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The formation and analysis of ten microporous triazolyl isophthalate based MOFs, including nine isomorphous and one isostructural compound is presented. The compounds 1 M – 3 M with the general formula [ M ( R 1 - R 2 - trz - ia ) ] ∞ 3 ·x H 2 O (M 2+ = Co 2+ , Cu 2+ , Zn 2+ , Cd 2+ ; R 1 = H, Me; R 2 = 2py, 2pym, prz (2py = 2-pyridinyle; 2pym = 2-pyrimidinyle; prz = pyrazinyle)) crystallize with rtl topology. They are available as single crystals and also easily accessible in a multi-gram scale via refluxing the metal salts and the protonated ligands in a solvent. Their isomorphous structures facilitate the synthesis of heteronuclear MOFs; in case of 2 M , Co 2+ ions could be gradually substituted by Cu 2+ ions. The Co 2+ :Cu 2+ ratios were determined by ICP-OES spectroscopy, the distribution of Co 2+ and Cu 2+ in the crystalline samples are investigated by SEM-EDX analysis leading to the conclusions that Cu 2+ is more favorably incorporated into the framework compared to Co 2+ and, moreover, that the distribution of the two metal ions between the crystals and within the crystals is inhomogeneous if the crystals were grown slowly. The various compositions of the heteronuclear materials lead to different colors and the sorption properties for CO 2 and N 2 are dependent on the integrated metal ions.
A Simple and Reliable HPTLC Method for the Quantification of the Intense Sweetener Sucralose®
(2003)
This paper describes a simple and fast thin layer chromatography (TLC) method for the monitoring of the relatively new intense sweetener Sucralose® in various food matrices. The method requires little or no sample preparation to isolate or concentrate the analyte. The Sucralose® extract is separated on amino‐TLC‐plates, and the analyte is derivatized “reagent‐free” by heating the developed plate for 20 min at 190°C. Spots can be measured either in the absorption or fluorescence mode. The method allows the determination of Sucralose® at the levels of interest regarding foreseen European legislation (>50 mg/kg) with excellent repeatability (RSD = 3.4%) and recovery data (95%).
HPTLC on amino plates, with simple heating of the plates for derivatization, has been used for quantification of glucosamine in nutritional supplements. On heating the plate glucosamine reacts to form a compound which strongly absorbs light between 305 and 330 nm, with weak fluorescence. The reaction product can be detected sensitively either by absorption of light or by fluorescence detection. The detection limit in absorption mode is approximately 25 ng per spot. In fluorescence mode a detection limit of 15 ng is achievable. A calibration plot for absorption detection is linear in the range 25 to 4000 ng glucosamine. The derivative formed from glucosamine by heating is stable for months, and the relative standard deviation is 1.64% for 600 ng glucosamine. The amounts of glucosamine found in nutritional supplements were in agreement with the label declarations.
We present an improved quantification method for urethane found in spirits. The quantification is based on a derivatization reaction using cinnamaldehyde in combination with phosphoric acid. Measurements were carried out in the wavelength range from 445 to 460 nm using a diode-TLC device. An LED was used for illumination purposes. It emits very dense light at 365 nm. The quantification range of urethane is in the lower ng range. By applying 20 µL of sprits, the urethane quantification range is from 320 µg/L to 8.1 mg urethane per litre of spirit. The range of linearity covers nearly two magnitudes. The method is cheap, fast and reliable, and is able to monitor all European legislation limits without time-consuming sample pre-treatments.
A printed electronics technology has the advantage of additive and extremely low-cost fabrication compared with the conventional silicon technology. Specifically, printed electrolyte-gated field-effect transistors (EGFETs) are attractive for low-cost applications in the Internet-of-Things domain as they can operate at low supply voltages. In this paper, we propose an empirical dc model for EGFETs, which can describe the behavior of the EGFETs smoothly and accurately over all regimes. The proposed model, built by extending the Enz-Krummenacher-Vittoz model, can also be used to model process variations, which was not possible previously due to fixed parameters for near threshold regime. It offers a single model for all the operating regions of the transistors with only one equation for the drain current. Additionally, it models the transistors with a less number of parameters but higher accuracy compared with existing techniques. Measurement results from several fabricated EGFETs confirm that the proposed model can predict the I-V more accurately compared with the state-of-the-art models in all operating regions. Additionally, the measurements on the frequency of a fabricated ring oscillator are only 4.7% different from the simulation results based on the proposed model using values for the switching capacitances extracted from measurement data, which shows more than 2× improvement compared with the state-of-the-art model.
Decentralized applications (dApp) have proliferated in recent years, but their long-term viability is a topic of debate. However, for dApps to be sustainable, and suitable for integration into a larger service networks, they need to attract users and promise reliable availability. Therefore, assessing their longevity is crucial. Analyzing the utilization trajectory of a service is, however, challenging due to several factors, such as demand spikes, noise, autocorrelation, and non-stationarity. In this study, we employ robust statistical techniques to identify trends in currently popular dApps. Our findings demonstrate that a significant proportion of dApps, across a range of categories, exhibit statistically significant positive overall trends, indicating that success in decentralized computing can be sustainable and transcends specific fields. However, there is also a substantial number of dApps showing negative trends, with a disproportionately high number from the decentralized finance (DeFi) category. Furthermore, a more detailed inspection of time series segments shows a clearly diminishing proportion of positive trends from mid-2021 to the present. In summary, we conclude that the dApp economy might have lost some momentum, and that there is a strong element of uncertainty regarding its future significance.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.
The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as minimizing the computational complexity of consensus algorithms or blockchain storage requirements, have received attention. However, to realize the full potential of blockchain in IoT, the inefficiencies of its inter-peer communication must also be addressed. For example, blockchain uses a flooding technique to share blocks, resulting in duplicates and inefficient bandwidth usage. Moreover, blockchain peers use a random neighbor selection (RNS) technique to decide on other peers with whom to exchange blockchain data. As a result, the peer-to-peer (P2P) topology formation limits the effective achievable throughput. This paper provides a survey on the state-of-the-art network structures and communication mechanisms used in blockchain and establishes the need for network-based optimization. Additionally, it discusses the blockchain architecture and its layers categorizes existing literature into the layers and provides a survey on the state-of-the-art optimization frameworks, analyzing their effectiveness and ability to scale. Finally, this paper presents recommendations for future work.
In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.
In this paper, a temperature-dependent viscoplasticity model is presented that describes thermal and cyclic softening of the hot work steel X38CrMoV5-3 under thermomechanical fatigue loading. The model describes the softening state of the material by evolution equations, the material properties of which can be determined on the basis of a defined experimental program. A kinetic model is employed to capture the effect of coarsening carbides and a new isotropic cyclic softening model is developed that takes history effects during thermomechanical loadings into account. The temperature-dependent material properties of the viscoplasticity model are determined on the basis of experimental data measured in isothermal and thermomechanical fatigue tests for the material X38CrMoV5-3 in the temperature range between 20 and 650 ∘C. The comparison of the model and an existing model for isotropic softening shows an improved description of the softening behavior under thermomechanical fatigue loading. A good overall description of the experimental data is possible with the presented viscoplasticity model, so that it is suited for the assessment of operating loads of hot forging tools.
The DMFC is a promising option for backup power systems and for the power supply of portable devices. However, from the modeling point of view liquid-feed DMFC are challenging systems due to the complex electrochemistry, the inherent two-phase transport and the effect of methanol crossover. In this paper we present a physical 1D cell model to describe the relevant processes for DMFC performance ranging from electrochemistry on the surface of the catalyst up to transport on the cell level. A two-phase flow model is implemented describing the transport in gas diffusion layer and catalyst layer at the anode side. Electrochemistry is described by elementary steps for the reactions occurring at anode and cathode, including adsorbed intermediate species on the platinum and ruthenium surfaces. Furthermore, a detailed membrane model including methanol crossover is employed. The model is validated using polarization curves, methanol crossover measurements and impedance spectra. It permits to analyze both steady-state and transient behavior with a high level of predictive capabilities. Steady-state simulations are used to investigate the open circuit voltage as well as the overpotentials of anode, cathode and electrolyte. Finally, the transient behavior after current interruption is studied in detail.
Featherweight Generic Go (FGG) is a minimal core calculus modeling the essential features of the programming language Go. It includes support for overloaded methods, interface types, structural subtyping, and generics. The most straightforward semantic description of the dynamic behavior of FGG programs is to resolve method calls based on runtime type information of the receiver. This article shows a different approach by defining a type-directed translation from FGG− to an untyped lambda-calculus. FGG− includes all features of FGG but type assertions. The translation of an FGG− program provides evidence for the availability of methods as additional dictionary parameters, similar to the dictionary-passing approach known from Haskell type classes. Then, method calls can be resolved by a simple lookup of the method definition in the dictionary. Every program in the image of the translation has the same dynamic semantics as its source FGG− program. The proof of this result is based on a syntactic, step-indexed logical relation. The step index ensures a well-founded definition of the relation in the presence of recursive interface types and recursive methods. Although being non-deterministic, the translation is coherent.
A Validated Quantification of Sudan Red Dyes in Spicery using TLC and a 16-bit Flatbed Scanner
(2018)
We present a video-densitometric quantification method for Sudan red dyes in spices and spice mixtures, separated by TLC. Application was done band-wise in small dots using a 5 μL glass pipette. For separation, the RP-18 plates (20 × 20 cm with fluorescent dye; Merck, Germany, 1.05559) were developed in a vertical developing chamber without vapor saturation from the starting point to a distance of 70 mm by using acetonitrile, methanol, and aqueous ammonia solution (25%; 8 + 1.8 + 0.2, v/v) as mobile phase. The quantification is based on direct measurements using an inexpensive 16-bit flatbed scanner for color measurements (in red, green, and blue). Evaluation of only the green channel makes the measurements very specific. For linearization, an extended Kubelka-Munk expression for data transformation was used. The range of linearity covers more than two magnitudes and lies between 20 and 500 ng. The extraction from a 2 g sample with acetonitrile, evaporation, and reconstitution to 200 μL with methanol and the band-wise application (7 mm) of a 10 μL sample allows a statistically defined LOD of less than 500 ppb of Sudan red dyes. To perform the analysis, a separation chamber, RP-18 plates, 5 μL glass pipettes, and a 16-bit flatbed scanner for 105 € are needed; therefore, the separation method is inexpensive, fast, and reliable.
We present a densitometric quantification method for triclosan in toothpaste, separated by high-performance thin-layer chromatography (HPTLC) and using a 48-bit flatbed scanner as the detection system. The sample was band-wise applied to HPTLC plates (10 × 20 cm), with fluorescent dye, Merck, Germany (1.05554). The plates were developed in a vertical developing chamber with 20 min of chamber saturation over 70 mm, using n-heptane–methyl tert-butyl ether–acetic acid (92:8:0.1, V/V) as solvent. The RF value of triclosan is hRF = 22.4, and quantification is based on direct measurements using an inexpensive 48-bit flatbed scanner for color measurements (in red, green, and blue) after plate staining with 2,6-dichloroquinone-4-chloroimide (Gibbs' reagent). Evaluation of the red channel makes the measurements of triclosan very specific. For linearization, an extended Kubelka–Munk expression was used for data transformation. The range of linearity covers more than two orders of magnitude and is between 91 and 1000 ng. The separation method is inexpensive, fast and reliable.
Background
To assess the in-field walking mechanics during downhill hiking of patients with total knee arthroplasty five to 14 months after surgery and an age-matched healthy control group and relate them to the knee flexor and extensor muscle strength.
Methods
Participants walked on a predetermined hiking trail at a self-selected, comfortable pace wearing an inertial sensor system for recording the whole-body 3D kinematics. Sagittal plane hip, knee, and ankle joint angles were evaluated over the gait cycle at level walking and two different negative slopes. The concentric and eccentric lower extremity muscle strength of the knee flexors and extensors isokinetically at 50 and 120°/s were measured.
Findings
Less knee flexion angles during stance have been measured in patients in the operated limb compared to healthy controls in all conditions (level walking, moderate downhill, steep downhill). The differences increased with steepness. Muscle strength was lower in patients for both muscle groups and all measured conditions. The functional hamstrings to quadriceps ratio at 120°/sec correlated with knee angle during level and downhill walking at the moderate slope in patients, showing higher ratios with lower peak knee flexion angles.
Interpretation
The study shows that even if rehabilitation has been completed successfully and complication-free, five to 14 months after surgery, the muscular condition was still insufficient to display a normal gait pattern during downhill hiking. The muscle balance between quadriceps and hamstring muscles seems related to the persistence of a stiff knee gait pattern after knee arthroplasty. LoE: III.
For the treatment of bone defects, biodegradable, compressive biomaterials are needed as replacements that degrade as the bone regenerates. The problem with existing materials has either been their insufficient mechanical strength or the excessive differences in their elastic modulus, leading to stress shielding and eventual failure. In this study, the compressive strength of CPC ceramics (with a layer thickness of more than 12 layers) was compared with sintered β-TCP ceramics. It was assumed that as the number of layers increased, the mechanical strength of 3D-printed scaffolds would increase toward the value of sintered ceramics. In addition, the influence of the needle inner diameter on the mechanical strength was investigated. Circular scaffolds with 20, 25, 30, and 45 layers were 3D printed using a 3D bioplotter, solidified in a water-saturated atmosphere for 3 days, and then tested for compressive strength together with a β-TCP sintered ceramic using a Zwick universal testing machine. The 3D-printed scaffolds had a compressive strength of 41.56 ± 7.12 MPa, which was significantly higher than that of the sintered ceramic (24.16 ± 4.44 MPa). The 3D-printed scaffolds with round geometry reached or exceeded the upper limit of the compressive strength of cancellous bone toward substantia compacta. In addition, CPC scaffolds exhibited more bone-like compressibility than the comparable β-TCP sintered ceramic, demonstrating that the mechanical properties of CPC scaffolds are more similar to bone than sintered β-TCP ceramics.
In pandemic times, the possibilities for conventional sports activities are severely limited; many sports facilities are closed or can only be used with restrictions. To counteract this lack of health activities and social exchange, people are increasingly adopting new digital sports solutions—a behavior change that had already started with the trend towards fitness apps and activity trackers. Existing research suggests that digital solutions increase the motivation to move and stay active. This work further investigates the potentials of digital sports incorporating the dimensions gender and preference for team sports versus individual sports. The study focuses on potential users, who were mostly younger professionals and academics. The results show that the SARS-CoV-19 pandemic had a significant negative impact on sports activity, particularly on persons preferring team sports. To compensate, most participants use more digital sports than before, and there is a positive correlation between the time spent physically active during the pandemic and the increase in motivation through digital sports. Nevertheless, there is still considerable skepticism regarding the potential of digital sports solutions to increase the motivation to do sports, increase performance, or raise a sense of team spirit when done in groups.
There is a growing trend for the use of thermo-active building systems (TABS) for the heating and cooling of buildings, because these systems are known to be very economical and efficient. However, their control is complicated due to the large thermal inertia, and their parameterization is time-consuming. With conventional TABS-control strategies, the required thermal comfort in buildings can often not be maintained, particularly if the internal heat sources are suddenly changed. This paper shows measurement results and evaluations of the operation of a novel adaptive and predictive calculation method, based on a multiple linear regression (AMLR) for the control of TABS. The measurement results are compared with the standard TABS strategy. The results show that the electrical pump energy could be reduced by more than 86%. Including the weather adjustment, it could be demonstrated that thermal energy savings of over 41% could be reached. In addition, the thermal comfort could be improved due to the possibility to specify mean room set-point temperatures. With the AMLR, comfort category I of the comfort norms ISO 7730 and DIN EN 15251 are observed in about 95% of occasions. With the standard TABS strategy, only about 24% are within category I.
Adaptive predictive control of thermo-active building systems (TABS) based on a multiple regression algorithm: First practical test. Available from: https://www.researchgate.net/publication/305903009_Adaptive_predictive_control_of_thermo-active_building_systems_TABS_based_on_a_multiple_regression_algorithm_First_practical_test [accessed Jul 7, 2017].
Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.
The technique of laser ultrasonics perfectly meets the need for noncontact, noninvasive, nondestructive mechanical probing of nanometer- to millimeter-size samples. However, this technique is limited to the excitation of low-amplitude strains, below the threshold for optical damage of the sample. In the context of strain engineering of materials, alternative optical techniques enabling the excitation of high-amplitude strains in a nondestructive optical regime are needed. We introduce here a nondestructive method for laser-shock wave generation based on additive superposition of multiple laser-excited strain waves. This technique enables strain generation up to mechanical failure of a sample at pump laser fluences below optical ablation or melting thresholds. We demonstrate the ability to generate nonlinear surface acoustic waves (SAWs) in Nb-SrTiO3 substrates, with associated strains in the percent range and pressures up to 3 GPa at 1 kHz repetition rate and close to 10 GPa for several hundred shocks. This study paves the way for the investigation of a host of high-strain SAW-induced phenomena, including phase transitions in conventional and quantum materials, plasticity and a myriad of material failure modes, chemistry and other effects in bulk samples, thin layers, and two-dimensional materials.
In this study, a facile method to fabricate a cohesive ion‐gel based gate insulator for electrolyte‐gated transistors is introduced. The adhesive and flexible ion‐gel can be laminated easily on the semiconducting channel and electrode manually by hand. The ion‐gel is synthesized by a straightforward technique without complex procedures and shows a remarkable ionic conductivity of 4.8 mS cm−1 at room temperature. When used as a gate insulator in electrolyte‐gated transistors (EGTs), an on/off current ratio of 2.24×104 and a subthreshold swing of 117 mV dec−1 can be achieved. This performance is roughly equivalent to that of ink drop‐casted ion‐gels in electrolyte‐gated transistors, indicating that the film‐attachment method might represent a valuable alternative to ink drop‐casting for the fabrication of gate insulators.
The separation of nitrogen and methane from hydrogen-rich mixtures is systematically investigated on a recently developed binder-free zeolite 5A. For this adsorbent, the present work provides a series of experimental data on adsorption isotherms and breakthrough curves of nitrogen and methane, as well as their mixtures in hydrogen. Isotherms were measured at temperatures of 283–313 K and pressures of up to 1.0 MPa. Breakthrough curves of CH4, N2, and CH4/N2 in H2 were obtained at temperatures of 300–305 K and pressures ranging from 0.1 to 6.05 MPa with different feed concentrations. An LDF-based model was developed to predict breakthrough curves using measured and calculated data as inputs. The number of parameters and the use of correlations were restricted to focus on the importance of measured values. For the given assumptions, the results show that the model predictions agree satisfactorily with the experiments under the different operating conditions applied.
Adsorption of N2 and CO2 on Activated Carbon, AlO(OH) Nanoparticles, and AlO(OH) Hollow Spheres
(2015)
Adsorption behaviors of nitrogen and CO2 on Norit R1 Extra and AlO(OH) nanoparticles and hollow spheres were measured under different temperature and pressure conditions using a magnetic suspension balance. Independent from the substrate investigated, all isotherms increase at lower pressure, reach a maximum, and then decrease with increasing pressure. In addition, selected experimental data were correlated with different model approaches and compared with reliable literature data. In case of CO2 on AlO(OH), capillary condensation was observed at two defined temperatures. The results suggest that the conversion of the liquid into a supercritical adsorbate phase does not take place suddenly.
A systematic toxicological analysis procedure using high-performance thin layer chromatography in combination with fibre optical scanning densitometry for identification of drugs in biological samples is presented. Two examples illustrate the practicability of the technique. First, the identification of a multiple intake of analgesics: codeine, propyphenazone, tramadol, flupirtine and lidocaine, and second, the detection of the sedative diphenhydramine. In both cases, authentic urine specimens were used. The identifications were carried out by an automatic measurement and computer-based comparison of in situ UV spectra with data from a compiled library of reference spectra using the cross-correlation function. The technique allowed a parallel recording of chromatograms and in situ UV spectra in the range of 197–612 nm. Unlike the conventional densitometry, a dependency of UV spectra by concentration of substance in a range of 250–1000 ng/spot was not observed.
The significant market growth of stationary electrical energy storage systems both for private and commercial applications has raised the question of battery lifetime under practical operation conditions. Here, we present a study of two 8 kWh lithium-ion battery (LIB) systems, each equipped with 14 lithium iron phosphate/graphite (LFP) single cells in different cell configurations. One system was based on a standard configuration with cells connected in series, including a cell-balancing system and a 48 V inverter. The other system featured a novel configuration of two stacks with a parallel connection of seven cells each, no cell-balancing system, and a 4 V inverter. The two systems were operated as part of a microgrid both in continuous cycling mode between 30% and 100% state of charge, and in solar-storage mode with day–night cycling. The aging characteristics in terms of capacity loss and internal resistance change in the cells were determined by disassembling the systems for regular checkups and characterizing the individual cells under well-defined laboratory conditions. As a main result, the two systems showed cell-averaged capacity losses of 18.6% and 21.4% for the serial and parallel configurations, respectively, after 2.5 years of operation with 810 (serial operation) and 881 (parallel operation) cumulated equivalent full cycles. This is significantly higher than the aging of a reference single cell cycled under laboratory conditions at 20 °C, which showed a capacity loss of only 10% after 1000 continuous full cycles.
The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.
Membrane distillation (MD) is a thermal separation process which possesses a hydrophobic, microporous
membrane as vapor space. A high potential application for MD is the concentration of hypersaline brines, such as
e.g. reverse osmosis retentate or other saline effluents to be concentrated to a near saturation level with a Zero
Liquid Discharge process chain. In order to further commercialize MD for these target applications, adapted MD
module designs are required along with strategies for the mitigation of membrane wetting phenomena. This
work presents the experimental results of pilot operation with an adapted Air Gap Membrane Distillation
(AGMD) module for hypersaline brine concentration within a range of 0–240 g NaCl /kg solution. Key performance
indicators such as flux, GOR and thermal efficiency are analyzed. A new strategy for wetting mitigation
by active draining of the air gap channel by low pressure air blowing is tested and analyzed. Only small reductions
in flux and GOR of 1.2% and 4.1% respectively, are caused by air sparging into the air gap channel.
Wetting phenomena are significantly reduced by avoiding stagnant distillate in the air gap making the air blower
a seemingly worth- while additional system component.
Am 1. Juli 2022 trafen sich im Rahmen des Abschlusskolloquiums des Projekts ACA-Modes rund 60 Teilnehmende aus Forschung, Lehre und Industrie zu einer internationalen Konferenz an der Hochschule Offenburg. Hier wurden die Projektergebnisse rund um die erfolgreiche Implementierung modellprädiktiver Regelstrategien vorgestellt, aktuelle Fragestellungen diskutiert und Entwicklungspfade hin zu einem netzdienlichen Betrieb von Energieverbundsystemen skizziert.
Hintergrund
In diesem Artikel wird ein Überblick und Vergleich der am häufigsten verwendeten zementierten Hüftschäfte, gruppiert in die verschiedenen Schafttypen und Zementmanteldicken, gegeben, um zu sehen, welche Kombination gut abschneidet.
Methodik
Aus dem Endoprothesenregister Deutschland wurden die Revisionsraten zementierter Schaftarten kategorisiert und die Revisionsraten von 3 und 5 Jahren erfasst und analysiert. Für die Recherche lag die Konzentration auf den Schäften Exeter, C‑Stem, MS-30, Excia, Bicontact, Charnley, Müller Geradschaft, Twinsys, Corail, Avenir, Quadra und dem Lubinus SP II. Ein wichtiger Aspekt lag darin, welcher Schaft favorisiert implantiert wird und welche Zementiertechnik in Hinblick auf die geplante Zementmanteldicke angewendet wird. Um einen Trend in der zementierten Hüftendoprothetik herauszufinden, wurden zusätzlich die Daten des dänischen, schwedischen, norwegischen, schweizerischen, neuseeländischen, englischen und australischen Endoprothesenregister verglichen.
Ergebnisse und Schlussfolgerung
Die meisten Länder nutzen zementierte Prothesen nach dem Kraftschlussprinzip (Exeter, MS30, C‑Stem etc.) oder dem Formschlussprinzip (Charnley, Excia, Bicontact), welche mit einer Zementmanteldicke von 2–4 mm implantiert werden. Jedoch hat sich in Deutschland und der Schweiz ein Trend zur Line-to-Line-Technik, mit einer geplanten Zementmanteldicke von 1 mm (Twinsys, Corail, Avenir, Quadra) aufgezeigt, dem Prinzip der Müller-Geradschaft-Prothese und der Kerboul-Charnley-Prothese folgend, auch wenn diese an sich als „french paradoxon“ postuliert werden. In den EPRD-5-Jahres-Ergebnissen scheinen die neueren Line-to-Line-Prothesen etwas schlechter abzuschneiden. Die besten Ergebnisse erzielt der „MS 30“ in Deutschland und der „Exeter“ in England. Hierbei handelt es sich um polierte Geradschäfte mit Zentraliser und Subsidence-Raum an der Spitze mit einem 2–4 mm Zementmantel in guter Zementiertechnik.
In this report, we have studied field-effect transistors (FETs) using low-density alumina for electrolytic gating. Device layers have been prepared starting from the structured ITO glasses by printing the In 2 O 3 channels, low-temperature atomic layer deposition (ALD) of alumina (Al 2 O 3 ), and printing graphene top gates. The transistor performance could be deliberately changed by alternating the ambient humidity; furthermore, ID,ON/ID,OFF-ratios of up to seven orders of magnitude and threshold voltages between 0.66 and 0.43 V, decreasing with an increasing relative humidity between 40% and 90%, could be achieved. In contrast to the common usage of Al 2 O 3 as the dielectric in the FETs, our devices show electrolyte-typegating behavior. This is a result from the formation of protons on the Al 2 O 3 surfaces at higher humidities. Due to the very high local capacitances of the Helmholtz double layers at the channel surfaces, the operation voltage can be as low as 1 V. At low humidities (≤30%), the solid electrolyte dries out and the performance breaks down; however, it can fully reversibly be regained upon a humidity increase. Using ALD-derived alumina as solid electrolyte gating material, thus, allows low-voltage operation and provides a chemically stable gating material while maintaining low process temperatures. However, it has proven to be highly humidity-dependent in its performance.
Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. To reveal model weaknesses, adversarial attacks are specifically optimized to generate small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained by using adversarial examples during training, which in most cases reduces the measurable model attackability. Unfortunately, this technique can lead to robust overfitting, which results in non-robust models. In this paper, we analyze adversarially trained, robust models in the context of a specific network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from downsampling artifacts, aka. aliasing, than baseline models. In the case of robust overfitting, we observe a strong increase in aliasing and propose a novel early stopping approach based on the measurement of aliasing.
All you need is sleep
(2016)
In 21st century, the century when the humanity hopes to embark on interplanetary travel, we are yet to fully reach an understanding of our very own idiosyncratic terra incognita – the human sleep. Sleep is a highly conserved evolutionary process that constitutes approximately one third of our life, and the lack or inadequate sleep may lead to impairment across multiple cognitive domains (Tononi and Cirelli, 2014; Lim and Dinges, 2010). Sleep deprivation also leads to aberrant brain functioning, immunological and metabolic collapse, and if it is sufficiently prolonged it will ultimately lead to death (Tononi and Cirelli, 2014).
There is increasing evidence of central hyperexcitability in chronic whiplash-associated disorders (cWAD). However, little is known about how an apparently simple cervical spine injury can induce changes in cerebral processes. The present study was designed (1) to validate previous results showing alterations of regional cerebral blood flow (rCBF) in cWAD, (2) to test if central hyperexcitability reflects changes in rCBF upon non-painful stimulation of the neck, and (3) to verify our hypothesis that the missing link in understanding the underlying pathophysiology could be the close interaction between the neck and midbrain structures. For this purpose, alterations of rCBF were explored in a case-control study using H215O positron emission tomography, where each group was exposed to four different conditions, including rest and different levels of non-painful electrical stimulation of the neck. rCBF was found to be elevated in patients with cWAD in the posterior cingulate and precuneus, and decreased in the superior temporal, parahippocampal, and inferior frontal gyri, the thalamus and the insular cortex when compared with rCBF in healthy controls. No differences in rCBF were observed between different levels of electrical stimulation. The alterations in regions directly involved with pain perception and interoceptive processing indicate that cWAD symptoms might be the consequence of a mismatch during the integration of information in brain regions involved in pain processing.
In the literature, many studies have described the 3D printing of ceramic-based scaffolds (e.g., printing with calcium phosphate cement) in the form of linear structures with layer rotations of 90°, although no right angles can be found in the human body. Therefore, this work focuses on the adaptation of biological shapes, including a layer rotation of only 1°. Sample shapes were printed with calcium phosphate cement using a 3D Bioplotter from EnvisionTec. Both straight and wavy spokes were printed in a round structure with 12 layers. Depending on the strand diameter (200 and 250 µm needle inner diameter) and strand arrangement, maximum failure loads of 444.86 ± 169.39 N for samples without subsequent setting in PBS up to 1280.88 ± 538.66 N after setting in PBS could be achieved.
n this work a mathematical model for describing the performance of lithium-ion battery electrodes consisting of porous active material particles is presented. The model represents an extension of the Newman-type model, accounting for the agglomerate structure of the active material particles, here Li(Ni1/3Co1/3Mn1/3)O2 (NCM) and Li(Ni1/3Co1/3Al1/3)O2 (NCA). To this goal, an additional pore space is introduced on the active material level. The space is filled with electrolyte and a charge-transfer reaction takes place at the liquid-solid interface within the porous active material particles. Volume-averaging techniques are used to derive the model equations. A local Thiele modulus is defined and provides an insight into the potentially limiting factors on the active material level. The introduction of a liquid-phase ion transport within the active material reduces the overall transport losses, while the additional active surface area within the agglomerate lowers the charge-transfer resistance. As a consequence, calculated discharge capacities are higher for particles modeled as agglomerates. This finding is more pronounced in the case of high C-rates
Running footwear is continuously being modified and improved; however, running-related overuse injury rates remain high. Nevertheless, novel manufacturing processes enable the production of individualized running shoes that can fit the individual needs of runners, with the potential to reduce injury risk. For this reason, it is essential to investigate functional groups of runners, a collective of runners who respond similarly to a footwear intervention. Therefore, the objective of this study was to develop a framework to identify functional groups based on their individual footwear response regarding injury-specific running-related risk factors for Achilles tendinopathy, Tibial stress fractures, Medial tibial stress syndrome, and Patellofemoral pain syndrome. In this work, we quantified the footwear response patterns of 73 female and male participants when running in three different footwear conditions using unsupervised learning (k-means clustering). For each functional group, we identified the footwear conditions minimizing the injury-specific risk factors. We described differences in the functional groups regarding their running style, anthropometric, footwear perception, and demographics. The results implied that most functional groups showed a tendency for a single footwear condition to reduce most biomechanical risk factors for a specific overuse injury. Functional groups often differed in their hip and pelvis kinematics as well as their subjective rating of the footwear conditions. The footwear intervention only partially affected biomechanical risk factors attributed to more proximal joints. Due to its adaptive nature, the framework could be applied to other footwear interventions or performance-related biomechanical variables.
This paper has the objective of creating a framework for a different cultural dimension of corporate entrepreneurship leading to corporate entrepreneurial culture (CEC). The analysis of CEC is based on a review of existing concepts of organisational culture and entrepreneurship. They are combined to create a framework of CEC, including macro- and microlevels and examples of subcultures. Core ideas of the framework are validated by qualitative interviews with ten experts. The identified organisational category of the CEC framework is defined by the levels of micro-cultures or subcultures and includes the upper levels of the hierarchy, including the industry level. Geographic categories such as regional or national culture are also part of the system. The individual category of the CEC framework is characterised by competencies (including aspects such as motivation, creativity, mobilising others, coping with uncertainty, teamwork and social competencies) and entrepreneurial personalities. The results of the interviews show the importance of these individual competencies for a lively CEC. The different levels, such as national and professional cultures, as a dimension of the organisational category of the framework are also confirmed by the interviews. The findings indicate that the individual category of CEC could be used for job satisfaction or engagement and the degree of CEC of an organisation could be defined and developed by the organisational category. The identified framework contributes to an understanding of this complex topic and supports companies in the implementation of entrepreneurial ideas in different organisational contexts.
Rubber materials are characterized by a variety of inelasticities such as softening behavior, hysteresis loops and permanent set. In order to calculate the inelastic material behavior, constitutive models, that describe rubber as a homogeneous continuum, have to make use of damping or friction elements.
On the nanoscale, there is no need to adopt such rheological models. Inelastic material behavior can be explained and simulated by a continuous rearrangement of bonds, in particular, the van der Waals interactions, and by the polymer chains transitioning between cis and trans equilibrium torsion angles. The discrete molecular dynamics simulations presented in this paper are performed in an explicit FEM environment using nonlinear but elastic force field potentials. From a structural mechanics point of view, topological changes of the polymer network can be interpreted as a sequence of local material instability problems due to negative tangential bond stiffnesses.
In order to obtain representative results within reasonable computational time, the model is optimized with respect to the number of atoms and the loading velocity. It is shown that by increasing the model size, the stress–strain curves become independent of both the atoms initial state and the strain amplitudes.
An Extraction Method for 17α-Ethinylestradiol from Water using a new kind of monolithic Stir-bar
(2015)
We present a video-densitometric high-performance thin-layer chromatography (HPTLC) quantification method for patulin in apple juice, developed in a vertical chamber from the starting point to a distance of 50 mm, using MTBE, n-pentane (9 + 5, v/v) as mobile phase. After separation the plate is sprayed with methyl-benzothiazolinone hydrazone hydrochloride monohydrate (MBTH) solution (40 mg in 20 mL methanol) and heated at 105 °C for 15 min. Patulin zones are transformed into yellow spots. The quantification is based on direct measurements using an inexpensive 48-bit flatbed scanner for color measurements (in red, green, and blue). Evaluation of the blue channel makes the measurements very specific. Quantification in fluorescence was also done by use of a 16-bit CCD-camera and UV-366 nm illumination as well as using a HPTLC DAD-scanner. For linearization the extended Kubelka–Munk expression for data transformation was used. The range of linearity covers more than two magnitudes and lies between 5 and 800 ng patulin. The extraction of 20 g apple juice and an extract application on plate up to 50 µL allows statistically defined checking the limit of detection (LOD) of 50 ng patulin per track, which is equivalent to 50 µg patulin per kg apple juice.
An in-depth study of U-net for seismic data conditioning: Multiple removal by moveout discrimination
(2024)
Seismic processing often involves suppressing multiples that are an inherent component of collected seismic data. Elaborate multiple prediction and subtraction schemes such as surface-related multiple removal have become standard in industry workflows. In cases of limited spatial sampling, low signal-to-noise ratio, or conservative subtraction of the predicted multiples, the processed data frequently suffer from residual multiples. To tackle these artifacts in the postmigration domain, practitioners often rely on Radon transform-based algorithms. However, such traditional approaches are both time-consuming and parameter dependent, making them relatively complex. In this work, we present a deep learning-based alternative that provides competitive results, while reducing the complexity of its usage, and, hence simplifying its applicability. Our proposed model demonstrates excellent performance when applied to complex field data, despite it being exclusively trained on synthetic data. Furthermore, extensive experiments show that our method can preserve the inherent characteristics of the data, avoiding undesired oversmoothed results, while removing the multiples from seismic offset or angle gathers. Finally, we conduct an in-depth analysis of the model, where we pinpoint the effects of the main hyperparameters on real data inference, and we probabilistically assess its performance from a Bayesian perspective. In this study, we put particular emphasis on helping the user reveal the inner workings of the neural network and attempt to unbox the model.
Rectifiersare vital electronic circuits for signal and power conversion in various smart sensor applications. The ability to process low input voltage levels, for example, from vibrational energy harvesters is a major challenge with existing passive rectifiers in printed electronics, stemming mainly from the built-in potential of the diode's p-njunction. To address this problem, in this work, we design, fabricate, and characterize an inkjet-printed full-wave rectifier using diode-connected electrolyte-gated thin-film transistors (EGTs). Using both experimental and simulation approaches, we investigate how the rectifier can benefit from the near-zero threshold voltage of transistors, which can be enabled by proper channel geometry setting in EGT technology. The presented circuit can be operated at 1-V input voltage, featuring a remarkably small voltage loss of 140 mV and a cutoff frequency of ~300 Hz. Below the cutoff frequency, more than 2.6-μW dc power is obtained over the load resistances ranging from 5 to 20 kQ. Furthermore, experiments show that the circuit can work with an input amplitude down to 500 mV. This feature makes the presented design highly suitable for a variety of energy-harvesting applications.
The aim of this paper is to identify indicators at country level that could prove useful in improving the effectiveness of fraud detection in European Structural and Investment Funds. We analyse data for 454 funds, belonging to the period 2014-2020, from the 28 countries that were members of the European Union in 2014. Explanatory results suggest the convenience of tracking funds, especially in countries with higher GDP and higher transparency levels, and the lesser relevance of the number of irregularities for countries with higher GDP and those receiving larger funds. Fraud and fraud detection rates in individual funds vary significantly across states. Federal states, such as the Federal Republic of Germany, are comparatively successful in detecting fraud in EU funds.
An isomorphous series of 10 microporous copper-based metal–organic frameworks (MOFs) with the general formulas ∞3[{Cu3(μ3-OH)(X)}4{Cu2(H2O)2}3(H-R-trz-ia)12] (R = H, CH3, Ph; X2– = SO42–, SeO42–, 2 NO32– (1–8)) and ∞3[{Cu3(μ3-OH)(X)}8{Cu2(H2O)2}6(H-3py-trz-ia)24Cu6]X3 (R = 3py; X2– = SO42–, SeO42– (9, 10)) is presented together with the closely related compounds ∞3[Cu6(μ4-O)(μ3-OH)2(H-Metrz-ia)4][Cu(H2O)6](NO3)2·10H2O (11) and ∞3[Cu2(H-3py-trz-ia)2(H2O)3] (12Cu), which are obtained under similar reaction conditions. The porosity of the series of cubic MOFs with twf-d topology reaches up to 66%. While the diameters of the spherical pores remain unaffected, adsorption measurements show that the pore volume can be fine-tuned by the substituents of the triazolyl isophthalate ligand and choice of the respective copper salt, that is, copper sulfate, selenate, or nitrate.