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High-tech running shoes and spikes ("super-footwear") are currently being debated in sports. There is direct evidence that distance running super shoes improve running economy; however, it is not well established to which extent world-class performances are affected over the range of track and road running events.
This study examined publicly available performance datasets of annual best track and road performances for evidence of potential systematic performance effects following the introduction of super footwear. The analysis was based on the 100 best performances per year for men and women in outdoor events from 2010 to 2022, provided by the world governing body of athletics (World Athletics).
We found evidence of progressing improvements in track and road running performances after the introduction of super distance running shoes in 2016 and super spike technology in 2019. This evidence is more pronounced for distances longer than 1500 m in women and longer than 5000 m in men. Women seem to benefit more from super footwear in distance running events than men.
While the observational study design limits causal inference, this study provides a database on potential systematic performance effects following the introduction of super shoes/spikes in track and road running events in world-class athletes. Further research is needed to examine the underlying mechanisms and, in particular, potential sex differences in the performance effects of super footwear.
Objective: To identify and evaluate the evidence of the most relevant running-related risk factors (RRRFs) for running-related overuse injuries (ROIs) and to suggest future research directions.
Design: Systematic review considering prospective and retrospective studies. (PROSPERO_ID: 236832)
Data sources: Pubmed. Connected Papers. The search was performed in February 2021.
Eligibility criteria: English language. Studies on participants whose primary sport is running addressing the risk for the seven most common ROIs and at least one kinematic, kinetic (including pressure measurements), or electromyographic RRRF. An RRRF needed to be identified in at least one prospective or two retrospective studies.
Results: Sixty-two articles fulfilled our eligibility criteria. Levels of evidence for specific ROIs ranged from conflicting to moderate evidence. Running populations and methods applied varied considerably between studies. While some RRRFs appeared for several ROIs, most RRRFs were specific for a particular ROI. The biomechanical measurements performed in many studies would have allowed for consideration of many more RRRFs than have been reported, highlighting a potential for more effective data usage in the future.
Conclusion: This study offers a comprehensive overview of RRRFs for the most common ROIs, which might serve as a starting point to develop ROI-specific risk profiles of individual runners. Future work should use macroscopic (big data) approaches involving long-term data collections in the real world and microscopic approaches involving precise stress calculations using recent developments in biomechanical modelling. However, consensus on data collection standards (including the quantification of workload and stress tolerance variables and the reporting of injuries) is warranted.
A benchmark analysis of Long Range (LoRaTM) Communication at 2.45 Ghz for safety applications
(2014)
Rotation of an elastic medium gives rise to a shift of frequency of its acoustic modes, i.e., the time-period vibrations that exist in it. This frequency shift is investigated by applying perturbation theory in the regime of small ratios of the rotation velocity and the frequency of the acoustic mode. In an expansion of the relative frequency shift in powers of this ratio, upper bounds are derived for the first-order and the second-order terms. The derivation of the theoretical upper bounds of the first-order term is presented for linear vibration modes as well as for stable nonlinear vibrations with periodic time dependence that can be represented by a Fourier series.
Mice and rats make up 95% of all animals used in medical research and drug discovery and development. Monitoring of physiological functions such as ECG, blood pressure, and body temperature over the entire period of an experiment is often required. Restraining of the animals in order to obtain this data can cause great inconvenience. The use of telemetric systems solves this problem and provides more reliable results. However, these devices are mostly equipped with batteries, which limit the time of operation or they use passive power supplies, which affects the operating range. The semi-passive telemetric implant being presented is based on RFID technology and overcomes these obstacles. The device is inductively powered using the magnetic field of a common RFID reader device underneath the cage, but is also able to operate for several hours autonomously. Being independent from the battery capacity, it is possible to use the implant over a long period of time or to re-use the device several times in different animals, thus avoiding the disadvantages of existing systems and reducing the costs of purchase and refurbishment.
Formal Description of Inductive Air Interfaces Using Thévenin's Theorem and Numerical Analysis
(2014)
With the development of new integrated circuits to interface radio frequency identification protocols, inductive air interfaces have become more and more important. Near field communication is not only able to communicate, but also possible to transfer power wirelessly and to build up passive devices for logistical and medical applications. In this way, the power management on the transponder becomes more and more relevant. A designer has to optimize power consumption as well as energy harvesting from the magnetic field. This paper discusses a model with simple equations to improve transponder antenna matching. Furthermore, a new numerical analysis technique is presented to calculate the coupling factors, inductions, and magnetic fields of multiantenna systems.
A Survey of Channel Measurements and Models for Current and Future Railway Communication Systems
(2016)
Bluetooth Low Energy extends the Bluetooth standard in version 4.0 for ultra-low energy applications through the extensive usage of low-power sleeping periods, which inherently difficult in frequency hopping technologies. This paper gives an introduction into the specifics of the Bluetooth Low Energy protocol, shows a sample implementation, where an embedded device is controlled by an Android smart phone, and shows the results of timing and current consumption measurements.
In the area of cloud computing, judging the fulfillment of service-level agreements on a technical level is gaining more and more importance. To support this we introduce privacy preserving set relations as inclusiveness and disjointness based ao Bloom filters. We propose to compose them in a slightly different way by applying a keyed hash function. Besides discussing the correctness of set relations, we analyze how this impacts the privacy of the sets content as well as providing privacy on the sets cardinality. Indeed, our solution proposes to bring another layer of privacy on the sizes. We are in particular interested how the overlapping bits of a Bloom filter impact the privacy level of our approach. We concretely apply our solution to a use case of cloud security audit on access control and present our results with real-world parameters.
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. 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.
An interlaboratory comparison was carried out to evaluate the effectiveness of a method based on HPTLC in which reagent-free derivatization is followed by UV/fluorescence detection. The method was tested for the determination of sucralose (C12H19C13O8; (2R,3R,4R,5S,6R)-2-[(2R,3S,4S,5S)-2,5-bis(chloromethyl)-3,4-dihydroxyoxolan-2-yl]oxy-5-chloro-6-hydroxymethyl)oxane-3, 4-diol; CAS Registry No. 56038-13-2) in carbonated and still beverages at the proposed European regulatory limits. For still beverages, a portion of the sample was diluted with methanol-water. For carbonated beverages, a portion of the sample was degassed in an ultrasonic bath before dilution. Turbid beverages were filtered after dilution through an HPLC syringe filter. The separation of sucralose was performed by direct application on amino-bonded (NH2) silica gel HPTLC plates (no cleanup needed) with the mobile phase acetonitrile-water. Sucralose was determined after reagent-free derivatization at 190 degrees C; it was quantified by measurements of both UV absorption and fluorescence. The samples, both spiked and containing sucralose, were sent to 14 laboratories in five different countries. Test portions of a sample found to contain no sucralose were spiked at levels of 30.5, 100.7, and 299 mg/L. Recoveries ranged from 104.3 to 124.6% and averaged 112% for determination by UV detection; recoveries ranged from 98.4 to 101.3% and averaged 99.9% for determination by fluorescence detection. On the basis of the results for spiked samples (blind duplicates at three levels), as well as sucralose-containing samples (blind duplicates at three levels and one split level), the values for the RSDr ranged from 10.3 to 31.4% for determinations by UV detection and from 8.9 to 15.9% for determinations by fluorescence detection. The values for the RSDR values ranged from 13.5 to 31.4% for determinations by UV detection and from 8.9 to 20.7% for determinations by fluorescence detection.
We present a videodensitometric quantification method for methadone in syrup, separated by thin-layer chromatography (TLC). The quantification is based on a derivation reaction with Dragendorf reagent. Measurements were carried out using a 16-bit flatbed scanner. The range of linearity covers two magnitudes of power using the Kubelka-Munk expression for data transformation. The separation method is inexpensive, fast, and reliable.
Diode-array planar chromatography is a versatile tool for identification of pharmaceutical substances In this paper thirty-three compounds with benzodiazepine properties were investigated and the separating conditions for silica gel HPTLC plates and three mobile phases were optimized. Diode-array HPTLC makes it possible to identify all the compounds with high certainty down to a level of 20 ng. An algorithm for spectral recognition which is combined with R F values from the three separation steps into one fit factor is presented. This set of data is unique for each of the compounds investigated and enables unequivocal identification. The method is rapid, inexpensive, and sensitive down to a level of 20 ng mL −1.
In thin-layer chromatography the development step distributes the sample throughout the layer, a process which strongly affects the reflection signals. The essential requirement for quantitative thinlayer chromatography is not a constant sample concentration but constant sample distribution in each sample spot. This makes evaporation of the mobile phase extremely important, because all tracks of a TLC plate must be dried uniformly. This paper shows that quantitative TLC is possible even if the concentration of the sample is not constant throughout the layer or if the distribution of the sample is not known. With uniform sample distribution, classical Kubelka-Munk theory is valid for isotropic scattering only. In the absence of this constraint classical Kubelka-Munk theory must be extended to situations where scattering is asymmetric. This can be achieved by modification of the original Kubelka-Munk equation. Extended theory is presented which is not only capable of describing asymmetrical scattering in TLC layers but also includes a formula for absorption and fluorescence in diode-array TLC. With this new theory all different formulas for diode-array thin-layer chromatographic evaluation are combined in one expression.
The communication technologies for automatic me-ter reading (smart metering) and for energy production and distribution networks (smart grid) have the potential to be one of the first really highly scaled machine-to-machine-(M2M)-applications. During the last years two very promising devel-opments around the wireless part of smart grid communication were initialized, which possibly have an impact on the markets far beyond Europe and far beyond energy automation. Besides the specifications of the Open Metering System (OMS) Group, the German Federal Office for Information Security (Bundesamt für Sicherheit in der Informationstechnik, BSI) has designed a protection profile (PP) and a technical directive (TR) for the communication unit of an intelligent measurement sys-tem (smart meter gateway), which were released in March 2013. This design uses state-of-the-art technologies and prescribes their implementation in real-life systems. At first sight the expenditures for the prescribed solutions seem to be significant. But in the long run, this path is inevitable and comes with strategic advantages.
CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision.
In contrast to their traditional, non-interactive counterparts, interactive dynamic visualisations allow users to adapt their form and content to their individual cognitive skills and needs. Provided that the interactive features allow for intuitive use without increasing cognitive load, interactive videos should therefore lead to more efficient forms of learning. This notion was tested in an experimental study, where participants learned to tie four nautical knots of different complexity by watching either non-interactive or interactive videos. The results show that in the interactive condition, participants used the interactive features like stopping, replaying, reversing or changing speed to adapt the pace of the video demonstration. This led to an uneven distribution of their attention and cognitive resources across the videos, which was more pronounced for the difficult knots. Consequently users of non-interactive video presentations, needed substantially more time than users of the interactive videos to acquire the necessary skills for tying the knots.
With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global level explain model behavior and on a local level explain sample predictions. We propose a visual analytics workflow to support seamless transitions between global and local explanations, focusing on attributions and counterfactuals on time series classification. In particular, we adapt local XAI techniques (attributions) that are developed for traditional datasets (images, text) to analyze time series classification, a data type that is typically less intelligible to humans. To generate a global overview, we apply local attribution methods to the data, creating explanations for the whole dataset. These explanations are projected onto two dimensions, depicting model behavior trends, strategies, and decision boundaries. To further inspect the model decision-making as well as potential data errors, a what-if analysis facilitates hypothesis generation and verification on both the global and local levels. We constantly collected and incorporated expert user feedback, as well as insights based on their domain knowledge, resulting in a tailored analysis workflow and system that tightly integrates time series transformations into explanations. Lastly, we present three use cases, verifying that our technique enables users to (1)~explore data transformations and feature relevance, (2)~identify model behavior and decision boundaries, as well as, (3)~the reason for misclassifications.
The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research has shown, how such detection can be achieved by deep learning methods. However, that particular approach is limited to the identification of only 4 types of vulnerabilities. Subsequently, we analyze to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardized LLVM Intermediate Representation. The vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, the methodology presented allows an accurate detection of 23 (compared to 4) vulnerabilities.
Multi-agent systems are a subject of continuously increasing interest in applied technical sciences. Smart grids are one evolving field of application. Numerous smart grid projects with various interpretations of multi-agent systems as new control concept arose in the last decade. Although several theoretical definitions of the term ‘agent’ exist, there is a lack of practical understanding that might be improved by clearly distinguishing the agent technologies from other state-of-the-art control technologies. In this paper we clarify the differences between controllers, optimizers, learning systems, and agents. Further, we review most recent smart grid projects, and contrast their interpretations with our understanding of agents and multi-agent systems. We point out that multi-agent systems applied in the smart grid can add value when they are understood as fully distributed networks of control entities embedded in dynamic grid environments; able to operate in a cooperative manner and to automatically (re-)configure themselves.
Micro-cracks give rise to non-analytic behavior of the stress-strain relation. For the case of a homogeneous spatial distribution of aligned flat micro-cracks, the influence of this property of the stress-strain relation on harmonic generation is analyzed for Rayleigh waves and for acoustic wedge waves with the help of a simple micromechanical model adopted from the literature. For the efficiencies of harmonic generation of these guided waves, explicit expressions are derived in terms of the corresponding linear wave fields. The initial growth rates of the second harmonic, i.e., the acoustic nonlinearity parameter, has been evaluated numerically for steel as matrix material. The growth rate of the second harmonic of Rayleigh waves has also been determined for microcrack distributions with random orientation, using a model expression for the strain energy in terms of strain invariants known in a geophysical context.
Hybrid SPECT/US
(2014)
(1) Background: Little is known about the baroque composer Domenico Scarlatti (1685-1757), whose life was centred behind closed doors at the royal court in Spain. There are no reports about his illnesses. From his compositions, mainly for harpsichord, an outstanding virtuosity can be read. (2) Case Presentation: In this case report, the only known oil painting of Domenico Scarlatti is presented, on which he is about 50 years old. In it one recognizes conspicuous hands with hints of watch glass nails and drumstick fingers. (3) Discussion: Whether Scarlatti had chronic hypoxia of peripheral body regions as a sign of, e.g., bronchial cancer or a severe heart disease, is not known. (4) Conclusions: The above-mentioned signs recorded in the oil painting, even if they were not interpretable at that time, are clearly represented and recorded for us and are open to diagnostic discussion from today's point of view.
The aim of this data collection is to enforce evidence of SCS effectiveness in treating neuropathic chronic pain and the very low percentage of undesired side effects of complications reported in our case series suggests that all implants should be performed by similarly well-trained and experienced professionals.
The Raman spectra from the chemical compounds toluene and cyclohexane obtained using a Fourier Transform (FT)-Raman spectrometer prototype have been contrasted with the Raman spectra of these same materials collected with two different commercial FT-Raman devices. The FT-Raman spectrometer consist of a Michelson interferometer, a self-designed photon counter and a reference photo-detector. The evaluation methodology of the spectral information, contrary to the commercial devices that commonly use the zero-crossing method, is carried out by re-sampling the Raman scattering and by accurately extracting the optical path information of the Michelson interferometer. The FTRaman arrangement has been built using conventional parts without disregarding the spectral frequency precision that usually such a FTRaman instruments deliver. No additional complex hardware components or costly software modules have been included in this FT-Raman device. The main Raman lines from the spectra obtained with the three FT-Raman devices have been compared with the Raman lines from the standard Raman spectra of these two materials. The values obtained using the FT-Raman spectrometer prototype have shown a frequency accuracy comparable to that obtained with the commercial devices without facing the need for a large investment. Although the proposed FT-Raman prototype cannot be directly compared to the last generation of FT-Raman spectrometers from the commercial manufacturers, such a device could give an opportunity to users that require high frequency precision in their spectral analysis and are provided with rather scarce resources.
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
Todays‘ traffic support environments are distributed by nature. In many cases the monitoring, control and guidance of traffic is effected by a federation of coordinating centers, often managed by different organizations, using differing local IT technology and system architecture. Despite the federative character of such systems, maintenance of a consistent overall traffic state is indispensable for a safe operation. This project develops a new type of middleware supporting federative systems
in the domain of Air Traffic Control (ATC), using OMG‘s DDS (Data Distribution Service) standard as contributor.