HES-SO Arc

Background Informed end-of-life decision-making requires a high level of death literacy. We still know little about the general population’s level of knowledge and its determinants. Aim To assess knowledge of the general population regarding the legal status and definitions of various end-of-life practices, and to compare the level of knowledge according to individual characteristics known to influence death literacy. Design A self-administered questionnaire featuring two evolving vignettes was used to assess participants’ knowledge relating to the legal status of various end-of-life practices and whether these practices are Medical Aid in Dying (MAiD), which is legal in Canada. The questionnaire also assessed participants’ individual characteristics such as their experience as caregivers for someone who received palliative care, their perception of health, and their financial situation. Setting/participants Participants were community-based community-based Canadian adults able to read French or English. Results In total, 27% of the participants associated the description of care withholding with MAiD, 39% incorrectly associated the description of continuous palliative sedation with MAiD, and 34% incorrectly indicated that the described intervention was illegal. Having cared for someone who received palliative care, at a younger age, a higher level of education, and having participated in advance care planning were associated with better knowledge regarding end-of-life practices. Conclusion Gaps in knowledge about end-of-life practices exist in the general population, they are associated with different individual characteristics and may limit citizens’ capacity to engage in informed end-of-life decision-making. Community-based interventions adapted to different audiences are essential to ensure a quality end-of-life for all.

Intermed, a primary care support nurse (PCN) model, was piloted from 2014 to 2021 in a general practice in La Chaux-de-Fonds. In collaboration with the physician and medical assistant, the PCN supports a proactive Chronic Care Model organization, and focuses specifically on patients in complex chronic situations. Integrated into the medical center, the PCN benefits from a close relationship with the physician. Her services, without apparent additional cost, often clarify the functioning of the network around the patients while allowing the latter to regain control over their care. However, her independent status makes her activity economically unviable within the framework of the LAMal and would require a mode of salaried employment which remains to be invented.

This study investigates the effects of metronome walking on gait dynamics in older adults, focusing on long-range correlation structures and long-range attractor divergence (assessed by maximum Lyapunov exponents). Sixty older adults participated in indoor walking tests with and without metronome cues. Gait parameters were recorded using two triaxial accelerometers attached to the lumbar region and to the foot. We analyzed logarithmic divergence of lumbar acceleration using Rosenstein's algorithm and scaling exponents for stride intervals from foot accelerometers using detrended fluctuation analysis (DFA). Results indicated a concomitant reduction in long-term divergence exponents and scaling exponents during metronome walking, while short-term divergence remained largely unchanged. Furthermore, long-term divergence exponents and scaling exponents were significantly correlated. Reliability analysis revealed moderate to good intrasession consistency for long-term divergence exponents, but poor reliability for scaling exponents. Our results suggest that long-term divergence exponents could effectively replace scaling exponents for unsupervised gait quality assessment in older adults. This approach may improve the assessment of attentional involvement in gait control and enhance fall risk assessment.

Molecular networking has been identified as a key enabling technology for Internet-of-Nano-Things (IoNT): microscopic devices that can monitor, process information, and take action in a wide range of medical applications. As the research matures into prototypes, the cybersecurity challenges of molecular networking are now being researched on at both the cryptographic and physical layer level. Due to the limited computation capabilities of IoNT devices, physical layer security (PLS) is of particular interest. As PLS leverages on channel physics and physical signal attributes, the fact that molecular signals differ significantly from radio frequency signals and propagation means new signal processing methods and hardware is needed. Here, we review new vectors of attack and new methods of PLS, focusing on 3 areas: (1) information theoretical secrecy bounds for molecular communications, (2) key-less steering and decentralized key-based PLS methods, and (3) new methods of achieving encoding and encryption through bio-molecular compounds. The review will also include prototype demonstrations from our own lab that will inform future research and related standardization efforts.

The paper describes a real-time job scheduling method designed for production of goods with different characteristics on machines with different processing parameters. The objective is to maximize the global reward of the factory as a sum of the rewards for each machine job. Traditionally, the task assignment problems deal with the assignment of m-tasks to n-agents and are calculated by analytical methods or heuristics. The proposed method is based on online auctions that distributes the tasks to the machines using the software agents. The method is implemented using the ERTS (Erlang Real-Time System) that allows adopting the features of fault-tolerance and real-time processing. The paper starts with introduction and review of related state-of-the-art. The following chapter briefly describes the problem and the specific requirements. The following chapter describes the software architecture of the online auction system, the use-case and the motivation for developing this method. The proposed method auction-based task assignment and its implementation are described in the next chapters. At the end of the work, the results of the method are presented in comparison with the optimal solution and the performance characteristics are also described. In the conclusion, possible advances and future work are proposed.

L’engagement en formation constitue l’un de moteurs de l’apprentissage et de la réussite d’une formation. Cet engagement mérite d’être examiné pour en comprendre les sources et les conséquences au travers de divers contextes formatifs. Dans le cas du contexte de la formation professionnelle initiale duale en Suisse, les facteurs liés au degré d’engagement des apprenti·es sur les deux principaux lieux de formation (entreprise formatrice et école professionnelle) sont méconnus. Ainsi, cette étude avait pour objectif d’examiner comment les perceptions des apprenti·es quant à la qualité de leur formation peuvent expliquer leur engagement dans chacun des deux principaux lieux de formation. Les analyses ont permis de confirmer que les perceptions de la qualité de la formation prédisaient l’engagement au-delà de multiples caractéristiques individuelles ainsi que d’aspects motivationnels. Les résultats permettent de conclure à la pertinence de tenir compte de la façon dont les apprenti·es considèrent la qualité de leur formation pour constituer d’éventuels leviers sur lesquels agir pour soutenir leur engagement.

An elastomeric optical fiber core was fabricated by drawing a 1160-D thermoplastic polyurethane preform. The cut-back technique was used to measure fiber attenuation for a light spectrum ranging from 550 nm to 875 nm, resulting in an average value of less than 0.25 dB/cm, which is suitable for use in short-distance applications. The potential of the fiber was evaluated in two applications where the advantages of this type of optical fiber, its elasticity and flexibility, are important. In the first application, for weight measurement, the sensor response showed asymptotic behavior at high weights that can be divided into two linear sections of 2.9 dB/N for weights ranging from 0 N up to 1 N, and 2.1 dB/N for 1 N to 1.7 N. In the second application, as a vibration sensor, signals were detected between 1 kHz and 20 kHz with an amplitude of approximately 35 dB above the background noise.

Predictive methods represent techniques commonly met in Industry 4.0 that offer a way to early predict or detect faults of machines, devices or tools. This is useful to anticipate failures with the main goal of improving maintenance planning. Making such predictions could decrease the unexpected malfunction operation or manufacturing downtime and consequently the overall maintenance costs. In this paper we present the basis of the architecture designed for predictive maintenance in the project Social Network of Machines (SOON) under the paradigm of Industry 4.0, as well as a brief literature state-of-the-art survey of the topic. A particular implementation of this architecture, a testbed for electrical motors failure detection, is shown and evaluated.

Microbial fuel cell stacks (MFC-Stack) are often confronted with voltage reversals, likely due to an interplay between microbial community dynamics and insufficient electric circuit balancing. Herein, we provide new insight into voltage reversals by examining the microbiomes of twelve MFC units of a 12-liter Pilot-MFC-Stack during repair. Different biofilm repair methods (self-healing, electrostimulation, and re-acclimatization upon cross-inoculation) were used to evaluate the microbial community response. In addition, MFC-Stack simulation was performed based on Kirchhoff’s Second Law to predict values for source potentials and post-evaluate internal resistances. Analysis of the 16S rRNA amplicon sequencing data suggests that the biofilm repair methods could slowly heal damaged biofilms. Notably, severely voltage reversed MFC units had low electrogen relative abundances (18%) and positive anode potentials, while strong bioanodes and contained more than 50% electrogens and had negative anode potentials. Between-community analyses (beta diversity ordination and multinomial regression) of the voltage reversed MFC units revealed differences among biofilms in contrast to healthy/strong MFC units. Permutational multivariate analysis of variance (PERMANOVA) confirmed that reversed biofilms were, indeed, significantly (p < 0.05) different from stronger ones. Overall, these analyses demonstrated the utility of combining electrotechnical and microbial community analyses, especially beta diversity ordination and multinomial regression, to understand problematic MFC units and the potential success of a biofilm repair method. Finally, thicker biofilms were usually healthier and stronger, although thickness was no guarantee for proper structure and power function as all factors were interdependent. There was an evolutionary trend that strong anodes became stronger/healthier and others weaker. This spontaneous trend has to be considered to avoid irreversible voltage reversals and to repair electrogenic biofilms in an MFC-Stack.

This article examines the entanglement between feelings of stress and discomfort, physiological arousal and urban experiences of persons living with early psychosis. It adopts a biosocial approach, using mixed methods combining ambulatory skin conductance monitoring, mobile interviews and contextual data, collected through GPS and video recordings. The study draws on and strives to cross-fertilize two recent strands of research. The first relates to the use of digital phenotyping in mental health research. The second explores stress and emotional arousal in cities using ambulatory physiological measures. Empirically, the paper is based on fieldwork in Basel, Switzerland, with nine participants recruited within the Basel Early Treatment Service (BEATS), and four controls. We focus on three salient elements in our results: visual perception of moving bodies, spatial transitions and openness and enclosure of the built environment. The analysis shows how these elements elicit physiological responses of arousal and expressed feelings of discomfort. In the concluding section we discuss the methodological implications of these results and suggest the notion of regime of attention as a focus for future biosocial research on urban mental health.

Background Urinary tract infection (UTI) is one of the most common bacterial infections responsible for increased annual incidence of antimicrobial resistance (AMR) cases. Clinical diagnosis of UTI AMR relies heavily on conventional urine culture and antibiotic susceptibility testing (AST) which has a turnaround time of ∼3 days. Often, irrespective of the infection status, antibiotics are prescribed to patients even before the test results are available, leading to non-judicious use of antibiotics. Over the years, several technologies have been developed for the rapid detection and diagnosis of UTI AMR, however, most of them are limited to traditional microbiological techniques and large laboratory equipment that are not readily available in low-to-middle income countries (LMICs). To address these diagnostic limitations, we are developing a rapid and affordable UTI-AMR diagnostic microfluidic device that is clinical friendly aimed at improving UTI management and AMR stewardship. Results Our device enables the flow of a large volume of urine specimens for the capture/enrichment of uropathogenic bacteria and determination of AST via a porous membrane that is augmented with a multifunctional polymer-based material. Important objectives for the development of UTI AMR diagnostic microfluidic device are: (i) development of a multifunctional polymer-based material; and (ii) validation of UTI AMR diagnostic device. We have successfully developed a polysaccharide-based platform to (i) selectively capture uropathogenic bacteria from urine specimen by immobilizing concanavalin A (con A) lectin as bacterial capture agent on the polymer surface via chemical modification; (ii) encapsulate and release bacterial nutrient media and antibiotics for AST; and (iii) detect AST via encapsulation of bacterial growth indicator. In addition, we have also determined the development of methacrylate-based and acrylamide-based synthetic polymer-based material for our application. Further, we have demonstrated the uniform augmentation of the polysaccharide-based polymer onto porous membrane via dip-coating technique for on-chip bacterial capture/enrichment and AST in fluid (urine) flow conditions. The porous membrane is a conducting material which enables us to perform electrochemical measurements such as impedance spectroscopy that accelerates the detection process of antibiotic susceptibility. As a proof-of-concept, we have determined the capture of biosafety level I Escherichia coli expressing kanamycin resistance gene on chemically surface modified polysaccharide-based polymer containing con A and the antibiotic susceptibility of captured bacteria against different antibiotics with and without the porous membrane. We have quantitatively determined the limit of detection of E. coli on multifunctional polysaccharide-based polymer material. Conclusions The utility of the UTI AMR microfluidic device in clinical settings enables clinicians to make informed decisions on the most appropriate antibiotic for treatment in less than a day. Integration of impedance spectroscopy will further accelerate the detection by significantly reducing the time of detection. Further, the device allows for off-chip analysis by retrieving the captured uropathogenic bacteria to perform high throughput sequencing for identifying AMR genetic determinants. Therefore, with the ability to selectively capture uropathogenic bacteria and determine AST in a short time, our technology has the potential to overcome some of the current limitations in UTI AMR diagnostics.

Conditionally automated cars share the driving task with the driver. When the control switches from one to another, accidents can occur, especially when the car emits a takeover request (TOR) to warn the driver that they must take the control back immediately. The driver’s physiological state prior to the TOR may impact takeover performance and as such was extensively studied experimentally. However, little was done about using Machine Learning (ML) to cluster natural states of the driver. In this study, four unsupervised ML algorithms were trained and optimized using a dataset collected in a driving simulator. Their performances for generating clusters of physiological states prior to takeover were compared. Some algorithms provide interesting insights regarding the number of clusters, but most of the results were not statistically significant. As such, we advise researchers to focus on supervised ML using ground truth labels after experimental manipulation of drivers’ states.

The aim of the Social Network of machines (SOON) project is to investigate the impact of using of autonomous social agents to optimize manufacturing processes in the framework of Industry 4.0. In this article, we present the multi-agent SOON architecture and the built solutions aiming at optimizing the scheduling of tasks. Two different scheduling approaches are proposed. The first approach is based on an ‘auction’ paradigm where the task assignment is decided according to the capability of a machine agent to bid for a task. The second approach is built on a heterarchical agents network where agents learn the acquisition of cooperative tasks. Both solutions are capable of managing and synchronizing the communication between agents while performing their tasks. To describe each approach, two industrial use cases are illustrated: wire rod mill manufacturing and mechanical part manufacturing. Finally, in the heterarchical network, agents are trained with reinforcement learning to maximize the cumulative reward and optimize the manufacturing scheduling. Results show that reinforcement learning allows learning the optimal behavior in multiple scenarios.

In the 1980s, Switzerland’s Jura Arc region was a globally competitive ‘new industrial space’ in the Third Industrial Revolution’s flexible accumulation regime based on information and communication technology (ICT) and automation processes. Recently, this nowadays ‘old industrial space’ has been experiencing the implementation of Industry 4.0. Caught between the use of existing productive assets and the development of platform-based market ecosystems, this region illustrates the challenges inherent in implementing ‘forking innovation’, which requires the development not only of new business models, but also of collaborative and investment models in order to scale up and increase local value capture.

The COVID-19 pandemic’s first wave required considerable adaptation efforts on the part of healthcare workers. The literature on resilient healthcare describes how the collective regulation strategies implemented by frontline employees make essential contributions to institutions’ abilities to cope with major crises. The present mixed-methodology study was thus conducted among a large sample of employees in a variety of Swiss healthcare institutions and focused on problematic real-world situations experienced by them and their managers during the pandemic’s first wave. It highlighted the anticipatory and adaptive strategies implemented by institutions, teams and individuals. The most frequently cited problematic situations involved organisational changes, interpersonal conflicts and workloads. In addition to the numerous top-down measures implemented by institutions, respondents also identified personal or team regulation strategies such as increasing staff flexibility, prioritising tasks, interprofessional collaboration, peer support or creating new communication channels to families. The present findings underlined the importance of taking greater account of healthcare support staff and strengthening managerial capacity to support interprofessional teams including those support staff.