Input Conditions Research Articles

Performance evaluation and validation of OpenMC code for criticality analysis of an MTR-type research reactor

This paper assesses the performance of OpenMC, an open-source Monte Carlo code for particle transport, through criticality analysis of an MTR-type research reactor. The evaluation involves validation against experimental data from the NUR research reactor's first fresh fuel core and a code-to-code comparison with MCNP5, using a consistent methodology ensuring identical input conditions. Key parameters, including excess reactivity, control rod worths, shutdown margin, critical configurations, kinetics parameters, neutron flux, power distributions, and peaking factors, were examined. Results indicate good agreement between OpenMC simulations, experimental data, and MCNP5 results, with marginal differences well within acceptable ranges. Discrepancies with experimental data are below 5 %, considering measurement accuracy, while code-to-code comparisons reveal relative differences below 1 % in most cases. This study supports OpenMC's reliability for MTR reactor criticality analysis, affirming the validity of the developed NUR reactor model for future applications and highlighting the code's credibility and applicability in the nuclear reactor simulations.

Agronomic advantage of bacterial biological nitrogen fixation on wheat plant growth under contrasting nitrogen and phosphorus regimes

IntroductionGiven their remarkable capacity to convert atmospheric nitrogen into plant-accessible ammonia, nitrogen-fixing microbial species hold promise as a sustainable alternative to chemical nitrogen fertilizers, particularly in economically significant crops like wheat. This study aimed to identify strains with optimal attributes for promoting wheat growth sustainably, with a primary emphasis on reducing reliance on chemical nitrogen fertilizers.MethodsWe isolated free nitrogen-fixing strains from diverse rhizospheric soils across Morocco. Subsequently, we conducted a rigorous screening process to evaluate their plant growth-promoting traits, including nitrogen fixation, phosphate solubilization, phytohormone production and their ability to enhance wheat plant growth under controlled conditions. Two specific strains, Rhodotorula mucilaginosa NF 516 and Arthrobacter sp. NF 528, were selected for in-depth evaluation, with the focus on their ability to reduce the need for chemical nitrogen supply, particularly when used in conjunction with TSP fertilizer and natural rock phosphate. These two sources of phosphate were chosen to assess their agricultural effectiveness on wheat plants.Results and discussionTwenty-two nitrogen-fixing strains (nif-H+) were isolated from various Moroccan rhizospheric soils, representing Bacillus sp., Pseudomonas sp., Arthrobacter sp., Burkholderia sp. and a yeast-like microorganism. These strains were carefully selected based on their potential to promote plant growth. The findings revealed that the application of Rhodotorula mucilaginosa NF 516 and Arthrobacter sp. NF 528 individually or in combination, significantly improved wheat plant growth and enhanced nutrients (N and P) uptake under reduced nitrogen regimes. Notably, their effectiveness was evident in response to both natural rock phosphate and TSP, demonstrating their important role in wheat production under conditions of low nitrogen and complex phosphorus inputs. This research underscores the significant role of nitrogen-fixing microorganisms, particularly Rhodotorula mucilaginosa NF 516 and Arthrobacter sp. NF 528, in wheat production under conditions of low nitrogen and complex phosphorus inputs. It showcases their potential to reduce chemical nitrogen fertilization requirements by up to 50% without compromising wheat plant yields. Our study emphasizes the importance of bacterial biological nitrogen fixation in meeting the remaining nitrogen requirements beyond this reduction. This underscores the vital role of microbial contributions in providing essential nitrogen for optimal plant growth and highlights the significance of biological nitrogen fixation in sustainable agriculture practices.

Experimental Study of Taper Shape Bistable Beam for Improved Vibration Energy Harvesting

Abstract Unwanted vibrations cause discomfort and affect the accuracy of the machinery. Vibrations of low frequency (<30 Hz) are usually inutile. Various researchers focus on harvesting and enhancing energy output through low-frequency vibrations. This paper focuses on improving energy harvesting from low-frequency vibration sources through taper shape (TAP) bistable beam fabricated using thin Piezoelectric Polyvinylidene fluoride (PVDF) film sandwiched between thin copper and aluminium films on both sides. Voltage power output through a PVDF film depends on the film's strain distribution; hence, in this study, five different beam designs are studied for strain distribution through the ANSYS workbench. Further, an experimental harmonic analysis study for voltage output is performed on rectangular beam (RECT) and TAP beam for both straight and bistable configuration using three different harmonic input conditions, which concluded that the voltage output for the TAP beam is much more than that of the RECT beam, with a much more significant voltage output increase in bistable conditions for all three harmonic input conditions.

Enhanced efficiency nitrogen fertilizers (EENFs) can reduce nitrous oxide emissions and maintain high grain yields in a rain-fed spring maize cropping system

ContextEnhanced efficiency nitrogen fertilizers (EENFs) have provided opportunities for the simultaneous mitigation of nitrous oxide (N2O) emissions and increases in crop productions. However, the practice of combining EENFs with optimal fertilization under conditions of misaligned water and fertilizer inputs in rain-fed spring maize systems remains inadequately understood. ObjectiveThis study aims to determine the factors controlling N2O emissions under the soil climatic conditions in rain-fed plastic mulching cropping systems treated with different nitrogen (N) fertilization rates and EENFs, and to determine the most effective N fertilization measure to reduce N2O emissions while maintaining crop yields. MethodsA 3-year field experiment was conducted in a spring maize system located in the semiarid rain-fed region of the Loess Plateau, applying two types of EENFs: a nitrification inhibitor dicyandiamide (DCD) and a slow-release fertilizer (SRF). It comprised five fertilization treatments: unfertilized (CK), conventional N rate (CON, 200 kg N ha−1), optimal N rate (OPT, 160 kg N ha−1), OPT with the addition of DCD (OPT+DCD), and OPT using SRF (OPT-SRF). ResultsThe average annual cumulative N2O emissions over the 3-year experimental period ranged from 0.74 to 1.87 kg N2O-N ha–1. Fertilizer N contributed 26−60% to annual N2O emissions. The highest N2O fluxes (50–161 μg N2O-N m–2 h–1) typically occurred within the initial 10 days following fertilization, likely induced by strong nitrification processes, constituting 12–19% of the annual N2O emissions during this brief period. In comparison to the CON treatment, the OPT, OPT+DCD, and OPT-SRF treatments resulted in significant reductions in annual N2O emissions of 24%, 46%, and 34%, respectively, without causing any significant decrease in maize grain yields. The application of DCD led to increased ammonium (NH4+-N) concentrations while reducing nitrate (NO3–-N). Conversely, using SRF resulted in a decrease in both NH4+-N and NO3–-N concentrations. ConclusionsUsing EENFs at the ideal fertilization rate significantly curtailed yearly N2O emissions without adversely affecting maize yields in a rain-fed plastic mulching spring maize system prevalent in the Loess Plateau. Most N2O emissions occurred post-fertilization, with rainfall events further influencing these emissions. ImplicationsOur findings recommend the incorporation of nitrification inhibitors, such as DCD, as the most effective fertilizer measure for reducing N2O emissions in the rain-fed region of the Loess Plateau.

Manipulation of geometric parametric sidebands via elliptical Gaussian beam in graded-index multimode fibers

Geometric parametric instability is a class of processes where the revival of optical beam in a nonlinear multimode graded-index fiber induces a spatial index modification and in turn generates frequency sidebands due to space-time coupling. While current studies have been focusing on breathing Gaussian beams in such settings, the evenly spaced eigenvalue in parabolic fiber indicates that any input condition will experience self-imaging effects under linear conditions. Here we investigate the nonlinear propagation and the corresponding geometric parametric instability sidebands generation associated with elliptical Gaussian beams where the long- and short- axis of the beam oscillates with different phases. We show that, depending on the initial conditions, there are four possible propagation regimes: over-trapping, self-trapping, under-trapping and self-focusing. As a result, the spacing and strength of generated sidebands can be judiciously tuned by the elliptical ratio of elliptical Gaussian beams. At certain conditions, missing-order effect occurs where a set of sidebands completely disappear. We experimentally observed the multiple and a few sidebands of the Gaussian beam and elliptical Gaussian beam in a standard graded-index multimode fiber. In addition, experiments show that GPI sidebands have well-defined and stable bell-shaped near-field profiles. These results pave the way for developing tunable light sources and ultra-broadband supercontinuum generation.

Bias induced circular current in a loop nanojunction with AAH modulation: Role of hopping dimerization

Abstract In this work, we investigate the interplay between correlated disorder and hopping dimerization on bias driven circular
current in a loop conductor that is clamped between two electrodes. The correlated disorder is introduced in site energies of the
ring in the form of Aubry-Andr\'e-Harper (AAH) model. Simulating the quantum system within a tight-binding framework all the results
are worked out based on the wave-guide theory. Unlike transport current, circular current in the loop conductor can get
enhanced with increasing disorder strength. This enhancement becomes much effective when hopping dimerization is included which is
taken following the Su-Schrieffer-Heeger (SSH) model. The characteristic features of bias driven circular current are studied under
different input conditions and we find the results are robust for wide range of physical parameters. For the sake
of completeness, uncorrelated disorder is also considered. Our analysis may provide a new insight in analyzing transport behavior
in different disordered lattices in presence of additional restrictions in hopping integrals.

Design and Analysis of Piston by using Ansys Workbench

Abstract: In this study, structural analysis is investigated on conventional piston made of cast iron. Secondly analysis are performed on piston made of aluminum, titanium alloy , structural steel and fiber glass. The material used for the design of piston should have light weight, low cost, structurally and thermally withstand at very high pressure and temperature condition that will occur in combustion process. In this project, It has been decide to study a particular piston design And its capability for maximum gas pressure. In this work, initial planning is to make piston model using solid modeling software CATIA V5. It has been decided to mesh the geometry analyze using ANSYS. For the analysis of piston input conditions and process of analysis, a lot of literature survey has been done. High combustion gas pressure will act as a mechanical loads and causes major stresses in the critical region of the piston. Detailed static structural analysis is carried out for various loading conditions like maximum gas pressure load.

Optimization of a syngas-fueled SOFC-based multigeneration system: Enhanced performance with biomass and gasification agent selection

In light of abundant sources of biomass feedstocks and regarding the higher performance in syngas-fueled-based SOFC systems, this work proposes a novel multigeneration system based on the combination of syngas-fuel SOFC, Kalina cycle, humidification-dehumidification desalination unit, and proton exchange membrane electrolyzer. This system is designed for simultaneous production of power, heating, freshwater, and hydrogen. Comprehensive energy, exergy, exergoeconomic, environmental, and economic analyses have been conducted to evaluate its performance. The optimum input biomasses, gasification agents, and operating conditions were determined using a coupled approach of an artificial neural network, multi-objective Particle Swarm Optimization algorithm, and the Linear Programming Technique for Multidimensional Analysis of Preference decision-making method. Under base input conditions, the energy and exergy efficiencies of the proposed system were found to be 53.66 % and 38.2 %, respectively. The system also demonstrated the capability to produce 0.1633 kg/s of freshwater and generate a net power output of 377.6 kW. The results indicate that using oxygen-enriched air in the gasification process notably enhances efficiency while reducing emissions. Straw and CO₂ were identified as the optimal feedstock and gasification agent, yielding an exergy efficiency of 42.71 % and a total product cost of 6.29 $/GJ at the optimum point. Moreover, with a selling price of $0.20/kWh for electricity and a fuel cost of $6/GJ, the system can achieve total revenue of approximately $1.65 million with a payback period of 4.01 years. These findings underscore the proposed plant's profitability under specific pricing conditions, making it an attractive investment opportunity.

Experimental investigation and optimization of heat pipe heat exchanger performance under two distinct heat transport mediums through TOPSIS approach

This paper presents a comprehensive thermal performance analysis of the heat pipe heat exchanger by varying the input process parameters and optimizing the best condition for waste heat recovery through the TOPSIS technique. In this work, the heat pipe is constructed with a copper tube and segmented into evaporator and condenser sections carrying hot and cold fluids with a half-fill ratio. The heat pipe is oriented in 0° horizontally with the wick material made from stainless steel and works based on the capillary principle. The efficiency of the HPHE is evaluated by varying the input operational parameters, namely heat input, mass flow rate of hot fluid, inlet temperature of hot fluid, and mass flow rate of cold fluid, on response variables, specifically thermal resistance, energy balance ratio, universal heat transfer coefficient, and effectiveness. The experiment was designed for four factors at three levels as per Taguchi’s L27 orthogonal array, and the best optimising condition was determined by employing the multi-objective technique for order preference by similarity to ideal solution (TOPSIS) approach. The best input condition found was A3B1C1D3, in which the heat input was at 90 W, the mass flow rate of hot fluid was at 0.2 kg min−1, the mass flow rate of cold fluid was at 0.1 kg min−1, and the inlet temperature was at 60 °C. The effect of process parameters and their significance on output responses was analysed by executing an analysis of variance (ANOVA) statistical method. The mathematical regression model was generated, and validation was made to investigate the prediction error percentage.

Practical approaches for evaluating radiative heat from high pressure hydrogen flame

As the construction of hydrogen stations and other hydrogen-related facilities progresses, it becomes increasingly crucial for safety designs to account for scenarios where leaked hydrogen might ignite. Since a hydrogen flame is usually invisible, detection must rely on radiative heat or temperature. While it is possible to predict accurate radiative heat flux from a high pressure hydrogen jet flame through detailed numerical calculations of hydrogen combustion with its radiation, the objective of this study is to provide a simpler and more accurate prediction method. Previous experimental data of radiative heat flux, Qr from high pressure hydrogen jet flames were compiled, and a simple experimental equation Qr=1463.3(Lo/Lf)−2.32 was derived, where Lf and Lo represent the flame length and the distance perpendicular to the flame axis from the midpoint of the flame length to the observer, respectively. Radiative heat fluxes were computed through detailed numerical simulations using three types of water vapor radiation models: Goody's narrow random band model, the exponential broad band model by Edward and Elsasser's narrow regular band model. The temperature and water vapor partial pressure distributions obtained from numerical simulations were used as input conditions. The results were found to be in good agreement with the experimental equation. Moreover, a simple diagram representing the radiative heat flux from a hydrogen flame was derived using the method of cylinder-like approximation. The results of this study show that if the hydrogen pressure and leakage aperture diameter are given, the radiative heat flux at a certain distance can be predicted with high accuracy. Additionally, the average value of emissivity for the hydrogen diffusion flame was found to be on the order of 0.03.

Mode-dependent analysis of a fiber optic curvature sensor with sensitive zone

In this paper, mode-dependent analysis of fiber optic curvature sensor with precision machined structural imperfections (teeth) as sensitive zone is conducted. Using controlled input or light source launching conditions sensor response is measured for different modes excited in the multimode optical fiber. Study shows that as curvature changes there is significant difference in how sensitive zone affects low and high order modes propagating through the fiber. Obtained measurement results provide further insight into FOCS response and open possibilities for development of new measurement approaches which represent a significant step forward for FOCS based measurement systems. Guidelines for maximizing sensor sensitivity, increasing measurement reliability and even avoiding intensity based measurement by measuring changes in the width of the sensor’s output light intensity distribution are discussed in this work.

Abrasive waterjet machining of titanium alloy using an integrated approach of taguchi-based passing vehicle search algorithm

AbstractThe titanium alloy, Ti6Al4V, is a popularly used material in aerospace and medical applications due to its specific attributes, such as excellent strength-to-weight ratio and resistance to corrosion. Ti6Al4V is challenging to machine using conventional methods due to its poor thermal conductivity, which necessitates the use of unconventional machining methods like abrasive water jet machining (AWJM). In this work, AWJM was preferred for machining Ti6Al4V, considering three key process variables: nozzle traverse speed (Tv), abrasive flow rate (Af), and stand-off distance (Sd). The performance of the AWJM process was assessed using three main metrics: Material removal rate (MRR), Surface roughness (SR), and Kerf taper angle (θ), which were crucial for evaluating the effectiveness of the AWJM. Taguchi’s L9 array, a design of experiments method, was used to plan the experiments. The adequacy of the developed models was assessed by analysis of variance (ANOVA). ANOVA results have shown that Tv, Af, and Sd were found to have a significant effect on MRR, SR, θ with contributions of 73.15%, 49.72%, and 78.35% respectively. The Passing Vehicle Search algorithm was adopted to find the global optimal solution. Simultaneous optimization results using the PVS algorithm have shown the optimal MRR, SR, and θ values of 0.17 g/min, 3.28 μm, and 2.45, respectively, at Tv of 237 mm/min, Af of 450 g/min, and Sd of 2.0 mm. The optimization results with Pareto points will help to achieve desired outcomes by selecting appropriate input conditions.

Cooperative treatment and resource utilization of municipal solid waste by industrial coke ovens: Effects of doping ratio and management

For the collaborative treatment and resource utilization of an industrial coke oven on municipal solid waste (MSW), this study used MSW to prepare refuse-derived fuel (RDF). The influence of different mix ratio between RDF and coal on coking products was considered, and technical and economic analyses were conducted. The results showed that under the low mixture ratio (3%), the pyrolytic carbon obtained basically met the class III standard of metallurgical carbon. In the experiment of medium mixture ratio (25%, 30%, 35%), the product contained a certain amount of calorific value, lower than that of the metallurgical coke. It might be that the ash content increased, and such product can be used for civil coke or chemical coke. When the mixture ratio was 100%, the calorific value of pyrolysis carbon was 7.53 MJ/kg, representing 25.7% of the calorific value of ordinary coke. The gas production rate of the discharged gas was 0.35, and the calorific value is 14.0 MJ/kg, reaching 73.6% of the coke oven gas. Under the condition of no coal input, the MSW was converted into usable energy. The results demonstrated that coke oven pyrolysis with 100% RDF addition is the most feasible way to recycle resources and energy.

Modeling and simulation for prediction of multiple sclerosis progression

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns.The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach

In this Paper solar desiccant air conditioning system integrated with cross flow Maisotsenko cycle (M-cycle) indirect evaporative cooler is used to investigate the performance of whole system in different range of parameters. Solar evacuated tube electric heater is used to supply the regeneration temperature to the desiccant wheel, whereas, Desiccant Wheel (DW) and M-cycle is used to handle latent load and sensible load separately. Major contribution of this research is to predict system level performance parameters of a Solar Assisted Desiccant Air Conditioning (Sol-DAC) system using Radial Basis Function Neural Network (RBF-NN) under real transient experimental inlet conditions. Nine parameters are mainly considered as input parameters to train the RBF-NN model, which are, supply Air temperature at the process side of desiccant wheel, supply air humidity ratio at process side of the desiccant wheel, outlet temperature from the desiccant wheel at process side, outlet humidity ratio from the desiccant wheel at process side, regeneration temperature at regeneration side of the DW, outlet temperature from the heat recovery wheel at process side, outlet humidity ratio out from the Heat Recovery Wheel (HRW) at process side, temperature before heat recovery wheel regeneration side of the system, humidity ratio before heat recovery wheel regeneration side of the system. Four parameters are considered as the output of the RBF-NN model, namely: output temperature, output humidity, Cooling Capacity (CC), and Coefficient of Performance (COP). The results of the RBF-NN model shows that the best Mean Squared Error (MSE) and Regression coefficient (R) for outlet temperature prediction are 0.00998279 and 0.99832 when regeneration temperature is 70 °C and inlet humidity at 18 g/kg. Best MSE and R for predication of outlet humidity are 0.0102932 and 0.99485 when the regeneration temperature is 70 °C and inlet humidity at 16 g/kg. Best MSE and R for predication of COP are 0.0106691 and 0.9981 when the regeneration temperature is 70 °C and inlet humidity 12 g/kg. Best MSE and R for predication of CC are 0.0144943 and 0.99711 when the regeneration temperature is 70 °C and inlet humidity 14 g/kg. Experimental and predicted performance parameters were in close agreement and showed minimal deviation. Investigations of predicted results revealed that trained RBF-NN model was capable of predicting the trend of output result under the varying input condition.

Millets as Nutricereal Climate Resilient Smart Crop: A Review

Worldwide climate change and extreme weather variations are the most threatening challenge to agriculture and allied activities. The entire food production system has faced serious challenges due to increase in average temperature, intensity and frequency of drought and flood, aberration of rainfall patterns, and elevation in CO2 concentration. In today’s world climate change is a major concern. Review of different literature from different reports and studies for different nutritional benefit of millets and how it can thrive in climate change conditions are important. Different literature have been searched from different sources and complied. Millets are considered highly nutrition-rich and climate-resilient coarse grain cereals and it can enhance income as well as improve food and nutrition security. Millets have climate-resilient features and that’ s why they can thrive in adjust to a vast changing ecological conditions, less water requirements, low nutrient input conditions and also they have more resistance to environmental hassels. In comparison to cereals, millets have more dietary fibres, resistant starches and are nutritionally superior. Millet require less time to complete its life cycle than rice and wheat, so they have the capacity to escape the stresses. Millets also provide several health benefits as they have nutritious. The cost of cultivation is also less compare to other cereals. As population growth increases and climate is changing day by day so millets are the alternative choice for today’s world. In Karnataka most of the farmers already have started to cultivate drought resistant millets than more water required crop like rice, sugarcane, and maize to due to climate change. They commonly grown millets from domestic purposes are sorghum, pearl millet, finger - millet, barnyard, foxtail, kodo, proso and Little millet.

Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model

This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90 %; for arsenate: RMSE on test set = 4.84 %). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model.

Sheathless and high-throughput separation of multi-target particles combining inertia and deterministic lateral displacement (DLD) in a microchannel

Precisely separation of multicomponent particle mixtures has great potential to clinical diagnosis. Herein, we propose a novel two-stage separation platform combining deterministic lateral displacement (DLD) with inertia which can achieve high-throughput, sheathless, and precise multi-target particle separation. The first-stage inertial unit, consisting of contraction–expansion arrays, was used for both separating the large particles and prefocusing the remaining particles. The second-stage DLD unit was then used to further separate medium and small particles. In particular, the inertial unit can also replace sheath flow in the DLD unit to prefocus and limit the entry position of the particle flow. Moreover, flow matching of the two-stage connection region has ensured that the input flow of the second stage meets the conditions for effective separation of the DLD unit. The structure parameters and input conditions of each unit were optimized based on the evaluation of separation performance by numerical simulation and particle experiment. The results show that continuous and rapid separation of 5 μm, 10 μm and 20 μm particles can be achieved with high efficiency (96.3 %, 94.7 % and 100 %) and high purity (98.65 %, 92.65 % and 85.5 %) based on the superposition of inertia and DLD units. Our two-stage separation platform is capable of high throughput operation while maintaining separation accuracy, which has strong practical significance in biological, clinical and point-of-care applications.

Synthesizing 3D Multi-Contrast Brain Tumor MRIs Using Tumor Mask Conditioning.

Data scarcity and data imbalance are two major challenges in training deep learning models on medical images, such as brain tumor MRI data. The recent advancements in generative artificial intelligence have opened new possibilities for synthetically generating MRI data, including brain tumor MRI scans. This approach can be a potential solution to mitigate the data scarcity problem and enhance training data availability. This work focused on adapting the 2D latent diffusion models to generate 3D multi-contrast brain tumor MRI data with a tumor mask as the condition. The framework comprises two components: a 3D autoencoder model for perceptual compression and a conditional 3D Diffusion Probabilistic Model (DPM) for generating high-quality and diverse multi-contrast brain tumor MRI samples, guided by a conditional tumor mask. Unlike existing works that focused on generating either 2D multi-contrast or 3D single-contrast MRI samples, our models generate multi-contrast 3D MRI samples. We also integrated a conditional module within the UNet backbone of the DPM to capture the semantic class-dependent data distribution driven by the provided tumor mask to generate MRI brain tumor samples based on a specific brain tumor mask. We trained our models using two brain tumor datasets: The Cancer Genome Atlas (TCGA) public dataset and an internal dataset from the University of Texas Southwestern Medical Center (UTSW). The models were able to generate high-quality 3D multi-contrast brain tumor MRI samples with the tumor location aligned by the input condition mask. The quality of the generated images was evaluated using the Fréchet Inception Distance (FID) score. This work has the potential to mitigate the scarcity of brain tumor data and improve the performance of deep learning models involving brain tumor MRI data.

PENGUJIAN BLACKBOX PADA SISTEM PEMESANAN UNTUK SALES ORDER DI PT BUKIT MURIA JAYA BERBASIS EQUIVALENCE PARTITIONS

This research aims to conduct blackbox testing on the sales order booking system at PT. BMJ based on the concept of equivalence partitions. The blackbox testing method is employed to assess the functionality of the system without considering its internal implementation. The study identifies representative equivalence partitions within the input and output domains of the sales order booking system and utilizes these partitions to design test cases. Through a series of tests, the system's performance is analyzed under various input conditions representing each partition. The testing results provide a better understanding of the system's reliability, identify potential defects, and offer recommendations for further improvement. This study contributes to enhancing the quality of the sales order booking system at PT. BMJ through a systematic and structured blackbox testing approach.