Design Of Systems Research Articles

Advanced Bioinspired Multifunctional Platforms Focusing on Gut Microbiota Regulation.

Gut microbiota plays a crucial role in maintaining host homeostasis, impacting the progression and therapeutic outcomes of diseases, including inflammatory bowel disease, cancer, hepatic conditions, obesity, cardiovascular pathologies, and neurologic disorders, via immune, neural, and metabolic mechanisms. Hence, the gut microbiota is a promising target for disease therapy. The safety and precision of traditional microbiota regulation methods remain a challenge, which limits their widespread clinical application. This limitation has catalyzed a shift toward the development of multifunctional delivery systems that are predicated on microbiota modulation. Guided by bioinspired strategies, an extensive variety of naturally occurring materials and mechanisms have been emulated and harnessed for the construction of platforms aimed at the monitoring and modulation of gut microbiota. This review outlines the strategies and advantages of utilizing bioinspired principles in the design of gut microbiota intervention systems based on traditional regulation methods. Representative studies on the development of bioinspired therapeutic platforms are summarized, which are based on gut microbiota modulation to confer multiple pharmacological benefits for the synergistic management of diseases. The prospective avenues and inherent challenges associated with the adoption of bioinspired strategies in the refinement of gut microbiota modulation platforms are proposed to augment the efficacy of disease treatment.

An experiential account with recommendations for the design, installation, operation and maintenance of a farm-scale soil moisture sensing and mapping system

Context The research explores the benefits of real time tracking of soil moisture for various land management contexts and the importance of spatio-temporal modelling and mapping to gain clear and visual understanding of soil moisture fluxes across a farm. Aims This research aims to outline the key processes required for building an operational on-farm soil moisture monitoring system where the product is highly granular daily soil moisture maps depicting variations temporally, spatially and vertically. Methods We describe processes of capacitance soil moisture probe installation, data collection infrastructure, sensor calibration, spatio-temporal modelling, and mapping. Key results An out-of-bag soil moisture evaluation modelling system was tested for nearly 2 years. We found a model accuracy (RMSE) estimate of 0.002 cm−3 cm−3 and concordance of 0.96 were found. This result is averaged over this period but fluctuated daily, and related to rainfall patterns across the target farm, which were not directly incorporated into the modelling framework. As expected, incorporating prior estimates of soil moisture into the modelling framework contributed to very accurate estimates of real time available soil moisture. Conclusions This research promotes the importance of iterative improvements to the soil moisture monitoring system, particularly in areas of sensor recalibration and spatio-temporal modelling. We stress the need for a longer-term view and plan for ongoing maintenance and improvement of such systems in the emerging digital farming ecosystem. Implications The results of this research will be useful for researchers and practitioners involved in the design and implementation of on-farm soil monitoring systems.

Enhanced thermal management strategy for supercapacitors using phase change materials: A numerical investigation

The integration of phase change materials (PCMs) presents a promising possibility for enhancing the thermal management of supercapacitors (SCs), which are vital components in various energy storage systems. This research focuses on investigating the thermal behavior of SCs with integrated PCM, employing finite element method (FEM) simulations to solve the transient thermal problem. Through a comparative analysis at various time instants, the transient thermal response of SCs is examined, shedding light on the efficacy of PCM-based thermal management strategies. Moreover, parametric studies are conducted to investigate the influence of PCM key characteristics, like melting temperature and layer thickness on the SC thermal response. Additionally, the impact of enhanced PCM thermal conductivity is explored by integrating high-conductive short carbon fibers (CF) within the PCM matrix. This investigation encompasses various PCM melting points, allowing for a comprehensive understanding of the interplay between PCM properties and SC thermal response. The results indicate that a notable decline in the highest temperatures of the SC can be achieved, leading to a reduction of about 9 °C depending on the PCM melting temperature and the improved thermal conductivity. The obtained results emphasize the effectiveness and practical feasibility of the proposed thermal management strategy. The modeling approach presented provides a robust tool with significant efficiency in reducing computational time for analyzing the thermal behavior of large models, as the utilization of the homogenization technique notably decreases the computational time. The findings of this study not only provide insights into optimizing PCM-based thermal management strategies for SCs but also contribute to advancing the design and performance of energy storage systems by addressing crucial thermal challenges.

Design and optimization of piezoelectric energy harvesting systems for enhanced performance in wireless sensor networks

Design and optimization of piezoelectric energy harvesting systems for enhanced performance in wireless sensor networks

π-π Stacking Network-Based Supramolecular Peptide Nanoprobe for Visualization of the ICB-Enhanced Ferroptosis Process.

The construction of coassembled peptide nanoprobes based on structural adaptation provides an effective template for stable monitoring of the molecular events in physiological and pathological processes. This also greatly expands their applications in biomedicine, such as multimodal combined diagnosis and treatment. However, the insufficient understanding of the physicochemical properties and structural features of different molecules still makes it difficult to construct the coassembled probes with mutually reinforcing functions, leading to unpredictable effects. Here, we showed how to utilize the π-π stacking network on β-sheets formed by PD-L1-targeting peptides to capture small molecules with ferroptosis functions, thus, coassembling them into a visual probe with synergistic effects. Compared with individual components, the coassembled strategy could significantly improve the stability of the nanoprobe, inducing stronger ferroptosis effects and immune checkpoint blocking effects, and track and reflect the process. This study provides new insights into the design of multicomponent collaborative coassembly systems with biological effects.

Design of 2-DOF Thrust vector control system for Rockets and Missiles

This paper outlines the development and implementation of a Thrust Vector Control (TVC) system tailored specifically for solid propellant rocket engines, aimed at enhancing the portability of hybrid and liquid propellant rocket systems. The proposed system ensures trajectory stability and rapid response to flight emergencies by effectively addressing external perturbations like wind. Key components are Gyroscopic (GYRO) sensors, good micro-controller, and Servo motors, collectively referred to as Thrust vectoring components, which dynamically adjust thrust direction opposite to the rocket's trajectory to maintain stability. Drawing from an extensive literature review analyzing existing TVC system designs, with a focus on thrust characteristics and engine combustion timings, the design integrates considerations of geometric properties, kinematics, forces, energy requirements, safety, cost, and control methods, aligning with both mechanical and avionic design principles. The anticipated outcome is an advancement in rocket propulsion technology, characterized by improved angular speed control, trajectory linearity, and rapid response capabilities.

Low-Temperature and High-Rate Rechargeable Aluminum Batteries Enabled by Ternary Eutectic Electrolytes.

Rechargeable aluminum batteries (RABs) have garnered extensive scientific attention as a promising alternative chemistry due to the inherent advantages associated with aluminum (Al) metal anodes, including their high theoretical capacities, cost-effectiveness, environmental friendliness, and inherent non-flammable properties. Nonetheless, the practical energy density of RABs is constrained by the electrolytes that support lower operational voltage windows. Herein, we report a ternary eutectic electrolyte composed of 1-ethyl-3-methylimidazolium chloride ([C2C1im]Cl):1-butyl-3-methylimidazolium chloride ([C4C1im]Cl):aluminum chloride (AlCl3) for the application of RABs. The electrolyte exhibits a high operational potential window (~3V vs. Al/Al3+ on SS 316) and high ionic conductivity (~8.3 mS.cm-1) while exhibiting only a low temperature glass transition at -65 oC suitable for all-climate conditions. Al||graphene nanoplatelets cell delivers a high capacity of ~117 mAh/g, and ~43 mAh/g at a very high current densities of 1A/g and 5A/g, respectively. The cells render a reversible capacity of 20 mAh/g at -20 oC and 17 mAh/g at -40 oC, indicating their suitability for operation under extreme environmental conditions. We comprehensively evaluated the design and optimization of carbon paper-based battery systems. The ternary eutectic electrolyte demonstrates exceptional electrochemical performance, thus signifying its substantial potential for utilization in high-performance energy storage systems in all climates.

Interfacial linkage modulated amorphous molybdenum sulfide/bismuth halide perovskite heterojunction for enhanced visible-light-driven photocatalytic hydrogen evolution

Photocatalytic hydrogen evolution is a promising approach for direct solar-to-fuel conversion. Although significant research efforts have been put on the development of lead-free metal halide perovskites to reach excellent optoelectronic properties, their rational design for efficient heterojunction photocatalytic systems still poses challenges. Here, we report a new strategy to tailor the interface of hybrid tri(dimethylammonium) hexaiodobismuthate (DMA3BiI6) and amorphous molybdenum sulfide (a-MoSx) heterojunctions. Specifically, a-MoSx was prepared with abundant apical S2– or bridging S22– ligands to allow coupling with DMA3BiI6 via an interfacial Mo–S–Bi linkage. The as-obtained heterostructures were found to show an improved visible-light-driven photocatalytic hydrogen evolution in hydroiodic acid splitting under mild conditions reaching a superior hydrogen evolution rate of around 750 µmol g−1 h−1 and an apparent quantum efficiency (AQE) of 13.0 % at 420 nm. The high activity was kept after a long-term performance test for 3 days.

Predicting Wall Pressure in Shock Wave/Boundary Layer Interactions with Convolutional Neural Networks

Within the dynamic realm of variable-geometry shock wave/boundary layer interactions, the wall parameters of the flow field undergo real-time fluctuations. The conventional approach to sensing these changes in wall pressure through sensor measurements is encumbered by a cumbersome process, leading to diminished efficiency and an inability to provide swift predictions of wall parameters. This paper introduces a data-driven methodology that leverages non-contact schlieren imaging to predict wall pressure within the flow field, a technique that holds promise for informing the optimized design of variable-geometry systems. A sophisticated deep learning framework, predicated on Convolutional Neural Networks (CNN), has been engineered to anticipate alterations in wall pressure stemming from high-speed shock wave/boundary layer interactions. Utilizing an impulsive wind tunnel with a Mach number of 6, we have procured a sequence of schlieren images and corresponding wall pressure measurements, capturing the continuous variations induced by an attack angle from a shock wave generator. These data have been instrumental in compiling a comprehensive dataset for the training and evaluation of the CNN. The CNN model, once trained, has adeptly deduced the distribution of wall pressure from the schlieren imagery. Notwithstanding, it was observed that the CNN’s predictive prowess is marginally diminished in regions where pressure variations are most pronounced. To assess the model’s generalization capabilities, we have segmented the dataset according to different temporal intervals for network training. Our findings indicate that while the generalization of all models crafted was less than optimal, Model 4 demonstrated superior generalization. It is thus suggested that augmenting the training set with additional samples and refining the network architecture will be a worthwhile endeavor in subsequent research initiatives.

Многофакторный анализ эффективности автономных транспортных средств на основе метода анализа среды функционирования

The design of robotic and unmanned vehicles includes a virtual commissioning stage that provides an analysis of the operation of objects using digital twins. Conducting a large number of experiments on models leads to the use of methods for evaluating the results obtained to choose the best design solution. A method of multifactorial anal-ysis of the efficiency of the complex of autonomous vehicles (AV) for agro-industrial purposes is proposed. Multivariate analysis is performed at the virtual commissioning stage in order to plan early maintenance and repair activities. The paper uses the method of analyzing the operating environment for a comparative assessment of various operating scenarios. The method is based on solving a complex of optimization problems of linear programming. A formal description of virtual test scenarios is proposed. The method of multifactorial analysis is implemented in the process of virtual tests using optimal purpose system models and AV simulation. A procedure for virtual AV tests has been developed, including step-by-step modeling and multivariate analysis. The sets of input and output key parameters of the AV and analyzed scenarios are determined. It is proposed to perform two consecutive efficiency assessment tasks. The first task is to compare the effectiveness of individual AV and determine the limits of their effectiveness. The solution of the second task makes it possible to evaluate the effectiveness of AV system operation scenarios. The target values of changes in the key parameters of the AV and operating scenarios are obtained, leading to an increase in the efficiency of operation and maintenance. Conducting a multifactorial analysis of the results of virtual tests allows you to formulate requirements and recommendations for the design of AV maintenance systems.

Folic Acid-Functionalized β-Cyclodextrin for Delivery of Organelle-Targeted Peptide Chemotherapeutics in Cancer.

Recent emphasis on the design of drug delivery systems typically involves the effective transport of a pharmaceutical substance to the disease site with the desired therapeutic efficacy and minimal cytotoxicity. Organelle-targeted peptides have become an integral part of designing an important class of prodrug/prodrug assemblies for new supramolecular therapeutics owing to their favorable biocompatibility, synthetic ease, tunability of their aggregation behavior, and desired functionalization for site-specificity. However, it is still limited due to the low selectivity. We designed a folic acid-functionalized β-cyclodextrin (FA-CD) as a delivery platform for specific and selective delivery of organelle-targeted (such as microtubule, lysosome, and mitochondria) peptide chemotherapeutics to the folate receptor (FR) overexpressing cancer cell lines. Low toxicity was found for the FA-CD and organelle-targeted peptide inclusion complex in FR-negative normal cells, but superior inhibition of tumor growth with no in vivo toxicity was found for the inclusion complex in the xenograft tumor model.

Optimization algorithms for transmit waveform design in radar-centric dual-function radar-communication systems

We study the transmit waveform optimization problem in multiple-input-multiple-output (MIMO) dual-function radar-communication (DFRC) systems. A penalty-based formulation is adopted where engineering concerns on radar and communication are integrated in a weighted manner. We transform the original problem into an unconstrained formulation and propose a novel waveform synthesis algorithm via combination of multiple algorithm updates. The combination operation experimentally reaches a better objective value than without. We develop two combination strategies and invent a shrinkage-expansion line search method for monotonicity maintenance. Numerical experiments show that the proposed algorithm suffices to generate constant modulus waveforms which achieve radar and communication purposes.

Electrochemical-thermal analysis of large-sized lithium-ion batteries: Influence of cell thickness and cooling strategy in charging

With the transition to electric propulsion accelerating, lithium-ion batteries (LIBs) are essential due to their high energy density and efficiency. However, higher energy requirements and rapid charging pose significant safety and performance challenges, particularly for large cells. This research uniquely investigates the thermal and electrochemical responses of large-sized prismatic LiFePO4 cells under various cell thicknesses (ranging from 11.3 mm to 90 mm), charging conditions (ranging from 0.3C to 1C), and cooling strategies. The model reveals that higher charging rates markedly increase temperature and variability within the cells. Charging a 45 mm-thick cell at a 1C rate and with natural air cooling results in a temperature rise of 24.3K and a deviation of 6.18K. In contrast, charging at 0.3C results in 5.4K and 1.17K, respectively. Despite the smaller heat exchange surface, bottom-cooling is most efficient for thick cells due to significant anisotropic heat conduction. Single-side cooling becomes more effective for thinner cells. A critical cell thickness for such transition exists at around 22.5 mm. Double-side cooling consistently outperforms single-side cooling, significantly reducing temperature deviations across various cell thicknesses. Best cooling performance is seen in the case of the thinnest cell (11.3 mm) with double-side cooling, with only 3K temperature rise and 1K deviation, at 1C charging. Additionally, improved thermal management during charging slightly increases heat generation of the cell, due to the higher resistance at lower temperatures, elevating ohmic heat generation from separators and electrodes. Polarization heat from the negative electrode is identified as the dominant heat source, driven by increased overpotential at higher temperatures due to elevated Li ion concentration at the electrode surface. The findings provide crucial insights into the thermal and electrochemical behaviors of large-sized LIBs, offering valuable guidance for the design and management of these LIB systems to enhance safety, performance, and longevity in electric vehicle applications, addressing a pressing need in the field.

Baffle-Enhanced Scour Mitigation in Rectangular and Trapezoidal Piano Key Weirs: An Experimental and Machine Learning Investigation

The assessment of scour depth downstream of weirs holds paramount importance in ensuring the structural stability of these hydraulic structures. This study presents groundbreaking experimental investigations highlighting the innovative use of baffles to enhance energy dissipation and mitigate scour in the downstream beds of rectangular piano key weirs (RPKWs) and trapezoidal piano key weirs (TPKWs). By leveraging three state-of-the-art supervised machine learning algorithms—multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and support vector regression (SVR)—to estimate scour hole parameters, this research showcases significant advancements in predictive modeling for scour analysis. Experimental results reveal that the incorporation of baffles leads to a remarkable 18–22% increase in energy dissipation and an 11–14% reduction in scour depth for both RPKWs and TPKWs. Specifically, introducing baffles in RPKWs resulted in a noteworthy 26.7% reduction in scour hole area and a 30.3% decrease in scour volume compared to RPKWs without baffles. Moreover, novel empirical equations were developed to estimate scour parameters, achieving impressive performance metrics with an average R2 = 0.951, RMSE = 0.145, and MRPE = 4.429%. The MLP models demonstrate superior performance in predicting maximum scour depth across all scenarios with an average R2 = 0.988, RMSE = 0.035, and MRPE = 1.036%. However, the predictive capabilities varied when estimating weir toe scour depth under diverse circumstances, with the XGBoost model proving more accurate in scenarios involving baffled TPKWs with R2 = 0.965, RMSE = 0.048, and MRPE = 2.798% than the MLP and SVR models. This research underscores the significant role of baffles in minimizing scouring effects in TPKWs compared to RPKWs, showcasing the potential for improved design and efficiency in water-management systems.

Scientific achievements in Siberian agriculture and their development

A historical review of the formation and development of scientific farming in Siberia is given. The main achievements of the leading research institutes of the region are highlighted. The contribution of Siberian scientists to the development of scientific farming, including its constituent directions, is shown. It presents almost 100 years of development of research on agriculture - from the Omsk experimental farm (XIX century), agricultural experimental stations - to modern federal scientific centers. The evolution of farming from individual crop cultivation techniques to the formation of zonal and then adaptive-landscape farming systems and modern agro-technologies is demonstrated. Examples of completed scientific developments on farming, ready for implementation in production, the number of which runs into thousands, are given. Implementation of knowledge-intensive agro-technologies guarantees grain production in Siberia up to 25-30 million tons, sustainable development of farming, stable food supply and high return on non-renewable resources. The evolution of plant protection from individual methods against the most dangerous pests to the formation of integrated systems that take into account the phytosanitary state of agrocenoses and agro-ecological conditions of agricultural crops cultivation is shown. The most important techniques to optimize the relationship between the crop and pests are the selection of plant species and varieties resistant to their effects, optimization of mineral nutrition of plants and sowing dates. At present, the development of scientific farming is associated with the design of adaptive-landscape farming systems, selection and support of agro-technologies, rational use of land resources, improvement of approaches to the conservation and reproduction of soil fertility, soil treatment systems, fertilizers and integrated plant protection, formation of adaptive crop rotations, research of technological and biological processes in the cultivation of crops, use of GIS-technologies, remote sensing, and the use of remote sensing.

Development of a high-pressure 700 bar metal hydride hydrogen compressor

HySA Systems and TF Design have recently developed a single-stage prototype high-pressure metal hydride hydrogen compressor (MHHC) which is able to compress hydrogen from 100 to >700 bar at the working temperatures from 20 to 150 °C, with estimated productivity about 1 Nm3/h. The MHHC comprises of two 2 m-long fibre-wound high-pressure MH containers developed by the authors earlier and assembled in two modules operating in a mode of a cyclic lower-pressure H2 absorption (on cooling) and high-pressure H2 desorption (on heating). The containers operate using a self-developed multicomponent C14 Laves type Ti–based AB2 intermetallic alloy. The article considers phase-structural and hydrogen sorption properties of the utilized metal hydride material, hydrogen compression performances of the metal hydride container, as well as layout and the expected performance characteristics of the compressor assembly.

A location fingerprinting approach for the automated radio telemetry of wildlife and comparison to alternative methods

BackgroundAutomated radio telemetry (ART) systems enable high-temporal resolution data collection for species unsuited to satellite-based methods. A challenge of ART systems is estimating the location of radio tagged animals from the radio signals received on multiple antennas within an ART array. Localisation methods for ART systems with omni-directional receivers have undergone rapid development in recent years, with the inclusion of machine learning techniques. However, comparable machine learning methods for ART systems with directional antennas are unavailable, despite their potential for improved accuracy and greater versatility. To address this, we introduce an open-source machine learning-based location fingerprinting method for directional antenna-based ART systems. We compare this method to two alternative localisation approaches. Both alternatives use relative signal strengths recorded among multiple antennas to estimate the signal’s angle of arrival at each receiver. In the ‘biangulation’ approach, the location is estimated by finding the intersection of these angles from two receivers. In contrast, the ‘linear regression’ approach uses a linear regression model to estimate the distance from the receiver along the angle of arrival, providing a location estimate. We evaluate these methods using an ART data set collected for the southern black-throated finch (Poephila cincta cincta), in the Desert Uplands Bioregion of Queensland, Australia.ResultsThe location fingerprinting method performed slightly better than the best performing alternative, the linear regression method, with mean positional errors of 308 m (SE = 17.7) and 335 m (SE = 18.5), respectively. The biangulation method performed substantially worse, with a mean positional error of 550 m (SE = 42.9, median = 540 m). Improved accuracy was observed with shorter distances between transmitters and receivers, higher signal strengths, and a greater number of detecting receivers, suggesting that increasing receiver density improves localisation accuracy, albeit with potential trade-offs in system coverage or cost. Furthermore, shorter pulse intervals of transmitters resulted in greater accuracy, highlighting the trade-offs among battery life, transmitter weight and radiative power.ConclusionsThe open-source location fingerprinting method offers an improved and versatile localisation approach suitable for a wide variety of ART system designs, addressing the challenge of developing study-specific localisation methods using alternative approaches.

Predicting bentonite swelling pressure: optimized XGBoost versus neural networks

The swelling pressure of bentonite and bentonite mixtures is critical in designing barrier systems for deep geological radioactive waste repositories. Accurately predicting the maximum swelling pressure is essential for ensuring these systems' long-term stability and sealing characteristics. In this study, we developed a constrained machine learning model based on the extreme gradient boosting (XGBoost) algorithm tuned with grey wolf optimization (GWO) to determine the maximum swelling pressure of bentonite and bentonite mixtures. A dataset containing 305 experimental data points was compiled, including relevant soil properties such as montmorillonite content, liquid limit, plastic limit, plasticity index, initial water content, and soil dry density. The GWO-XGBoost model, incorporating a penalty term in the loss function, achieved an R2 value of 0.9832 and an RMSE of 0.5248 MPa in the testing phase, outperforming feed-forward and cascade-forward neural network models. The feature importance analysis revealed that dry density and montmorillonite content were the most influential factors in predicting maximum swelling pressure. While the developed model demonstrates high accuracy and reliability, it may have limitations in capturing extreme values due to the complex nature of bentonite swelling behavior. The proposed approach provides a valuable tool for predicting the maximum swelling pressure of bentonite-based materials under various conditions, supporting the design and analysis of effective barrier systems in geotechnical engineering applications.

A review on development approaches for 3D gestural embodied human-computer interaction systems

The integration of 3D gestural embodied human-computer interaction (eHCI) has provided an avenue for contactless interaction with systems. The design of gestural systems employs two approaches: Technology-based approach and Human-based approach. This study reviews the existing literature on development approaches for 3D gestural eHCI to understand the current state of research in 3D gestural eHCI using these approaches and identify potential areas for future exploration. Articles were gathered from three databases: PsycInfo, Science Direct and IEEE Xplore. A total of 35 articles were identified, of which 18 used human-based methods, and 17 used technology-based methods. Findings shed light on inconsistencies between developers and users in preferred hand gesture poses and identify factors influencing users’ gesture choice. Implementation of the consolidated findings has the potential to improve human readiness for 3D gestural eHCI technologies.

Thermal characteristics of nanofluid ice slurry flowing through a spiral tube: A computational study

Nanofluid ice slurry (NICS) has recently attracted significant attention in the field of thermal engineering as an alternative refrigerant, offering a cost-effective, stable, and environmentally friendly cold storage solution. To maximize its potential, a thorough understanding of its heat transfer characteristics is crucial. Unfortunately, this information is not available for spiral tubes which are commonly used in thermal systems due to their superior heat transfer performance. This may hinder further development and implementation of this technology. Hence, the present investigation aims to analyze the flow characteristics and heat transfer mechanisms of a graphene oxide hybrid NICS within a spiral tube employing a computational fluid dynamics (CFD) methodology. More specifically, an interphase heat and mass transfer model, derived from the Euler–Euler model, is formulated to accurately represent the phase transition phenomena occurring within the nanofluid. The results indicate that the spiral tube structure enhances ice crystal accumulation inside the tube, leading to lower outlet temperatures and improved heat transfer without causing ice blockage. The NICS shows a 3% higher pressure drop compared to pure water ice slurry. Increased nanoparticle concentrations enhance thermal conductivity, benefiting heat transfer, but also raise viscosity, resulting in greater internal friction. A Nusselt correlation based on Prandtl and Dean numbers is formulated to aid future studies and the design of NICS systems. When the Reynolds number increases from 3600 to 6600, the Nusselt number rises by approximately 21% for pure water ice slurry and 22% for nanofluid ice slurry.