Processing System Research Articles

Development and implementation of ion cyclotron emission diagnostic system on J-TEXT tokamak

An ion cyclotron emission (ICE) diagnostic based on a magnetic probe has been designed and installed on the J-TEXT tokamak. This ICE diagnostic is used to study the high-frequency magnetic fluctuations driven by energetic particles. The signals detected by the ICE probe are divided into two equal power signals, which enter the two systems respectively. The first system converts high-frequency signals into power signals in different frequency bands using bandpass filters and power detectors. This reduces the amount of data to be processed and allows the data to be uploaded in time after a discharge. Thus, the approximate time resolution, frequency range (10 MHz-150 MHz), and power of the high-frequency signals can be quickly judged. The second system uses low-pass filters and an oscilloscope to collect data by Nyquist sampling law, so that the frequency spectrum of signals with frequencies from 7 kHz to 200 MHz can be analyzed. Therefore, if significant signals are detected in the first system on the data server, corresponding stored data can be extracted from the second system for large-scale data processing. This structure enhances the efficiency of data storage and analysis. The high-frequency magnetic fluctuations driven by fast electrons or runaway electrons under ohmic heating have been measured on J-TEXT, which verified the feasibility of the system.

Leveraging business intelligence solutions for urban parking management

Efficient parking management is essential for enhancing customer experience, mobility, accessibility, and overall quality of life in urban areas. In recent years, parking analytics have emerged as valuable tools for understanding drivers’ behavior and developing data-driven management strategies. However, the application of these tools is often hindered by the complexity of data extraction, transformation, loading and analysis. Additionally, the implementation of these tools can be time-consuming and costly, further limiting their practical use for operators and urban authorities. To address this issue, this paper presents the development of a Business Intelligence tool specifically designed to facilitate parking management through the automated flow of transaction data between collection, processing, and analysis systems. The tool provides easy-to-use analytical capabilities that allow parking managers to analyze parking transaction data, identify trends and patterns, and make informed decisions about parking management quickly and easily. The cost-effective implementation of this tool presents a valuable solution for managing on-street parking in urban areas. This study highlights the potential of Business Intelligence tools for parking management and contributes to improving the effectiveness of parking management.

Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms.

Development of a Csy4-Processing TRV-Based CRISPR/Cas9 Genome Editing System in Nicotiana benthamiana

AbstractThe CRISPR/Cas9 system is a site-specific genome editing tool that has been widely used in various plant species. The plant virus-based gRNA (guide RNA) delivery system, which differs from the typical Agrobacterium-mediated transformation, is an attractive method to facilitate the application of CRISPR/Cas9. The virally delivered gRNA is usually driven by heterologous plant U6 or viral promoters (e.g., pea early-browning virus, PEBV; barley stripe mosaic virus, BSMV). However, heterologous promoters may have poor performance in some cases. In this paper, a feasible option to detach gRNA(s) from the virus genome is employed. Specifically, the Csy4-RNA processing system is used to release gRNA(s) from the tobacco rattle virus (TRV). The coding sequences of Cas9 and Csy4 nucleases are cloned into a single polycistronic expression cassette under an estrogen-inducible promoter in a binary vector, and the gRNA is cloned into the TRV genome flanked by two 20 bp Csy4 recognition sites. The results show that the Csy4-processing TRV-based delivery system works effectively in targeting single and multiple sites, nucleotide replacement, and large fragment deletion in Cas9-mediated genome editing via transient expression in Nicotiana benthamiana. The Csy4-TRV is a promising gRNA(s) processing and delivery system for CRISPR/Cas9 genome editing. This method can be easily adapted to other plant RNA viruses, facilitating the application of the CRISPR/Cas9 system in plants.

Edge computing based real-time Nephrops (Nephrops norvegicus) catch estimation in demersal trawls using object detection models

In demersal trawl fisheries, the unavailability of the catch information until the end of the catching process is a drawback, leading to seabed impacts, bycatches and reducing the economic performance of the fisheries. The emergence of in-trawl cameras to observe catches in real-time can provide such information. This data needs to be processed in real-time to determine the catch compositions and rates, eventually improving sustainability and economic performance of the fisheries. In this study, a real-time underwater video processing system counting the Nephrops individuals entering the trawl has been developed using object detection and tracking methods on an edge device (NVIDIA Jetson AGX Orin). Seven state-of-the-art YOLO models were tested to discover the appropriate training settings and YOLO model. To achieve real-time processing and accurate counting simultaneously, four frame skipping ideas were evaluated. It has been shown that adaptive frame skipping approach, together with YOLOv8s model, can increase the processing speed up to 97.47 FPS while achieving correct count rate and F-score of 82.57% and 0.86, respectively. In conclusion, this system can improve the sustainability of the Nephrops directed trawl fishery by providing catch information in real-time.

How Does Digital Leadership Foster Employee Innovative Behavior: A Cognitive-Affective Processing System Perspective.

Employee innovative behavior is crucial for organizations to engage in innovative activities and gain competitive advantages in the context of digital transformation. Despite many studies having focused on the relationship between leadership and employee innovative behavior, the role of digital leadership and the underlying mechanisms for employee innovative behavior remain unclear. Using the cognitive-affective processing system framework, the study investigated the dual mediating role of psychological empowerment and affective commitment between digital leadership and employee innovative behavior and the moderating role of a proactive personality in such relationships. Employing data from 359 employees, the study conducted structure equation modeling to examine the hypotheses. The results show that digital leadership influences employee innovative behavior through psychological empowerment but not affective commitment. Furthermore, a proactive personality does not moderate the direct effect of digital leadership on psychological empowerment and affective commitment or the indirect effect of digital leadership on employee innovative behavior. Theoretical and practical implications are discussed.

Blockchain and differential privacy-based data processing system for data security and privacy in urban computing

Recently, big data related to human movement, air quality, and meteorology have been generated in urban computing through sensing technology and the computing infrastructure. However, security problems arise as data utilization increases. If the sensing data from internet of things devices are constantly exposed, the users’ private information can be determined, a critical security risk that could result in privacy breaches. This paper proposes a secure data processing system using the blockchain and differential privacy for data security and privacy protection in urban computing. When a service provider requests information, the system generates it from urban computing data using machine learning. We apply differential privacy to these data to protect privacy. However, if a query repeats, differential privacy may provide insufficient privacy protection. Therefore, we reduce the total privacy cost by reusing noise for the same data and privacy parameters using the blockchain. Machine learning accuracy may decrease when noisy data are used for training. Thus, we increase accuracy by storing and appropriately using the model parameters generated by the same data in the blockchain. We design, simulate, and analyze the results of an experimental environment for reusing noise for differential privacy and parameter utilization of machine learning using the blockchain. The proposed approach reduces privacy costs compared to the existing mechanism while protecting data privacy. We demonstrate that, through parameter utilization, the accuracy improves compared to conventional mechanisms.

A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding–blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

Construction of large scale English teaching corpus and e-learning system based on Apache Hadoop algorithm

Currently, the contradiction between traditional English teaching and the need for dynamic learning is becoming increasingly prominent. Therefore, the organic integration of new teaching aids with traditional English teaching is conducive to achieving a new concept design and innovation in English teaching. On this basis, a large-scale English teaching corpus was established by combining MapReduce with Apache Hadoop algorithm. At the same time, information theory and methods based on the MapReduce computing model are proposed and applied to the parallelism analysis of big data, effectively solving practical problems that are difficult to expand certain datasets. Secondly, this article will design a new algorithm, Salsa20, based on the construction characteristics of Apache Hadoop. By utilizing the parallel characteristics of this algorithm, we can parallelize the Apache Hadoop algorithm, improve the system's data processing ability, ensure the security of data in MapReduce operation mode, and ensure its efficient data processing ability. In addition, the comprehensive and systematic use of large-scale corpora to establish an English teaching system based on them can effectively improve some of the problems that arise in English teaching, greatly enhance the teaching effectiveness of English courses.

Design and FPGA Implementation of 4×4 Vedic Multiplier using Different Architectures

The need of high speed multiplier is increasing as the need of high speed processors are increasing. A Multiplier is one of the key hardware blocks in most of the fast processing systems which is not only a high delay block but also a major source of power dissipation. A conventional processor requires substantially more hardware resources and processing time in the multiplication operation, rather than addition and subtraction. This Project describes about the design of 4-bit, 8-bit and 32-bit Vedic multiplier using ancient Vedic mathematics which helps in delay and power reduction. Simulation is done in Xilinx VIVADO software using VHDL and display on LCD. The results for Vedic multiplier using various architecture and their delay comparison are done. Key Words: FPGA (Artix-7), adders, 4x4 Array Multiplier, Vedic Mathematics

Implementing Distribution Requirement Planning in Medan City Health Department's Medicine Distribution System

One of the pharmacy installations located in the Medan city area was tasked with overseeing the management of pharmaceutical inventory for public health facilities, ensuring adequate stock levels, and processing medication-related data, including receiving supplies and LPLPO forms from 41 Public Health Centers. Supervisors at the pharmacy installation were responsible for dispensing medications, while medication managers at the Public Health Centers handled medication requests by completing LPLPO forms and sending them to the installation. Issues arose regarding the accuracy of medication data within its operations, encompassing aspects such as initial stock, receipt of medications, inventory management, medication disbursement (including usage, damaged, or expired items), remaining stock, medication requests, and discrepancies between reported and actual medication quantities. The objective of this study was to establish a web-based data processing system utilizing the Distribution Requirement Planning (DRP) methodology. The DRP approach offered significant insights for forecasting medication stock demands and effectively guided the pharmacy installation in meeting the medication needs of the Public Health Centers. Furthermore, the DRP method shed light on the distribution process costs, thus serving as a valuable tool for enhancing cost efficiency and effectiveness. Results obtained through the DRP approach provided a more efficient distribution process, yielding a notable 93% reduction in expenditure. Additionally, the DRP method successfully anticipated future requirements by employing structured calculations that delineated demand levels experienced by each Public Health Center, accounting for the distinct needs of each facility.

Inactivation kinetic parameters of hydrogen peroxide application in commercial sterility of aseptic processing systems

Hydrogen peroxide (H2O2) is a commonly used sterilant for aseptic processing and packaging systems. Surrogate bacterial spores are commonly used for the microbiological validation of the sterilization process. However, limited information is available on the resistance of these surrogate spores in liquid H2O2 (LHP) and vapor H2O2 (VHP). This study aims to fill the gaps in understanding of microbial inactivation parameters and the impact of temperature on VHP effectiveness. 35 % LHP experiments were conducted at temperatures ranging from 25 to 70 °C, while VHP tests were performed using vaporized sterilant technology (VST) and temperatures ranging from 43.3 to 60 °C. Exposure times ranged from 1 to 18 s, and VHP concentrations of 2500 and 4450 PPM were used while maintaining a relative humidity of 0.5%. The model performance of the log-linear and Weibull model was assessed using Root Mean Square Error (RMSE), the sum of the square, and Akaike Information Criterion (AIC) scores. The Weibull model outperformed the log-linear model in terms of parameter estimation and statistical indices.

Discrete linear canonical shearlet transform

In this paper, we introduce the continuous and discrete version of the linear canonical shearlet transform (LCST). The authors begin with the definition of the LCST and then establish a relationship between the linear canonical transform (LCT) and the LCST. Next, the paper derives several basic properties like Parseval’s Formula, inversion formula, and the characterization of the transform’s range. In addition to the continuous version of the transform, the authors also present a discrete version of the LCST. This discrete version allows for practical implementation and efficient computation of the transform in digital signal processing systems. Lastly, the paper establishes a frame condition for the discrete LCST, which thereby helps in establishing the reconstruction formula for the discrete LCST. The paper ends with a conclusion.

Iterative kernel regression with preconditioning

Kernel methods are popular in nonlinear and nonparametric regression due to their solid mathematical foundations and optimal statistical properties. However, scalability remains the primary bottleneck in applying kernel methods to large-scale data regression analysis. This paper aims to improve the scalability of kernel methods. We combine Nyström subsampling and the preconditioned conjugate gradient method to solve regularized kernel regression. Our theoretical analysis indicates that achieving optimal convergence rates requires only [Formula: see text] memory and [Formula: see text] time (up to logarithmic factors). Numerical experiments show that our algorithm outperforms existing methods in time efficiency and prediction accuracy on large-scale datasets. Notably, compared to the FALKON algorithm [A. Rudi, L. Carratino and L. Rosasco, Falkon: An optimal large scale kernel method, in Advances in Neural Information Processing Systems (Curran Associates, 2017), pp. 3891–3901], which is known as the optimal large-scale kernel method, our method is more flexible (applicable to non-positive definite kernel functions) and has a lower algorithmic complexity. Additionally, our established theoretical analysis further relaxes the restrictive conditions on hyperparameters previously imposed in convergence analyses.

Thermal performance of a motile-microorganism within the two-phase nanofluid flow for the distinct non-Newtonian models on static and moving surfaces

Nanofluid plays a crucial role in addressing the heat transmission challenges facing the industries including chemical processing systems, automobile radiators, spacecraft design, concrete heating, solar thermal conversion systems, etc. Consequently, this research is devoted to analyzing the solar radiation mechanism, thermophoresis and Brownian motion under unique conditions, specifically, temperature gradient within liquids with limiting viscosities or plastic dynamic viscosity at zero and infinite shear rate. To increase the model novelty, a model involving two phases of Casson–Carreau fluid conveying tiny particles is developed to analyze the flow of solar radiation mechanism over stationary and moving surfaces. Furthermore, the impacts of heat generation, chemical reaction and activation energy are also taken into account. The dimensionless equations are solved through numerical methods using the Galerkin-weighted residual technique and analytically employing the Homotopy analysis method with the assistance of MATHMATIACA 11.3 software. Our study reveals that the fluid temperature reduces for greater values of Weissenberg number, and Casson parameter. Additionally, the distribution of gyrotactic microorganisms decreased against the bio-convective Schmidt number and Peclet number.

Novel heat pump assisted distillation configurations for energy and economic savings and their application on propylene/propane and I-butane/N-butane separation systems

Various configurations of vapor recompression heat pumps were previously proposed for enhancing its thermodynamic and/or economic performance. The outcomes of prior publications were documented using fundamentally distinct separation systems, making it challenging for process engineers to decisively determine the optimal processing options for a particular separation task. This difficulty arises from the variations in thermophysical properties exhibited by the materials involved. Despite limited research on bottom flashing heat pumps, they offer advantages over vapor recompression such as lower operating pressure and temperature in process sections. This can result in cost savings and safer operation. In this paper, novel configurations of bottom flashing are proposed, based on the ideas and schemes, previously proposed for vapor recompression. The novel processing systems were proposed and applied on propylene/propane and i-butane/n-butane separation processes as samples of close-boiling distillation systems. This study showed that the bottom flashing systems showed greater economic performances compared to vapor recompression for i-butane/n-butane separation. total annual costs savings of 19.0% was obtained, using the novel configuration of heat recuperated bottom flashing configuration on isobutane/n-butane separation. Also, annualized costs of the systems based on bottom flashing were 10.9%, 4.2% and 7.3% lower than their equivalent vapor recompression processes.

IoT Based Smart Kitchen Using Fuzzy Intelligence and Signal Processing

Kitchen management remains a challenging task, despite many technological advancements. As a result, it's clearly time for us to get the kitchen work done with ease. The ingenious Kitchen uses the Internet of Things for design and features a mobile application as a user interface. Smart Kitchen includes a grocery management system, a fuzzy system for adaptive cooking and signal processing for identifying the cooker whistle. In the grocery management system, an ultra-sonic sensor measures the inventory level and using a Wi-Fi module, data updates the value of a particular key in the cloud. Users can see the amount of item left, be notified of shortage, and get the shopping list using the mobile application.

Multi-Sensor Origami Platform: A Customizable System for Obtaining Spatiotemporally Precise Functional Readouts in 3D Models.

Bioprinting technology offers unprecedented opportunities to construct in vitro tissue models that recapitulate the 3D morphology and functionality of native tissue. Yet, it remains difficult to obtain adequate functional readouts from such models. In particular, it is challenging to position sensors in desired locations within pre-fabricated 3D bioprinted structures. At the same time, bioprinting tissue directly onto a sensing device is not feasible due to interference with the printer head. As such, a multi-sensing platform inspired by origami that overcomes these challenges by "folding" around a separately fabricated 3D tissue structure is proposed, allowing for the insertion of electrodes into precise locations, which are custom-defined using computer-aided-design software. The multi-sensing origami platform (MSOP) can be connected to a commercial multi-electrode array (MEA) system for data-acquisition and processing. To demonstrate the platform, how integrated 3D MEA electrodes can record neuronal electrical activity in a 3D model of a neurovascular unit is shown. The MSOP also enables a microvascular endothelial network to be cultured separately and integrated with the 3D tissue structure. Accordingly, how impedance-based sensors in the platform can measure endothelial barrier function is shown. It is further demonstrated the device's versatility by using it to measure neuronal activity in brain organoids.

Load Balanced PIM-Based Graph Processing

Graph processing is widely used for many modern applications, such as social networks, recommendation systems, and knowledge graphs. However, processing large-scale graphs on traditional Von Neumann architectures is challenging due to the irregular graph data and memory-bound graph algorithms. Processing-in-memory (PIM) architecture has emerged as a promising approach for accelerating graph processing by enabling computation to be performed directly on memory. Despite having many processing units and high local memory bandwidth, PIM often suffers from insufficient global communication bandwidth and high synchronization overhead due to load imbalance. This paper proposes GraphB, a novel PIM-based graph processing system, to address all these issues. From the algorithm perspective, we propose a degree-aware graph partitioning algorithm that can generate balanced partitioning at a low cost. From the architecture perspective, we introduce tile buffers incorporated with an on-chip 2D-Mesh, which provides high bandwidth for inter-node data transfer. Dataflow in GraphB is designed to enable computation-communication overlap and dynamic load balancing. In a PyMTL3-based cycle-accurate simulator with five real-world graphs and three common algorithms, GraphB achieves an average 2.2 × and maximum 2.8 × speedup compared to the SOTA PIM-based graph processing system GraphQ.

Valley Pseudospin Polarized Evanescent Coupling between Microwave Ring Resonator and Waveguide in Phononic Topological Insulators.

A coupled ring-waveguide structure is at the core of bosonic wave-based information processing systems, enabling advanced wave manipulations such as filtering, routing, and multiplexing. However, its miniaturization is challenging due to momentum conservation issues in rings with larger curvature that induce significant backscattering and radiation leakage and hampering stable operation. Here, we address it by taking an alternative approach of using topological technology in wavelength-scale and microwave ring-waveguide coupled systems built in nanoengineered phononic crystals. Our approach, which leverages pseudospin conservation in valley topological systems, eliminates phonon backscattering and achieves directional evanescent coupling. The resultant hypersonic waves in the tiny ring exhibit robust transport and resonant circulation. Furthermore, the ring-waveguide hybridization enables critical coupling, where valley-dependent ring-waveguide interference blocks the transmission. Our findings reveal the capability of topological phenomena for managing ultrahigh-frequency phonons in nano/microscale structures and pave the way for advanced phononic circuits in classical and quantum signal processing applications.