Open Access

Volatile organic compound (VOC) analysis of exhaled breath is highly promising for non invasive medical diagnosis. In this paper, we present the design of a software system for the classification of gases through the combination of Internet of Things (IoT) technologies, machine learning (ML), spectrometry, and metal oxide (MOX) sensors. Although the hardware elements were pre-existing, we present the software implementation that allows for the efficient and reliable detection of respiratory gases, which can be extended to the detection of disease-specific biomarkers. Although the physical sensing platform is currently on a single device, Internet Of Things (IoT) principles were embedded in the software architecture to deliver future scalability, remote monitoring, and integration with larger healthcare systems—enabling distributed data collection and centralized analysis required for widespread clinical adoption. The implemented software solution consists of two components: a web application with a redesigned interface and Python-based backend software enabling secure real-time processing. Despite the fact that there are no real VOC measurements yet, the system shows the potential for measuring gas concentration effectively. Additional development will cover measurement of mixture gases to establish more realistic conditions and enhance the precision of ML models. Future development will focus on the combination of the most beneficial features of both software components, expanding real-time data processing, and refining measurement accuracy with advanced software algorithms. This research work provides the software framework necessary for innovative, non-invasive respiratory diagnostic techniques that can be utilized for early disease detection in the future.

This work presents a comprehensive analysis of the variability and reliability of the resistive switching (RS) behavior in Prussian Blue (a mixed-valence iron(III/II) hexacyanoferrate compound) thin films, used as the active layer. These films are fabricated through a simple and scalable electrochemical process, and exhibit robust bipolar resistive switching, making them suitable both for neuromorphic computing applications and hardware cryptography. A detailed statistical evaluation was conducted over 100 consecutive switching cycles using multiple parameter extraction techniques to assess cycle-to-cycle (C2C) variability in key RS parameters, including set/reset voltages and corresponding currents. One and two-dimensional coefficients of variation (1DCV and 2DCV) were calculated to quantify variability and identify application potential. Results demonstrate moderate variability compatible with neuromorphic computing and cryptographic functionalities, including physical unclonable functions and true random number generation. These findings position Prussian Blue-based memristors as promising candidates for low-cost, stable, and multifunctional memory.

An individual’s digital mindset is crucial to navigating digital transformation. Current studies reveal a lack of construct clarity jeopardizing further research. We address this gap by conceptualizing the construct and its multidimensionality, and by developing and validating a scale. Following a multi-grounded theory approach (n = 28) we identify three dimensions of digital mindset: digital consciousness, digital expertise, and digital business acumen. Subsequently, we developed and validated the digital mindset scale in five phases. We generated 95 items. Exploratory (n = 167) and confirmatory (n = 658) factor analyses supported the dimensions. We added items and reassessed the psychometric properties (n = 152), and established convergent and discriminant validity (n = 243). Finally, we examined relationships with innovative and entrepreneurial behavior, supporting nomological and incremental validity (n = 145). Our research paves the way for empirical studies and equips practitioners to assess employees’ digital mindset throughout the professional lifecycle.

Cold atmospheric plasma-aerosol (CAP-A) offers a promising alternative to conventional sterilisation and disinfection methods, which are often unsuitable for thermolabile medical devices due to high temperatures, toxic chemicals or radiation. CAP-A efficiently inactivates microorganisms and viruses without compromising the material integrity. Given the ongoing risk of infection associated with ultrasound probes and other delicate diagnostic instruments, this study investigates whether an indirect CAP-A method can meet all requirements for effective and safe disinfection of thermolabile medical devices. The disinfection of thermolabile medical devices was carried out in a container saturated with indirect CAP-A. A transvaginal ultrasound probe was used as a reference product. The study involved six test organisms, with five measurements taken at six different measurement points. The study showed that Enterococcus hirae (mean logarithmic reduction factor (LRF)  6.23), Staphylococcus aureus (mean LRF  6.51), and Enterococcus faecium (mean LRF  6.16) demonstrated a germ reduction of 99.9999%. For Pseudomonas aeruginosa (mean LRF  5.40) and Escherichia coli (mean LRF  5.29), a germ reduction of  99.999% was achieved, and for Candida albicans (mean LRF  4.95) and Clostridioides difficile (mean LRF  4.62), a germ reduction of  99.99% was demonstrated. The log reduction demonstrates a complete inactivation of the six tested microorganisms. The initially defined requirements for an effective disinfection process for thermolabile medical devices were met in the CAP-A method. Regarding highly tenacious microorganisms, such as Clostridioides difficile, the method of CAP-A proved effective, superior to alcohol-based methods, and with no resistance development observed. Its efficacy is otherwise only known in corrosive chemicals, such as hydrogen peroxide, chlorine, and chlorine dioxide. However, these chemicals have corrosive-oxidative effects on the surfaces to be disinfected and are critical in terms of market launch and hazardous material classification. Therefore, the method of CAP-A, provides an effective, material-friendly alternative.