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By developing innovative solutions to social and environmental problems, sustainable ventures carry greatpotential. Entrepreneurship which focuses especially on new venture creation can be developed through education anduniversities, in particular, are called upon to provide an impetus for social change. But social innovations are associatedwith certain hurdles, which are related to the multi-dimensionality, i.e. the tension between creating social,environmental and economic value and dealing with a multiplicity of stakeholders. The already complex field ofentrepreneurship education has to face these challenges. This paper, therefore, aims to identify starting points for theintegration of sustainability into entrepreneurship education. To pursue this goal experiences from three differentproject initiatives between the partner universities: Lapland University of Applied Sciences, FH Aachen University ofApplied Sciences and Turiba University are reflected and findings are systematically condensed into recommendationsfor education on sustainable entrepreneurship.
Background
Hip fractures are a common and costly health problem, resulting in significant morbidity and mortality, as well as high costs for healthcare systems, especially for the elderly. Implementing surgical preventive strategies has the potential to improve the quality of life and reduce the burden on healthcare resources, particularly in the long term. However, there are currently limited guidelines for standardizing hip fracture prophylaxis practices.
Methods
This study used a cost-effectiveness analysis with a finite-state Markov model and cohort simulation to evaluate the primary and secondary surgical prevention of hip fractures in the elderly. Patients aged 60 to 90 years were simulated in two different models (A and B) to assess prevention at different levels. Model A assumed prophylaxis was performed during the fracture operation on the contralateral side, while Model B included individuals with high fracture risk factors. Costs were obtained from the Centers for Medicare & Medicaid Services, and transition probabilities and health state utilities were derived from available literature. The baseline assumption was a 10% reduction in fracture risk after prophylaxis. A sensitivity analysis was also conducted to assess the reliability and variability of the results.
Results
With a 10% fracture risk reduction, model A costs between $8,850 and $46,940 per quality-adjusted life-year ($/QALY). Additionally, it proved most cost-effective in the age range between 61 and 81 years. The sensitivity analysis established that a reduction of ≥ 2.8% is needed for prophylaxis to be definitely cost-effective. The cost-effectiveness at the secondary prevention level was most sensitive to the cost of the contralateral side’s prophylaxis, the patient’s age, and fracture treatment cost. For high-risk patients with no fracture history, the cost-effectiveness of a preventive strategy depends on their risk profile. In the baseline analysis, the incremental cost-effectiveness ratio at the primary prevention level varied between $11,000/QALY and $74,000/QALY, which is below the defined willingness to pay threshold.
Conclusion
Due to the high cost of hip fracture treatment and its increased morbidity, surgical prophylaxis strategies have demonstrated that they can significantly relieve the healthcare system. Various key assumptions facilitated the modeling, allowing for adequate room for uncertainty. Further research is needed to evaluate health-state-associated risks.
Background
Post-COVID-19 syndrome (PCS) is a lingering disease with ongoing symptoms such as fatigue and cognitive impairment resulting in a high impact on the daily life of patients. Understanding the pathophysiology of PCS is a public health priority, as it still poses a diagnostic and treatment challenge for physicians.
Methods
In this prospective observational cohort study, we analyzed the retinal microcirculation using Retinal Vessel Analysis (RVA) in a cohort of patients with PCS and compared it to an age- and gender-matched healthy cohort (n = 41, matched out of n = 204).
Measurements and main results
PCS patients exhibit persistent endothelial dysfunction (ED), as indicated by significantly lower venular flicker-induced dilation (vFID; 3.42% ± 1.77% vs. 4.64% ± 2.59%; p = 0.02), narrower central retinal artery equivalent (CRAE; 178.1 [167.5–190.2] vs. 189.1 [179.4–197.2], p = 0.01) and lower arteriolar-venular ratio (AVR; (0.84 [0.8–0.9] vs. 0.88 [0.8–0.9], p = 0.007). When combining AVR and vFID, predicted scores reached good ability to discriminate groups (area under the curve: 0.75). Higher PCS severity scores correlated with lower AVR (R = − 0.37 p = 0.017). The association of microvascular changes with PCS severity were amplified in PCS patients exhibiting higher levels of inflammatory parameters.
Conclusion
Our results demonstrate that prolonged endothelial dysfunction is a hallmark of PCS, and impairments of the microcirculation seem to explain ongoing symptoms in patients. As potential therapies for PCS emerge, RVA parameters may become relevant as clinical biomarkers for diagnosis and therapy management.
Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.
The eVTOL industry is a rapidly growing mass market expected to start in 2024. eVTOL compete, caused by their predicted missions, with ground-based transportation modes, including mainly passenger cars. Therefore, the automotive and classical aircraft design process is reviewed and compared to highlight advantages for eVTOL development. A special focus is on ergonomic comfort and safety. The need for further investigation of eVTOL’s crashworthiness is outlined by, first, specifying the relevance of passive safety via accident statistics and customer perception analysis; second, comparing the current state of regulation and certification; and third, discussing the advantages of integral safety and applying the automotive safety approach for eVTOL development. Integral safety links active and passive safety, while the automotive safety approach means implementing standardized mandatory full-vehicle crash tests for future eVTOL. Subsequently, possible crash impact conditions are analyzed, and three full-vehicle crash load cases are presented.
We present the production of 58mCo on a small, 13 MeV medical cyclotron utilizing a siphon style liquid target system. Different concentrated iron(III)-nitrate solutions of natural isotopic distribution were irradiated at varying initial pressures and subsequently separated by solid phase extraction chromatography. The radio cobalt (58m/gCo and 56Co) was successfully produced with saturation activities of (0.35 ± 0.03) MBq μA−1 for 58mCo with a separation recovery of (75 ± 2) % of cobalt after one separation step utilizing LN-resin.
Density reduction effects on the production of [11C]CO2 in Nb-body targets on a medical cyclotron
(2023)
Medical isotope production of 11C is commonly performed in gaseous targets. The power deposition of the proton beam during the irradiation decreases the target density due to thermodynamic mixing and can cause an increase of penetration depth and divergence of the proton beam. In order to investigate the difference how the target-body length influences the operation conditions and the production yield, a 12 cm and a 22 cm Nb-target body containing N2/O2 gas were irradiated using a 13 MeV proton cyclotron. It was found that the density reduction has a large influence on the pressure rise during irradiation and the achievable radioactive yield. The saturation activity of [11C]CO2 for the long target (0.083 Ci/μA) is about 10% higher than in the short target geometry (0.075 Ci/μA).
Meitner-Auger-electron emitters have a promising potential for targeted radionuclide therapy of cancer because of their short range and the high linear energy transfer of Meitner-Auger-electrons (MAE). One promising MAE candidate is 197m/gHg with its half-life of 23.8 h and 64.1 h, respectively, and high MAE yield. Gold nanoparticles (AuNPs) that are labelled with 197m/gHg could be a helpful tool for radiation treatment of glioblastoma multiforme when infused into the surgical cavity after resection to prevent recurrence. To produce such AuNPs, 197m/gHg was embedded into pristine AuNPs. Two different syntheses were tested starting from irradiated gold containing trace amounts of 197m/gHg. When sodium citrate was used as reducing agent, no 197m/gHg labelled AuNPs were formed, but with tannic acid, 197m/gHg labeled AuNPs were produced. The method was optimized by neutralizing the pH (pH = 7) of the Au/197m/gHg solution, which led to labelled AuNPs with a size of 12.3 ± 2.0 nm as measured by transmission electron microscopy. The labelled AuNPs had a concentration of 50 μg (gold)/mL with an activity of 151 ± 93 kBq/mL (197gHg, time corrected to the end of bombardment).
Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.