Central Processing Unit Research Articles

Histological tissue classification with a novel statistical filter-based convolutional neural network.

Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image-based challenges. The high-performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter-based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.

Artificial intelligence, robotics, and automation viewed through the context of the previous four decades.

Computers and applications of computers into our world have changed dramatically during the past five decades, from early days of minimal central processing unit capacity, limited memory and without advantage of global networking. In this article, the author highlights the application of predictive artificial intelligence in use globally over the last 40 years in process industries. It discusses the novel application of process automation and robotics in health clinical high-volume laboratory use that began as a Canadian innovation initiative and followed by similar innovation extending to other countries.

An Improved Onboard Adaptive Aero-Engine Model Based on an Enhanced Neural Network and Linear Parameter Variance for Parameter Prediction

Achieving measurable and unmeasurable parameter prediction is the key process in model-based control, for which an accurate onboard model is the most important part. However, neither nonlinear models like component level models or LPV models, nor linear models like state–space models can fully meet the requirements. Hence, an original ENN-LPV linearization strategy is proposed to achieve the online modelling of the state–space model. A special network structure that has the same format as the state–space model’s calculation was applied to establish the state–space model. Importantly, the network’s modelling ability was improved through applying multiple activation functions in the single hidden layer and an experience pool that records data of past sampling instants, which strengthens the ability to capture the engine’s strongly nonlinear dynamics. Furthermore, an adaptive model, consisting of a component-level model with adaptive factors, a linear Kalman filter, a predictive model, an experience pool, and two ENN-LPV networks, was developed using the proposed linearization strategy as the core process to continuously update the Kalman filter and the predictive model. Simulations showed that the state space model built using the ENN-LPV linearization strategy had a better model identification ability in comparison with the model built using the OSELM-LPV linearization strategy, and the maximum output error between the ENN-LPV model and the simulated engine was 0.1774%. In addition, based on the ENN-LPV linearization strategy, the adaptive model was able to make accurate predictions of unmeasurable performance parameters such as thrust and high-pressure turbine inlet temperature, with a maximum prediction error within 0.5%. Thus, the effectiveness and the advantages of the proposed method are demonstrated.

Design and dynamic analysis of the scissor deployable mechanism used in the drill for replicating ice cores from the borehole wall

The deep ice cores preserved within the polar ice sheets hold a wealth of valuable information, and the demand for ice cores in related scientific research is on the rise. Thus, the replication of ice cores from the borehole wall holds significant importance. However, current methods face limitations such as difficulties in whipstock retrieval, extended auxiliary working hours, structural complexity, and the necessity of ice core depth calibration. To address this, this study proposes a novel method for replicating ice cores from the borehole wall utilizing a thermal coring drill bit. The design and dynamic analysis of the scissor deployable mechanism employed to drive the drill bit movement were thoroughly examined through theoretical and simulation studies. The motion characteristics of the drill bit during the replication coring process and the stress–strain behavior of the scissor rods were determined and validated through drill bit movement experiments, demonstrating a maximum error of 10%. The findings reveal that the mechanism can meet the strength requirements across three operating stages: horizontal drilling, vertical coring, and ice core recovery, with maximum loads of approximately 284 N, 749 N, and 970 N, respectively. Increasing the thickness of the scissor rods and reducing their length can augment the load-bearing capacity of the structure. Additionally, the speed transmission characteristics derived from the scissor mechanism can offer theoretical support for the drill's control system.

VSP wavefields separating method based on parallel local slope scanning

Due to the detectors being distributed in the target medium, vertical seismic profile (VSP) is a seismic observation method that has the advantages of high resolution and a high signal-to-noise ratio. Separating the mixed wavefields into upgoing and downgoing waves can obtain more obvious dynamic and kinematic characteristics of seismic waves, guiding subsequent imaging and interpretation. The traditional method is mainly based on the pickup of first breaks. Flattening the global seismic data by first breaks can enhance the seismic events through techniques like median filter and singular value decomposition (SVD). However, this method relies on high-precision pickup of first breaks and is limited by zero offset. To address this limitation, we introduce an improved median filtering separation method. This method employs separations of the local dip angles into positive and negative through multi-window scanning (MWS). Due to the high accuracy and robustness of this method in 2-D VSP data, we propose to use the local dip angle of the wavefields to median filter the wavefield through positive and negative angles to obtain upgoing and downgoing waves. This method for wavefield separation is optimized by iteratively identifying the directions of the seismic data. However, this optimization comes at the cost of increased computational requirements, especially under high-precision conditions. To help alleviate this problem, we use multi-thread parallel computing technique on a multi-core central processing unit (CPU) to improve computational efficiency. Finally, we validate the proposed method by testing it on synthetic seismic data and field VSP data, respectively. The results show that this wavefield separation method has advantages in terms of accuracy and robustness compared to the median filtering method based on the first break picking.

Correction of the calculation method of CT state energy in ITIC and Y6 acceptor systems

The charge transfer (CT) state at the donor (D)/acceptor (A) interface in organic solar cell (OSC) is the core of exciton dissociation and geminate recombination processes, which are affected by geometries and electron transition. Recently, the CT mechanism developed by fullerenes has produced some unsuitable phenomena in the application of high performance NFA materials, due to the transformation of the highly (three dimensional) symmetric structure to a linear structure. In order to improve the calculation accuracy and custom-matched calculation models for NFA, two benzo[1,2-b:4,5-b′]difuran (BDF) polymers (P-FT)/3,9-bis(2-methylene-((3-(1,1-dicyanomethylene)-6/7-methyl)-indanone))-5,5,11,11-tetrakis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d’]-s-indaceno[1,2-b:5,6-b’]dithiophene (m-ITIC) and P-FT/2,2′-((2Z,2′Z)-((12,13-bis(2-ethylhexyl)-3,9-diundecyl-12,13-dihydro-[1,2,5]thiadiazolo[3,4-e]thieno[2″,3″:4′,5′]thieno[2′,3′:4,5]pyrrolo[3,2-g]thieno[2′,3′:4,5]thieno[3,2-b]indole-2,10-diyl) bis (methanylylidene))bis(5,6-difluoro-3-oxo-2,3-dihydro-1H-indene-2,1-diylidene))dimalononitrile (Y6) were selected as the research objects, the four levels with gradient variation were constructed, including identification methods (CDD or NTO), range-separated hybrid (RSH) functionals, polarizable continuum models (PCM) and related ω parameter. The results show that RSH functional is the dominant factor affecting CT state. The ωB97XD functional should be selected without distinguishing CT state types. However, when considering the types and high-energy of CT states, it should be noted that there is a strong system dependence. In addition, if a solvent model needs to be added, solvent optimization should be performed on the ω parameter. This work aims to define uniform ECT calculation models and standards to improve accuracy and provide suitable target selection for relevant research.

Lightweight Hardware-Based Cache Side-Channel Attack Detection for Edge Devices (Edge-CaSCADe)

Cache Side-Channel Attacks (CSCAs) have been haunting most processor architectures for decades now. Existing approaches to mitigation of such attacks have certain drawbacks, namely software mishandling, performance overhead, and low throughput due to false alarms. Hence, “mitigation only when detected” should be the approach to minimize the effects of such drawbacks. We propose a novel methodology of fine-grained detection of timing-based CSCA using a hardware-based detection module. We discuss the design, implementation, and use of our proposed detection module in processor architectures. Our approach successfully detects attacks that flush secret victim information from cache memory like Flush+Reload, Flush+Flush, Prime+Probe, Evict+Probe, and Prime+Abort, commonly known as cache timing attacks. Detection is on time with minimal performance overhead. The parameterizable number of counters used in our module allows detection of multiple attacks on multiple sensitive locations simultaneously. The fine-grained nature ensures negligible false alarms, severely reducing the need for any unnecessary mitigation. The proposed work is evaluated by synthesizing the entire detection algorithm as an attack detection block, Edge-CaSCADe, in a RISC-V processor as a target example. The detection results are checked under different workload conditions with respect to the number of attackers and the number of victims having RSA-, AES-, and ECC-based encryption schemes like ECIES, and on benchmark applications like MiBench and Embench. More than 98% detection accuracy within 2% of the beginning of an attack can be achieved with negligible false alarms. The detection module has an area and power overhead of 0.9% to 2% and 1% to 2.1% for the targeted RISC-V processor core without cache for one to five counters, respectively. The detection module does not affect the processor critical path and hence has no impact on its maximum operating frequency.

Fast approximation of the traveling salesman problem shortest route by rectangular cell clustering pattern to parallelize solving

A method of quickly obtaining an approximate solution to the traveling salesman problem (TSP) is suggested, where a dramatic computational speedup is guaranteed. The initial TSP is broken into open-loop TSPs by using a clustering method. The clustering method is based on either imposing a rectangular lattice on the nodes or dividing the dataset iteratively until the open-loop TSPs become sufficiently small. The open-loop TSPs are independent and so they can be solved in parallel without synchronization, whichever the solver is. Then the open-loop subroutes are assembled into an approximately shortest route of the initial TSP via the shortest connections. The assemblage pattern is a symmetric rectangular closed-loop serpentine. The iterative clustering can use the rectangular assembling approach as well. Alternatively, the iterative clustering can use the centroid TSP assembling approach, which requires solving a supplementary closed-loop TSP whose nodes are centroids of the open-loop-TSP clusters. Based on results of numerical simulation, it is ascertained that both the iterative clustering and rectangular cell clustering pattern are roughly equally accurate, but the latter is way more computationally efficient on squarish datasets. Fast approximation of the TSP shortest route serves as an upper bound. In addition, the route can be studied to find its bottlenecks, which subsequently are separated and another bunch of open-loop TSPs is approximately solved. For big-sized TSPs, where the balance of the accuracy loss and computational time is uncertain, the rectangular cell clustering pattern allows obtaining fast solution approximations on multitudinous non-synchronized parallel processor cores.

SJFO: Sail Jelly Fish Optimization enabled VM migration with DRNN-based prediction for load balancing in cloud computing

ABSTRACT The dynamic workload is evenly distributed among all nodes using balancing methods like hosts or VMs. Load Balancing as a Service (LBaaS) is another name for load balancing in the cloud. In this research work, the load is balanced by the application of Virtual Machine (VM) migration carried out by proposed Sail Jelly Fish Optimization (SJFO). The SJFO is formed by combining Sail Fish Optimizer (SFO) and Jellyfish Search (JS) optimizer. In the Cloud model, many Physical Machines (PMs) are present, where these PMs are comprised of many VMs. Each VM has many tasks, and these tasks depend on various parameters like Central Processing Unit (CPU), memory, Million Instructions per Second (MIPS), capacity, total number of processing entities, as well as bandwidth. Here, the load is predicted by Deep Recurrent Neural Network (DRNN) and this predicted load is compared with a threshold value, where VM migration is done based on predicted values. Furthermore, the performance of SJFO-VM is analysed using the metrics like capacity, load, and resource utilization. The proposed method shows better performance with a superior capacity of 0.598, an inferior load of 0.089, and an inferior resource utilization of 0.257.

SoC-FPGA Based Concept of Hardware Aided Quantum Simulation

Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables to delegate of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows to overcome encountered limitations and maintain the flexibility known from high-level solutions and high performance achieved with low-level programming, without the need for a high financial background.

High-Performance Krawtchouk Polynomials of High Order Based on Multithreading

Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various recurrence algorithms have been proposed so far to address the challenge of numerical instability for large values of orders and signal sizes. The computation of DKraP coefficients was typically computed using sequential algorithms, which are computationally extensive for large order values and polynomial sizes. To this end, this paper introduces a computationally efficient solution that utilizes the parallel processing capabilities of modern central processing units (CPUs), namely the availability of multiple cores and multithreading. The proposed multi-threaded implementations for computing DKraP coefficients divide the computations into multiple independent tasks, which are executed concurrently by different threads distributed among the independent cores. This multi-threaded approach has been evaluated across a range of DKraP sizes and various values of polynomial parameters. The results show that the proposed method achieves a significant reduction in computation time. In addition, the proposed method has the added benefit of applying to larger polynomial sizes and a wider range of Krawtchouk polynomial parameters. Furthermore, an accurate and appropriate selection scheme of the recurrence algorithm is introduced. The proposed approach introduced in this paper makes the DKraP coefficient computation an attractive solution for a variety of applications.

DarA-the central processing unit for the integration of osmotic with potassium and amino acid homeostasis in Bacillus subtilis.

Cyclic di-adenosine monophosphate (c-di-AMP) is a second messenger involved in diverse metabolic processes including osmolyte uptake, cell wall homeostasis, as well as antibiotic and heat resistance. This study investigates the role of the c-di-AMP receptor protein DarA in the osmotic stress response in Bacillus subtilis. Through a series of experiments, we demonstrate that DarA plays a central role in the cellular response to osmotic fluctuations. Our findings show that DarA becomes essential under extreme potassium limitation as well as upon salt stress, highlighting its significance in mediating osmotic stress adaptation. Suppressor screens with darA mutants reveal compensatory mechanisms involving the accumulation of osmoprotectants, particularly potassium and citrulline. Mutations affecting various metabolic pathways, including the citric acid cycle as well as glutamate and arginine biosynthesis, indicate a complex interplay between the osmotic stress response and metabolic regulation. In addition, the growth defects of the darA mutant during potassium starvation and salt stress in a strain lacking the high-affinity potassium uptake systems KimA and KtrAB can be rescued by increased affinity of the remaining potassium channel KtrCD or by increased expression of ktrD, thus resulting in increased potassium uptake. Finally, the darA mutant can respond to salt stress by the increased expression of MleN , which can export sodium ions.IMPORTANCEEnvironmental bacteria are exposed to rapidly changing osmotic conditions making an effective adaptation to these changes crucial for the survival of the cells. In Gram-positive bacteria, the second messenger cyclic di-AMP plays a key role in this adaptation by controlling (i) the influx of physiologically compatible organic osmolytes and (ii) the biosynthesis of such osmolytes. In several bacteria, cyclic di-adenosine monophosphate (c-di-AMP) can bind to a signal transduction protein, called DarA, in Bacillus subtilis. So far, no function for DarA has been discovered in any organism. We have identified osmotically challenging conditions that make DarA essential and have identified suppressor mutations that help the bacteria to adapt to those conditions. Our results indicate that DarA is a central component in the integration of osmotic stress with the synthesis of compatible amino acid osmolytes and with the homeostasis of potassium, the first response to osmotic stress.

Responsible learning organizations: a framework to embed responsible innovation within organizations

Purpose The purpose of this paper is to explore the extent to which the concept of learning organization can support the embedding of responsible innovation (RI) in organizations. Design/methodology/approach Based on literature in the fields of corporate social responsibility, learning organizations and quadruple helix collaborations, the authors constructed the responsible learning organization (RLO) framework for RI. With the framework, the authors want to show that the RLO can enable RI within organizations. Findings Based on this framework, the distinction is made between, on the one hand, the learning processes inside the organization, which resemble reflexivity, and, on the other hand, the learning processes that take place with stakeholders outside the organization, which resemble the other three core processes of RI: anticipation, inclusion and responsiveness. Based on these insights, the authors argue that if an organization wants to do good on innovation, which is seen as the core of RI, organization’s core values should guide that. Practical implications Organizational core values should be developed by means of learning inside the organization. Therefore, the process of reflexivity should be stressed more, and employees should be empowered to take part in developing these values, which in return can guide the organization as a compass through all the uncertainty it will encounter during the learning outside the organization when interacting with stakeholders. Originality/value The RLO framework for RI shows what learning processes organizations should facilitate first and what content should be at stake during these learning processes to embed RI. Furthermore, the framework puts emphasis on reflexivity as a condition for responsiveness, inclusion and anticipation.

RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8

Currently, weed control robots that can accurately identify weeds and carry out removal work are gradually replacing traditional chemical weed control techniques. However, the computational and storage resources of the core processing equipment of weeding robots are limited. Aiming at the current problems of high computation and the high number of model parameters in weeding robots, this paper proposes a lightweight weed target detection model based on the improved YOLOv8 (You Only Look Once Version 8), called RVDR-YOLOv8 (Reversible Column Dilation-wise Residual). First, the backbone network is reconstructed based on RevCol (Reversible Column Networks). The unique reversible columnar structure of the new backbone network not only reduces the computational volume but also improves the model generalisation ability. Second, the C2fDWR module is designed using Dilation-wise Residual and integrated with the reconstructed backbone network, which improves the adaptive ability of the new backbone network RVDR and enhances the model’s recognition accuracy for occluded targets. Again, GSConv is introduced at the neck end instead of traditional convolution to reduce the complexity of computation and network structure while ensuring the model recognition accuracy. Finally, InnerMPDIoU is designed by combining MPDIoU with InnerIoU to improve the prediction accuracy of the model. The experimental results show that the computational complexity of the new model is reduced by 35.8%, the number of parameters is reduced by 35.4% and the model size is reduced by 30.2%, while the mAP50 and mAP50-95 values are improved by 1.7% and 1.1%, respectively, compared to YOLOv8. The overall performance of the new model is improved compared to models such as Faster R-CNN, SSD and RetinaNet. The new model proposed in this paper can achieve the accurate identification of weeds in farmland under the condition of limited hardware resources, which provides theoretical and technical support for the effective control of weeds in farmland.

Impact of Digital Technology on Processes at Universities from Teachers’ Perspective

Abstract Digital technology has become inseparable from all core and supportive processes because of the intensive use of different digital tools. The paper aimed to determine teachers’ views on the impact of digital technology on business processes in higher education institutions - whether they recognize the impact and how they evaluate it. The survey was conducted among the teachers of the public University in Bosnia and Herzegovina. The results show that digital technology has a positive and mostly strong or medium impact on business processes in higher education institutions. The work on international research and educational projects and the teaching process are the most influenced by digital technology. It has a stronger impact on the core processes than on supportive activities. Teachers recognize the influence of digital transformation on all processes at universities, both core and supporting. The influence is stronger with core processes.

Development of complete hydrometallurgical processes for gold recovery from ICs and CPUs using ionic liquids

Integrated circuits (ICs) and central processing units (CPUs), essential components of electrical and electronic equipment (EEE), are complex composite materials rich in recyclable high-value strategic and critical metals, with many in concentrations higher than in their natural ores. With gold the most valuable metal present, increase in demand for gold for EEE and its limited availability have led to a steep rise in the market price of gold, making gold recycling a high priority to meet demand. To overcome the limitations associated with conventional technologies for recycling e-waste, the use of greener technologies (ionic liquids (ILs) as leaching agents), offers greater potential for the recovery of gold from e-waste components. While previous studies have demonstrated the efficiency and feasibility of using ILs for gold recovery, these works predominantly concentrate on the extraction stage and often utilise simulated solutions, lacking the implementation of a complete process validated with real samples to effectively assess its overall effectiveness. In this work, a simulated Model Test System was used to determine the optimal leaching and extraction conditions before application to real samples. With copper being the most abundant metal in the e-waste fractions, to access the gold necessitated a two-stage pre-treatment (nitric acid leaching followed by aqua regia leaching) to ensure complete removal of copper and deliver a gold-enriched leach liquor. Gold extraction from the leach liquor was achieved by liquid-liquid extraction using Cyphos 101 (0.1 M in toluene with an O:A = 1:1, 20 °C, 150 rpm, and 15 min) and as a second process by sorption extraction with loaded resins (Amberlite XAD-7 with 300 mg of Cyphos 101/g of resins at 20 °C, 150 rpm and 3 h). In both processes, complete stripping and desorption of gold was achieved (0.5 M thiourea in 0.5 M HCl) and gold recovered, as nanoparticles of purity ≥95%, via a reduction step using a sodium borohydride solution (0.1 M NaBH4 in 0.1 M NaOH). These two hydrometallurgical processes developed can achieve overall efficiencies of ≥95% for gold recovery from real e-waste components, permit the reuse of the IL and resins up to five consecutive times, and offer a promising approach for recovery from any e-waste stream rich in gold.

Matching workloads ro systems with deep reinforcement learning

Along with the evolution of computer microarchitecture over the years, the number of dies, cores, and embedded multi-die interconnect bridges has grown. Optimizing the workload running on a central processing unit (CPU) to improve the computer performance has become a challenge. Matching workloads to systems with optimal system configurations to achieve the desired system performance is an open challenge in both academic and industrial research. In this paper, we propose two reinforcement learning (RL) approaches, a deep reinforcement learning (DRL) approach and an evolutionary deep reinforcement learning (EDRL) approach, to find an optimal system configuration for a given computer workload with a system performance objective. The experimental results demonstrate that both approaches can determine the optimal system configuration with the desired performance objective. The comparison studies illustrate that the DRL approach outperforms the standard RL approaches. In the future, these DRL approaches can be leveraged in system performance auto-tuning studies.

GPUPRSI: GPU implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings

Passive seismic exploration is an environmentally friendly, economical, and highly accessible exploration method that is widely used in different-scale subsurface imaging. Retrieving reflections from ambient noise is a newly developed passive seismic method that can be used in many fields such as mineral exploration and near-surface imaging, but the interferometry calculation is time-consuming because it requires a longer period of data acquisition to improve the signal-to-noise ratio of the retrieved reflections. In this study, we introduced a graphical processing unit (GPU)- based implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings. Because all traces are involved in the computation process, but the size of passive source data often exceeds available memory, repeated disk reads lead to a decrease in computational efficiency. We design a strategy of grouping computations followed by stacking to minimize disk input and output, simultaneously keeping the memory requirements low. Passive source data is read and written only once, and there is no requirement for the memory size to be greater than the data size. Additionally, acceleration technologies such as asynchronous execution, asynchronous memory transfer, and GPU-accelerated libraries are used. We test the efficiency using short data where the number of sampling points per trace is on the order of 30,000, and long data, where the number of sampling points per trace is on the order of 30,000,000. For short data, the average speedup is 4 compared with a multi-core central processing unit (CPU); for long data, the speedup can reach 24. The GPU-based implementation of interferometry greatly reduces the calculation time for retrieving reflections from passive source seismic recordings, providing a solution to the problem of large calculation in three-dimensional (3D) passive source reflection exploration.

Smart measurement and monitoring system for aquaculture fisheries with IoT-based telemetry system

The instrumentation design of an online monitoring device for aquaculture media is discussed in this article. The main processor in this internet of things (IoT) real-time telemetry system is an ESP32 board. Temperature, acidity level, conductivity level, dissolved oxygen (DO) level, and degree of oxygen reduction in the water were the aquaculture parameters measured. The ESP32 collects data from each sensor, groups it into a dataset, displays it on the LCD, saves it to the SD card, and then uploads it to the real-time database. In addition, an Android application is being developed for users. This device has been tested to ensure that each measured parameter is accurate and precise. The accuracy test, one of the major results of laboratory scale tests, demonstrates that each parameter has a different measurement error that represents with average error absolute. Six tested sensors/instruments were subjected to the test. Average absolute error for temperature sensor is +0.76%, pH sensor is +1.52%, electrical conductivity (EC) sensor is +10.8%, oxidation reduction potential (ORP) sensor is +14.6%, DO sensor is +9.3%, and total dissolve solids (TDS) sensor is +13.2%. This device is very dependable and convenient for monitoring the condition of aquaculture media in real-time and accurately.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events, causing surveillance and energy loss. This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters. Initially, the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes. Subsequently, it delves into communication and data processing mechanisms, specifically focusing on central data processing (CDP), communication routers (CRs), and phasor measurement units (PMUs), to re-establish an equipment recovery model based on these data transmission methodologies. Furthermore, it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution. The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system. The findings suggest that the proposed branch- and-bound algorithm significantly augments the observational capabilities of a power system with limited resources, thereby bolstering its stability and emergency response mechanisms.