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In a randomized controlled cross-over study ten male runners (26.7 ± 4.9 years; recent 5-km time: 18:37 ± 1:07 min:s) performed an incremental treadmill test (ITT) and a 3-km time trial (3-km TT) on a treadmill while wearing either carbon fiber insoles with downwards curvature or insoles made of butyl rubber (control condition) in light road racing shoes (Saucony Fastwitch 9). Oxygen uptake, respiratory exchange ratio, heart rate, blood lactate concentration, stride frequency, stride length and time to exhaustion were assessed during ITT. After ITT, all runners rated their perceived exertion, perceived shoe comfort and perceived shoe performance. Running time, heart rate, blood lactate levels, stride frequency and stride length were recorded during, and shoe comfort and shoe performance after, the 3-km TT. All parameters obtained during or after the ITT did not differ between the two conditions [range: p = 0.188 to 0.948 (alpha value: 0.05); Cohen's d = 0.021 to 0.479] despite the rating of shoe comfort showing better scores for the control insoles (p = 0.001; d = −1.646). All parameters during and after the 3-km TT showed no differences (p = 0.200 to 1.000; d = 0.000 to 0.501) between both conditions except for shoe comfort showing better scores for control insoles (p = 0.017; d = −0.919). Running with carbon fiber insoles with downwards curvature did not change running performance or any submaximal or maximal physiological or biomechanical parameter and perceived exertion compared to control condition. Shoe comfort is impaired while running with carbon fiber insoles. Wearing carbon fiber insoles with downwards curvature during treadmill running is not beneficial when compared to running with control insoles.
Batteries typically consist of multiple individual cells connected in series. Here we demonstrate single-cell state of charge (SOC) and state of health (SOH) diagnosis in a 24 V class lithium-ion battery. To this goal, we introduce and apply a novel, highly efficient algorithm based on a voltage-controlled model (VCM). The battery, consisting of eight single cells, is cycled over a duration of five months under a simple cycling protocol between 20 % and 100 % SOC. The cell-to-cell standard deviations obtained with the novel algorithm were 1.25 SOC-% and 1.07 SOH-% at beginning of cycling. A cell-averaged capacity loss of 9.9 % after five months cycling was observed. While the accuracy of single-cell SOC estimation was limited (probably owed to the flat voltage characteristics of the lithium iron phosphate, LFP, chemistry investigated here), single-cell SOH estimation showed a high accuracy (2.09 SOH-% mean absolute error compared to laboratory reference tests). Because the algorithm does not require observers, filters, or neural networks, it is computationally very efficient (three seconds analysis time for the complete data set consisting of eight cells with approx. 780.000 measurement points per cell).
Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.
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.
Background/Purpose
Several methods are used to evaluate the outcome of total hip arthroplasty (THA), however, their relationship at different time points after surgery is unclear. The purpose of this exploratory study was to investigate correlations between self-report function, performance-based tests (PBTs) and biomechanical parameters in patients 12 months after THA.
Methods
Eleven patients were included in this preliminary cross-sectional study. Hip disability and Osteoarthritis Outcome Score (HOOS) was completed for self-reported function. As PBTs, the Timed-up-and-Go test (TUG) and 30-Second-Chair-Stand test (30CST) were used. Biomechanical parameters were derived from analyses of hip strength, gait and balance. Potential correlations were calculated using Spearman correlation coefficient r.
Results
HOOS scores and parameters of PBTs showed moderate to strong correlations (0.3 < r < 0.7). Correlation analysis between HOOS scores and biomechanical parameters revealed moderate to strong correlations for hip strength whereas correlations with gait parameters and balance were rather weak (r < 0.3). Moderate to strong correlations were also found between parameters of hip strength and 30CST.
Conclusion
For THA outcome assessment 12 months after surgery, our first results indicate that self-report measures or PBTs could be used. Analysis of hip strength also appears to be reflected in HOOS and PBT parameters and may be considered as an adjunct. Given the weak correlations with gait and balance parameters, we suggest that gait analysis and balance testing should be performed in addition to PROMs and PBTs as they may provide supplementary information, especially for THA patients that are at risk for falls.
Lithium-ion batteries exhibit slow voltage dynamics on the minute time scale that are usually associated with transport processes. We present a novel modelling approach toward these dynamics by combining physical and data-driven models into a Grey-box model. We use neural networks, in particular neural ordinary differential equations. The physical structure of the Grey-box model is borrowed from the Fickian diffusion law, where the transport domain is discretized using finite volumes. Within this physical structure, unknown parameters (diffusion coefficient, diffusion length, discretization) and dependencies (state of charge, lithium concentration) are replaced by neural networks and learnable parameters. We perform model-to-model comparisons, using as training data (a) a Fickian diffusion process, (b) a Warburg element, and (c) a resistor-capacitor circuit. Voltage dynamics during constant-current operation and pulse tests as well as electrochemical impedance spectra are simulated. The slow dynamics of all three physical models in the order of ten to 30 min are well captured by the Grey-box model, demonstrating the flexibility of the present approach.
A novel peptidyl-lys metalloendopeptidase (Tc-LysN) from Tramates coccinea was recombinantly expressed in Komagataella phaffii using the native pro-protein sequence. The peptidase was secreted into the culture broth as zymogen (~38 kDa) and mature enzyme (~19.8 kDa) simultaneously. The mature Tc-LysN was purified to homogeneity with a single step anion-exchange chromatography at pH 7.2. N-terminal sequencing using TMTpro Zero and mass spectrometry of the mature Tc-LysN indicated that the pro-peptide was cleaved between the amino acid positions 184 and 185 at the Kex2 cleavage site present in the native pro-protein sequence. The pH optimum of Tc-LysN was determined to be 5.0 while it maintained ≥60% activity between pH values 4.5—7.5 and ≥30% activity between pH values 8.5—10.0, indicating its broad applicability. The temperature maximum of Tc-LysN was determined to be 60 °C. After 18 h of incubation at 80 °C, Tc-LysN still retained ~20% activity. Organic solvents such as methanol and acetonitrile, at concentrations as high as 40% (v/v), were found to enhance Tc-LysN’s activity up to ~100% and ~50%, respectively. Tc-LysN’s thermostability, ability to withstand up to 8 M urea, tolerance to high concentrations of organic solvents, and an acidic pH optimum make it a viable candidate to be employed in proteomics workflows in which alkaline conditions might pose a challenge. The nano-LC-MS/MS analysis revealed bovine serum albumin (BSA)’s sequence coverage of 84% using Tc-LysN which was comparable to the sequence coverage of 90% by trypsin peptides.
This article presents the development, parameterization, and experimental validation of a pseudo-three-dimensional (P3D) multiphysics aging model of a 500 mAh high-energy lithium-ion pouch cell with graphite negative electrode and lithium nickel manganese cobalt oxide (NMC) positive electrode. This model includes electrochemical reactions for solid electrolyte interphase (SEI) formation at the graphite negative electrode, lithium plating, and SEI formation on plated lithium. The thermodynamics of the aging reactions are modeled depending on temperature and ion concentration and the reactions kinetics are described with an Arrhenius-type rate law. Good agreement of model predictions with galvanostatic charge/discharge measurements and electrochemical impedance spectroscopy is observed over a wide range of operating conditions. The model allows to quantify capacity loss due to cycling near beginning-of-life as function of operating conditions and the visualization of aging colormaps as function of both temperature and C-rate (0.05 to 2 C charge and discharge, −20 °C to 60 °C). The model predictions are also qualitatively verified through voltage relaxation, cell expansion and cell cycling measurements. Based on this full model, six different aging indicators for determination of the limits of fast charging are derived from post-processing simulations of a reduced, pseudo-two-dimensional isothermal model without aging mechanisms. The most successful aging indicator, compared to results from the full model, is based on combined lithium plating and SEI kinetics calculated from battery states available in the reduced model. This methodology is applicable to standard pseudo-two-dimensional models available today both commercially and as open source.
Phytases are widely used food and feed enzymes to improve phosphate availability and reduce anti-nutritional factors. Despite the benefits, enzyme usage is restricted by the harsh conditions in a gastrointestinal tract (pH 2–6) and feed pelleting conditions at high temperatures (60–90 °C). The commercially available phytase Quantum® Blue has been immobilized as CLEAs using glutardialdehyde and soy protein resulting in a residual activity of 33%. The influence of the precipitating agent, precipitant concentration, cross-linker concentration and cross-linking time, sodium borohydride as well as the proteic feeders gluten, soy protein and bovine serum albumin (BSA) has been optimized. The best conditions were 90% (v/v) ethyl lactate as precipitating reagent, 100 mM glutardialdehyde and a soy protein concentration of 227 mg/L with a cross-linking time of 1 h. The intrinsically stable phytase remained its high thermal stability and temperature optimum. The phytase-CLEA achieved a 425% increase of residual activity under harsh acidic conditions between pH 2.2 and 3.5 compared to the free enzyme. The free and immobilized phytase were deployed in an in vitro assay simulating the acidic conditions in the gizzard of poultry at pH 2. The hydrolysis of phytate was monitored using a novel high-performance thin-layer chromatography (HPTLC) analysis and DAD scanner to study the InsPx fingerprint. All lower inositol phosphate pools (InsP1–InsP6) and free phosphate were separated and analyzed. The phytase-CLEA efficiently released 80% of the total phosphate within 180 min, whereas the free enzyme only released 6% in the same time under the same conditions.
Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.
Purpose
To (1) identify neuromuscular and biomechanical injury risk factors in elite youth soccer players and (2) assess the predictive ability of a machine learning approach.
Material and Methods
Fifty-six elite male youth soccer players (age: 17.2 ± 1.1 years; height: 179 ± 8 cm; mass: 70.4 ± 9.2 kg) performed a 3D motion analysis, postural control testing, and strength testing. Non-contact lower extremities injuries were documented throughout 10 months. A least absolute shrinkage and selection operator (LASSO) regression model was used to identify the most important injury predictors. Predictive performance of the LASSO model was determined in a leave-one-out (LOO) prediction competition.
Results
Twenty-three non-contact injuries were registered. The LASSO model identified concentric knee extensor peak torque, hip transversal plane moment in the single-leg drop landing task and center of pressure sway in the single-leg stance test as the three most important predictors for injury in that order. The LASSO model was able to predict injury outcomes with a likelihood of 58% and an area under the ROC curve of 0.63 (sensitivity = 35%; specificity = 79%).
Conclusion
The three most important variables for predicting the injury outcome suggest the importance of neuromuscular and biomechanical performance measures in elite youth soccer. These preliminary results may have practical implications for future directions in injury risk screening and planning, as well as for the development of customized training programs to counteract intrinsic injury risk factors. However, the poor predictive performance of the final model confirms the challenge of predicting sports injuries, and the model must therefore be evaluated in larger samples.
Photovoltaic-heat pump (PV-HP) combinations with battery and energy management systems are becoming increasingly popular due to their ability to increase the autarchy and utilization of self-generated PV electricity. This trend is driven by the ongoing electrification of the heating sector and the growing disparity between growing electricity costs and reducing feed-in tariffs in Germany. Smart control strategies can be employed to control and optimize the heat pump operation to achieve higher self-consumption of PV electricity. This work presents the evaluation results of a smart-grid ready controlled PV-HP-battery system in a single-family household in Germany, using 1-minute-high-resolution field measurement data. Within 12 months evaluation period, a self-consumption of 43% was determined. The solar fraction of the HP amounts to 36%, enabled also due to higher set temperatures for space heating and domestic hot water production. Accordingly, the SPF decreases by 4.0% the space heating and by 5.7% in the domestic hot water mode. The combined seasonal performance factor for the heat pump system increases from 4.2 to 6.7, when only considering the electricity taken from the grid and disregarding the locally generated electricity supplied from photovoltaic and battery units.
Photovoltaic thermal (PVT) technology has been drawing attention recently. Electrification of the heating sector with heat pumps run by carbon-free electricity sources like photovoltaics is setting the ground for the interest. This article gives insight into PVT technologies and collector designs according to application and operating temperatures. For most conventional designs, examples like prototypes from Research & Development projects are presented. In addition, commercial products are listed along these categories, and the influence on the gross thermal and electrical yield is depicted based on Solar Keymark certification data. The process of certification is presented in a comprehensive way, showing current limitations, giving an outlook on the most recent approach for enhanced procedures and specifications. Finally, different system layouts are presented, and examples from installations combined with a heat pump are given with their specific performances. Real performance data of several PVT installations are compared to conventional heat pump systems. The identified seasonal performance factors are in a range from 3.4 to 4.2 and in between air source and ground source heat pumps. Continuous monitoring and derived data are enablers to discover the decisive influence of the system layout and dimensioning on performance indicators like, for example, operating temperatures over the year.
Purpose
To summarize the mechanical loading of the spine in different activities of daily living and sports.
Methods
Since the direct measurement is not feasible in sports activities, a mathematical model was applied to quantify spinal loading of more than 600 physical tasks in more than 200 athletes from several sports disciplines. The outcome is compression and torque (normalized to body weight/mass) at L4/L5.
Results
The data demonstrate high compressive forces on the lumbar spine in sport-related activities, which are much higher than forces reported in normal daily activities and work tasks. Especially ballistic jumping and landing skills yield high estimated compression at L4/L5 of more than ten times body weight. Jumping, landing, heavy lifting and weight training in sports demonstrate compression forces significantly higher than guideline recommendations for working tasks.
Conclusion
These results may help to identify acute and long-term risks of low back pain and, thus, may guide the development of preventive interventions for low back pain or injury in athletes.
Appraising the Methodological Quality of Sports Injury Video Analysis Studies: The QA-SIVAS Scale
(2023)
Background
Video analysis (VA) is commonly used in the assessment of sports injuries and has received considerable research interest. Until now, no tool has been available for the assessment of study quality. Therefore, the objective of this study was to develop and evaluate a valid instrument that reliably assesses the methodological quality of VA studies.
Methods
The Quality Appraisal for Sports Injury Video Analysis Studies (QA-SIVAS) scale was developed using a modified Delphi approach including expert consensus and pilot testing. Reliability was examined through intraclass correlation coefficient (ICC3,1) and free-marginal kappa statistics by three independent raters. Construct validity was investigated by comparing QA-SIVAS with expert ratings by using Kendall’s tau analysis. Rating time was studied by applying the scale to 21 studies and computing the mean time for rating per study article.
Results
The QA-SIVAS scale consists of an 18-item checklist addressing the study design, data source, conduct, report, and discussion of VA studies in sports injury research. Inter- and intra-rater reliability were excellent with ICCs > 0.97. Expert ratings revealed a high construct validity (0.71; p < 0.001). Mean rating time was 10 ± 2 min per article.
Conclusion
QA-SIVAS is a reliable and valid instrument that can be easily applied to sports injury research. Future studies in the field of VA should adhere to standardized methodological criteria and strict quality guidelines.
Maintaining stability while walking on arbitrary surfaces or dealing with external perturbations is of great interest in humanoid robotics research. Increasing the system’s autonomous robustness to a variety of postural threats during locomotion is the key despite the need to evaluate noisy sensor signals. The equations of motion are the foundation of all published approaches. In contrast, we propose a more adequate evaluation of the equations of motion with respect to an arbitrary moving reference point in a non-inertial reference frame. Conceptual advantages are, e.g., getting independent of global position and velocity vectors estimated by sensor fusions or calculating the imaginary zero-moment point walking on different inclined ground surfaces. Further, we improve the calculation results by reducing noise-amplifying methods in our algorithm and using specific characteristics of physical robots. We use simulation results to compare our algorithm with established approaches and test it with experimental robot data.
In 4D printing, an additively manufactured component is given the ability to change its shape or function in an intended and useful manner over time. The technology of 4D printing is still in an early stage of development. Nevertheless, interesting research and initial applications exist in the literature. In this work, a novel methodical approach is presented that helps transfer existing 4D printing research results and knowledge into solving application tasks systematically. Moreover, two different smart materials are analyzed, used, and combined following the presented methodical approach to solving the given task in the form of recovering an object from a poorly accessible space. This is implemented by self-positioning, grabbing, and extracting the target object. The first smart material used to realize these tasks is a shape-memory polymer, while the second is a polymer-based magnetic composite. In addition to the presentation and detailed implementation of the methodical approach, the potentials and behavior of the two smart materials are further examined and narrowed down as a result of the investigation. The results show that the developed methodical approach contributes to moving 4D printing closer toward a viable alternative to existing technologies due to its problem-oriented nature.
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.
This paper presents a framework for numerical building validation enhancement based on detailed building specifications from in-situ measurements and evidence-based validation assessment undertaken on a detached sustainable lightweight building in a semi-arid climate. The validation process has been undergone in a set of controlled experiments – a free-float period, and steady-state internal temperatures. The validation was conducted for a complete year with a 1-min time step for the hourly indoor temperature and the variable refrigerant flow (VRF) energy consumption. The initial baseline model was improved by three series of validation steps per three different field measurements including thermal transmittance, glazing thermal and optical properties, and airtightness. Then, the accurate and validated model was used for building energy efficiency assessment in 12 regions of Morocco. This study aims to assess the effect of accurate building characteristics values on the numerical model enhancement. The initial CV(RMSE) and NMBE have improved respectively from 14.58 % and −11.23 %–7.85 % and 1.86 % for the indoor temperature. Besides, from 31.17 % to 14.37 %–20.57 % and 9.77 % for energy consumption. The findings demonstrate that the lightweight construction with the use of a variable refrigerant flow system could be energy efficient in the southern regions of Morocco.
Lithium-ion battery cells exhibit a complex and nonlinear coupling of thermal, electrochemical,and mechanical behavior. In order to increase insight into these processes, we report the development of a pseudo-three-dimensional (P3D) thermo-electro-mechanical model of a commercial lithium-ion pouch cell with graphite negative electrode and lithium nickel cobalt aluminum oxide/lithium cobalt oxide blend positive electrode. Nonlinear molar volumes of the active materials as function of lithium stoichiometry are taken from literature and implemented into the open-source software Cantera for convenient coupling to battery simulation codes. The model is parameterized and validated using electrical, thermal and thickness measurements over a wide range of C-rates from 0.05 C to 10 C. The combined experimental and simulated analyses show that thickness change during cycling is dominated by intercalation-induced swelling of graphite, while swelling of the two blend components partially cancel each other. At C-rates above 2 C, electrochemistry-induced temperature increase significantly contributes to cell swelling due to thermal expansion. The thickness changes are nonlinearly distributed over the thickness of the electrode pair due to gradients in the local lithiation, which may accelerate local degradation. Remaining discrepancies between simulation and experiment at high C-rates might be attributed to lithium plating, which is not considered in the model at present.