University of Applied Sciences and Arts of Southern Switzerland

Introduction China faces the dual challenge of high air pollution and an increasing burden of cardiovascular disease (CVD). We aimed to estimate the healthcare costs associated with CVD and the quality-adjusted life years (QALYs) under scenarios of improved air quality in China. Methods A health prediction model was developed to estimate 10-year CVD-related costs and QALY associated with PM2.5 levels in 2015, as well as two hypothetical improved air quality scenarios: (1) the China national PM2.5 target of 35 µg/m³, and (2) the World Health Organization’s (WHO) PM2.5 guideline of 5 µg/m³. Population CVD risks were estimated from the 2015 China Health and Retirement Longitudinal Study. Hazard ratios from WHO risk curves were subsequently applied to baseline cardiovascular risks to predict national 10-year estimates of ischaemic stroke and coronary heart disease-related healthcare expenditures and QALYs for individuals aged 45–85 under the three air quality scenarios. Results Under PM2.5 levels in 2015, we estimated a cumulative 10-year incidence of 35.40 million CVD events, resulting in healthcare costs of US$96.12 billion and 4.44 billion QALYs. Under the national target of 35 µg/m³, the projected 10-year CVD incidence was 31.92 million cases, resulting in cost savings of US$9.29 billion and 3.43 million QALY gains compared with 2015 levels. If PM2.5 concentration levels meet the WHO’s guideline of 5 µg/m³, the projected number of CVD events would decrease to 24.18 million, translating to cost savings of approximately US$30.10 billion and gains of 11.29 million QALYs. Conclusion Our findings indicate that achieving the WHO recommended PM2.5 concentration level of 5 µg/m³ could lead to over threefold greater health and economic benefits than those achievable under national standards of 35 µg/m³. This underscores the potential need for stricter future national PM2.5 standards. Our findings also inform other low- and middle-income countries in establishing effective long-term PM2.5 targets.

Non-specialised nurses play a crucial role in managing chronic wounds, necessitating fundamental knowledge of wound assessment and debridement techniques. Chronic wounds require accurate clinical assessment to determine appropriate debridement methods, such as autolytic, mechanical, or sharp debridement, depending on wound characteristics and patient needs. Effective debridement should be integrated into a comprehensive care plan, focusing on an after debridement and patient-centered care plan. Nurses must recognize their limits and refer patients to specialized wound care teams when necessary. Training in debridement, should comprise theoretical aspects which are supported by mentorship and practical training to ensure patient safety and obtain optimal patient outcomes.

Aims It is widely recognized that the COVID-19 pandemic exerted an impact on the mental health of the general population, but epidemiological evidence is surprisingly sparse. We aimed to explore the association between serologically confirmed SARS-CoV-2 infection and psychological distress – assessed by symptoms of depression, anxiety and stress – in the general adult population in southern Switzerland, a region widely affected by the pandemic. We also investigated whether this association varied over time and between pandemic waves from late 2020 through 2021. Methods We used data from 305 adults who participated in the Corona Immunitas Ticino prospective seroprevalence study in southern Switzerland, including results of the serological tests of SARS-CoV-2 infection collected in June 2021, and explored associations with depression, anxiety and stress scores as measured by the 21-item Depression, Anxiety and Stress Scale at three time points between December 2020 and August 2021, accounting for socio-demographic and health characteristics. Results In our sample, 84.3% of the participants (mean age of 51.30 years, SD = ±.93) were seronegative at baseline. Seropositive (i.e., infected) participants had a decreasing probability of being depressed and anxious through the COVID-19 pandemic waves compared to the seronegative (non-infected) participants. Further, seropositivity at baseline was also associated with more rapid decline in depressive, anxiety and stress symptomatology, and younger age and the presence of chronic diseases were independently associated with mild anxiety (OR = .97; P = 0.013; 95% CI = 0.95, 0.99; OR = 3.47; P = 0.001; 95% CI = 1.71, 7.04) and stress (OR = .96; P = 0.003; 95% CI = .94, .99; OR = 2.56; P = 0.010; 95% CI = 1.25, 5.22). Conclusions Our results suggest that the MH consequences of the pandemic may not be due to the SARS-CoV-2 infection per se, but to fears associated with the risk of infection, and to the pandemic uncertainties.

This article presents the performance analysis of a new dynamic and vertically oriented building‐integrated photovoltaic (BIPV) shading device. The work forms part of a Swiss Pilot and Demonstration project. From technology readiness levels 5–7, it aims to validate the technology's consistency and replicability in the operational building and cost‐effectiveness for marketability. The shading slats comprise two‐string PV modules realized with laminated glazing layered by an outer white satin glass pane. For the small‐scale mock‐up, two designs are compared to quantify the effectiveness of temperature reduction and energy gain: 1) optimized—each string is connected to a bypass diode, and 2) standard—two strings are connected to a bypass diode. It is demonstrated that optimized slats have consistently lower module temperatures and higher energy yield, achieving more than 20% gain during spring and summer. There are no additional risks for consideration since no extreme temperature and humidity measurements are observed for the pilot installation at the actual building. The first floor's system produces a lower specific energy yield due to partial shading. Still, it can be worsened if the standard PV is used instead, highlighting the importance of a BIPV‐specific consultancy for successfully implementing BIPV systems.

Multilayer vesicles with an onion-like architecture are obtained by aggregation of an amphiphilic Au 3 (pyrazolate) 3 complex in aqueous solution.

This study aimed to analyze how career adaptability and job control can influence apprentices’ perceptions of their work as being decent and stressful. The participants included 530 Swiss apprentices enrolled in a three-year vocational education training (VET) program. Structural equation modeling showed a positive association between career adaptability and job control and a strong negative association between decent work perception and occupational stress. Career adaptability had a specific positive relationship with decent work perception, whereas job control had a significant relationship with both decent work and occupational stress. Finally, results indicate that decent work could be a possible mediator between career adaptability and occupational stress and between job control and occupational stress. The study suggests that some personal resources and specific work conditions, such as physical and psychological safety and working hours that allow leisure and rest, could help apprentices perceive their work environment as positive and manage occupational stress.

Phytoplankton is an essential resource in aquatic ecosystems, situated at the base of aquatic food webs. Plastic pollution can impact these organisms, potentially affecting the functioning of aquatic ecosystems. The interaction between plastics and phytoplankton is multifaceted: while microplastics can exert toxic effects on phytoplankton, plastics can also act as a substrate for colonisation. By reviewing the existing literature, this study aims to address pivotal questions concerning the intricate interplay among plastics and phytoplankton/phytobenthos and analyse impacts on fundamental ecosystem processes (e.g. primary production, nutrient cycling). This investigation spans both marine and freshwater ecosystems, examining diverse organisational levels from subcellular processes to entire ecosystems. The diverse chemical composition of plastics, along with their variable properties and role in forming the “plastisphere”, underscores the complexity of their influences on aquatic environments. Morphological changes, alterations in metabolic processes, defence and stress responses, including homoaggregation and extracellular polysaccharide biosynthesis, represent adaptive strategies employed by phytoplankton to cope with plastic‐induced stress. Plastics also serve as potential habitats for harmful algae and invasive species, thereby influencing biodiversity and environmental conditions. Processes affected by phytoplankton–plastic interaction can have cascading effects throughout the aquatic food web via altered bottom‐up and top‐down processes. This review emphasises that our understanding of how these multiple interactions compare in impact on natural processes is far from complete, and uncertainty persists regarding whether they drive significant alterations in ecological variables. A lack of comprehensive investigation poses a risk of overlooking fundamental aspects in addressing the environmental challenges associated with widespread plastic pollution.

Sensor ontology is the kernel technique of the Intelligent Sensor System, which provides a structured framework to organize and interpret the knowledge of the Internet of Things (IoT). However, the ontology heterogeneity issue hampers the communication of sensor ontologies. Sensor Ontology Matching (SOM) can find semantically identical entities between two ontologies, which is an effective method to address this issue. However, due to their complicated semantic relationships, it is a challenge to construct an effective Similarity Feature (SF) to distinguish the heterogeneous sensor entities. Although Evolutionary Algorithms (EAs) based matching techniques have shown their effectiveness in the ontology matching field, they suffer from drawbacks such as high computational complexity and expert-dependent solution evaluation. To overcome these drawbacks, this paper proposes a novel Light Genetic Programming (L-GP) to automatically construct SF for SOM. First, a simplified evolutionary mechanism is designed to improve the efficiency of the SOM process. Second, a novel fitness function based on the approximate evaluation metric is introduced to automatically guide the search direction of L-GP. Lastly, a two-stage tournament selection operator is presented to balance the quality and complexity of the solutions, improving the accuracy of the SOM results. The experiment uses ten pairs of real-world SOM tasks to test the performance of L-GP, and the experimental results show that L-GP significantly outperforms state-of-the-art matching techniques.

This study investigated the role of domain-specific and domain-general factors in predicting early literacy skills in Italian children. A sample of 239 first-grade students was evaluated using a broad neuropsychological battery to assess their cognitive skills. The results showed that phonological awareness, rapid automatized naming, speed of processing, and attentional control all played a role in predicting reading and writing abilities. These findings support the importance of considering not just domain-specific language skills, but also domain-general cognitive skills when identifying children at risk of difficulties in reading and writing. The study supports the adoption of a multifactorial-probabilistic model to accurately diagnose specific learning disorders.

This chapter starts by providing a historical account geared toward shedding light about how recent historical events have been shaping the complexity of our times. It is fundamental to understand that the evolution of different narratives has been modifying international relations and the way these international dynamics are re-polarizing the world we experience today. A world which represents important challenges at the levels of governance, ecology, equality, and inclusion. The second section of the chapter focuses on the concept of polycrisis and risk perception. Here, I contend that the public's perception of risk is intricately intertwined with how influential groups shape the international and political agenda. Consequently, the sensation of uncertainty and vulnerability to uncontrollable events arises because of constructed narratives that seek, whether directly or indirectly, to polarize and fragment societies and the entire international system. The chapter finalizes considering that a humanistic leadership approach can help to mitigate the consequences of the aforementioned problems. I focus on three main elements that by playing together with a humanistic approach could ensure durable effects on leading a paradigm shift: a humanistic approach to business education; a responsible approach to innovation; and the implementation of the principles of a H2H marketing approach.

The act of telling stories is a fundamental part of what it means to be human. This work introduces the concept of narrative information, which we define as the overlap in information space between a story and the items that compose the story. Using contrastive learning methods, we show how modern artificial neural networks can be leveraged to distill stories and extract a representation of the narrative information. We then demonstrate how evolutionary algorithms can leverage this to extract a set of narrative template curves and how these—in tandem with a novel curve-fitting algorithm we introduce—can reorder music albums to automatically induce stories in them. In doing so, we give statistically significant evidence that (1) these narrative information template curves are present in existing albums and that (2) people prefer an album ordered through one of these learned template curves over a random one. The premises of our work extend to any form of (largely) independent media, and as evidence, we also show that our method works with image data.

Requests for environmental assessments and reports are becoming more frequent, prompting companies to include sustainability-related declarations in their strategy. Similarly, claims about circular economy performance are of growing interest to limit waste output and resource use. For the practical implementation of sustainability and circularity approaches, measuring the environmental and circularity performances of products and companies has been widely acknowledged as a key prerequisite. A life cycle sustainability assessment (LCSA) covering social, economic and environmental dimensions and circularity analysis uses a combination of several methodologies, as there is no universally recognized method for the execution of LCSA, making the interpretation and use of the results difficult for decision-making. In this context, the chapter is meant to derive and provide integrated LCSA (iLCSA)’s best practices. An analysis of state-of-the-art practices and standards in product-level sustainability areas is presented, together with the methodologies available for sustainability assessments integration, followed by phases of identification, classification and selection of environmental, economic and social indicators. Those phases are carried out in the context of electrical and electronic equipment, referring to circular use cases, with the aim to identify information flows for the calculation process and the actors involved. Finally, addressing iLCSA for individual actors of product life cycle and linking it to labelling allow a wide adoption by end users.

Supramolecular polymers are composed of monomers that self-assemble non-covalently generating distributions of fibers in continuous exchange-and-communication with each other and the surrounding. Intriguing collective properties may emerge in such molecular-scale complex systems, following mechanisms often difficult to ascertain. Here we show how non-trivial collective behaviors may emerge in dynamical supramolecular polymer systems already at low-complexity levels. We combine minimalistic models, simulations, and advanced statistical analyses investigating how cooperative and non-cooperative supramolecular polymer systems respond to a specific stimulus: i.e., the addition of molecular sequestrators perturbing their equilibrium. Our data show how, while in a non-cooperative system all assemblies populating the system suffer uniformly the perturbation, in a cooperative system the larger/stronger assemblies survive at the expense of the smaller/weaker entities. Collective behaviors typical of larger-scale and more complex (social, economic, etc.) systems may thus emerge even in relatively simple self-assembling systems from the internal (microscopic) dynamic heterogeneity of their ensembles.

Most individuals with sleep-disorders remain undiagnosed due to unawareness of symptoms or the high cost of polysomnographic (PSG) studies, impacting quality of life. Despite evidence that sleep-disorders alter sleep-stage-dynamics, clinical practice resists including these parameters in PSG-reports. We introduce a novel digital sleep-fingerprint, leveraging the matrix of sleep-stage-transition-proportions, enabling the derivation of several novel digital-markers and investigation of dynamics mechanisms. Using causal inference we address confounding in an observational clinical database and estimate personalized markers across ages, genders, and Obstructive-Sleep-Apnea (OSA) severities. Notably, our approach adjusts for five categories of sleep-wake-related-comorbidities, an aspect ignored in existing research, impacting 48.6% of OSA-subjects in our data. Key markers proposed, including NREM-REM-oscillations and sleep-stage-specific-fragmentations, were significantly increased across all OSA-severities and demographics. We also identified several OSA-gender-phenotypes, suggesting higher vulnerability of females to awakening and REM-sleep disruptions. Considering advances in automated-sleep-scoring and wearables, our approach can enable novel, low-cost screening tools.

As AI increasingly enters classrooms, educational designers have begun investigating students' learning processes vis‐à‐vis simultaneous feedback from active sources—AI and the teacher. Nevertheless, there is a need to delve into a more comprehensive understanding of the orchestration of interactions between teachers and AI systems in educational settings. The research objective of this paper is to identify the challenges and opportunities when AI intertwines with instruction and examine how this hybrid teaching intelligence is being perceived by the students. The insights of this paper are extracted by analysing a case study that utilizes an AI‐driven system (MOVES‐NL) in the context of learning integer arithmetic. MOVES‐NL is an advanced interactive tool that deploys whole‐body movement and immediate formative feedback in a room‐scale environment designed to enhance students' learning of integer arithmetic. In this paper, we present an in‐situ study where 29 students in grades 6–8 interacted individually with MOVES‐NL for approximately 1 hour each with the support of a facilitator/instructor. Mixed‐methods analyses of multimodal data sources enabled a systematic multifaceted account of students' cognitive–affective experiences as they engaged with MOVES‐NL while receiving human support (eg, by asking students to elaborate on their digital actions/decisions). Finally, we propose design insights for instructional and technology design in support of student hybrid learning. The findings of this research contribute to the ongoing discourse on the role of hybrid intelligence in supporting education by offering practical insights and recommendations for educators and designers seeking to optimize the integration of technology in classrooms. Practitioner notes What is already known about this topic Students and teachers develop different relations with and through AI, beyond just interacting with it. AI can support and augment the teachers' capabilities. Hybrid intelligence (HI) has already demonstrated promising potential to advance current educational theories and practices. What this paper adds This research identifies the important learning opportunities and adversities emerging when AI intertwines with instruction and examines how learners perceive those moments. The results show that the system and the facilitator's feedback were complementary to the success of the learning experience. AI‐enabled students to reflect upon and test their previous knowledge and guided teachers to work with students to consolidate challenging topics. Findings provide insights into how the teacher–AI collaboration could engage and motivate students to reflect conceptually upon mathematical rules. Implications for practice and/or policy This study encourages practitioners and scholars to consider hybrid teaching intelligence when designing student‐centred AI learning tools, focusing on supporting the development of effective teacher–AI collaborative technologies.

Human land-use intensification threatens arthropod (for example, insect and spider) biodiversity across aquatic and terrestrial ecosystems. Insects and spiders play critical roles in ecosystems by accumulating and synthesizing organic nutrients such as polyunsaturated fatty acids (PUFAs). However, links between biodiversity and nutrient content of insect and spider communities have yet to be quantified. We relate insect and spider richness to biomass and PUFA-mass from stream and terrestrial communities encompassing nine land uses. PUFA-mass and biomass relate positively to biodiversity across ecosystems. In terrestrial systems, human-dominated areas have lower biomass and PUFA-mass than more natural areas, even at equivalent levels of richness. Aquatic ecosystems have consistently higher PUFA-mass than terrestrial ecosystems. Our findings reinforce the importance of conserving biodiversity and highlight the distinctive benefits of aquatic biodiversity.

We introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson’s disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson’s disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects’ age and motor impairments highlights the versatility of our approach.