Key Performance Parameters Research Articles

A Novel Technique in Determining Mud Cake Permeability in SiO2 Nanoparticles and KCl Salt Water Based Drilling Fluid using Deep Learning Algorithm

The permeability of the mud cake formed at the formation-wellbore interface is an important factor in the designing of water-based drilling fluids. This study presents a novel approach to utilizing experimental thixotropic and rheological parameters of polymeric water-based drilling fluids having varying concentrations of SiO2 nanoparticles and KCl salt. A fully connected feed-forward multi-layered neural network, more commonly known as a Multilayer Perceptron (MLP) was developed to predict the mud cake permeability using input parameters such as SiO2 & KCl concentration, differential pressure, temperature, mud cake thickness, API LPLT and HPHT filter loss volume and spurt loss volume. The results suggested that the developed Multilayer Perceptron model effectively determined the mud cake permeability based on the input parameters of the WBDF mentioned above. The model converged on the global minima, minimizing the loss function using the Gradient descent algorithm. A higher Coefficient of Determination (R2) value i.e., 0.8781, and a lesser Root Mean Square Error (RMSE) value i.e., 0.04378 indicates the higher accuracy of the model. Pearson’s Coefficient of Correlation obtained via the heatmap indicates that mud cake permeability is strongly influenced by the differential pressure followed by filter loss volume, spurt loss volume, mud cake thickness, and temperature. Previous similar studies have focused on using machine learning algorithms, this study utilized a robust deep learning algorithm i.e., Multilayer Perceptron (MLP) neural network to simultaneously model the combined effects of SiO2 nanoparticles and KCl salt concentrations on mud cake permeability, offering an unprecedented level of accuracy in predicting key WBDF performance parameters

Analysis of G+20 Horizontally Connected Buildings with and Without Fluid Viscous Dampers

As urban populations continue to grow globally, the demand for new cities and high-rise structures is increasing due to limited land resources and expanding social and commercial activities. To address these challenges, this study investigates the seismic performance of G+20 horizontally connected buildings, focusing on the effects of fluid viscous dampers (FVDs) on structural response. A 3D model of two G+20 buildings, one with and one without FVDs, was developed using ETABS software, and seismic analysis was conducted for a Zone V seismic region in India. The study evaluates key performance parameters, including storey displacement, storey drift, and storey shear, to assess the effectiveness of the dampers. Results showed that adding FVDs significantly reduced overall building displacement, particularly in the X-direction at the top storey, with a reduction of approximately 40%. The Y-direction saw a more modest 5% reduction, attributed to geometric and stiffness irregularities. Additionally, storey drifts in the X-direction were higher than in the Y-direction but were significantly reduced after installing dampers. The findings highlight the importance of FVDs in improving the seismic resilience of horizontally connected high-rise buildings and offer valuable insights for future structural design in earthquake prone areas.

Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis

Solar cell research aims to improve power conversion efficiency (PCE). This field has an extensive body of literature on the Web of Science. For researchers, it is impossible to understand the development of the entire field comprehensively through traditional reading methods. Knowledge is recorded in the literature by text and numbers. Researchers acquire knowledge through literature surveying, text reading, and thinking. The conversion from text and numbers to knowledge can be automatically completed by machines, which can avoid path‐dependent perspectives. In this work, an intelligent machine learning method for literature structure delineation and information extraction is proposed. As an example, a knowledge base of organic solar cells (OSCs) is extracted including topic analysis of literature, numerical characteristics of performance, and material information. Seven major research directions of OSCs are identified. The correlations between key performance parameters, including PCE, short‐circuit current density (JSC), open‐circuit voltage (VOC), and fill factor (FF), are revealed from text mining. A donor–acceptor material map of PCE is constructed which provides a road map for OSCs, indicating the bottleneck of this field. Moreover, the method of machine intelligence developed here can be used in any other materials field, aiding a comprehensive understanding of the development quickly.

Prototype of a New Head Grabber for Robotic Strawberry Harvesting with a Vision System

This paper presents the design of a strawberry fruit head gripper unit, together with the concept of a control system for the operation of its mechanisms and vision system. The developed design consists of three specialised mechanisms: positioning, grasping, and cutting off of the fruit. A Finite Element Method (FEM) model was developed for the described design. Next, calculations were carried out, based on which the construction materials were selected. The key performance parameters of the functional model, built on the basis of the developed design concept, were verified under laboratory conditions. In tests carried out on the possible hematoma caused by exceeding the breaking stress induced by the pressure of the encompassing jaws on the fruit, it was found that none of the fruit tested suffered mechanical damage as a result of the sensor triggering force, and the average length of the trimmed stalk was approximately 14 mm. The designed head gripper, together with the proposed automation system, will contribute to improving harvesting precision, and this will favour a reduction in the quantitative and qualitative losses of the harvested crop. The experimental tests conducted under harvesting conditions showed a high efficiency of 95% in identifying ripe fruit, and the harvesting efficiency of the robotic arm was 90%.

Evaluating crop nitrogen status in maize leaves: A predictive modelling approach using chlorophyll fluorescence parameters

The consumption of chemical fertilizers has increased eight-fold since the 19th century, outstripping crop yields increases and, emphasizing the need for precise nitrogen (N) assessment in crops to optimize fertilization and mitigate environmental impacts. This study developed a model using chlorophyll fluorescence technology to accurately evaluate the N status in maize leaves while addressing the limitations of current labor-intensive and environmentally sensitive methods. Based on a long-term experiment initiated in 2011, maize hybrid Fumin 985 was sampled in 2021 and 2022 under two crop-straw management strategies (SM: no tillage with surface straw mulch, SP: plough tillage with straw incorporation) and six N application rates. Partial least squares regression (PLSR) models were formulated using chlorophyll fluorescence parameters (ChlF) to assess leaf N content (N leaf). The results indicated that a N application rate of 270 kg ha−1 sufficed to meet crop N requirements. Leaf characteristics such as N leaf, total pigment content (TP), and leaf dry weight (DW leaf) changed significantly with increasing N application rates, influencing rapid chlorophyll fluorescence (OJIP) dynamics. Principal component analysis (PCA) reduced ChlF from 35 to 21, and four models were developed, among which, the model using ChlF and TP was more accurate than the model using DW alone. Key ChlF parameters for PLSR model performance included ABS/RC, φ(Eo), ETo/CSm, and δ(Ro)/(1–δ(Ro)). Although non-destructive N leaf detection using chlorophyll fluorescence technology proved feasible, additional leaf characteristics, such as TP, are necessary to improve model accuracy. Considering local field conditions is essential for the application of this technology at a larger scale. Precise evaluation of N status using chlorophyll fluorescence is beneficial for a more efficient N management and sustainable agriculture.

Association of H-reflex parameters and peroneal nerve conduction velocity with dynamic balance and agility in university athletes: a cross-sectional study

Abstract Athletic performance parameters encompass a wide range of physical, physiological, and biomechanical measures that are crucial for evaluating and optimising athletes’ capabilities. There are various intrinsic factors that can contribute to athletic skills, which subsequently impact overall performance. Thus, there is a need to explore significant intrinsic factors associated with athletic parameters, that can provide insights into the physiological mechanisms underlying performance. In this study, we examined the potential relationship of two such intrinsic factors, namely the H-reflex and the peroneal nerve conduction velocity, with athletic skills like dynamic balance and agility. Forty-five university-level athletes of all genders playing for different teams of Jamia Millia Islamia University, New Delhi, India were selected. All athletes underwent tibial H-reflex, peroneal NCV, mSEBT (for dynamic balance), and agility T-test evaluations. The Pearson correlation coefficient was calculated to evaluate the association of nerve function parameters (H-reflex and NCV) with athletic performance parameters (dynamic balance and agility). Results showed that H-reflex amplitude showed a significant negative correlation with both dynamic balance scores (Dominant Leg [DL]: r = −0.321 and ; Non-dominant leg [NDL]: r = −0.397 and ) and T-test scores (DL: r = −0.342; NDL: r = −0.321, ), respectively. The peroneal NCV was significantly positively correlated with dynamic balance (DL: r = 0.438 and ; NDL: r = 0.418 and ) and agility scores (DL: r = 0.639 and ; NDL: r = 0.641 and ) in athletes. In conclusion, significant associations were found between H-reflex and Peroneal nerve conduction velocity and key athletic performance parameters like dynamic balance and agility among university athletes. This implies that neural function is closely linked with athletic skills and performance. Registered in Clinical Trials Registry-India (CTRI) under registration number: REF/2023/11/075394.

Short-Circuit Detection in Lithium-Ion Batteries Using Machine Learning: Analysis and Comparison with Physics-Based Method

Early detection of short circuits in battery-powered systems is critical in preventing potential catastrophic failures. However, nascent short-circuit signatures are extremely weak and challenging to detect using existing algorithms without compromising on prediction accuracy. Traditional physics-based approaches rely on hand-crafted models to establish relationships between battery operating parameters and short resistance, which limits their ability to capture all relevant details, resulting in sub-optimal accuracies. In this study, we present a machine learning-based approach that leverages rest period voltage data to detect short circuits. Our method employs a 1D convolutional neural network (CNN) classifier/estimator that extracts temporal dynamic features relevant to the short circuit prediction problem from both the long and short tails of the rest period voltage profile. The approach is validated using commercial battery data, generated at different conditions including temperatures, and short circuits of varying severities; with prediction accuracies greater than 90% even for soft shorts of 500Ω. The key performance parameters of the 1D CNN model are compared against a physics-based short detection approach, demonstrating its superior performance and cost-effectiveness. Overall, our work represents a significant advancement in the field of short circuit detection in battery-powered systems, offering improved accuracy, efficiency, and cost-effectiveness.

An Interference Cancelation and Signal Decoding Method for Non-orthogonal Multiple Access Based on Deep Learning

In wireless communication, spectral efficiency is one of the key performance parameters. The available limited resources must be effectively shared among different applications. Due to the tremendous demand for wireless applications, an effective technique could be used for sharing the resources (time/frequency). Non-orthogonal Multiple Access (NOMA), is a wireless network technique aimed at boosting spectral efficiency. In NOMA, Users in the downlink have received a super-imposed signal from a Base Station (BS) with varying power allocations. Depending on the power allocation, the receiver employs interference cancelation techniques to decode its own data. The effectiveness of interference cancelation hinges on achieving a perfect channel condition, which is challenging to achieve in practical scenarios. This study introduces an unsupervised Deep Learning (DL) approach known as autoencoder, aimed at interference cancelation to ensure signal decoding from super-imposed signal. In contrast to the traditional approach, simulation results of the proposed structure show that the DL-based receiver achieves superior performance in correctly decoding the signal.

Deformation measurement of aero-engine rotating twisted blade based on multi-vision overlapping area feature registration method

Due to the complexity of the spatial surface of the twisted blade of the aero-engine, there are occlusions and shadows between the blades, resulting in visual blind spots. This causes the loss of certain information in the deformation measurement of twisted blade. A deformation measurement method of rotating twisted blades based on multi-vision overlapping area feature registration is proposed. It aims to solve the problem of limited field of view (FOV) in the deformation measurement of twisted blade, and overcome the problems of small measurement range and difficult measurement of traditional contact sensor method in the rotating state of the blade. First, a calibration and image-matching method based on multi-vision digital image correlation method is proposed. Under the premise of ensuring the simultaneous calibration of multi-vision camera stereo calibration, the matching of image detection points on the twisted blade surface is realized. Second, a three-dimensional reconstruction method based on the feature registration of the overlapping area of the FOV is proposed. It is used to solve the problem that the traditional image stitching method is prone to mismatching and to provide complete point cloud information for the deformation field calculation. Finally, the strain of each point is solved by piecewise point-by-point fitting of the local subdomains of the discrete displacement data, which suppresses the amplification of noise and improves the accuracy of blade strain calculation. In this paper, the deformation measurement experiment is carried out for the rotating twisted blade (up to 6000 rpm). The experimental results show that the proposed method can achieve a displacement measurement accuracy of 5 μm and the strain measurement accuracy of 50 με. This method can provide an important guarantee for the accurate evaluation and safe operation of the key performance parameters of the engine.

Design and multi parameter performance optimization of the bionic robotic fish driven by tail fin

In this paper, we investigate the optimization of body contour parameters and the parameters of the tail fin in bionic robotic fish. A new type of robotic fish with fully flexible shell was proposed, followed by the analysis and optimization of the resistance properties of the profile. Subsequently, a dynamic model considering tail fin deformation was established. We use a combination of Fluent simulation and particle swarm optimization (PSO) algorithm to seek the key parameters for an enchanced performance. Upon multi-parameter performance optimization, the obtained results indicate that the fully flexible shell robotic fish can achieve a high swimming speed of 0.69 m/s (equivalent to 1.2 BL/s) and exhibit excellent turning maneuverability with a turning radius of 0.8 body lengths. This paper provides insights for optimizing the performance of robotic fish and enhancing its underwater operational capabilities.

Insights into solid-contact ion-selective electrodes based on laser-induced graphene: Key performance parameters for long-term and continuous measurements.

This work aims to serve as a comprehensive guide to properly characterize solid-contact ion-selective electrodes (SC-ISEs) for long-term use as they advance toward calibration-free sensors. The lack of well-defined SC-ISE performance criteria limits the ability to compare results and track progress in the field. Laser-induced graphene (LIG) is a rapid and scalable method that, by adjusting the CO2 laser parameters, can create LIG substrates with tunable surface properties, including wettability, surface chemistry, and morphology. Herein, we fabricate laser-induced graphene (LIG) solid-contact electrodes using different laser settings and subsequently convert them into ion-selective sensors using a potassium-selective membrane. We measure the aforementioned tunable surface properties and correlate them with resultant low-frequency capacitance and water layer formation in an effort to pinpoint their effects on the sensitivity (Nernstian response), reproducibility (E°' variation), and potential stability of the LIG-based SC-ISEs. More specifically, we demonstrate that the surface wettability of the LIG substrate, which can be tuned by controlling the lasing parameters, can be modified to exhibit hydrophobic (contact angle > 90°) and even highly hydrophobic surfaces (contact angle ≈ 130°) to help reduce sensor drift. Recommendations are also provided to ensure proper and robust characterization of SC-ISEs for long-term and continuous measurements. Ultimately, we believe that a comprehensive understanding of the correlation between LIG tunable surface properties and SC-ISE performance can be used to improve the electrochemical behavior and stability of SC-ISEs designed with a wide range of materials beyond LIG.

On the Temporal Evolution of Key Hemofilter Parameters-In Vitro Study under Co-Current Flow.

Effective permeability KP, the ultrafiltration coefficient (KUF), the sieving coefficient (SC), and the loss/permeation of proteins (primarily albumin) are key parameters/specifications characterizing hemofilter (HF) performance. However, there are uncertainties regarding their determination. This work aims (a) to demonstrate that the co-current flow (of blood and dialysate) can lead to beneficial unidirectional filtration (from blood/plasma to dialysate) under a fairly uniform local trans-membrane pressure (TMP), unlike the presently employed counter-current flow; (b) to study the temporal evolution of key HF performance parameters under co-current flow, particularly during the important early stage of hemocatharsis (HC). Experiments with human plasma and BSA solutions in co-current flow mode (for which a fluid mechanical model is developed) show a fairly uniform local/axial TMP, which also improves the local/axial uniformity of protein membrane fouling, particularly under (currently favored) high convective flux operation. Due to incipient membrane fouling, a significant temporal variability/decline in the effective KP is observed, and, in turn, of other parameters (i.e., the Kuf, SC, and permeation/mass flux Mm for albumin and total proteins). A satisfactory correlation of the albumin/protein mass flux Mm with permeability KP is obtained, indicating strong inter-dependence. In conclusion, co-current flow, allowing for a fair local TMP axial uniformity, enables the acquisition of accurate/representative data on the evolution of HF parameters, facilitating their interpretation and correlation. The new results provide a basis for exploring the clinical application of the co-current flow.

Climate change modulates the impact of nitrogen and enhances phosphorus-driven top-down control by zooplankton on harmful algae

Eutrophication and climate change are the main drivers of the increase in Microcystis biomass. These environmental factors also regulate the dynamics between planktonic predators and their prey. Here, we present the results of a controlled laboratory study investigating the top-down control efficiency of Paramecium on Microcystis under different nutrient (nitrogen and phosphorus) levels and climate conditions (current climate: 25 °C, 400 ppm; predicted future climate: 30 °C, 750 ppm). The results showed that Paramecium completely eliminated Microcystis populations under all combinations of nutrient levels and climate conditions. Furthermore, the ability of Paramecium to control Microcystis was modulated by nutrient enrichment and climate change. Specifically, key performance parameters—including the Microcystis population decline index, time for Microcystis elimination, ingestion rate and specific growth rate of Paramecium, and time to reach maximum Paramecium abundance—were inhibited by increasing nitrogen levels but improved by increasing phosphorus levels. This indicates that nitrogen enrichment weakened the ability of Paramecium to control Microcystis, whereas phosphorus enrichment enhanced it. Further analysis revealed that, regardless of nutrient availability, these parameters were optimized under the high-temperature-CO2 scenario, suggesting that climate change has consistently enhanced the top-down control of Microcystis by Paramecium. Notably, an in-depth examination of the overall change patterns of performance parameters and principal component analysis (PCA) revealed that climate change altered the response patterns of these parameters (or their comprehensive scores) to increased nitrogen and enhanced their response magnitude to increased phosphorus. Essentially, climate change compensated for the negative effects of increased nitrogen levels on the performance of Paramecium in controlling Microcystis and amplified the positive effects of increased phosphorus levels. These findings reveal that protozoan predators might enhance their top-down control of harmful algae under the interaction of eutrophication and climate change. This study, using Paramecium and Microcystis as models, provides valuable insights for managing freshwater ecosystems and applying protozoa to mitigate harmful algal blooms.

Применение теории множеств для разработки ключевых параметров эффективности трудового сектора пенитенциарной системы

The article considers the development of information management systems, decision support and modelling for production processes in penitentiary institutions. The work outlines the problem of insufficient study of production and labour sector data both from a scientific viewpoint and from the standpoint of developing new software and practical application. It is found that the first steps to solve the above-mentioned problems will be proper data structuring and identifying key performance parameters. Presenting data as sets and studying the interaction between them can be a good solution to the problems posed. Thus, the work formulates the task of determining the key performance parameters of the labour sector of the penitentiary system, and considers the principles of set theory as tools for solving this problem. In addition, the authors use previously created developments such as an indexing system and a software package for collecting statistical data to settle this issue. As a result of the study, rules are determined, as well as relationships that can be applied to detect the key performance parameters and develop an information system, as well as to create databases with improved structuring that are more practically oriented.

Data-driven prediction of scaling and ignition of inertial confinement fusion experiments

Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.

Artificial Amacrine Retinal Circuits.

Event-based imaging represents a new paradigm in visual information processing that addresses the speed and energy efficiency shortcomings inherently present in the current complementary metal oxide semiconductor-based machine vision. Realizing such imaging systems has previously been sought using very large-scale integration technologies that have complex circuitries consisting of many photodiodes, differential amplifiers, capacitors, and resistors. Here, we demonstrate that event-driven sensing can be achieved using a simple one-resistor, one-capacitor (1R1C) circuit, where the capacitor is modified with colloidal quantum dots (CQDs) to have a photoresponse. This sensory circuit emulates the motion-tracking function of the biological retina, in which the amacrine cells in the bipolar-to-ganglion synaptic pathway produce a transient spiking signal only in response to changes in light intensity but remain inactive under constant illumination. When extended to a 2D imaging array, the individual sensors work independently and output signals only when a change in the light intensity is detected; hence, the concept of the frame in image processing is thereby removed. In this work, we present the fabrication and characterization of a CQD photocapacitor-based 1R1C circuit that has a spectral response at 1550 nm in the short-wave infrared (SWIR). We report on the key performance parameters including peak responsivity, noise, and optical noise equivalent power and discuss the operating mechanism that is responsible for spiking responses in these artificial retinal circuits. The present work sets the foundation for expanding the bioinspired vision sensor capability toward midwave infrared (MWIR) and long-wave infrared (LWIR) spectral regions that are invisible to human eyes and mainstream semiconductor technologies.

Distinct microdiversity of phosphate accumulating organisms (PAOs) between side-stream and conventional enhanced biological phosphorus removal (EBPR) systems with performance implications

Polyphosphate Accumulating Organisms (PAOs) microdiversity is a key factor to elucidate the mechanisms involved in the side-stream enhanced biological phosphorus removal (S2EBPR) systems, which has been shown to improve the process stability over conventional EBPR. However, fast, effective and cost-efficient methods to resolve PAO microdiversity in real-world activate sludge samples is still in absence. In this study, we applied oligotyping analysis following the regular 16S rRNA gene amplicon sequencing standard operation pipeline (SOP) to resolve subgenus-level PAO oligotypes, which cannot be achieved using traditional 16S rRNA sequencing SOP. The identified oligotype profiles of PAO-containing genera Ca. Accumulibacter, Tetrasphaera and Comamonas showed distinguished community-level differences across 12 water resource recovery facilities (WRRFs), which would not be revealed at the genus level. The WRRF-level differences were observed larger than the temporal differences in the same WRRF, indicating intrinsic sub-genus level microdiversity fingerprint between EBPR/S2EBPR systems. The identified oligotypes can be associated with known PAO clades phylogenetically, suggesting that oligotyping can suffice as a fast and cost-efficient approach for PAO microdiversity profiling. In addition, network analysis can be used to identify coexistence patterns between oligotypes with respect to EBPR/S2EBPR configurations and performance, enabling more detailed analysis between EBPR system performance and PAOs microdiversity. Correlation analyses between oligotype profiles and key EBPR performance parameters revealed potential different biological functional traits among these PAO species with P-removal performance implications.

Exploring the Performance of Alkaline Water Electrolyzers Under Dynamic Operational Conditions

Green hydrogen, produced through water electrolysis driven by Renewable Energy Sources (RES), is a compelling solution for storing renewable energy. Directly coupling RES with water electrolyzers enhances system efficiency and cost-effectiveness. Alkaline water electrolysis, a mature and cost-effective technology, faces challenges operating under fluctuating RES conditions using AC/DC convertor , which supply rippled DC. A comprehensive understanding of electrolyzer performance in dynamic conditions is crucial for developing durable, efficient systems.In the present experimental study, we extensively explored the performance of alkaline water electrolyzers and the key factors influencing it under dynamic operational conditions. The electrolytic cell was constructed using a zero-gap configuration, comprising a NiCoOx-based anode deposited on a Ni-mesh and an Ln-doped RuOx cathode deposited on a fine mesh of Ni metal. The Zirfon Perl UTP500 membrane served as a separator between the two cell compartments [1]. A 7.0 Molar KOH electrolyte at a room temperature of approximately 30°C was employed. The cell underwent dynamic operational mode through the application of a square wave cell voltage, shown in Fig.1 (a), utilizing the NF BP4620 power supply. Square waves with different frequencies, specifically 1, 100, 1000, and 3000 Hz, were employed. Steady-state operations were conducted while applying various DC voltages, serving as a reference study. The resulting current and the actual cell voltage waveforms were monitored and recorded using a high-resolution oscilloscope (YOKOGAWA DL850E). The flow of H2 gas was measured using a film flow meter (HORIBA STEC, SF series VP-3). The apparent power, real power, and reactive power components were calculated using formulas from a previously published report [2]. Subsequently, the specific energy consumption was determined for each condition.The applied cell voltages were categorized into two groups. In the first category, the maximum cell voltage (Vmax) was set at 2.0 V, a value sufficiently high and suitable for industrial cell operation when applied in a DC mode. Within this category, the minimum cell voltage (Vmin) took various values, namely 1.8, 1.6, and 1.4 V. In the second category, the average cell voltage was maintained at a constant 2.0 V, while the amplitude of the waveform was adjusted to 0.2 and 0.4 V by modifying both Vmax and Vmin. Figure 1(b) depicts the resulting current of the voltage waveform at 100 Hz, illustrating three different Vmin values. The figure reveals that at a high Vmin of 1.8V, the current consistently maintained positive values during the voltage cycle, indicating that the double-layer capacitance remained charged throughout the operational period. However, with Vmin set at 1.6V, a relatively small capacitive current emerged, suggesting partial discharge of the double-layer capacitance. Moreover, at a lower Vmin of 1.4V, the cell voltage became insufficient to drive water electrolysis in the current cell. Consequently, a relatively large negative (capacitive) current manifested during the Vmin period, denoting a deep discharge of the double-layer capacitance.The obtained results revealed a profound influence of the charge and discharge status of the double-layer capacitance on the AWE cell's performance throughout the voltage cycles. Notably, when the double-layer capacitor remained fully charged during the voltage cycles, minimal impact of the frequency was observed on key performance parameters, including H2 flow rate that shown in figure 1 (c), specific energy consumption that shown in figure 1 (d), and power factor. However, under conditions of partial discharge within the cell voltage cycles, a noticeable correlation between the size and frequency of the voltage waveform and the AWE cell's performance emerged. Deep discharge of the double-layer capacitance led to a frequency-dependent reduction in H2 flow rate, attributed to incomplete charge/discharge cycles. Hence, the critical factor influencing the performance of the electrolytic cell during dynamic operation is the condition (charging/discharging) of the double-layer capacitance. These findings offer valuable insights into optimizing the dynamic operation of AWE cells for enhanced efficiency and performance. Acknowledgments: This research was partially supported by New Energy and Industrial Technology Development Organization (NEDO).

The Aqueous Aluminium-Ion Battery: Optimising the Electrode Compression Ratio through Image-Based Modelling

The aqueous Al-ion battery potentially has improved safety and environmental advantages over the incumbent lithium-ion technology [1,2]. Using 3D carbon-felt matrix electrodes, the performance of these batteries can be optimised. Typically, the electrodes are compressed to improve electrical conductivity by improving contact between adjacent fibres, which also closes the electrolyte pores, reducing the ionic diffusivity [3]. In this work, we set out to understand the role of electrode compression on the interplay between these key performance parameters for the aq. Al-ion battery.Synchrotron X-ray computed tomography was used to examine the 3D morphology of the carbon felt electrodes under 11 different compression ratios [4]. A bespoke in-situ tensile/compression rig was used to measure displacement and loading for three electrode types: raw carbon felt, positive electrodes loaded with a copper hexacyanoferrate ink and negative electrodes loaded with TiO2 anatase powder. Data was acquired using two voxel sizes (330nm, and 540nm) to compare the effects of spatial resolution and imaged volume size on subsequent image analysis. The tomograms were reconstructed using Paganin phase retrieval, to improve the contrast of the weakly attenuating carbon, and a filtered back projection algorithm. A U-net convolutional neural network was trained on uncompressed, fully compressed and partially compressed (three volumes) data, and then used to fully segment all tomograms.A high-throughput, image-based model was then used to analyse how compression affects the porosity, tortuosity, volume-specific area, ionic diffusivity, and electrical conduction of the electrodes. A finite differences-based model was used to solve the equilibrium partial differential equations directly on the voxel datasets, with no additional regularisation [5]. The heterogeneity of the electrode samples is quantified by comparing the effect of representative elementary volume on the value of the computed parameters [6]. Finally, aq. Al-ion batteries are manufactured using the predicted optimal compression ratio and compared to the simulated results.This is the first in-situ study of the compression effect on the aqueous aluminium-ion battery, and the largest XCT-based modelling study known to the authors (99 XCT tomograms). This work aims to improve the understanding of the effect of manufacturing parameters on the aqueous Al-ion battery and other similar batteries, and ultimately, its performance.[1] Melzack, N. "Advancing battery design based on environmental impacts using an aqueous Al-ion cell as a case study." Scientific Reports 12, no. 1 (2022): 8911.[2] Melzack, Nicole, R. G. A. Wills, and Andrew Cruden. "Cleaner energy storage: Cradle-to-gate life cycle assessment of aluminum-ion batteries with an aqueous electrolyte." Frontiers in Energy Research 9 (2021): 699919.[3] Mckerracher, R. D., A. Holland, A. Cruden, and R. G. A. Wills. "Comparison of carbon materials as cathodes for the aluminium-ion battery." Carbon 144 (2019): 333-341.[4] Reinhard, Christina, Michael Drakopoulos, Sharif I. Ahmed, Hans Deyhle, Andrew James, Christopher M. Charlesworth, Martin Burt et al. "Beamline K11 DIAD: A new instrument for dual imaging and diffraction at Diamond Light Source." Journal of Synchrotron Radiation 28, no. 6 (2021): 1985-1995.[5] Le Houx, James, and Denis Kramer. "Openimpala: Open source image based parallisable linear algebra solver." SoftwareX 15 (2021): 100729.[6] Le Houx, James, Siul Ruiz, Daniel McKay Fletcher, Sharif Ahmed, and Tiina Roose. "Statistical Effective Diffusivity Estimation in Porous Media Using an Integrated On-site Imaging Workflow for Synchrotron Users." Transport in Porous Media 150, no. 1 (2023): 71-88.

Relationship between physical fitness parameters and match time motion performance in international footballers with cerebral palsy

ABSTRACT The goal of this study was to describe the physical performance of international football players with cerebral palsy (CP) using commonly employed field tests to determine whether these characteristics are correlated with, or even predictive of, physical performance during competitive matches. Ten international footballers with CP performed a physical performance assessment that included anthropometry, sprint capacity, change of direction ability and intermittent endurance conducted before a worldwide competition. Match running performances were recorded using global positioning system devices during five official matches, including speed and distance variables. Moderate-to-high correlations were found between 5-m and 30-m sprints with maximal acceleration, deceleration and distances covered at moderate-to-high intensities. The change of direction capacity correlated to maximal deceleration and high-intensity variables. Furthermore, intermittent endurance correlated with average velocity, total distance and distances covered at varying intensities. Multiple linear regression showed predictive relationships for maximal acceleration, average velocity, total distance and intensity-specific distances. Practitioners must consider the main test that correlates with the demands of the sport and key performance parameters in CP football. The results offer practical insights for optimising training strategies tailored to specific performance variables for match football performance.