might reduce their effectiveness. Future-self avatar interventions have resulted in behavior change in related fields, yet evidence of whether such interventions can change health behavior is lacking. Objective: We aimed to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior and examine the feasibility of a novel automated nutrition tracking system. We also aimed to understand how this intervention impacts related attitudinal and motivational constructs. Methods: We conducted a 12-week parallel randomized controlled trial (RCT), followed by semistructured interviews. German-speaking smartphone users aged ≥18 years living in Switzerland and using at least one of the two leading Swiss grocery loyalty cards, were recruited for the trial. Data were collected from November 2020 to April 2021. The intervention group received the FutureMe intervention, a physical activity and food purchase tracking mobile phone app that uses a future-self avatar as the primary interface and provides participants with personalized food basket analysis and shopping tips. The control group received a conventional text- and graphic-based primary interface intervention. We pioneered a novel system to track nutrition by leveraging digital receipts from loyalty card data and analyzing food purchases in a fully automated way. Data were consolidated in 4-week intervals, and nonparametric tests were conducted to test for within- and between-group differences. Results: We recruited 167 participants, and 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The median age was 44 years (IQR 19), and the gender ratio was balanced (female 52/95, 55%). Attrition was unexpectedly high with only 30 participants completing the intervention, negatively impacting the statistical power. The FutureMe intervention led to small statistically insignificant increases in physical activity (median +242 steps/day) and small insignificant improvements in the nutritional quality of food purchases (median −1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased in the FutureMe group, but decreased in the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and accepted fully automated nutrition tracking system. Conclusions: Preventative future-self avatar mHealth interventions promise to encourage improvements in physical activity and food purchasing behavior in healthy population groups. A full-powered RCT is needed to confirm this preliminary evidence and to investigate how future-self avatars might be modified to reduce attrition, overcome present bias, and promote sustainable behavior change. Trial Registration: ClinicalTrials.gov NCT04505124; https://clinicaltrials.gov/ct2/show/NCT04505124

There is a long history in environmental sciences to investigate and understand the fate of chemicals in the environment. For pesticides, this has led to systematic assessments of compounds by both academic and regulatory bodies, particularly for soil and water. As we show here, in recent years, there is an increasing interest in the potential presence of pesticide residues in air and related exposure risks. Based on a literature review for the years 2002–2022, we find a growing number of air monitoring studies with an average of 6.7 studies/year since 2020, with passive sampling methods contributing significantly to this rise. Most studies are concentrated in Europe and North America, with France leading in the number of monitoring studies. However, due to a lack of harmonization, and thus, the use of diverse methods and approaches, it remains challenging to derive potential exposure risks, to assess data quality of studies, and to compare datasets. In this perspective, we focus on current and emerging trends of different air monitoring approaches and highlight how they influence the interpretation of data. To improve the comparability and utility of data, and to ensure that air monitorings meet certain quality requirements, we propose a path forward, including: (1) Standardization and harmonization of methods: Adopting well-characterized and widely applied methods from air quality research as a basis for standardizing pesticide monitoring, with a clear distinction between relevant exposure and total air concentrations. (2) Tiered approach for monitoring programs: A dynamic concept where initial passive sampling identifies potential exposure risks, followed by active sampling for quantitative data, and, if necessary, extensive monitoring programs. This approach balances the need for detailed data with resource constraints. (3) Data interpretation and transparency: Public availability of data and clear reporting of methods, analysis, and uncertainties are crucial for the credibility and utility of monitoring studies. Overall, we see that harmonization of standards is critical for assessing exposure risks from pesticides in air and for informing regulatory decisions and mitigation strategies. Collaboration with the air quality and atmospheric research community is strongly recommended to leverage existing expertise in sampling, analysis, and data interpretation.

Buildings employ an ensemble of technical systems like those for heating and ventilation. Ontologies such as Brick, IFC, SSN/SOSA, and SAREF have been created to describe such technical systems in a machine-understandable manner. However, these focus on describing system topology , whereas several relevant use cases (e.g., automated fault detection and diagnostics (AFDD)) also need knowledge about the physical processes. While mathematical simulation can be used to model physical processes, these are practically expensive to run and are not integrated with mainstream technical systems ontologies today. We propose to describe the effect of component actuation on underlying physical mechanisms within component stereotypes. These stereotypes are linked to actual component instances in the technical system description, thereby accomplishing an integration of knowledge about system structure and physical processes. We contribute an ontology for such stereotypes and show that it covers 100% of Brick heating, ventilation, and air-conditioning (HVAC) components. We further show that the ontology enables automatically inferring relationships between components in a real-world building in most cases, except in two situations where component dependencies are underreported. This is due to missing component models for passive parts like splits and join in ducts, and hence points at concrete future extensions of the Brick ontology. Finally, we demonstrate how AFDD applications can utilize the resulting knowledge graph to find expected consequences of an action, or conversely, to identify components that may be responsible for an observed state of the process.

Balancing the immune response after solid organ transplantation (SOT) and vascularized composite allotransplantation (VCA) remains an ongoing clinical challenge. While immunosuppressants can effectively reduce acute rejection rates following transplant surgery, some patients still experience recurrent acute rejection episodes, which in turn may progress to chronic rejection. Furthermore, these immunosuppressive regimens are associated with an increased risk of malignancies and metabolic disorders. Despite significant advancements in the field, these IS related side effects persist as clinical hurdles, emphasizing the need for innovative therapeutic strategies to improve transplant survival and longevity. Cellular therapy, a novel therapeutic approach, has emerged as a potential pathway to promote immune tolerance while minimizing systemic side-effects of standard IS regiments. Various cell types, including chimeric antigen receptor T cells (CAR-T), mesenchymal stromal cells (MSCs), regulatory myeloid cells (RMCs) and regulatory T cells (T regs ), offer unique immunomodulatory properties that may help achieve improved outcomes in transplant patients. This review aims to elucidate the role of cellular therapies, particularly MSCs, T cells, T regs , RMCs, macrophages, and dendritic cells in SOT and VCA. We explore the immunological features of each cell type, their capacity for immune regulation, and the prospective advantages and obstacles linked to their application in transplant patients. An in-depth outline of the current state of the technology may help SOT and VCA providers refine their perioperative treatment strategies while laying the foundation for further trials that investigate cellular therapeutics in transplantation surgery.

Artificial Intelligence (AI) is increasingly used worldwide to make decisions, be it by public administrations, industry, banks, or insurers. One area with a particularly high impact on citizens is its use in judiciary processes. To this day, there has been little investigation into why countries decide to adopt AI in this area. In this article, we show that the mere promise of efficiency gains is not enough of an explanation for why countries adopt AI within judiciary processes. Instead, we show that the administrative burden (as measured by the number of days to trial), a government’s leaning within the political spectrum (namely towards the left), and the level of adoption of technology by governments in neighboring countries predict countries’ announcement to adopt AI in their judiciary. Taken together, our findings derived from a data analysis of 28 advanced democracies constitute an important step towards a better understanding of the factors shaping AI’s integration into judiciary processes.

We prove a lower bound for the free energy (per unit volume) of the two-dimensional Bose gas in the thermodynamic limit. We show that the free energy at density$\\unicode[STIX]{x1D70C}$and inverse temperature$\\unicode[STIX]{x1D6FD}$differs from the one of the noninteracting system by the correction term$4\\unicode[STIX]{x1D70B}\\unicode[STIX]{x1D70C}^{2}|\\ln \\,a^{2}\\unicode[STIX]{x1D70C}|^{-1}(2-[1-\\unicode[STIX]{x1D6FD}_{\\text{c}}/\\unicode[STIX]{x1D6FD}]_{+}^{2})$. Here,$a$is the scattering length of the interaction potential,$[\\cdot ]_{+}=\\max \\{0,\\cdot \\}$and$\\unicode[STIX]{x1D6FD}_{\\text{c}}$is the inverse Berezinskii–Kosterlitz–Thouless critical temperature for superfluidity. The result is valid in the dilute limit$a^{2}\\unicode[STIX]{x1D70C}\\ll 1$and if$\\unicode[STIX]{x1D6FD}\\unicode[STIX]{x1D70C}\\gtrsim 1$.

Background Studying dynamic processes in living organisms with MRI is one of the most promising research areas. The use of paramagnetic compounds as contrast agents (CA), has proven key to such studies, but so far, the lack of appropriate techniques limits the application of CA-technologies in experimental plant biology. The presented proof-of-principle aims to support method and knowledge transfer from medical research to plant science. Results In this study, we designed and tested a new approach for plant Dynamic Contrast Enhanced Magnetic Resonance Imaging (pDCE-MRI). The new approach has been applied in situ to a cereal crop ( Hordeum vulgare ). The pDCE-MRI allows non-invasive investigation of CA allocation within plant tissues. In our experiments, gadolinium-DTPA, the most commonly used contrast agent in medical MRI, was employed. By acquiring dynamic T 1 -maps, a new approach visualizes an alteration of a tissue-specific MRI parameter T 1 (longitudinal relaxation time) in response to the CA. Both, the measurement of local CA concentration and the monitoring of translocation in low velocity ranges (cm/h) was possible using this CA-enhanced method. Conclusions A novel pDCE-MRI method is presented for non-invasive investigation of paramagnetic CA allocation in living plants. The temporal resolution of the T 1 -mapping has been significantly improved to enable the dynamic in vivo analysis of transport processes at low-velocity ranges, which are common in plants. The newly developed procedure allows to identify vascular regions and to estimate their involvement in CA allocation. Therefore, the presented technique opens a perspective for further development of CA-aided MRI experiments in plant biology.

Governments around the world are increasingly turning to algorithms to support or fulfil administrative tasks – from automating social benefit regulation to the automatic determination of the merit of court cases. With this global trend came along an increasing number of scandals that often resulted from a mismatch between implementation of an algorithmic process by the government and public expectation. These (violated) expectations concern several factors of the deployment, which are currently understudied. This article analyses how six of these factors – institution, aim, legal field, data provenance, trigger, and level of automation – influence social acceptance of automating administrative and legal processes in France. From our factorial vignette study with 6 variables across 540 vignettes and N=7,560 assessments, we draw three main conclusions. First, we find that the institution, aim, data provenance, trigger and level of automation are statistically significant in driving the acceptance of automatically processable regulations. Second, we find that acceptance across all conditions is only slightly positive on average, when expressed on a scale from −5 to +5. Overall, our study confirms that the social acceptability of the implementation of algorithms in administrative and legal processes is highly dependent on specific implementation and contextual details; public discourse on the implementation of such algorithms hence requires information about specifics of their deployment.

Severe physical injuries and associated traumatic brain injury and/or hemorrhagic shock (HS) remain leading causes of death worldwide, aggravated by accompanying extensive inflammation. Retrospective clinical data indicated an association between mild hyperoxemia and improved survival and outcome. However, corresponding prospective clinical data, including long-term resuscutation, are scarce. Therefore, the present study explored the effect of mild hyperoxemia for 24 hours in a prospective randomized controlled trial in a long-term resuscitated model of combined acute subdural hematoma (ASDH) and HS. ASDH was induced by injecting 0.1 ml × kg −1 autologous blood into the subdural space and HS was triggered by passive removal of blood. After 2 hours, the animals received full resuscitation, including retransfusion of the shed blood and vasopressor support. During the first 24 hours, the animals underwent targeted hyperoxemia (P a O 2 = 200 – 250 mmHg) or normoxemia (P a O 2 = 80 – 120 mmHg) with a total observation period of 55 hours after the initiation of ASDH and HS. Survival, cardiocirculatory stability, and demand for vasopressor support were comparable between both groups. Likewise, humoral markers of brain injury and systemic inflammation were similar. Multimodal brain monitoring, including microdialysis and partial pressure of O 2 in brain tissue, did not show significant differences either, despite a significantly better outcome regarding the modified Glasgow Coma Scale 24 hours after shock that favors hyperoxemia. In summary, the present study reports no deleterious and few beneficial effects of mild targeted hyperoxemia in a clinically relevant model of ASDH and HS with long-term resuscitation in otherwise healthy pigs. Further beneficial effects on neurological function were probably missed due to the high mortality in both experimental groups. The present study remains exploratory due to the unavailability of an a priori power calculation resulting from the lack of necessary data.