Central Processing Unit Research Articles

Pericentricity on the Congolese copperbelt: how the DRC shapes Chinese cobalt supply chains and the low-carbon transition

ABSTRACT Through the case of cobalt from the Democratic Republic of Congo (DRC), we examine the role of local politics in critical mineral sourcing and its implications for the lead processing and battery manufacturing industry in China. We propose a pericentric and multiscalar analysis of cobalt extraction to understand the connections and contingencies within the cobalt supply chain and DRC’s place within it. Pericentricity acknowledges geopolitics while emphasizing localized power dynamics that shape cobalt supply, as seemingly peripheral actors in the DRC influence core extractors, processors, and traders. Our analysis contributes to scholarship on African agency amid resource extractivism, highlighting Chinese dependency on foreign extractive political economies. While Chinese firms remain central in cobalt mining, processing, and battery manufacturing, the complex dependencies on local, provincial, and national systems must be understood in their intricacy and flux.

Consistent Thermal Conductivities of Spring-Like Structured Polydimethylsiloxane Composites under Large Deformation.

Flexible and highly thermally conductive materials with consistent thermal conductivity (λ) during large deformation are urgently required to address the heat accumulation in flexible electronics. In this study, spring-like thermal conduction pathways of silver nanowire (S-AgNW) fabricated by 3D printing are compounded with polydimethylsiloxane (PDMS) to prepare S-AgNW/PDMS composites with excellent and consistent λ during deformation. The S-AgNW/PDMS composites exhibit a λ of 7.63W m-1 K-1 at an AgNW amount of 20 vol%, which is ≈42 times that of PDMS (0.18W m-1 K-1) and higher than that of AgNW/PDMS composites with the same amount and random dispersion of AgNW (R-AgNW/PDMS) (5.37W m-1 K-1). Variations in the λ of 20 vol% S-AgNW/PDMS composites are less than 2% under a deformation of 200% elongation, 50% compression, or 180° bending, which benefits from the large deformation characteristics of S-AgNW. The heat-transfer coefficient (0.29W cm-2 K-1) of 20 vol% S-AgNW/PDMS composites is ≈1.3 times that of the 20 vol% R-AgNW/PDMS composites, which reduces the temperature of a full-stressed central processing unit by 6.8°C compared to that using the 20 vol% R-AgNW/PDMS composites as a thermally conductive material in the central processing unit.

On the total energy consumption of scalable cache-aided multi-CPU cell-free massive MIMO systems

In conventional cell-free (CF) systems, the scalability and feasibility are hindered by the fact that a single central processing unit (CPU) manages all access points and the stringent synchronization requirement for coherent transmission (CT) across the entire network. Multi-CPU CF systems emerge as a scalable and feasible alternative, yet they still encounter the typical CF system challenge of high downlink transmit energy consumption from extensive fronthaul and backhaul transmissions. To tackle this issue, we propose a scalable downlink partial coherent transmission (PCT) strategy for multi-CPU CF systems to provide high spectral efficiency (SE), reduce transmission time and thereby lower energy consumption, while keeping decoding complexity manageable. Based on the PCT strategy, we introduce scalable cache-aided multi-CPU CF systems and develop the corresponding total energy consumption (TEC) model. A successive convex approximation algorithm is proposed to obtain the suboptimal cache placement to minimize the TEC. Simulation results indicate that the proposed PCT-based scalable cache-aided multi-CPU CF systems can significantly reduce the TEC and consistently maintain the TEC at a relatively low level across all Zipf parameters by leveraging the high SE of CT and the independent transmission of non-coherent transmission.

Commodity chain as a negotiated process: Empirical analysis of benefit allocation, governance, and powers of upstream and downstream actors in matsutake mushroom trade in Shangri-La, Yunnan Province

The commercialization of non-timber forest products beyond in-house consumption is a strategy for alleviating rural poverty and forest degradation. With the implementation of the ecological civilization program, the Chinese government has explored the value realization mechanism of sustainable ecological products, and many successful cases of agricultural product commercialization have emerged. Focusing on the matsutake of Shangri-La as an example, this study applies both qualitative and quantitative analysis of semi-structured interviews, participatory observation, secondary data collection, and statistical analysis for the dynamics of the matsutake commodity chain construction in the southwest plateau of China to obtain unique insights into contemporary rural China. Specifically, the interrelationships and functional logic of diverse participants in the core process of commodity management were analyzed. We found that the upstream and downstream participants in the matsutake commodity chain are subject to institutional constraints, such as land tenure and non-unified standards, and that markets are crucial to matsutake's trading in Shangri-La. Certain participants along the commodity chain have inefficient information sources, while middlemen have greater autonomy in the matsutake transaction process and a larger profit margin. Additionally, the involvement of the government and non-governmental organizations impacts the preservation of natural resources and the creation of a stable market order, but policies and funds tend to be short-lived. To improve the commodity chain sustainability of non-timber forest products in plateau areas, it is necessary to consider social and cultural factors, ensure the equity of different participants in the commodity chain, and protect traditional local knowledge related to natural resource management.

Benchmarking parallel programming for single-board computers

Within the computing continuum, SBCs (single-board computers) are essential in the Edge and Fog, with many featuring multiple processing cores and GPU accelerators. In this way, parallel computing plays a crucial role in enabling the full computational potential of SBCs. However, selecting the best-suited solution in this context is inherently complex due to the intricate interplay between PPI (parallel programming interface) strategies, SBC architectural characteristics, and application characteristics and constraints. To our knowledge, no solution presents a combined discussion of these three aspects. To tackle this problem, this article aims to provide a benchmark of the best-suited parallelism PPIs given a set of hardware and application characteristics and requirements. Compared to existing benchmarks, we introduce new metrics, additional applications, various parallelism interfaces, and extra hardware devices. Therefore, our contributions are the methodology to benchmark parallelism on SBCs and the characterization of the best-performing parallelism PPIs and strategies for given situations. We are confident that parallel computing will be mainstream to process edge and fog computing; thus, our solution provides the first insights regarding what kind of application and parallel programming interface is the most suited for a particular SBC hardware.

Sustainable Value Aggregation: A Framework for Environmental Solutions and Business Opportunities

Objective: The objective of this study is to investigate the intellectual framework that enables the aggregation of sustainable value. Theoretical Framework: The generation of value and businesses are at the core of the economic exchange process and have a direct impact on sustainability. In this regard, sustainable businesses have as their main characteristic the ability to maximize the efficiency of material and energy use. However, the factors necessary for the implementation of sustainable businesses are a phenomenon that has not yet received attention from researchers. Method: The methodology adopted for this research comprises bibliometric techniques and content analysis. Data collection was carried out in Scopus database, covering a time horizon from 1995 to 2022. Results and Discussion: The results demonstrate that the conducive environment for sustainable businesses can be identified and explained by four main factors: (1) sustainability issues exist in a particular context; (2) people are becoming aware of these issues; (3) viable solutions or technologies exist to address the problem; and (4) where possible, people change their habits in pursuit of sustainability. Research Implications: by understanding the process of sustainable value aggregation, policymakers can create policies aimed at incentivizing these solutions, investing in research and development, creating financing mechanisms for companies, fostering businesses, etc. Legislators can adapt legislation to the new reality, favouring the development of sustainable businesses; in this regard, the awareness of the population regarding the need for sustainability is crucial. On the other hand, entrepreneurs can identify opportunities and develop interesting businesses from economic, social, and environmental perspectives. Originality/Value: the present study initiates a discussion on the conditions that favour the emergence of so-called sustainable businesses.

Automated design of embedded digital signal processing systems on SOC platform

The object of the study is the procedures for automated design and analysis of digital signal processing algorithms on the SoC technology platform. The subject of the study is models, methods and procedures for designing and optimal selection of SoC components for the implementation of digital signal processing algorithms for audio spectrum. The aim of the study is to develop models and procedures for determining the possibilities of a compromise distribution of signal processing algorithm computations in the cycle of computer-aided design on the SoC technology platform in terms of performance and the feasibility of using hardware and software algorithms realization. The article solves the following tasks: consideration of the procedures for interacting the processor core with programmable logic as part of system-on-chip systems; development of procedures for computer-aided design and analysis of signal processing systems using programming languages and hardware description languages for the implementation of embedded systems. The following methods are being used: implementation of digital signal processing algorithms in the C programming language and high-level synthesis tools for realizing IP blocks, diagnostic experiment by generating test signal patterns, and analysis of the processing results at the system output. The results achieved. Based on the analysis of the procedures for the interaction of the processor core and programmable logic on the selected SoC platform, a model of the audio spectrum signal processing system is designed. The practical implementation was performed based on the Vivado/Vitis/Vitis HLS CAD tool stack. The proposed model was verified using a programmable test signal generator and analyzing the obtained characteristics of digital filters at the system output. Conclusions. The article analyzes the principles of designing embedded information processing systems implemented in system-on-chip. The principles of building and analyzing digital signal processing systems based on system-on-chip containing programmable logic and processor parts are considered. The developed methods have been tested on the algorithms of CIC and FIR filters on the technological platform of SoC FPGA of the ZYNQ-7000 family of Xilinx company.

Micro-milling machinability prediction for crystalline materials via numerical-analytical hybrid modelling and strain rate-dependent grain-scale simulation

Micro-milling is a promising technology to produce miniature-sized components, while the machinability prediction of crystalline material micro-milling is difficult due to the inhomogeneous microstructure, anisotropic properties, nonlinear strain rate sensitivity and complex process characteristics. To achieve accurate and effective micro-milling machinability prediction of crystalline materials, this paper proposes a numerical-analytical hybrid method that comprehensively and simultaneously considers core material and process characteristics of the crystalline material micro-milling technique, which evaluates micro-milling forces and surface roughness via a series of adaptive strain rate-dependent micro-orthogonal cutting emulations in grain-scale and a set of analytical transformations. Taking Inconel-718 as the sample material, grain-scale mechanical parameters under 10−2–104 s−1 strain rate range were calibrated. Strain rate effect-considered grain-scale constitutive theory is proposed and strain rate-dependent micro-orthogonal cutting emulations were executed, which were experimentally verified to be accurate and robust on the forces and surface roughness prediction of micro-cutting. Furthermore, numerical-analytical hybrid modelling and machinability prediction of Incoenl-718 micro-milling process under 12 mm/min and 20 mm/min feed rates were carried out, the relative prediction errors of micro-milling forces and surface roughness are <15 % and 14 %, which confirm that the proposed hybrid method is accurate and robust on the micro-milling machinability prediction of crystalline materials.

A method to enhance Apache Spark performance based on data segmentation and configuration parameters settings

When using modern big data processing tools, there is a problem of increasing the productivity of using modern frameworks in the context of effective setting of various configuration parameters. The object of the research is computational processes of processing big data with the use of technologies of high-performance frameworks. The subject is methods and approaches to the effective setting of configuration parameters of frameworks in the conditions of limitations of virtualization environments and local resources. The purpose of the study is to improve the performance of Apache Spark and Apache Hadoop deployment modes based on a combined approach that includes preprocess segmentation of input data and setting of basic and additional configuration parameters that take into account the limitations of the virtual environment and local resources. Achieving the set goal involves the following tasks: create a synthesized set of WordCount test data for using input data segmentation methods. Determine the composition of general and specific Apache Spark and Apache Hadoop configuration parameters that most affect the performance of frameworks in Spark Standalone and Hadoop Yarn (FIFO) deployment modes. Justify changes in the values of the configuration parameters (accepted by default) by setting the level of parallelism, the number of partitions of the input file according to the number of processor cores, the number of tasks assigned to each core and the system executor. Conduct experimental research to substantiate theoretical results and prove their use in practice. Methods. The research used the following methods: statistical analysis; a method of generating test data based on defined segmentation characteristics with arbitrary volumes of data; a systematic approach for comprehensive evaluation and analysis of performance of frameworks based on selected configuration parameters. The results. On the basis of the developed system of parameters for evaluating the performance of the studied frameworks, experiments were carried out, which include: the application of the method of segmentation of input data based on the division of the input file into paragraphs (lines) for different values of the ranges of the number of words and the number of letters in each word; setting the main parameters and specific ones, in particular, partitioning and parallelism, taking into account the characteristics of the virtual environment and the local resource. According to the obtained results, a detailed analysis of the use of the proposed methods to improve the performance of the studied frameworks with recommendations for choosing the optimal values of data segmentation parameters and configuration parameters was carried out. You are snowmen. The obtained results of the experiments allow us to conclude that the use of the proposed methods of setting the configuration parameters of Spark and Hadoop will increase the processing productivity: for small files (0.5–1 GB) on average up to 25–30%, for large ones (1.5–2.5 GB ) – up to 10–20% on average. At the same time, the average value of the execution time of one task decreased by 10-15% for files of different sizes and with different number of words in a line.

Examining psychological flexibility in unaccompanied refugee minors: A network analysis

BackgroundRefugees, particularly unaccompanied refugee minors (URM), often report poor mental health. Psychological Flexibility (PF), derived from Acceptance and Commitment Therapy (ACT), appears to positively impact mental health in various populations, including adolescents and refugees. ObjectivesThis study aimed to examine the structure of the PF model and the connections among its core processes, as well as the structure and connections between mental health constructs (i.e., post-traumatic stress, depression, anxiety, stress, quality of life) and PF in URM. Methods100 URM aged 13–18 years living in shelters in the Republic of Cyprus completed four self-reports, assessing depression, anxiety, and stress (DASS-21), PF (Psy-Flex), PTSD (CRIES-13), and HRQL (KIDSCREEN-10). Network Analysis was used to examine the structure and connections of the constructs. ResultsMost core PF processes showed positive connections amongst each other, with the strongest edge between committed action and values. Together with self as context, these core processes exhibited the highest expected influence. The strongest positive connections in the mental health network were found among stress, anxiety, and depression. Stress had the highest expected influence, whereas PF had the lowest. A post hoc Johnson-Neyman analysis suggested a buffering effect of PF on the impact of PTSD on anxiety and stress. ConclusionsProposed areas of focus for clinicians working with URM include incorporating strategies that address stress symptoms and facilitate individuals in pursuing value-based behavior. It is equally important to encourage critical reflection on values and the conceptualized self in the context of culture.

Contributions of core, mantle and climatological processes to Earth’s polar motion

Earth’s spin axis slowly moves relative to the crust over time. A 120-year-long record of this polar motion from astronomical and more modern geodetic measurements displays interannual and multidecadal fluctuations of 20 to 40 milliarcseconds superimposed on a secular trend of about 3 milliarcseconds per year. Earth’s polar motion is thought to be driven by various surface and interior processes, but how these processes operate and interact to produce the observed signal remains enigmatic. Here we show that predictions made by an ensemble of physics-informed neural networks trained on measurements to capture geophysical processes can explain the main features of the observed polar motion. We find that glacial isostatic adjustment and mantle convection primarily account for the secular trend. Mass redistribution on the Earth’s surface—for example, ice melting and global changes in water storage—yields a relatively weak trend but explains about 90% of the interannual and multidecadal variations. We also find that core processes contribute to both the secular trend and fluctuations in polar motion, either due to variations in torque at the core–mantle boundary or dynamical feedback of the core in response to surface mass changes. Our findings provide constraints on core–mantle interactions for which observations are rare and global ice mass balance over the past century and suggest feedback operating between climate-related surface processes and core dynamics.

Efficient protein structure archiving using ProteStAr.

The introduction of Deep Minds' Alpha Fold 2 enabled the prediction of protein structures at an unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data, which hinders the effective use of predicted structures in large-scale analyses. Here, we present ProteStAr, a compressor dedicated to CIF/PDB, as well as supplementary PAE files. Its main contribution is a novel approach to predicting atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates, the largest component of the protein structure files. The compression is lossless by default, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, i.e. BinaryCIF, Foldcomp, PDC, our approach offers a superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of the multicore architecture of current central processing units and operates with speeds of about 1 GB/s. The presence of Python and C++ API further increases the usability of the presented method. The source code of ProteStAr is available at https://github.com/refresh-bio/protestar.

A grader for fresh jujube with electromagnetic synchronous actuator assembly

AbstractIn recent years, the post‐harvest commercialization of fresh jujube sorting technology is backward, the efficiency of manual grading is low, and the application of machine vision technology in fruit grading has been a research hotspot. To improve the grading accuracy and speed of the fresh jujube machine vision grading machine, this study designs a method of fresh jujube machine vision grading using an electromagnetic synchronous actuator. The grading machine consists of an automatic distribution and arrangement system, an image acquisition and processing system, and a synchronous actuator assembly, which uses a sensitive electromagnet to adsorb a silicon steel switching buckling plate to execute the action. The image processing process includes an image color component, Sobel operator filtering, Otsu method binarization, and ellipse fitting to extract the color and size eigenvalues. The experimental results show that the date images can be acquired quickly and processed accurately with a processing speed of 50 ms per frame. In addition, the proposed actuator can be sensitively triggered with a response time of 40 ms. The grader successfully classified jujube into five size classes and one color class. The grading accuracy and non‐destructive rate reached 93.7% and 100%, respectively. The grading efficiency is 20 jujubes per second, with a maximum production rate of 1400 kilograms per hour. This grading machine realizes the requirements of high accuracy, high speed, and non‐destructive grading of fresh jujube and provides an important technical and theoretical basis for the development of machine vision jujube rapid grading technology.Practical applicationsIn recent years, sorting technology in the post‐harvest commercialization of fruits has been outdated, and manual grading has been inefficient. The application of machine vision technology in fruit grading has been a research hotspot in recent years. This study designs a machine vision grading of fresh dates with an electromagnetic synchronous actuator. The grader was composed of a roller conveyor, synchronous actuator assembly, image main processor, and actuator controlling co‐processor. Experimental results indicated that jujube images could be acquired rapidly and processed accurately, with a processing speed of 50 ms per frame. In addition, the proposed actuator could be sensitively triggered with a response time of 40 ms. Finally, the grader successfully sorted jujubes into five size classes and one color class. The grading accuracy rate and non‐destructive rate reached 93.7% and 100%, respectively. The grading efficiency was 20 jujubes per second, and the maximum productivity was 1400 kg/h.

Digital Notice Board using Bluetooth and Arduino

In this project, Bluetooth and Arduino microcontrollers are used to build and operate a wireless noticeboard system. To disseminate real-time information across many contexts, a cutting-edge, interactive platform is now being developed. On conventional noticeboards, there are just physical notes and human modifications, both of which can be tedious and time-consuming. This solution makes use of Bluetooth connectivity and the adaptability of Arduino to let users send messages wirelessly from their cell phones or laptops. The Bluetooth-enabled devices used in the wireless noticeboard system allow users to enter messages. An Arduino microcontroller with a Bluetooth module collects the messages, processes them, and displays the results on a display device. Users can rapidly post messages to the noticeboard from their mobile devices using Bluetooth technology without having to engage with it physically. The system's core processing unit, the Arduino microcontroller, which also activates the display unit, receives and processes incoming messages before displaying them.

Research on Ecoguard Solar Powered IOT for Efficient Garbage Tracking

Abstract: The "Eco Guard" project offers a novel way to improve the effectiveness of waste tracking and management by utilizing solar-powered Internet of things technology coupled with an ESP32 microcontroller. The inability of traditional waste management systems to precisely monitor waste levels and optimize collection routes results in resource waste and negative environmental effects.The Eco Guard project seeks to overcome these obstacles by putting in place a system that powers Internet of Things (IoT) devices with sensors for monitoring waste levels in real-time using renewable solar energy. The central processing unit is the ESP32 microcontroller, which makes it easier to gather, process, and send data to a cloud platform or centralized server.The Eco Guard project's salient characteristics comprise:Solar-Powered Mechanism: Utilizing solar energy guarantees continuous

Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation

Recently, every aspect of human existence has been affected by modern technologies including agriculture. The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. This work uses a deep learning algorithm to investigate the classification of potato diseases as a case study which can be leveraged on by other agricultural products for reference. Potatoes play a vital role in global agriculture, constituting a significant portion of fresh produce consumption. In southern Africa, potatoes hold particular importance, comprising 39% of total fresh produce consumption. However, the industry faces challenges from diseases such as early blight and late blight, resulting in substantial yield losses. Early detection is crucial, prompting the exploration of Convolutional Neural Networks (CNNs) for their disease classification capabilities. CNNs have shown excellent results in plant disease classification based on image data set. This study proposes the potential of aligning existing software-based Central Processing Units (CPUs) and Graphic Processing Units (GPUs) with FPGA-based potato disease classification using CNNs. To this end, five CNN models were trained on the Plant Village dataset for analysis. The models were compared using various performance metrics such as sensitivity, F1-Score, precision and recall. Based on the analysis the most suitable CNN model for FPGA implementation was selected. The chosen model was optimized for size using quantization technique. Subsequently the model was converted to Hardware Description Language (HDL) using Vitis High level synthesis (HLS) tool performance in terms of resources required to implement the model in System on Chip (SoC) was analysed.

Development of Performance Monitoring Dashboard for Product Packaging Manufacturer by using Waterfall Methodology

Implementation of a robust and advanced management information system can enhance the organizational efficiency by streamlining data management, improving decision-making processes, optimizing resource allocation, and facilitating seamless communication across all departments. PT. MPM wanted to increase organizational efficiency but discovered that the organization currently lacks an appropriate system for measuring and monitoring the performance of its core processes. In this research, a business intelligence (BI) solution was used to address the issue by developing a BI dashboard, i.e., sales performance dashboard and production performance dashboard. The waterfall approach was used as the development framework and Google Data Studio was utilized for developing the dashboard itself. The BI Dashboard offered clear and understandable information, pertinent and accurate data, as well as an interactive user interface, so the implemented system aided the business in making decisions or developing a business strategy. The BI Dashboard was evaluated through alpha and beta testing, and the users were satisfied with the BI Dashboard with an average score of 82%.

Hybrid deep learning and optimized clustering mechanism for load balancing and fault tolerance in cloud computing

ABSTRACT Cloud services are one of the most quickly developing technologies. Furthermore, load balancing is recognized as a fundamental challenge for achieving energy efficiency. The primary function of load balancing is to deliver optimal services by releasing the load over multiple resources. Fault tolerance is being used to improve the reliability and accessibility of the network. In this paper, a hybrid Deep Learning-based load balancing algorithm is developed. Initially, tasks are allocated to all VMs in a round-robin method. Furthermore, the Deep Embedding Cluster (DEC) utilizes the Central Processing Unit (CPU), bandwidth, memory, processing elements, and frequency scaling factors while determining if a VM is overloaded or underloaded. The task performed on the overloaded VM is valued and the tasks accomplished on the overloaded VM are assigned to the underloaded VM for cloud load balancing. In addition, the Deep Q Recurrent Neural Network (DQRNN) is proposed to balance the load based on numerous factors such as supply, demand, capacity, load, resource utilization, and fault tolerance. Furthermore, the effectiveness of this model is assessed by load, capacity, resource consumption, and success rate, with ideal values of 0.147, 0.726, 0.527, and 0.895 are achieved.

Finite Element Axisymmetric Models for Conical Wedge Anchorage Used in the Production of Prestressed Steel Reinforced Concretes

The anchor system plays a very important part of the prestressed structure, that contacts and transmits force to the cables when stretching to create residual stress. The operating conditions of the anchor system in general and the anchor core in particular are extremely harsh. In this paper, &amp;lt;I&amp;gt;ABAQUS&amp;lt;/I&amp;gt; software was applied to model and numerically simulate the axisymmetric loading process of the anchor core in manufacturing prestressed concrete sleepers, to study the effects of tensile forces to the pressure resistance of the cylinder. To verify the analytical results, a Finite Element (&amp;lt;I&amp;gt;FE&amp;lt;/I&amp;gt;)-model with the same material and geometrical properties was created. The difference is the assumption of plane stress in the analytical solution, can be solved using two - dimensional finite elements, which are most conveniently described in cylindrical (&amp;lt;i&amp;gt;r, θ, z&amp;lt;/i&amp;gt;) coordinates. This articles also especially focused on calculating the change of stress on the edges of the anchor core teeth with different tensile forces. We also refered to experimental results to get data on traction force and working conditions to build a calculation model and select input parameters. Comparison of the stress generated on the teeth of the anchor core was performed with three different tensions of &amp;lt;i&amp;gt;171, 181&amp;lt;/i&amp;gt; and &amp;lt;i&amp;gt;191 kN&amp;lt;/i&amp;gt;. The maximum and concentrated stress at the top and root of the anchor core when tensioning the cable has been calculated.

Synaptic plasticity and associative learning in IGZO-based synaptic transistor

The increasing demand for efficient data access in high-performance computing systems has emphasized the limitations of the traditional von Neumann architecture, particularly due to the von Neumann bottleneck. This bottleneck arises from the significant power and time consumption required for data transfer between the central processing units and memory, impeding overall system efficiency. Neuromorphic computing offers a promising alternative. Neuromorphic systems emulate biological neurons and synapses, enabling parallel data processing and potentially overcoming the limitations of traditional computing architectures. This study focuses on synaptic transistors with In–Ga–Zn-O (IGZO) channels and TaOx charge trapping layers (Ox-CTL) to enhance neuromorphic computing capabilities. The devices were fabricated using reactive sputtering and atomic layer deposition, followed by comprehensive structural and compositional analyses. Experimental results demonstrated significant hysteresis, high on/off ratios, and multibit conductance states, suggestive of effective charge trapping and detrapping mechanisms. Evaluation through potentiation, depression, and excitatory postsynaptic current (EPSC) measurements, along with simulations using the Modified National Institute of Standards and Technology (MNIST) database, revealed improved pattern recognition accuracy. Additionally, associative learning experiments modeled after Pavlov’s dog conditioning emphasized the device's capability for both long- and short-term memory retention. These findings suggest that IGZO-based synaptic transistors are promising candidates for next-generation neuromorphic computing systems, offering enhanced data processing efficiency and adaptability.