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The global push for sustainable energy has heightened the demand for green hydrogen, which is crucial for decarbonizing heavy industry. However, current electrolysis plant capacities are insufficient. This research addresses the challenge through optimizing large-scale electrolysis construction via standardization, modularization, process optimization, and automation. This paper introduces H2Giga, a project for mass-producing electrolyzers, and HyPLANT100, investigating large-scale electrolysis plant structure and construction processes. Modularizing electrolyzers enhances production efficiency and scalability. The integration of AutomationML facilitates seamless information exchange. A digital twin concept enables simulations, optimizations, and error identification before assembly. While construction site automation provides advantages, tasks like connection technologies and handling cables, tubes, and hoses require pre-assembly. This study identifies key tasks suitable for automation and estimating required components. The Enapter Multicore electrolyzer serves as a case study, showcasing robotic technology for tube fittings. In conclusion, this research underscores the significance of standardization, modularization, and automation in boosting the electrolysis production capacity for green hydrogen, contributing to ongoing efforts in decarbonizing the industrial sector and advancing the global energy transition.

Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method’s potential benefits and implications for HRC.

Increasing volatility in manufacturing and rising sustainability requirements demand more efficient processes in production, especially in employee qualification and engineering during development and on-site adjustments before and after the start of production. One possible solution is using digital twins for virtual commissioning, which can speed up engineering processes, qualify employees, and save valuable resources. To solve these challenges, it is necessary to identify promising approaches for using the digital twin and virtual commissioning. Furthermore, creating an environment where these approaches can be optimally explored is essential. This paper presents promising research approaches and demonstrates the development of an assembly process and a production system with a digital twin designed to explore these aspects. The presented system is an interlinked production system for assembling an actual industrial product. It includes different levels of human–robot interaction and automation, which can be implemented virtually in the digital twin.

Affective computing (AC), like most other areas of computational research, has benefited tremendously from advances in deep learning (DL). These advances have opened up new horizons in AC research and practice. Yet, as DL dominates the community’s attention, there is a danger of overlooking other emerging trends in artificial intelligence (AI) research. Furthermore, over-reliance on one particular technology may lead to stagnating progress. In an attempt to foster the exploration of complementary directions, we provide a concise, easily digestible overview of emerging trends in AI research that stand to play a vital role in solving some of the remaining challenges in AC research. Our overview is driven by the limitations of the current state of the art as it pertains to AC.

Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems.

Life in modern societies is fast-paced and full of stress-inducing demands. The development of stress monitoring methods is a growing area of research due to the personal and economic advantages that timely detection provides. Studies have shown that speech-based features can be utilised to robustly predict several physiological markers of stress, including emotional state, continuous heart rate, and the stress hormone, cortisol. In this contribution, we extend previous works by the authors, utilising three German language corpora including more than 100 subjects undergoing a Trier Social Stress Test protocol. We present cross-corpus and transfer learning results which explore the efficacy of the speech signal to predict three physiological markers of stress—sequentially measured saliva-based cortisol, continuous heart rate as beats per minute (BPM), and continuous respiration. For this, we extract several features from audio as well as video and apply various machine learning architectures, including a temporal context-based Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). For the task of predicting cortisol levels from speech, deep learning improves on results obtained by conventional support vector regression—yielding a Spearman correlation coefficient (ρ) of 0.770 and 0.698 for cortisol measurements taken 10 and 20 min after the stress period for the two corpora applicable—showing that audio features alone are sufficient for predicting cortisol, with audiovisual fusion to an extent improving such results. We also obtain a Root Mean Square Error (RMSE) of 38 and 22 BPM for continuous heart rate prediction on the two corpora where this information is available, and a normalised RMSE (NRMSE) of 0.120 for respiration prediction (−10: 10). Both of these continuous physiological signals show to be highly effective markers of stress (based on cortisol grouping analysis), both when available as ground truth and when predicted using speech. This contribution opens up new avenues for future exploration of these signals as proxies for stress in naturalistic settings.

Abstract Preoperative homeostasis of sex hormones in testicular germ cell tumor (TGCT) patients is scarcely characterized. We aimed to explore regulation of sex hormones and their implications for histopathological parameters and prognosis in TGCT using a data-driven explorative approach. Pre-surgery serum concentrations of luteinizing hormone (LH), follicle-stimulating hormone (FSH), testosterone (T), estradiol (E2) and prolactin were measured in a retrospective multicenter TGCT cohort (n = 518). Clusters of patients were defined by latent class analysis. Clinical, pathologic and survival parameters were compared between the clusters by statistical hypothesis testing, Random Forest modeling and Peto-Peto test. Cancer tissue expression of sex hormone-related genes was explored in the publicly available TCGA cohort (n = 149). We included 354 patients with pure seminoma and 164 patients with non-seminomatous germ cell tumors (NSGCT), with a median age of 36 years. Three hormonal clusters were defined: ‘neutral’ (n = 228) with normal sex hormone homeostasis, ‘testicle’ (n = 91) with elevated T and E2, low pituitary hormones, and finally ‘pituitary’ subset (n = 103) with increased FSH and LH paralleled by low-to-normal levels of the gonadal hormones. Relapse-free survival in the hormonal subsets was comparable (p = 0.64). Cancer tissue expression of luteinizing hormone- and follicle-stimulating hormone-coding genes was significantly higher in seminomas, while genes of T and E2 biosynthesis enzymes were strongly upregulated in NSGCT. Substantial percentages of TGCT patients are at increased risk of sex hormone dysfunction at primary diagnosis before orchiectomy. TGCT may directly influence systemic hormonal homeostasis by in-situ synthesis of sex hormones.

Purpose This article will report results from the in-vivo application of a previously published model-predictive control algorithm for MR-HIFU hyperthermia. The purpose of the investigation was to test the controller’s in-vivo performance and behavior in the presence of heterogeneous perfusion. Materials and methods Hyperthermia at 42°C was induced and maintained for up to 30 min in a circular section of a thermometry slice in the biceps femoris of German landrace pigs (n=5) using a commercial MR-HIFU system and a recently developed MPC algorithm. The heating power allocation was correlated with heat sink maps and contrast-enhanced MRI images. The temporal change in perfusion was estimated based on the power required to maintain hyperthermia. Results The controller performed well throughout the treatments with an absolute average tracking error of 0.27 ± 0.15 °C and an average difference of 1.25 ± 0.22 °C between and The MPC algorithm allocates additional heating power to sub-volumes with elevated heat sink effects, which are colocalized with blood vessels visible on contrast-enhanced MRI. The perfusion appeared to have increased by at least a factor of ∼1.86 on average. Conclusions The MPC controller generates temperature distributions with a narrow spectrum of voxel temperatures inside the target ROI despite the presence of spatiotemporally heterogeneous perfusion due to the rapid thermometry feedback available with MR-HIFU and the flexible allocation of heating power. The visualization of spatiotemporally heterogeneous perfusion presents new research opportunities for the investigation of stimulated perfusion in hypoxic tumor regions.

Purpose Pancreatic cancer is typically diagnosed in a late stage with limited therapeutic options. For those patients, ultrasound-guided high-intensity focused ultrasound (US-HIFU) can improve local control and alleviate pain. However, MRI-guided HIFU (MR-HIFU) has not yet been studied extensively in this context. To facilitate related research and accelerate clinical translation, we report a workflow for the in vivo HIFU ablation of the porcine pancreas under MRI guidance. Materials and methods The pancreases of five healthy German landrace pigs (35–58 kg) were sonicated using a clinical MR-HIFU system. Acoustic access to the pancreas was supported by a specialized diet and a hydrogel compression device for bowel displacement. Organ motion was suspended using periods of apnea. The size of the resulting thermal lesions was assessed using the thermal threshold- and dose profiles, non-perfused volume, and gross examination. The effect of the compression device on beam path length was assessed using MRI imaging. Results Eight of ten treatments resulted in clearly visible damage in the target tissue upon gross examination. Five treatments resulted in coagulative necrosis. Good agreement between the four metrics for lesion size and a clear correlation between the delivered energy dose and the resulting lesion size were found. The compression device notably shortened the intra-abdominal beam path. Conclusions We demonstrated a workflow for HIFU treatment of the porcine pancreas in-vivo under MRI-guidance. This development bears significance for the development of MR-guided HIFU interventions on the pancreas as the pig is the preferred animal model for the translation of pre-clinical research into clinical application.

Background: A mother's own milk (MOM) is the gold standard for the feeding and nutrition of preterm and full term infants. When MOM is not available or there is not enough, donor human milk (DHM) should be used. Milk delivered to Human Milk Banks (HMBs) should be pasteurized to inactivate viral and bacterial agents. Currently, a pasteurization process at 62.5°C for 30 min (Holder pasteurization, HoP) is recommended in all international HMBs guidelines.State of the art: It is known that HoP affects some of the nutritional and biological components of human milk. Studies have demonstrated that temperature cycle in HoP is not always controlled or calibrated. A better check of these parameters in the pasteurizers on the market today may contribute to an improvement of the quality of HM, still maintaining some of the negative effects of the heat treatment of human milk. So, food industry, and dairy industry in particular, are evaluating innovative methodologies alternative to HoP to better preserve the nutritional and biological properties of fresh human milk, while assuring at least the same microbiological safety of HoP. The most studied processing techniques include High-Temperature-Short-Time (HTST) pasteurization, High Pressure Processing (HPP), and Ultraviolet-C (UV-C) irradiation. HTST is a thermal process in which milk is forced between plates or pipes that are heated on the outside by hot water at a temperature of 72°C for 5–15 s. HPP is a non-thermal processing method that can be applied to solid and liquid foods. This technology inactivates pathogenic microorganisms by applying a high hydrostatic pressure (usually 300–800 MPa) during short-term treatments (

Pasteurized donor human milk is provided by milk banks to very preterm babies where their maternal supply is insufficient or unavailable. Donor milk is currently processed by Holder pasteurization, producing a microbiologically safe product but significantly reducing immunoprotective components. Ultraviolet-C (UV-C) irradiation at 254 nm is being investigated as an alternative treatment method and has been shown to preserve components such as lactoferrin, lysozyme and secretory IgA considerably better than Holder pasteurization. We describe the inactivation of cytomegalovirus, a virus commonly excreted into breast milk, using UV-C irradiation. Full replication was ablated by various treatment doses. However, evidence of viral immediate early proteins within the cells was never completely eliminated indicating that some viral gene transcription was still occurring. In conclusion, UV-C may be a safe alternative to pasteurisation for the treatment of human donor milk that preserves the bioactivity. However, our data suggests that CMV inactivation will have to be carefully evaluated for each device designed to treat breast milk using UV-C irradiation.

BACKGROUND:Holder pasteurization (milk held at 62.5°C for 30 minutes) is the standard treatment method for donor human milk. Although this method of pasteurization is able to inactivate most bacteria, it also inactivates important bioactive components. Therefore, the objective of this study was to investigate ultraviolet irradiation as an alternative treatment method for donor human milk. METHODS:Human milk samples were inoculated with five species of bacteria and then UV-C irradiated. Untreated and treated samples were analysed for bacterial content, bile salt stimulated lipase (BSSL) activity, alkaline phosphatase (ALP) activity, and fatty acid profile. RESULTS:All five species of bacteria reacted similarly to UV-C irradiation, with higher dosages being required with increasing concentrations of total solids in the human milk sample. The decimal reduction dosage was 289±17 and 945±164 J/l for total solids of 107 and 146 g/l, respectively. No significant changes in the fatty acid profile, BSSL activity or ALP activity were observed up to the dosage required for a 5-log10 reduction of the five species of bacteria. CONCLUSION:UV-C irradiation is capable of reducing vegetative bacteria in human milk to the requirements of milk bank guidelines with no loss of BSSL and ALP activity and no change of FA.

Human milk possesses bacteriostatic properties, largely due to the presence of immunological proteins. Heat treatments such as Holder pasteurization reduce the concentration of immunological proteins in human milk and consequently increase the bacterial growth rate. This study investigated the bacterial growth rate and the immunological protein concentration of ultraviolet (UV-C) irradiated, Holder pasteurized and untreated human milk.Samples (n=10) of untreated, Holder pasteurized and UV-C irradiated human milk were inoculated with E. coli and S. aureus and the growth rate over 2 hours incubation time at 37°C was observed. Additionally, the concentration of sIgA, lactoferrin and lysozyme of untreated and treated human milk was analyzed.The bacterial growth rate of untreated and UV-C irradiated human milk was not significantly different. The bacterial growth rate of Holder pasteurized human milk was double compared to untreated human milk (p