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The demand for specialized hardware to train AI models has increased in tandem with the increase in the model complexity over the recent years. Graphics processing unit (GPU) is one such hardware that is capable of parallelizing operations performed on a large chunk of data. Companies like Nvidia, AMD, and Google have been constantly scaling-up the hardware performance as fast as they can. Nevertheless, there is still a gap between the required processing power and processing capacity of the hardware. To increase the hardware utilization, the software has to be optimized too. In this paper, we present some general GPU optimization techniques we used to efficiently train the optiGAN model, a Generative Adversarial Network that is capable of generating multidimensional probability distributions of optical photons at the photodetector face in radiation detectors, on an 8GB Nvidia Quadro RTX 4000 GPU. We analyze and compare the performances of all the optimizations based on the execution time and the memory consumed using the Nvidia Nsight Systems profiler tool. The optimizations gave approximately a 4.5x increase in the runtime performance when compared to a naive training on the GPU, without compromising the model performance. Finally we discuss optiGANs future work and how we are planning to scale the model on GPUs.

• A micro pulsed plasma thruster was developed and tested. • Specific impulse reaches 1600 s and efficiency greater than 10 %. • The designed thruster has been used in Hangsheng-1 Satellite. Pulsed plasma thrusters appeal to the propulsive tasks on miniaturized satellites because of the low volume and high specific impulse. However, further increasing efficiency and reducing the size of pulsed plasma thrusters still require more effort. In this paper, the thruster's spark plug, plate structure, ignition system, charging system, electromagnetic compatibility system, and propellant have been particularly designed to maximize its performance and miniaturize its dimension. With the conducted designs, the whole mass of the micro-pulsed plasma thruster is 450 g, and its dimension is 90 mm * 90 mm * 35 mm. According to the experimental results, the specific impulse reached 1600 s, and the efficiency was more significant than 9 %. Thanks to its excellent performance, we have used this thruster for attitude control and drag compensation tasks on the Hang-sheng-1 Satellite. [ABSTRACT FROM AUTHOR]

Modular multilevel converters (MMCs) have gained widespread adoption in high-voltage direct current (HVDC) transmission due to their high voltage levels, low harmonic content, and high scalability. However, conventional control methods such as finite control set model predictive control (FCS-MPC) suffer from a heavy computational burden and sensitivity to system parameter variations, limiting the performance of MMCs. This paper proposes a data-driven approach based on model-free adaptive control with an event-triggered mechanism that demonstrates superior robustness against parameter mismatches and enhanced dynamic performance in response to sudden output changes. Moreover, the introduction of the event-triggered mechanism effectively reduces redundant operations, decreasing the computational burden and switching losses. Finally, the proposed strategy is validated through a MATLAB/Simulink simulation model. [ABSTRACT FROM AUTHOR]

This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials. [ABSTRACT FROM AUTHOR]

In this study, the heat transfer model of a radiation–conduction–convection coupled lithium-ion battery pack is established through theoretical analysis. The temperature distribution and flow field distribution inside the battery pack are obtained by simulation using ANSYS Fluent software 2022 R1, and the reasonableness of the simulation model is verified with an experiment. This study also analyzes in detail the improvement effect of adding heat dissipation ribs, applying heat dissipation coatings, and adjusting the fan speed on the heat dissipation performance of the system. Under the same heat sink rib height conditions, the relationship between its thickness and total heat dissipation and thermal efficiency is studied in depth, and the temperature distribution of the cell under different rib thicknesses is obtained. At the same time, the emissivity of the heat sink coating under different coating thicknesses was measured by infrared thermography, and the relevant design values were determined through simulation experiments. Finally, based on the experimental test results of fan performance, a corresponding control strategy is proposed to construct an efficient and high-performance multiple-input multiple-output (MIMO) battery thermal management system. The experimental results show that optimizing the structure of the forced air cooling system through the above measures can ensure that the Li-ion battery operates within the efficient operating temperature range, thus extending its cycle life, improving its stability, and reducing the risk of thermal runaway. Meanwhile, the problem of excessive temperature difference between different modules is improved, and the output capacity of the energy storage system is increased. [ABSTRACT FROM AUTHOR]

With the development of measurement techniques and numerical simulation technologies, data-driven turbulence research has become a new approach in this field. In China, several wind tunnel laboratories and supercomputing centers have been established for turbulence simulations, resulting in a substantial collection of turbulence data. However, there is currently no centralized turbulence data management platform in China, which makes it difficult to achieve the exchange and share of the expensive experimental and simulation data. Turbulence data is characterized by its large volume, high dimensionality, precision and heterogeneity, which present problems in terms of storage, access and management efficiency. A turbulence big data distributed storage system called TDFS was designed, specifically targeting typical flow problems in aviation, aerospace, and marine applications. Considering the access characteristics of turbulence big data, the novel metadata management methods and data access interfaces were designed in TDFS. Experimental results demonstrate that TDFS achieves interface response speed improvements of 54.38% and 57.7% compared with HDFS and GlusterFS, respectively. Additionally, to reduce the storage overhead of turbulence big data, a lazy replication compression mechanism based on HDF5 was designed, resulting in 34% reduction in storage space, compared to the original replication storage approach. [ABSTRACT FROM AUTHOR]

In the existing large-scale distributed environments, there is still much room for improvement in the performance and computational efficiency of big data applications. However, performance analysis and optimization in large-scale environments requires a large number of human resources from domain experts. This paper proposes a general lowperformance query statement detection and optimization process for performance optimization in big data applications, summarizes four types of low-performance behaviors that significantly affect the performance of big data applications, and proposes specific optimization strategies for each type of low-performance behavior. Finally, through experimental evaluation, the effectiveness of the optimization scheme in actual large-scale cluster is verified. [ABSTRACT FROM AUTHOR]

With the increasing volume of data on the web, distributed file systems such as HDFS are widely used to provide efficient, fast and reliable access to the ever-growing volumes of data. Parameter tuning, a core performance management task, directly affects the performance of storage systems. However, large, non-linear, discrete and non-digital storage system configuration parameters significantly increase the storage system parameter tuning burden. Traditional manual tuning methods rely on a lot of a priori knowledge and have high tuning costs. In this paper, we propose a Smart gambler (SG) algorithm based on a simulated environment and Hill Climbing to reduce the probability of falling into a local optimum. In comparative experiments on publicly available datasets, SG achieves an average improvement of 3.14 % and 5.02 % in the highest tuning value over search algorithms such as Genetic Algorithm and an average time saving of 32.1 % and 50.6 % in tuning time for both workloads, respectively. [ABSTRACT FROM AUTHOR]

Sparse matrix-vector multiplication (SpMV) is one of the key operators in the field of high performance computing and also has significant applications in emerging deep learning domains. Existing research on SpMV often focuses on square sparse matrices, while there is still a lack of in-depth exploration for irregularly shaped sparse matrices (with unequal numbers of rows and columns). The characteristic of unequal numbers of rows and columns results in different storage features for these sparse matrices compared to square sparse matrices, leaving room for further optimization. Therefore, this paper establishes an SpMV performance model for irregularly shaped sparse matrices with unequal rows and columns, and analyzes that the performance bottleneck is caused by insufficient bandwidth for data exchange between cache and memory. At the same time, this paper carried out the following two optimization tasks: (1) Based on the commonly used CSR storage format for sparse matrices, a new RCSR storage format is proposed, which transforms and compresses a performance-limiting array in the CSR storage format, making SpMV more efficient; (2) An optimized SpMV algorithm based on the RCSR format is designed in conjunction with the SIMD instruction set extension of domestic processors. Thi paper tests regular and irregular sparse matrices on domestic Phytium processors. For regular sparse matrices, the comprehensive application of RCSR storage format, SIMD instructions, and OpenMP parallelization technology increases GFLOPS by 83.35% on average. For irregular sparse matrices, the performance improvement is related to the row-to-column ratio, and when the row-to- column ratio is not equal, the optimization effect is more obvious. [ABSTRACT FROM AUTHOR]

BACKGROUND: Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu. OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM). METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status. RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent. CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With increasing environmental pollution from ship exhaust emissions and increasingly stringent International Maritime Organization carbon regulations, there is a growing demand for cleaner and lower-carbon fuels and near-zero-emission marine engines worldwide. Liquefied natural gas is a low-carbon fuel, and when liquefied natural gas (LNG) is used on ships, dual-fuel methods are often used. The pre-chamber plays a key role in the working process of dual-fuel engines. In this paper, an effective three-dimensional simulation model based on the actual operating conditions and structural characteristics of a marine low-pressure dual-fuel engine is established. In addition, the effects of changing the Precombustion chamber (PCC) volume ratio and the PCC orifice diameter ratio on the mixture composition, engine combustion performance, and pollutant generation were thoroughly investigated. It was found that a small PPC volume ratio resulted in a higher flame jet velocity, a shorter stagnation period, and an acceleration of the combustion process in the main combustion chamber. When the PCC volume was large, the Nitrogen oxygen (NOx) ratio emission was elevated. Moreover, the angle of the PCC orifice affected the flame propagation direction of the pilot fuel. Optimizing the angle of the PCC orifice can improve combustion efficiency and reduce the generation of NOx. Furthermore, reasonable arrangement of the PCC structure can improve the stability of ignition performance and accelerate the flame jet velocity. [ABSTRACT FROM AUTHOR]

Modern database systems rely on indexes to accelerate data access. The recently proposed learned indexes can offer higher search performance with lower space costs than traditional indexes like B+-tree. We observe that existing main-memory learned indexes are particularly optimized for read-heavy workloads. However, such an optimization comes at the cost of model training and handling out-of-range key insertions, which will worsen the overall performance. We argue that workloads are not always read-heavy in real applications, and it is more important and practical to make learned indexes work efficiently for dynamic workloads with changing access patterns and data distributions. In this paper, we aim to improve the practicality of learned indexes by making them adaptive to dynamic workloads. Specifically, we propose a new polymorphic learned index named Morphtree, which can adaptively change the index structure to provide stable and high performance for dynamic workloads. The novelty of Morphtree lies in three aspects: (1) a decoupled tree structure for separating the inner search tree from the data layer consisting of leaf nodes, (2) a read-optimized learned inner tree for improving the performance of index search, and (3) an evolving data layer for automatically transforming node layouts into read friendly or write friendly according to workload changes. We evaluate these new ideas of Morphtree on various datasets and workloads. The comparative results with six up-to-date learned indexes, including ALEX, PGM-index, FITing-tree, LIPP, FINEdex, and XIndex, show that Morphtree can achieve, on average, 0.56x and 3x improvements in lookup and insertion performance, respectively. Moreover, when evaluated on dynamic workloads with changing lookup ratios and data distributions, Morphtree can achieve a sustained high throughput across different real-world datasets and query patterns, owing to its ability to automatically adjust the index structure according to workload changes. [ABSTRACT FROM AUTHOR]

Serverless computing has gained popularity for its scalability and cost-effectiveness, but managing performance and monitoring presents unique challenges. This paper explores the application of Artificial Intelligence (AI) techniques and tools for performance testing and monitoring in serverless computing. Through a comprehensive literature analysis, the study identifies AI's potential to optimize resource allocation, predict workload patterns, detect anomalies, and enhance overall performance and reliability. The findings highlight AI's role in addressing challenges in serverless computing, offering insights for improving efficiency and reliability. [ABSTRACT FROM AUTHOR]

Due to the shortage of lithium resource reserves and the pressure of rising prices, sodium-ion batteries have regained the attention of the public, and shown great potential for application in the fields of grid energy storage and low-speed vehicles to achieve the purpose of complementing lithium-ion batteries, so it is imperative to promote the commercial application of sodium-ion batteries. For sodium-ion battery anode materials, hard carbon is the material most likely to be used commercially. However, there is still much work to be done before its commercialization. This review provides a comprehensive overview of the current research status from the following three aspects. First, the microstructure and sodium storage active sites of hard carbon are described. Then, the mechanism of sodium storage in hard carbon is investigated, which can be broadly categorized into four model, "insertion–filling", "adsorption–insertion", "adsorption–filling", and "multistage". Finally, from the perspective of improving the electrochemical performance of hard carbon, the performance improvement strategies proposed in recent years are summarized. Combined with the performance of hard carbon commercial products of some enterprises, the future development goal of hard carbon is prospected, hoping that all sectors of society can work hard for this common goal. [ABSTRACT FROM AUTHOR]

Resistive random-access memories have attracted significant attention in memory applications, while the physical mechanism behind their resistive switching behavior is still unclear. As a key issue, the migration of oxygen vacancies (VO) directly influences the performance of devices in the formation and rupture of conductive filaments (CFs). In this work, the distance of VO migration is performed as electric field dependent and affects the performance of the device. Sufficient distance of VO migration is essential for formation and rupture of CF completely, leading to, ideally, SET/RESET transition of the device. Based on this mechanism, an enhanced electric field is designed that optimizes the stability of the device significantly, which is demonstrated to be a promising approach to optimize the properties of the device. The investigation is helpful for guiding the design of device structures. [ABSTRACT FROM AUTHOR]

The identification and severity assessment of plant leaf diseases is crucial to food security and sustainable agriculture. This study shows an innovative way to improve plant leaf disease detection. The recommended method uses Optuna for parameter optimization and the Genetic Algorithm for feature selection to improve plant leaf disease identification. We do this to improve diagnosis accuracy. This method improves classification accuracy and is called ECPLDD-OGA. Modern hyper parameter optimization framework Optuna is employed. This allows classification model parameters to be fine-tuned. A systematic feature selection method is the Genetic Algorithm. It finds the most useful characteristics in the input dataset. By applying the algorithm on the data. By facilitation, the iterative process helps create a simplified and meaningful subset of features. Contrary to parameter tinkering and feature selection, empirical data suggests that utilizing Optuna and the Genetic Algorithm simultaneously improves disease identification. The updated model recognizes sick plants more accurately and generalizes better. Optimization enabled both gains. The usage of this technology can improve agricultural operations and reduce crop losses by increasing productivity. The present ECPLDD-OGA technique helps integrate hyper parameter tweaking and feature selection into machine learning-based agricultural applications. [ABSTRACT FROM AUTHOR]

We explore the impact of pumping beams with different transverse intensity profiles on the performance of the spin-exchange relaxation-free (SERF) atomic magnetometers (AMs). We conduct experiments comparing the traditional Gaussian optically-pumped AM with that utilizing the flat-top optically-pumped (FTOP) method. Our findings reveal that the FTOP-based approach outperforms the conventional method, exhibiting a larger response, a narrower magnetic resonance linewidth, and a superior low-frequency noise performance. Specifically, the use of FTOP method leads to a 16% enhancement in average sensitivity within 1 Hz–30 Hz frequency range. Our research emphasizes the significance of achieving transverse polarization uniformity in AMs, providing insights for future optimization efforts and sensitivity improvements in miniaturized magnetometers. [ABSTRACT FROM AUTHOR]

In this research, SCAPS-1D simulation software (Version: 3.3.10) was employed to enhance the efficiency of CsSnX3 (X = Cl, Br, I) all-inorganic perovskite solar cells. By fine-tuning essential parameters like the work function of the conductive glass, the back contact point, defect density, and the thickness of the light absorption layer, we effectively simulated the optimal performance of CsSnX3 (X = Cl, Br, I) all-inorganic perovskite solar cells under identical conditions. The effects of different X-site elements on the overall performance of the device were also explored. The theoretical photoelectric conversion efficiency of the device gradually increases with the successive substitution of halogen elements (Cl, Br, I), reaching 6.09%, 17.02%, and 26.74%, respectively. This trend is primarily attributed to the increasing size of the halogen atoms, which leads to better light absorption and charge transport properties, with iodine (I) yielding the highest theoretical conversion efficiency. These findings suggest that optimizing the halogen element in CsSnX3 can significantly enhance device performance, providing valuable theoretical guidance for the development of high-efficiency all-inorganic perovskite solar cells. [ABSTRACT FROM AUTHOR]

Researching and improving the low-temperature cold-start performance of PEMFC plays an important role in the promotion and application of PEMFC. Among them, auxiliary preheating is one of the key technologies to achieve fast cold start of PEMFC. In order to reveal the influence law of auxiliary preheating parameters on the cold start of PEMFC, the auxiliary preheating control strategy is proposed. In this paper, a mathematical model for PEMFC performance analysis is first established. On this basis, the influence law of preheating exhaust gas temperature and flow rate on PEMFC temperature rise process is studied. Based on the Screening optimization algorithm, the input and output parameters of the preheating process were optimized, and the optimal auxiliary preheating control parameters were determined under different starting temperatures. Finally, a start-up auxiliary preheating control strategy for different low-temperature environments is designed and performance verification is carried out. The results show that the auxiliary preheating control strategy proposed in this paper reduces the start-up time by 43% at -10 compared without the auxiliary preheating control. The auxiliary strategy designed in this paper is able to achieve normal startup of the PEMFC at -20 and -30. However, the PEMFC startup fails in both without the auxiliary modes. [ABSTRACT FROM AUTHOR]

Heterogeneous multicore system could provide high flexibility for applications through integrating different types of cores. As the number of cores increases, more and more transistors are integrated which could lead to the rise of chip temperature, thus have a negative impact on the system performance. At present, the typical method to tackle this problem is dynamic voltage and frequency scaling (DVFS) which reduces the voltage and frequency to achieve cooling. However, DVFS will make the heterogeneous multicore system unable to take full advantage of its performance. This paper proposes a dynamic mapping method called TspDM which maximizes the system performance target under the premise of satisfying the temperature safety power. TspDM exploits the runtime performance counters of running threads to dynamically adjust thread mapping. Specifically, a lightweight Artificial Neural Network (ANN) model is proposed to obtain the thread-to-core type mapping when a remapping process is activated. Subsequently, TspDM determines the specific core locations of threads according to the number of threads mapped to a certain core type and the thermal safety power computation. The experimental results show that the proposed method is 21% better than that of PCMig while ensuring the thermal safety of heterogeneous platform. In addition, TspDM outperforms recent performance constrained and power constrained literature in terms of system throughput. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous high‐throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user‐friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error‐tolerant parsing of sequence records. In addition, three subcommands designed for real‐time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real‐time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a "Swiss army knife" of sequence and alignment processing—equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines. Highlights: SeqKit2 expands its capabilities, doubling the number of subcommands from 19 to 38, and adding support for three more compression file formats.SeqKit2 outperforms its predecessor and maintains competitive with other tools.SeqKit2 improves user‐friendliness with new features, like, autocompletion, progress bars, and enhanced error handling. [ABSTRACT FROM AUTHOR]

The significance of earth observation data spans diverse fields and domains, driving the need for efficient management. Nevertheless, the exponential increase in data volume brings new challenges that complicate processing and storing data. This article proposes an optimized multi-modular service for earth observation data management in response to these challenges. The suggested approach focuses on choosing the optimal configurations for the storage and processing layers to improve the performance and cost-effectiveness of managing data. By employing the recommended optimized strategies, earth observation data can be managed more effectively, resulting in fast data processing and reduced costs. [ABSTRACT FROM AUTHOR]

Modern high-performance processor systems universally employ hardware prefetch engines to address the "memory wall" issue. Nonetheless, prefetchers are typically activated with the default configuration at system startup, and this fixed configuration does not always achieve the intended performance in the face of varied programs and may even degrade performance. As a result, it is crucial to investigate the prefetch configuration tuning method that adapts to different program characteristics in order to take full advantage of hardware prefetching. In this study, a hardware prefetching tuning method based on program phase behavior is proposed to determine the prefetch configuration that maximizes the overall predicted performance of the program through low-overhead online profiling. In the profiling process, the branch instruction vector sampled by the hardware performance counter is used to dynamically classify the program phase behavior, and the performance profiling is performed for each type of phase. Simultaneously, the recurring program phases are no longer profiled to reduce overhead. Following the profiling, the prefetch configuration with the best predicted performance is derived by combining the performance data from each phase and its running time proportion. The results of the tests on prefetch-sensitive programs in SPEC2006, NPB, and PARSEC demonstrate that the prefetch configuration obtained using the suggested method has a geometric average performance improvement of 7.34% over the default configuration and achieves 99.34% of the optimal configuration. Furthermore, the profiling run adds only 2.24% extra overhead as compared to the default configuration. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Refrigeration is the property of Journal of Refrigeration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drilling fluid is pivotal for efficient drilling. However, the gelation performance of drilling fluids is influenced by various complex factors, and traditional methods are inefficient and costly. Artificial intelligence and numerical simulation technologies have become transformative tools in various disciplines. This work reviews the application of four artificial intelligence techniques—expert systems, artificial neural networks (ANNs), support vector machines (SVMs), and genetic algorithms—and three numerical simulation techniques—computational fluid dynamics (CFD) simulations, molecular dynamics (MD) simulations, and Monte Carlo simulations—in drilling fluid design and performance optimization. It analyzes the current issues in these studies, pointing out that challenges in applying these two technologies to drilling fluid gelation performance research include difficulties in obtaining field data and overly idealized model assumptions. From the literature review, it can be estimated that 52.0% of the papers are related to ANNs. Leakage issues are the primary concern for practitioners studying drilling fluid gelation performance, accounting for over 17% of research in this area. Based on this, and in conjunction with the technical requirements of drilling fluids and the development needs of drilling intelligence theory, three development directions are proposed: (1) Emphasize feature engineering and data preprocessing to explore the application potential of interpretable artificial intelligence. (2) Establish channels for open access to data or large-scale oil and gas field databases. (3) Conduct in-depth numerical simulation research focusing on the microscopic details of the spatial network structure of drilling fluids, reducing or even eliminating data dependence. [ABSTRACT FROM AUTHOR]

Traditional potato grading in China relies mostly on manual sorting, which is labor-intensive, time-consuming, costly, and inefficient. To enhance the operational performance of potato-grading devices, this paper focuses on optimizing the slide rail structure, which is the key component of a self-developed first-generation potato-grading device. A five-factor, three-level orthogonal experiment was designed, with the experimental factors being the height of the horizontal slide rail, angle of the first-stage inclined slide, angle of the second-stage inclined rail, chain horizontal movement speed, and conveyor belt speed. The indoor experiments were conducted using grading accuracy and grading efficiency as the experimental indicators. On the basis of the analysis of the orthogonal experiment results, two relatively optimal solutions were obtained, and validation experiments were conducted. The validation results show that when the height of the horizontal slide rail was 185 mm, the angle of the first-stage inclined rail was 4°, the angle of the second-stage inclined rail was 2.5°, the horizontal movement speed of the chain was 700 mm/s, and the movement speed of the conveyor belt was 275.60 mm/s, the performance of the movable rotating plate (MRP)-type grading device for potatoes reached its optimum. At this point, the grading accuracy was 94.88%, and the grading efficiency was 13.9477 t/h. Compared with the first-generation grading device, the optimized grading device achieved an improvement of 3.84% in grading accuracy and 12.94% in grading efficiency. The research methodology provided in this paper serves as a reference for the performance optimization of potato-grading devices. [ABSTRACT FROM AUTHOR]

Abstract This study focuses on optimizing and designing the Delayed-Fix-Later Awaiting Transmission Encoding (DEFLATE) algorithm to enhance its compression performance and reduce the compression time for models, specifically in the context of compressing NX three-dimensional (3D) image models. The DEFLATE algorithm, a dual-compression technique combining the LZ77 algorithm and Huffman coding, is widely employed for compressing multimedia data and 3D models. Three 3D models of varying sizes are selected as subjects for experimentation. The Wavelet algorithm, C-Bone algorithm, and DEFLATE algorithm are utilized for compression, with subsequent analysis of the compression ratio and compression time. The experimental findings demonstrate the DEFLATE algorithm’s exceptional performance in compressing 3D image models. Notably, when compressing small and medium-sized 3D models, the DEFLATE algorithm exhibits significantly higher compression ratios compared to the Wavelet and C-Bone algorithms while also achieving shorter compression times. Compared to the Wavelet algorithm, the DEFLATE algorithm enhances the compression performance of 3D image models by 15% and boosts data throughput by 49%. While the compression ratio of the DEFLATE algorithm for large 3D models is comparable to that of the Wavelet and C-Bone algorithms, it notably reduces the actual compression time. Furthermore, the DEFLATE algorithm enhances data transmission reliability in NX 3D image model compression by 12.1% compared to the Wavelet algorithm. Therefore, the following conclusions are drawn: the DEFLATE algorithm serves as an excellent compression algorithm for 3D image models. It showcases significant advantages in compressing small and medium-sized models while remaining highly practical for compressing large 3D models. This study offers valuable insights for enhancing and optimizing the DEFLATE algorithm, and it serves as a valuable reference for future research on 3D image model compression.