Refine
Year of publication
- 2023 (114) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (29)
- Fachbereich Elektrotechnik und Informationstechnik (21)
- Fachbereich Luft- und Raumfahrttechnik (20)
- ECSM European Center for Sustainable Mobility (18)
- Fachbereich Chemie und Biotechnologie (16)
- Fachbereich Energietechnik (13)
- INB - Institut für Nano- und Biotechnologien (11)
- IfB - Institut für Bioengineering (9)
- Fachbereich Wirtschaftswissenschaften (8)
- Fachbereich Maschinenbau und Mechatronik (6)
Language
- English (114) (remove)
Document Type
- Article (65)
- Conference Proceeding (35)
- Part of a Book (5)
- Habilitation (2)
- Preprint (2)
- Talk (2)
- Book (1)
- Conference: Meeting Abstract (1)
- Contribution to a Periodical (1)
Keywords
- Information extraction (3)
- Natural language processing (3)
- Associated liquids (2)
- Bacillaceae (2)
- Biotechnological application (2)
- CFD (2)
- Diversity Management (2)
- Engineering Habitus (2)
- Future Skills (2)
- Interdisciplinarity (2)
- Organizational Culture (2)
- Power plants (2)
- Subtilases (2)
- Subtilisin (2)
- Sustainability (2)
- additive manufacturing (2)
- factory planning (2)
- manufacturing flexibility (2)
- ultrasound (2)
- (Poly)saccharides (1)
- (R)- or (S)- gamma-valerolactone (1)
- 197m/gHg (1)
- 4-hydroxy valeric acid (1)
- Academia (1)
- Active learning (1)
- Acyl-amino acids (1)
- Aeroelasticity (1)
- Agent-based simulation (1)
- Agile development (1)
- Aloe vera (1)
- Aminoacylase (1)
- Anammox (1)
- Android (1)
- Anomaly detection (1)
- Anti-Bias (1)
- Antibias (1)
- Architectural design (1)
- Asymptotic relative efficiency (1)
- Automation (1)
- Automotive safety approach (1)
- Autonomy (1)
- Bacillus atrophaeus spores (1)
- Bacterial cellulose (1)
- Best practice sharing (1)
- Bioabsorbable (1)
- Blade element method (1)
- Bragg peak (1)
- Brake set-up (1)
- Braking curves (1)
- Brands (1)
- Broad pH spectrum (1)
- Building Automation (1)
- Business Process Intelligence (1)
- CO2 (1)
- CO2 emission reduction targets (1)
- CRISPR/Cas9 (1)
- Capacitive field-effect sensor (1)
- Carbon Dioxide (1)
- Carbon sources (1)
- Cellulose nanostructure (1)
- Change (1)
- Chaperone co-expression (1)
- Charging station (1)
- Chiralidon-R (1)
- Chiralidon-S (1)
- Chondroitin sulfate (1)
- Clustering (1)
- Cognitive assistance system (1)
- Competitiveness (1)
- Conductive Boundary Condition (1)
- Connected Automated Vehicle (1)
- Control (1)
- Cost function (1)
- Cost-effectiveness (1)
- Cramér-von-Mises test (1)
- Crashworthiness (1)
- Cross border adjustment mechanism (1)
- Culture media (1)
- Cyclotron production (1)
- DPA (dipicolinic acid) (1)
- Datasets (1)
- Decision theory (1)
- Deep learning (1)
- Design Thinking (1)
- Dietary supplements (1)
- Digital leadership (1)
- Digital transformation (1)
- Digital triage (1)
- Digital twin (1)
- District data model (1)
- District energy planning platform (1)
- Drag estimation (1)
- Driver assistance system (1)
- Driving cycle recognition (1)
- E-Mobility (1)
- ECMS (1)
- Earthquake (1)
- Education (1)
- Electrocardiography (1)
- Electrochemistry (1)
- Electronic vehicle (1)
- Elicit (1)
- Endothelial dysfunction (1)
- Energy Disaggregation (1)
- Energy management strategies (1)
- Energy market design (1)
- Energy storage (1)
- Energy system planning (1)
- Energy-intensive industry (1)
- Enterprise information systems (1)
- Fault approximation (1)
- Fault detection (1)
- Finite element method (1)
- Finland (1)
- Floor prices (1)
- Freight rail (1)
- Full-vehicle crash test (1)
- Future skills (1)
- Ga-68 (1)
- Gamification (1)
- Geriatric (1)
- Germany (1)
- Glucosamine (1)
- Gold nanoparticle (AuNP) (1)
- Gold nanoparticles (1)
- Guide Tube (1)
- Halotolerant protease (1)
- High-field NMR (1)
- Hip fractures (1)
- Home Assistant (1)
- Home Automation Platform (1)
- Human factors (1)
- Human-centered work design (1)
- Human-robot collaboration (1)
- Ice melting probe (1)
- Ice penetration (1)
- Icy moons (1)
- Inclusion bodies (1)
- Incomplete data (1)
- Instagram store (1)
- Interculturality (1)
- Inverse Scattering (1)
- Inverse scattering problem (1)
- Key competences (1)
- Label-free detection (1)
- Labyfrinth weirs (1)
- Lactobacillus rhamnosus GG (1)
- Large Eddy Simulation (1)
- Latvia (1)
- LbL films (1)
- Leaderboard (1)
- Levulinic acid (1)
- Local path planning (1)
- Long COVID (1)
- Luxury (1)
- MCDA (1)
- Machine Learning (1)
- Mainstream (1)
- Marginal homogeneity (1)
- Market modeling (1)
- Mars (1)
- Masonry partition walls (1)
- Medical radionuclide production (1)
- Medusomyces gisevi (1)
- Meitner-Auger-electron (MAE) (1)
- Metal contaminants (1)
- Microfluidic solvent extraction (1)
- Micromix (1)
- Minimum Risk Manoeuvre (1)
- Minor chemistry (1)
- Mobility transition (1)
- Model-driven software engineering (1)
- Mpc (1)
- Multi-criteria decision analysis (1)
- Multi-objective optimization (1)
- Multicell (1)
- Multiplexing (1)
- Multirotor UAS (1)
- Natural Language Processing (1)
- Natural language understanding (1)
- Navigation (1)
- Neural networks (1)
- Nitrogen removal (1)
- Nozzle (1)
- Obstacle avoidance (1)
- Ocean worlds (1)
- Open Source (1)
- Operational Design Domain (1)
- Operations (1)
- Organic acids (1)
- Out-of-plane capacity (1)
- PLS (1)
- Paired sample (1)
- Parking (1)
- Partial nitritation (1)
- Path planning (1)
- Path-following (1)
- Performance (1)
- Personality (1)
- Physical chemistry (1)
- Physical chemistry basics (1)
- Physical chemistry starters (1)
- Polylactide acid (1)
- Polysaccharides (1)
- Post-COVID-19 syndrome (1)
- Predictive battery discharge (1)
- Preference assessment (1)
- Prevention (1)
- Privacy (1)
- Process Model Extraction (1)
- Process optimization (1)
- Profile extraction (1)
- Propeller (1)
- Propeller elasticity (1)
- Prophylaxis (1)
- Prototype (1)
- Quality control (1)
- Query learning (1)
- Raman spectroscopy (1)
- Regionalization (1)
- Relation classification (1)
- Renewable energy integration (1)
- Reproducible research (1)
- Reservation system (1)
- Resistive temperature detector (1)
- Responsibility (1)
- Rotary encoder (1)
- SOA (1)
- Sensors comparison (1)
- Shunting (1)
- Silk fibroin (1)
- Simulation (1)
- Slab deflection (1)
- Smart Building (1)
- Sn₃O₄ (1)
- Social impact measurement (1)
- Society (1)
- Software (1)
- Software and systems modeling (1)
- Software development (1)
- Software testing (1)
- Spectroscopy (1)
- Steel industry (1)
- Streptomyces griseus (1)
- Streptomyces lividans (1)
- Stress testing (1)
- Suction (1)
- Sustainable engineering education (1)
- TICTOP (1)
- Targeted radionuclide therapy (TRT) (1)
- Teamwork (1)
- Text Mining (1)
- Text mining (1)
- Thermodynamics as minor (1)
- Time-series synchronization (1)
- Transdisciplinarity (1)
- Transformative Competencies (1)
- Transiton of Control (1)
- Transmission Eigenvalues (1)
- Triage-app (1)
- Trustworthy artificial intelligence (1)
- UAV (1)
- Utilization improvement (1)
- V2X (1)
- Vibrio natriegens (1)
- Volumes of confidence regions (1)
- Wastewater (1)
- Wearable electronic device (1)
- Wiegand sensor (1)
- Wind milling (1)
- Wind tunnel experiments (1)
- active learning (1)
- adaptive systems (1)
- aircraft engine (1)
- allocation (1)
- amperometric biosensors (1)
- anammox (1)
- artificial intelligence (1)
- aspergillus (1)
- assistance system (1)
- bacterial cellulose (1)
- bio-methane (1)
- biocompatible (1)
- biodegradabl (1)
- biofilms (1)
- biological dosimeter (1)
- biomechanics (1)
- biosensor (1)
- bubble column (1)
- central symmetry test (1)
- climate change (1)
- combustion (1)
- compression behavior (1)
- conditional excess distribution (1)
- conditional expectation principle (1)
- confidence interval (1)
- connective tissue (1)
- covariance principle (1)
- deficit irrigation (1)
- distribution grid simulation (1)
- e-mobility (1)
- eVTOL development (1)
- eVTOL safety (1)
- electromyography (1)
- emission index (1)
- encapsulation materials (1)
- energy efficiency (1)
- entrepreneurship education (1)
- enzyme cascade (1)
- exchangeability test (1)
- fibroin (1)
- field-effect sensor (1)
- filamentous fungi (1)
- forecast (1)
- fuel cell vehicle (1)
- fused filament fabrication (1)
- gamification (1)
- genome engineering (1)
- glucose oxidase (GOx) (1)
- goodness-of-fit test (1)
- heavy metals (1)
- horseradish peroxidase (HRP) (1)
- hydrogel (1)
- hydrogen (1)
- immobilization (1)
- independence test (1)
- infill strategy (1)
- intelligent control (1)
- intelligent energy management (1)
- lab work (1)
- locomotion (1)
- machine learning (1)
- mainstream deammonification (1)
- manufacturing (1)
- manufacturing data model (1)
- methanation (1)
- mix flexibility (1)
- nanobelts (1)
- neutrons (1)
- nitric oxides (1)
- nitrogen elimination (1)
- not identically distributed (1)
- onion (1)
- optical fibers (1)
- optical sensor setup (1)
- optical trapping (1)
- optimization system (1)
- overload (1)
- physiology (1)
- plug flow reactor (1)
- polyetheretherketone (PEEK) (1)
- portfolio risk (1)
- power-to-gas (1)
- prebiotic (1)
- production planning and control (1)
- professional skills (1)
- proton therapy (1)
- protons (1)
- purchase factor (1)
- qNMR (1)
- random effects (1)
- rapid tooling (1)
- recombinant expression (1)
- relative dosimetry (1)
- retinal microvasculature (1)
- service-oriented architectures (1)
- shopping behavior (1)
- smart-charging (1)
- sterilization (1)
- stretch-shortening cycle (1)
- structural equation model (1)
- technology planning (1)
- tobacco mosaic virus (TMV) (1)
- turnip vein clearing virus (TVCV) (1)
- volume flexibility (1)
- wastewater (1)
- water economy (1)
- yield (1)
- α-aminoacylase (1)
- ε-lysine acylase (1)
Subglacial environments on Earth offer important analogs to Ocean World targets in our solar system. These unique microbial ecosystems remain understudied due to the challenges of access through thick glacial ice (tens to hundreds of meters). Additionally, sub-ice collections must be conducted in a clean manner to ensure sample integrity for downstream microbiological and geochemical analyses. We describe the field-based cleaning of a melt probe that was used to collect brine samples from within a glacier conduit at Blood Falls, Antarctica, for geomicrobiological studies. We used a thermoelectric melting probe called the IceMole that was designed to be minimally invasive in that the logistical requirements in support of drilling operations were small and the probe could be cleaned, even in a remote field setting, so as to minimize potential contamination. In our study, the exterior bioburden on the IceMole was reduced to levels measured in most clean rooms, and below that of the ice surrounding our sampling target. Potential microbial contaminants were identified during the cleaning process; however, very few were detected in the final englacial sample collected with the IceMole and were present in extremely low abundances (∼0.063% of 16S rRNA gene amplicon sequences). This cleaning protocol can help minimize contamination when working in remote field locations, support microbiological sampling of terrestrial subglacial environments using melting probes, and help inform planetary protection challenges for Ocean World analog mission concepts.
Immunosorbent turnip vein clearing virus (TVCV) particles displaying the IgG-binding domains D and E of Staphylococcus aureus protein A (PA) on every coat protein (CP) subunit (TVCVPA) were purified from plants via optimized and new protocols. The latter used polyethylene glycol (PEG) raw precipitates, from which virions were selectively re-solubilized in reverse PEG concentration gradients. This procedure improved the integrity of both TVCVPA and the wild-type subgroup 3 tobamovirus. TVCVPA could be loaded with more than 500 IgGs per virion, which mediated the immunocapture of fluorescent dyes, GFP, and active enzymes. Bi-enzyme ensembles of cooperating glucose oxidase and horseradish peroxidase were tethered together on the TVCVPA carriers via a single antibody type, with one enzyme conjugated chemically to its Fc region, and the other one bound as a target, yielding synthetic multi-enzyme complexes. In microtiter plates, the TVCVPA-displayed sugar-sensing system possessed a considerably increased reusability upon repeated testing, compared to the IgG-bound enzyme pair in the absence of the virus. A high coverage of the viral adapters was also achieved on Ta2O5 sensor chip surfaces coated with a polyelectrolyte interlayer, as a prerequisite for durable TVCVPA-assisted electrochemical biosensing via modularly IgG-assembled sensor enzymes.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Residential and commercial buildings account for more than one-third of global energy-related greenhouse gas emissions. Integrated multi-energy systems at the district level are a promising way to reduce greenhouse gas emissions by exploiting economies of scale and synergies between energy sources. Planning district energy systems comes with many challenges in an ever-changing environment. Computational modelling established itself as the state-of-the-art method for district energy system planning. Unfortunately, it is still cumbersome to combine standalone models to generate insights that surpass their original purpose. Ideally, planning processes could be solved by using modular tools that easily incorporate the variety of competing and complementing computational models. Our contribution is a vision for a collaborative development and application platform for multi-energy system planning tools at the district level. We present challenges of district energy system planning identified in the literature and evaluate whether this platform can help to overcome these challenges. Further, we propose a toolkit that represents the core technical elements of the platform. Lastly, we discuss community management and its relevance for the success of projects with collaboration and knowledge sharing at their core.
This article describes an Internet of things (IoT) sensing device with a wireless interface which is powered by the energy-harvesting method of the Wiegand effect. The Wiegand effect, in contrast to continuous sources like photovoltaic or thermal harvesters, provides small amounts of energy discontinuously in pulsed mode. To enable an energy-self-sufficient operation of the sensing device with this pulsed energy source, the output energy of the Wiegand generator is maximized. This energy is used to power up the system and to acquire and process data like position, temperature or other resistively measurable quantities as well as transmit these data via an ultra-low-power ultra-wideband (UWB) data transmitter. A proof-of-concept system was built to prove the feasibility of the approach. The energy consumption of the system during start-up was analysed, traced back in detail to the individual components, compared to the generated energy and processed to identify further optimization options. Based on the proof of concept, an application prototype was developed.
Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.
Digital forensics of smartphones is of utmost importance in many criminal cases. As modern smartphones store chats, photos, videos etc. that can be relevant for investigations and as they can have storage capacities of hundreds of gigabytes, they are a primary target for forensic investigators. However, it is exactly this large amount of data that is causing problems: extracting and examining the data from multiple phones seized in the context of a case is taking more and more time. This bears the risk of wasting a lot of time with irrelevant phones while there is not enough time left to analyze a phone which is worth examination. Forensic triage can help in this case: Such a triage is a preselection step based on a subset of data and is performed before fully extracting all the data from the smartphone. Triage can accelerate subsequent investigations and is especially useful in cases where time is essential. The aim of this paper is to determine which and how much data from an Android smartphone can be made directly accessible to the forensic investigator – without tedious investigations. For this purpose, an app has been developed that can be used with extremely limited storage of data in the handset and which outputs the extracted data immediately to the forensic workstation in a human- and machine-readable format.
In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant.
Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis.
A method for detecting and approximating fault lines or surfaces, respectively, or decision curves in two and three dimensions with guaranteed accuracy is presented. Reformulated as a classification problem, our method starts from a set of scattered points along with the corresponding classification algorithm to construct a representation of a decision curve by points with prescribed maximal distance to the true decision curve. Hereby, our algorithm ensures that the representing point set covers the decision curve in its entire extent and features local refinement based on the geometric properties of the decision curve. We demonstrate applications of our method to problems related to the detection of faults, to multi-criteria decision aid and, in combination with Kirsch’s factorization method, to solving an inverse acoustic scattering problem. In all applications we considered in this work, our method requires significantly less pointwise classifications than previously employed algorithms.
The increasing share of renewable electricity in the grid drives the need for sufficient storage capacity. Especially for seasonal storage, power-to-gas can be a promising approach. Biologically produced methane from hydrogen produced from surplus electricity can be used to substitute natural gas in the existing infrastructure. Current reactor types are not or are poorly optimized for flexible methanation. Therefore, this work proposes a new reactor type with a plug flow reactor (PFR) design. Simulations in COMSOL Multiphysics ® showed promising properties for operation in laminar flow. An experiment was conducted to support the simulation results and to determine the gas fraction of the novel reactor, which was measured to be 29%. Based on these simulations and experimental results, the reactor was constructed as a 14 m long, 50 mm diameter tube with a meandering orientation. Data processing was established, and a step experiment was performed. In addition, a kLa of 1 h−1 was determined. The results revealed that the experimental outcomes of the type of flow and gas fractions are in line with the theoretical simulation. The new design shows promising properties for flexible methanation and will be tested.
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).
Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.
It has been shown that muscle fascicle curvature increases with increasing contraction level and decreasing muscle–tendon complex length. The analyses were done with limited examination windows concerning contraction level, muscle–tendon complex length, and/or intramuscular position of ultrasound imaging. With this study we aimed to investigate the correlation between fascicle arching and contraction, muscle–tendon complex length and their associated architectural parameters in gastrocnemius muscles to develop hypotheses concerning the fundamental mechanism of fascicle curving. Twelve participants were tested in five different positions (90°/105°*, 90°/90°*, 135°/90°*, 170°/90°*, and 170°/75°*; *knee/ankle angle). They performed isometric contractions at four different contraction levels (5%, 25%, 50%, and 75% of maximum voluntary contraction) in each position. Panoramic ultrasound images of gastrocnemius muscles were collected at rest and during constant contraction. Aponeuroses and fascicles were tracked in all ultrasound images and the parameters fascicle curvature, muscle–tendon complex strain, contraction level, pennation angle, fascicle length, fascicle strain, intramuscular position, sex and age group were analyzed by linear mixed effect models. Mean fascicle curvature of the medial gastrocnemius increased with contraction level (+5 m−1 from 0% to 100%; p = 0.006). Muscle–tendon complex length had no significant impact on mean fascicle curvature. Mean pennation angle (2.2 m−1 per 10°; p < 0.001), inverse mean fascicle length (20 m−1 per cm−1; p = 0.003), and mean fascicle strain (−0.07 m−1 per +10%; p = 0.004) correlated with mean fascicle curvature. Evidence has also been found for intermuscular, intramuscular, and sex-specific intramuscular differences of fascicle curving. Pennation angle and the inverse fascicle length show the highest predictive capacities for fascicle curving. Due to the strong correlations between pennation angle and fascicle curvature and the intramuscular pattern of curving we suggest for future studies to examine correlations between fascicle curvature and intramuscular fluid pressure.
Ambitious climate targets affect the competitiveness of industries in the international market. To prevent such industries from moving to other countries in the wake of increased climate protection efforts, cost adjustments may become necessary. Their design requires knowledge of country-specific production costs. Here, we present country-specific cost figures for different production routes of steel, paying particular attention to transportation costs. The data can be used in floor price models aiming to assess the competitiveness of different steel production routes in different countries (Rübbelke, 2022).
The Cramér-von-Mises distance is applied to the distribution of the excess over a confidence level. Asymptotics of related statistics are investigated, and it is seen that the obtained limit distributions differ from the classical ones. For that reason, quantiles of the new limit distributions are given and new bootstrap techniques for approximation purposes are introduced and justified. The results motivate new one-sample goodness-of-fit tests for the distribution of the excess over a confidence level and a new confidence interval for the related fitting error. Simulation studies investigate size and power of the tests as well as coverage probabilities of the confidence interval in the finite sample case. A practice-oriented application of the Cramér-von-Mises tests is the determination of an appropriate confidence level for the fitting approach. The adoption of the idea to the well-known problem of threshold detection in the context of peaks over threshold modelling is sketched and illustrated by data examples.
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.
Aspergillus oryzae is an industrially relevant organism for the secretory production of heterologous enzymes, especially amylases. The activities of potential heterologous amylases, however, cannot be quantified directly from the supernatant due to the high background activity of native α-amylase. This activity is caused by the gene products of amyA, amyB, and amyC. In this study, an in vitro CRISPR/Cas9 system was established in A. oryzae to delete these genes simultaneously. First, pyrG of A. oryzae NSAR1 was mutated by exploiting NHEJ to generate a counter-selection marker. Next, all amylase genes were deleted simultaneously by co-transforming a repair template carrying pyrG of Aspergillus nidulans and flanking sequences of amylase gene loci. The rate of obtained triple knock-outs was 47%. We showed that triple knockouts do not retain any amylase activity in the supernatant. The established in vitro CRISPR/Cas9 system was used to achieve sequence-specific knock-in of target genes. The system was intended to incorporate a single copy of the gene of interest into the desired host for the development of screening methods. Therefore, an integration cassette for the heterologous Fpi amylase was designed to specifically target the amyB locus. The site-specific integration rate of the plasmid was 78%, with exceptional additional integrations. Integration frequency was assessed via qPCR and directly correlated with heterologous amylase activity. Hence, we could compare the efficiency between two different signal peptides. In summary, we present a strategy to exploit CRISPR/Cas9 for gene mutation, multiplex knock-out, and the targeted knock-in of an expression cassette in A. oryzae. Our system provides straightforward strain engineering and paves the way for development of fungal screening systems.
Deammonification for nitrogen removal in municipal wastewater in temperate and cold climate zones is currently limited to the side stream of municipal wastewater treatment plants (MWWTP). This study developed a conceptual model of a mainstream deammonification plant, designed for 30,000 P.E., considering possible solutions corresponding to the challenging mainstream conditions in Germany. In addition, the energy-saving potential, nitrogen elimination performance and construction-related costs of mainstream deammonification were compared to a conventional plant model, having a single-stage activated sludge process with upstream denitrification. The results revealed that an additional treatment step by combining chemical precipitation and ultra-fine screening is advantageous prior the mainstream deammonification. Hereby chemical oxygen demand (COD) can be reduced by 80% so that the COD:N ratio can be reduced from 12 to 2.5. Laboratory experiments testing mainstream conditions of temperature (8–20°C), pH (6–9) and COD:N ratio (1–6) showed an achievable volumetric nitrogen removal rate (VNRR) of at least 50 gN/(m3∙d) for various deammonifying sludges from side stream deammonification systems in the state of North Rhine-Westphalia, Germany, where m3 denotes reactor volume. Assuming a retained Norganic content of 0.0035 kgNorg./(P.E.∙d) from the daily loads of N at carbon removal stage and a VNRR of 50 gN/(m3∙d) under mainstream conditions, a resident-specific reactor volume of 0.115 m3/(P.E.) is required for mainstream deammonification. This is in the same order of magnitude as the conventional activated sludge process, i.e., 0.173 m3/(P.E.) for an MWWTP of size class of 4. The conventional plant model yielded a total specific electricity demand of 35 kWh/(P.E.∙a) for the operation of the whole MWWTP and an energy recovery potential of 15.8 kWh/(P.E.∙a) through anaerobic digestion. In contrast, the developed mainstream deammonification model plant would require only a 21.5 kWh/(P.E.∙a) energy demand and result in 24 kWh/(P.E.∙a) energy recovery potential, enabling the mainstream deammonification model plant to be self-sufficient. The retrofitting costs for the implementation of mainstream deammonification in existing conventional MWWTPs are nearly negligible as the existing units like activated sludge reactors, aerators and monitoring technology are reusable. However, the mainstream deammonification must meet the performance requirement of VNRR of about 50 gN/(m3∙d) in this case.