Operating Points Research Articles

Prediction of pellet mass thresholds for ELM triggering in low-collisionality, ITER-like discharges

In ITER, pellets are calculated to require more than 8 times the mass than currently planned to reliably trigger edge-localized modes (ELMs). Unmitigated heat flux impulses from ELMs are intolerable in ITER at full power and current. Therefore, ITER operation relies on multiple approaches to control ELM heat fluxes. One method is pellet ELM pacing to instigate small rapid ELMs with low heat flux. Predicting the performance of pellet pacing is critical for ITER, which is expected to operate in a regime with a low-collisionality, peeling-limited pedestal. However, to trigger ELMs the local pressure increase in the expanding pellet cloud pushes the equilibrium over the ballooning stability limit. In this work, linear and nonlinear M3D-C1 simulations are used to predict pellet mass thresholds in DIII-D discharges and ITER scenarios with peeling-limited pedestals. It is found that the distance of the equilibrium’s operational point from the ballooning branch of the pedestal stability boundary strongly changes thresholds. Linear M3D-C1 simulations find a strong dependence of the pellet mass threshold on the poloidal injection location for ITER’s 15 MA, Q = 10 scenario. The required pellet mass at the planned injection locations is 8 to 17 times larger than currently considered. However, such linear simulations do not include pellet ablation physics or time evolution of density and temperature. A new scheme of 2D nonlinear simulations, coupled with linear stability analysis at various steps throughout the nonlinear time evolution, was developed to include such physics and improve on the linear results. These new nonlinear-to-linear simulations confirm previous findings. This result suggests that pellet ELM triggering in ITER could require pellets much larger than those currently planned, which makes ELM-pacing operationally challenging. On the other hand, fueling pellets injected from the high-field side will likely not unintentionally trigger ELMs in an otherwise ELM-stable plasma.

Startup characteristics of the large length–diameter ratio high-temperature heat pipe

Large length–diameter ratio high-temperature sodium heat pipes (HTSHPs) are uniquely structured heat-transfer devices with significant application potential in high-temperature solar thermal power and small-scale nuclear systems. This study tested HTSHPs' startup characteristics examining various parameters. Results indicate that the transition temperature for flow transition is 564 K, and the continuous flow transition temperature is 722 K. Heat pipes at 15 % fill ratio are adaptable across various inclination angles, whereas those at 25 % and 35 % fill ratios show optimal startup at smaller angles (15°, 30°), with decreasing startup time as angle increases, notably significant at 45°.The start-up time refers to the duration from the initiation of heating to the point when all the normal operational temperature points of the HTSHP reach the continuous flow transition temperature during the condensation section. During the experiments, heat pipes with a large length–diameter ratio exhibit heat transfer instability, temperature oscillation and local overheating. Reducing the fill ratio and inclination angle effectively reduces the probability of temperature oscillation and local overheating. This study proposes a novel method for optimizing temperature oscillations in heat pipe design, offering pioneering insights and experimental data crucial for specialized high-temperature heat pipe research.

EF-UODA: Underwater Object Detection Based on Enhanced Feature

The ability to detect underwater objects accurately is important in marine environmental engineering. Although many kinds of underwater object detection algorithms with relatively high accuracy have been proposed, they involve a large number of parameters and floating point operations (FLOPs), and often fail to yield satisfactory results in complex underwater environments. In light of the demand for an algorithm with the capability to extract high-quality features in complex underwater environments, we proposed a one-stage object detection algorithm called the enhanced feature-based underwater object detection algorithm (EF-UODA), which was based on the architecture of Next-ViT, the loss function of YOLOv8, and Ultralytics. First, we developed a highly efficient module for convolutions, called efficient multi-scale pointwise convolution (EMPC). Second, we proposed a feature pyramid architecture called the multipath fast fusion-feature pyramid network (M2F-FPN) based on different modes of feature fusion. Finally, we integrated the Next-ViT and the minimum point distance intersection over union loss functions in our proposed algorithm. Specifically, on the URPC2020 dataset, EF-UODA surpasses the state-of-the-art (SOTA) convolution-based object detection algorithm YOLOv8X by 2.9% mean average precision (mAP), and surpasses the SOTA ViT-based object detection algorithm real-time detection transformer (RT-DETR) by 2.1%. Meanwhile, it achieves the lowest FLOPs and parameters. The results of extensive experiments showed that EF-UODA had excellent feature extraction capability, and was adequately balanced in terms of the number of FLOPs and parameters.

Color image encryption algorithm based on novel dynamic DNA encoding and chaotic system* *This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS–202208), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant no. KJZD-M202000501), Sponsored by Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0139) and National Natural

To enhance the security of image data, prevent unauthorized access, tampering, and leakage, maintain personal privacy, protect intellectual property rights, and ensure the integrity of images during transmission and storage. This study introduces an innovative color image encryption scheme based on dynamic DNA encoding operations and chaotic systems. By simulating a quantum random walk, a random key is generated to enhance the security of the confidential system. In addition, we integrated the enhanced Josephus problem into DNA coding rules to create dynamic DNA coding rules. At the same time, we proposed a dynamic double-loop DNA XOR operation, which fully utilizes the random sequence generated by the generalized Hamiltonian chaos system to precisely control the loop direction, starting point, and number of operations. This enhances the complexity of the encryption algorithm. After sufficient experimental verification and in-depth research and analysis, our innovative design not only enhances the difficulty of cracking while ensuring image quality but also provides reliable protection for the security of image data.

A novel strategy for real-time optimal scheduling of grid-tied microgrid considering load management and uncertainties

This paper proposes an efficient bi-level energy management strategy (EMS) to optimize the operation cost of a grid-connected microgrid, considering the system operational constraints and uncertainties for renewable energy sources and load demand. The first level is optimal day-ahead scheduling based on two stages: the first stage is finding the optimal operating points of sources during the next day while the second one is controllable loads management. The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. Also, a recent meta-heuristic algorithm called Honey Badger Algorithm (HBA) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem. Results obtained demonstrate that the HBA based bi-level EMS provides the real-time optimal economic operation of a grid tied microgrid under uncertainties in weather, utility tariff and load forecasts.

PECULIARITIES OF NOISY IMAGE LOSSY COMPRESSION

This paper deals with lossy compression applied to grayscale noisy images using several coders. The case of additive white Gaussian noise is considered as the first step in studying the problem. A special attention is paid to quite new coders AVIF and HEIF for which, to the best of our knowledge, lossy compression of noisy images of different complexity has not been thoroughly considered yet. It is shown that optimal operation point is possible for all coders under certain conditions according to different quality metrics including conventional peak signal-to-noise ratio and visual quality metrics such as PSNR-HVS-M and MS-SSIM. The compression parameters (metrics) in optimal operation point significantly depend on image complexity and noise intensity where the existence of optimal operation point is more probable for simple structure images and/or intensive noise. Comparison of metric values in optimal operation point for the considered coders shows that slightly better characteristics are provided by better portable graphics (BPG) and advanced discrete cosine transform (ADCT) coders. The results for JPEG are significantly worse. The AVIF and HEIF encoders provide similar results and they outperform JPEG significantly. Optimal operation point for all studied coders is observed more rarely for visual quality metrics than for peak signal-to-noise ratio. General tendencies concerning dependence of optimal compression parameters on noise intensity are presented. It is shown that compression ratio in optimal operation point is often larger than 20 and can be as large as 60. The problem with AVIF and HEIF is that it is currently unclear how to choose quality factor for them to carry out lossy image compression in the neighborhood of the corresponding optimal operation point. Meanwhile, there is the tendency to optimal quality factor reduction if intensity of additive white Gaussian noise increases. The directions of further research are discussed.

Low-pressure gasoline direct injection system development and emissions analysis for a reciprocating two-stroke engine

This study presents the design and construction of an electronic low-pressure gasoline direct injection system for two-stroke engines, with the objective to study engine performance and the emission of exhaust polluting gases, maintaining the expectations of this type of engine. The investigation includes a test bench with an incorporated engine and the required control devices for this, as well as a gas analyzer Class 0, a computerized data acquisition and several tools for displaying and process all the electronic signals from the engine in real time. In order to manage this system, a custom electronic injection control unit has been designed and built, which allows to control the fuel injection timing and duration. This unit uses the signal from an inductive sensor installed in the crankshaft as a reference and synchronization point. Additionally, modifications have been made to the fuel feeder circuit and the electronic of the test engine, as well as to some parts of the mechanism in order to adapt them to this technology. The implementation of the engine tests is described, and the performance and operational points of the original system and those of the new injection system are evaluated.

A simple new approach predicting how microalgae culture systems will perform under sunlight and artificial light conditions

There are many problems associated with the methods for scaling microalgae culture systems. Mathematical models - predictive models in particular - are considered key, but most of them require specialist programming and computer science skills. Even where these skills are available, new parameters (such as radiative properties for determining the effect of light attenuation on photosynthetic growth) have to be determined with each application of the model for a new strain of microalgae, and this in itself is highly complex. Consequently, the methods used in the field are still mainly (semi-)empirical. A simplified method of determining maximal performance and the corresponding optimal operating points (i.e. biomass concentration, harvesting rate) for a given culture system, strain and culture condition (including solar) is presented here. The approach involves engineering equations, which are smartly adapted in this study to eliminate parameters that are difficult to obtain, with a few conventional small-scale experiments to determine the remaining key parameters relevant to the strain. The method is applied with two strains: Haematococcus pluvialis (in green phase and under continuous light) and a Picochlorum maculatum strain isolated from the Qatar desert (in both continuous and diurnal light cycles). A deviation of <10 % was achieved between the predicted data and the experimental data. The proposed method, which eliminates several months of experiments and is relatively simple to apply, appears to be an appropriate tool for optimizing and accelerating the scaling-up of microalgae culture systems.

Role of proton exchange membrane fuel cell in efficiency improvement of ammonia–hydrogen fusion zero-carbon powertrains for long-haul, heavy-duty transportation

Ammonia fuel, a transport zero-carbon energy carrier with a much lower life-cycle greenhouse gas footprint than existing fuels and the ability to overcome the storage and transportation challenges of hydrogen fuel, is an effective means of realizing the energy transition for long-haul, heavy-duty transportation such as heavy trucks. Although the efficient use of liquid ammonia fuel cannot be realized directly at this stage, it can be realized indirectly by converting ammonia fuel into hydrogen and combining the hydrogen generated with ammonia in an ammonia–hydrogen fusion fuel supply method. In this study, we assess four ammonia–hydrogen fusion-fueled zero-carbon powertrains for heavy-duty commercial vehicles, each of which is designed to determine its most efficient green zero-carbon fuel utilization scenario at the point of steady state operation as well as over the entire power-demand interval. It was found that the required engine capacity decreases with the increase of the hydrogen energy ratio in the ammonia–hydrogen composite fuel. Considering the indicated thermal efficiency of the engine and the combustion stability in the cylinder, the ammonia–hydrogen composite fuel with 30% hydrogen energy share was selected for the matching design of the powertrains. The study results show that powertrain equipped with engine and fuel cell are more energy efficient than others. The powertrain with range extender consisting of the engine and fuel cell takes ammonia dissociation separation unit and fuel cell as an important part of engine exhaust gas heat recovery, which reduces the dependence of ammonia cracking hydrogen production on electric heating power. By regulating the energy distribution ratio between the engine and the fuel cell, the system can be maintained at a high level of energy efficiency. Under different power requirements of highway application scenarios, the system efficiency is not less than 36%, and the system energy utilization efficiency is higher than 44%. Compared to engine direct drive powertrain, engine range-extended powertrain, and fuel cell range-extended powertrain, the average system efficiency increases by 30.8%, 8.68%, and 14.24%, while the average system energy utilization increased by 41.12%, 25.26%, and 39.43%, respectively.

Calculation and prediction of divertor detachment via impurity seeding by using one-dimensional model

Abstract Achieving the divertor detachment helps to alleviate excessive heat load and sputtering problems on the target plates, thereby prolonging the lifetime of divertor components for fusion devices. In order to provide a rapid but relatively reliable prediction of plasma parameters along the flux tube for future devices design, a one-dimensional (1D) modeling code for the impurity seeded detached divertor operational point has been developed based on the Python language, which is a fluid model based on the previous work.[1] The experimental observation of the onset of divertor detachment by neon (Ne) and argon (Ar) seeding in EAST is well reproduced by using the 1D modeling code. The comparison of the 1D modeling and 2D simulation by SOLPS-ITER code for CFETR detachment operation with Ne and Ar seeding also shows good consistency. The predictions of the radiative power loss and requirement of the corresponding impurity concentration for achieving divertor detachment via different impurity seeding for the high heating power scenarios on EAST and CFETR Phase II by the 1D model are also presented. Based on the predictions, the optimized parameter space for divertor detachment operation on EAST and CFETR has also been determined. Such a simple but reliable 1D model can provide a reasonable parameter inputs for a detailed and accurate analysis by 2D or 3D modeling tools through rapid parameter scanning.

On undecidability of unary non-nested PFP-operators for one successor function theory

We investigate the decidability of first-order logic extensions. For example, it is established in A. S. Zolotov’s works that a logic with a unary transitive closure operator for the one successor theory is decidable. We show that in a similar case, a logic with a unary partial fixed point operator is undecidable. For this purpose, we reduce the halting problem for the counter machine to the problem of truth of the underlying formula. This reduction uses only one unary non-nested partial fixed operator that is applied to a universal or existential formula.

On the Origin of “Rotating Instabilities”—Stability and Frequency Studies of Channel Flows

Abstract A group of unsteady flow phenomena in turbomachinery is often referred to as “rotating instabilities.” They occur at flow conditions with very asymmetric velocity profiles, as found downstream of blade rows in axial compressors with large rotor tip clearance in stable operating points close to compressor stall or off-design operating points in steam turbines. In the present work, it is shown that such instabilities do not depend on the blading but also occur in blading-free flow channels. The theoretical proof is carried out with the tools and computational programs of spatial stability theory and Reynolds number dependent jumps in the transverse wavelengths at characteristic perturbation frequencies of the present asymmetric turbulent channel flow are discovered. Experimental investigations in a straight flow channel confirm the calculated frequency field and other characteristics of the perturbations. In agreement to theoretical results, a very asymmetric velocity profile across the channel height was found to trigger disturbances within a limited range of frequencies and not bound to the existence of blading. The notion of rotating instability is misleading in the characterization of this disturbance.

A Scenario-Based DVFS-Aware Hybrid Application Mapping Methodology for MPSoCs

Sound techniques for mapping soft real-time applications to resources are indispensable for meeting the application deadlines and minimizing objectives such as energy consumption, particularly on heterogeneous MPSoC architectures. For applications with input-dependent workload variations, static mappings are not able to sufficiently cope with the run-time variation, which can lead to deadline misses or unnecessary energy consumption. As a remedy, hybrid application mapping (HAM) techniques combine a design-time optimization with run-time management that adapts the mappings dynamically to the changes of the arriving input. This paper focuses on scenario-based HAM techniques. Here, the application input space is systematically clustered such that data inside the same scenario exhibit similar characteristics concerning workload when being processed under the same operating points. This static clustering of the input space into data scenarios has proven to be a good abstraction layer for simplifying the design and employment of high-quality run-time managers. However, existing state-of-the-art scenario-based HAM approaches neglect or underutilize the synergistic interplay between mapping selection and the usage of dynamic voltage/frequency scaling (DVFS) when adapting to workload variation. By combining mapping and DVFS selection, variations in the input can be either compensated by a complete re-mapping of the application, evoking a potential high reconfiguration overhead or by just changing the DVFS settings of the resources, offering a low-overhead adaptation alternative and thus significantly reducing the necessary overhead compared to DVFS-agnostic HAM. Furthermore, DVFS enables a fine-grained adaptation of a mapped application to the input data variation, e.g., by slowing down tasks with no impact on the end-to-end latency for the current input using low-frequency DVFS settings. It is shown that this combined approach can save even more energy than a pure mapping adaptation scheme, especially in the presence of data scenarios. In particular, scenario-based design operates as a catalyst for eliciting the synergies between a combined DVFS and mapping optimization and the peculiarities inside a data scenario, i.e., exploiting the commonalities inside a data scenario by perfectly tailored DVFS settings and task mapping. In this scope, this paper proposes two supplementary scenario-based DVFS-aware HAM approaches that consistently outperform existing state-of-the-art mapping approaches in terms of the number of deadline misses and energy consumption as we demonstrate in an empirical study on the basis of four different applications and three different architectures. It is also shown that these benefits still apply to target architectures with increasing mapping migration overheads, thwarting frequent mapping reconfigurations.

IoT CONTROLLED LED NOTICE BOARD

The IoT Controlled LED sign board design represents a slice- edge bid aimed at revolutionizing display results for a multitude of purposes including advertising, information dispersion, and aesthetic improvement. At its core, the sign board features an intricate array of LEDs strictly arranged in a grid pattern, each strictly orchestrated by a central microcontroller unit( MCU) harmoniously integrated with a Bluetooth module for flawless wireless communication. This community enables druggies to apply their smartphones or any Bluetooth- enabled bias as important remote controls, decreeing the content showcased on the LED board with unknown ease and perfection. Through a devoted mobile operation, druggies gain a gateway to a realm of creative expression, empowered to transmit a myriad of content types ranging from brief textbook dispatches to witching, robustness and images for immediate display. specially, the system's hallmark lies in its capability to grease real- time content operation, preventing the need for physical intervention and fostering dynamic rigidity to evolving demands and scripts. likewise, the mobile operation's robust point set extends beyond bare content transmission, offering sophisticated tools for communication scheduling and contemporaneous operation of multiple sign boards, thereby accelerating scalability and usability across different surroundings similar as bustling retail spaces, bulging transportation capitals, and bustling public spaces likewise. With its intimately designed interface and multifaceted functionality, the IoT Controlled LED sign board design emerges as a lamp of invention, promising to review the veritably geography of information dispersion and visual communication in the digital age. KEYWORDS: IoT Controlled LED subscribe Board, Microcontroller Unit( MCU), Wireless Communication, Mobile Application, Management, Remote Control.

A Lightweight Model of Underwater Object Detection Based on YOLOv8n for an Edge Computing Platform

The visual signal object detection technology of deep learning, as a high-precision perception technology, can be adopted in various image analysis applications, and it has important application prospects in the utilization and protection of marine biological resources. While the marine environment is generally far from cities where the rich computing power in cities cannot be utilized, deploying models on mobile edge devices is an efficient solution. However, because of computing resource limitations on edge devices, the workload of performing deep learning-based computationally intensive object detection on mobile edge devices is often insufficient in meeting high-precision and low-latency requirements. To address the problem of insufficient computing resources, this paper proposes a lightweight process based on a neural structure search and knowledge distillation using deep learning YOLOv8 as the baseline model. Firstly, the neural structure search algorithm was used to compress the YOLOv8 model and reduce its computational complexity. Secondly, a new knowledge distillation architecture was designed, which distills the detection head output layer and NECK feature layer to compensate for the accuracy loss caused by model reduction. When compared to YOLOv8n, the computational complexity of the lightweight model optimized in this study (in terms of floating point operations (FLOPs)) was 7.4 Gflops, which indicated a reduction of 1.3 Gflops. The multiply–accumulate operations (MACs) stood at 2.72 G, thereby illustrating a decrease of 32%; this saw an increase in the AP50, AP75, and mAP by 2.0%, 3.0%, and 1.9%, respectively. Finally, this paper designed an edge computing service architecture, and it deployed the model on the Jetson Xavier NX platform through TensorRT.

Critical points of discrete periodic operators

We study the spectra of operators on periodic graphs using methods from combinatorial algebraic geometry. Our main result is a bound on the number of complex critical points of the Bloch variety, together with an effective criterion for when this bound is attained. We show that this criterion holds for {\mathbb{Z}}^{2} - and {\mathbb{Z}}^{3} -periodic graphs with sufficiently many edges and use our results to establish the spectral edges conjecture for some {\mathbb{Z}}^{2} -periodic graphs.

An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data-driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.

Comparison of Tabulated and Complex Chemistry Approaches for Ammonia–Diesel Dual-Fuel Combustion Simulation

&lt;div&gt;Using ammonia as a carbon-free fuel is a promising way to reduce greenhouse gas emissions in the maritime sector. Due to the challenging fuel properties, like high autoignition temperature, high latent heat of vaporization, and low laminar flame speeds, a dual-fuel combustion process is the most promising way to use ammonia as a fuel in medium-speed engines.&lt;/div&gt; &lt;div&gt;Currently, many experimental investigations regarding premixed and diffusive combustion are carried out. A numerical approach has been employed to simulate the complex dual-fuel combustion process to better understand the influences on the diffusive combustion of ammonia ignited by a diesel pilot. The simulation results are validated based on optical investigations conducted in a rapid compression–expansion machine (RCEM). The present work compares a tabulated chemistry simulation approach to complex chemistry-based simulations. The investigations evaluate the accuracy of both modeling approaches and point out the limitations and weaknesses of the tabulated chemistry approach. When using two fuels, the tabulated chemistry approach cannot reproduce misfiring events due to inherent model limitations. By adjusting the model parameters of the tabulated chemistry model, it is possible to reproduce experimental results accurately for a specific case. However, using the adjusted parameters for simulations with changed injection timing or interaction angle between the sprays shows that no predictive calculations are possible. The parameter set is only valid for a single operation point.&lt;/div&gt; &lt;div&gt;Further simulations show that the complex chemistry approach can capture the complex interaction between both directly injected fuels for different operation points. It correctly predicts the ignition as well as heat release. Therefore, the approach allows predictive combustion simulations. Furthermore, it reproduces the occurrence of misfiring in cases of unsuitable interaction of both sprays and injection timing.&lt;/div&gt;

Experimental Investigation of a Free-Form Honed Cylinder Liner for Heavy-Duty Engines

For future internal combustion engines, driven by regenerative fuels, efficiency is more important than ever. One approach to reduce the losses inside the piston cylinder unit (PCU) is to improve the alignment of the liner and the piston. Therefore, a cylinder liner with a free form was developed at the Institute of Technical Combustion (ITV) of the Leibniz University Hannover which compensates radial and linear deformations along the stroke. The layout is based on a FEM simulation. The liner was manufactured by the Institute of Production Engineering and Machine Tools (IFW) of Leibniz University of Hannover with a novel turn-milling process. The liner was investigated on the heavy-duty Floating-Liner engine of ITV with a displacement of 1991 ccm and a bore diameter of 130 mm. The experimental results show improvement in the friction losses over the whole engine map in the range of 9% and up to 17.3% compared to a serial liner. Sealing efficiency could be improved up to 28.8%, depending on the operational point. Overall, the investigation aims for lower fuel consumption which would in result fewer emissions.

An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.

Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease's spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model-but with improved accuracy-for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector. The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3-C5) Res-block bodies of the backbone architecture's C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed. The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone. The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model's performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision.