Rakesh Nadig, Vamanan Arulchelvan, Mayank Kabra
et al.
Solid-state drives (SSDs) are well suited for near-data processing (NDP) because they: (1) store large application datasets, and (2) support three NDP paradigms: in-storage processing (ISP), processing using DRAM in the SSD (PuD-SSD), and in-flash processing (IFP). A large body of prior SSD-based NDP techniques operate in isolation, mapping computations to only one or two NDP paradigms (i.e., ISP, PuD-SSD, or IFP) within the SSD. These techniques (1) are tailored to specific workloads or kernels, (2) do not exploit the full computational potential of an SSD, and (3) lack programmer-transparency. While several prior works propose techniques to partition computation between the host and near-memory accelerators, adapting these techniques to SSDs has limited benefits because they (1) ignore the heterogeneity of the SSD resources, and (2) make offloading decisions based on limited factors such as bandwidth utilization, or data movement cost. We propose Conduit, a general-purpose, programmer-transparent NDP framework for SSDs that leverages multiple SSD computation resources. At compile time, Conduit executes a custom compiler (e.g., LLVM) pass that (i) vectorizes suitable application code segments into SIMD operations that align with the SSD's page layout, and (ii) embeds metadata (e.g., operation type, operand sizes) into the vectorized instructions to guide runtime offloading decisions. At runtime, within the SSD, Conduit performs instruction-granularity offloading by evaluating six key features, and uses a cost function to select the most suitable SSD resource. We evaluate Conduit and two prior NDP offloading techniques using an in-house event-driven SSD simulator on six data-intensive workloads. Conduit outperforms the best-performing prior offloading policy by 1.8x and reduces energy consumption by 46%.
Abstract Determining thermodynamic properties in disordered systems remains a formidable challenge because of the difficulty in incorporating nuclear quantum effects into large‐scale and nonperiodic atomic simulations. In this study, we employ a machine learning deep potential model in conjunction with the quantum thermal bath method, enabling machine learning molecular dynamics to simulate thermodynamic quantities of liquid materials with satisfactory accuracy without significantly increasing computational costs. Using this approach, we accurately calculate the variations in various thermodynamic quantities of liquid metal gallium at temperatures ranging from zero to room temperature. The calculated thermodynamic properties accurately capture the solid‐liquid phase transition behavior of gallium, whereas classical molecular dynamics methods fail to reproduce realistic results. Through this approach, we offer a potential method for accurately calculating the thermodynamic properties of liquids and other disordered systems.
Materials of engineering and construction. Mechanics of materials, Computer engineering. Computer hardware
Abstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods.
Computer engineering. Computer hardware, Information technology
Abstract Industrial Cyber‐Physical Systems (iCPSs) represent a new generation of industrial systems that enable a profound integration of industrial processes and informational spaces, thereby empowering the fourth industrial revolution. iCPSs confront more severe safety and security (S&S) challenges compared to traditional industrial systems. One of the most critical challenges is the joint risk analysis of S&S. Many scholars have devoted their research to this area. However, there is a dearth of literature reviews encapsulating recent advancements, which provides the motivation for this study. The authors review the methodologies in this field, delve into the S&S relationships involved, and propose 12 criteria for evaluating these methods. Furthermore, the current research limitations were analysed and potential directions were suggested for future research.
The paper considers the electromechanical systems with cable-block transmissions. Their blocks have a small mass and are inertialess. In such transmissions, as a rule, one block is connected to the electric drive and serves as a drive block for transmitting forces, and the remaining movable and stationary transmitting blocks are passive. In this case, dry friction moments with previously unknown values act in all blocks. The author has carried out the analysis of the effect of dry friction in blocks of both types on the accuracy and stability of electromechanical systems. As a result of the mathematical description, taking into account the accepted assumptions, it is established that the effect of dry friction in the drive block is equivalent to the action of a relay β-link with a leading hysteresis characteristic. The effect of dry friction in passive inertialess blocks corresponds to the effect of backlashes with lagging hysteresis characteristics. A conclusion is made about the negative effect of dry friction in blocks of both types on the accuracy of electromechanical systems and the deterioration of their stability as a result of dry friction in passive blocks. The author developed the control algorithms that reduce the established negative effect of dry friction on the dynamics of electromechanical systems. The first adaptive algorithm is capable of accelerating the start of electromechanical system motion from a state of rest with an uncertain moment of dry friction, as well as when changing the direction of rotation of the blocks due to the rapid overcoming of the total moment of dry friction. The second robust algorithm implements a pseudo-linear law of correction of the phase-frequency characteristic of the system and creates a constant frequency-independent phase advance of the required value in it.
With the rapid development of information technology, computer hardware and software have been widely used in enterprise management. This paper aims to explore the application of computer hardware and software in enterprise management, first of all, the basic concept of computer hardware and software, and then analyzes the application of computer software and hardware in enterprise management, and then the computer hardware and software system in enterprise management advantage are summarized, including improve management efficiency, improve the quality of management, reduce management cost, etc. And through the commonly used enterprise management software system in enterprise management, the enterprise management, fully understand the depth of the meaning of the article, finally the computer hardware and software system in the enterprise management problems are discussed, and put forward the corresponding suggestions and countermeasures.
Considering the several usages of CO2, it is necessary to find a suitable CO2 purification method to satisfy each demand with low energy consumption. There are major four methods to separate CO2 from mixed gas, absorption, adsorption, membrane, and cryogenic separation. These four processes have different characteristics due to their basic separation properties. In this study, the characteristics of those processes were compared based on three performance indexes: CO2 product purity, recovery ratio, and energy consumption to find a suitable separation process for satisfying various CO2 demands. As a result, to obtain pure CO2 with a high recovery ratio and low energy consumption, it was found that vacuum pressure swing adsorption (VPSA) was the most promising among these processes and membrane separation, especially the vacuuming process was a potential candidate for getting low purity of CO2.
Chemical engineering, Computer engineering. Computer hardware
Abstract This paper introduces a novel and innovative approach to simulating random variates from two distinct probability distributions, namely the neutrosophic uniform distribution and the neutrosophic Weibull distribution. The primary objective of this research is to present a cutting-edge methodology for generating random variates by leveraging the accept-reject simulation method, particularly in the context of managing and addressing uncertainty. In addition to introducing the simulation methodology, this work will also provide comprehensive algorithms tailored to these proposed methods. These algorithms are essential for implementing the simulation techniques and will be instrumental in their practical applications. Furthermore, this study aims to explore the relationship between the level of indeterminacy and the resulting random variates. By investigating how varying degrees of indeterminacy impact random variates, we gain valuable insights into the dynamics of these distributions under different uncertainty conditions. Preliminary results suggest that random variates exhibit a trend of decreasing as indeterminacy levels increase, shedding light on the intriguing interplay between indeterminacy and random variate generation.
Computer engineering. Computer hardware, Information technology
Jacob Penney, João Felipe Pimentel, Igor Steinmacher
et al.
Computational thinking, and by extension, computer programming, is notoriously challenging to learn. Conversational agents and generative artificial intelligence (genAI) have the potential to facilitate this learning process by offering personalized guidance, interactive learning experiences, and code generation. However, current genAI-based chatbots focus on professional developers and may not adequately consider educational needs. Involving educators in conceiving educational tools is critical for ensuring usefulness and usability. We enlisted nine instructors to engage in design fiction sessions in which we elicited abilities such a conversational agent supported by genAI should display. Participants envisioned a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences. The insights obtained in this paper can guide future implementations of tutoring conversational agents oriented toward teaching computational thinking and computer programming.
This PhD dissertation investigates garbage-free reversible computing systems from abstract design to physical gate-level implementation. Designed in reversible logic, we propose a ripple-block carry adder and work towards a reversible circuit for general multiplication. At a higher-level, abstract designs are proposed for reversible systems, such as a small von Neumann architecture that can execute programs written in a simple reversible two-address instruction set, a novel reversible arithmetic logic unit, and a linear cosine transform. To aid the design of reversible logic circuits we have designed two reversible functional hardware description languages: a linear-typed higher-level language and a gate-level point-free combinator language. We suggest a garbage-free design flow, where circuits are described in the higher-level language and then translated to the combinator language, from which methods to place-and-route of CMOS gates can be applied. We have also made standard cell layouts of the reversible gates in complementary pass-gate CMOS logic and used these to fabricate the ALU design. In total, this dissertation has shown that it is possible to design non-trivial reversible computing systems without garbage and that support from languages (computer aided design) can make this process easier.
Patricio Harold Orejuela Franco, Lourdes del Rosario Hidalgo Castañeda, Silvia Isabel Chicaiza Morales
et al.
La pandemia provocada por el COVID-19 cambió drásticamente las actividades diarias a nivel mundial. En el sector educativo, la totalidad de los centros de enseñanza tuvieron que migrar sus actividades a una impartición de docencia virtual, todo lo cual tuvo importantes afectaciones al proceso de enseñanza-aprendizaje. El objeto de estudio de este trabajo analiza la educación secundaria en el Ecuador durante el COVID-19. El objetivo del manuscrito es analizar el impacto de la pandemia por COVID-19 sobre el rendimiento académico de los estudiantes del nivel secundario en el Ecuador. El estudio tiene un diseño no experimental, el alcance es descriptivo y enfoque es cuantitativo, avalado en la utilización de un cuestionario y métodos estadísticos descriptivos e inferenciales. Los resultados estadísticos correlacionales evidencian que los factores psicosociales y cognitivos asociados con la pandemia por COVID-19, los cuales se vieron muy afectados en el periodo de confinamiento por la delicada situación sanitaria vivida, tuvieron un elevado impacto sobre el rendimiento académico de los estudiantes del nivel secundario en el Ecuador. El grado de relación constatado entre la pandemia por COVID-19 y el rendimiento académico de los estudiantes fue estadísticamente significativo, con un valor elevado y lineal positivo. Por todo ello, se indica la adopción de estrategias educativas, en el orden didáctico y pedagógico, que propicien un mejoramiento de la situación actual, para posibilitar mayores niveles de calidad educativa en los estudiantes del nivel secundario.
To design foundations, embankments and other soil structures, geotechnical engineers require methods of assessing engineering properties of soils. Some of the more complex phenomena that occur in soils have often been difficult to recreate in a laboratory: seismic activity, vibration, unsaturated condition, control of principal stresses etc. are areas which have proven difficult to replicate, despite their importance of being understood. This was partly due to the lack of test systems capable of reproducing these effects and the complexity of test systems that were developed to carry out such work. A number of advanced computer/ software controlled systems allow the geotechnical engineer to perform the most complex test regimes via a user-friendly software interface. However, it is difficult to determine firstly parameters needed, e.g. shear speed in soil triaxial testing. In this paper we represent a new approach to determine this shear speed by solving the inverse problem using testing results obtained by the forward procedure. Direct search method, i.e. Adaptive Neuro-Fuzzy Inference System (ANFIS), is developed and applied to soil triaxial shear tests. It allows us to use the advanced sensor and actuator technologies in order to change the traditional triaxial shear apparatus from a mechanical system to a mechatronics system in next work.
Computer engineering. Computer hardware, Mechanics of engineering. Applied mechanics
Stefan Haller, Lorenz Feineis, Lisa Hutschenreiter
et al.
The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their performance claims are often hard to verify as evaluation on differing problem instances and criteria make the results incomparable. To address these shortcomings, we present a comparative study of graph matching algorithms. We create a uniform benchmark where we collect and categorize a large set of existing and publicly available computer vision graph matching problems in a common format. At the same time we collect and categorize the most popular open-source implementations of graph matching algorithms. Their performance is evaluated in a way that is in line with the best practices for comparing optimization algorithms. The study is designed to be reproducible and extensible to serve as a valuable resource in the future. Our study provides three notable insights: 1.) popular problem instances are exactly solvable in substantially less than 1 second and, therefore, are insufficient for future empirical evaluations; 2.) the most popular baseline methods are highly inferior to the best available methods; 3.) despite the NP-hardness of the problem, instances coming from vision applications are often solvable in a few seconds even for graphs with more than 500 vertices.
Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.
A spinning disc reactor (SDR) is a useful equipment to produce monodisperse nanoparticles with controllable properties, as particle size and particle size distribution. Since the late 90s, this technology has been successfully proven for the reaction and solvent-antisolvent precipitation process. This paper reviews the works on the use of SDR to produce inorganic and organic compounds. Firstly, the more significant works on the subject are presented concerning the produced compound, then the factors influencing the process performances are examined in the light of the results in the literature. Finally, some considerations on the fluid stream's hydrodynamics modelling along the disc surface are attempted.
Chemical engineering, Computer engineering. Computer hardware
With the rapid development of communication technology, the intelligent mobile terminal brings about great convenience to people’s life with rich applications, while its power consumption has become a great concern to researchers and consumers. Power modeling is the basis to understand and analyze the power consumption characteristics of the terminal. In this paper, we analyze the Bluetooth and hidden power consumption of the android platform and fix the power model of open-source Android platform. Then, a power consumption monitoring tool is implemented based on the model; the tool is divided into three layers, which are original information monitor layer, power consumption calculation layer, and application layer. The original monitor layer gets the power consumption data and running time of the different components under different states, the calculation layer calculates the power consumption of each hardware and each application based on the power model of each component, and the application layer displays the real-time power consumption of the software and hardware. Finally, we test our tool in real environment by using Xiaomi 9 Pro and perform comparison with actual instrument measurement; the error between the monitored value and the measured value is less than 5%.
This paper deals with gas separation by adsorption processes. The key objective is to investigate the adsorption suitability for Post-combustion Carbon Dioxide (CO2) Capture (PCC). Adsorption is a promising technology suitable for a high volume of diluted gas processing. Unlike commercialised amine-based absorption processes, adsorption seems to require less energy for sorbent regeneration and extends the sorbent lifetime. Two common industrial methods utilizing a difference in adsorption equilibrium of the gas components were investigated: 1) Pressure Swing Adsorption (PSA) including Vacuum Swing Adsorption (VSA), 2) Temperature Swing Adsorption (TSA). A comparison of their energy consumption, suitability for industrial use with consideration of Carbon Capture and Storage standards is evaluated. A complex mathematical model for the adsorption step of the fixed bed adsorber was proposed and solved by the structural programming. Three adsorbent materials: Mg-MOF-74, UTSA-16, and Zeolite 13X were evaluated based on their CO2 adsorption capacity, selectivity, and market availability. Zeolite 13X was further explored. As a benchmark case, a medium-sized natural gas cogeneration unit was used to study the potential of VSA unit. The lower limit of CO2 capture efficiency in simulations was 75 %. The results presented in this paper suggest that adsorption can be a feasible CO2 capture solution for a low-carbon emission power generation technologies. Optimal parameters for the adsorption step and column configuration are proposed.
Chemical engineering, Computer engineering. Computer hardware
Deeksha Dangwal, Meghan Cowan, Armin Alaghi
et al.
Users are demanding increased data security. As a result, security is rapidly becoming a first-order design constraint in next generation computing systems. Researchers and practitioners are exploring various security technologies to meet user demand such as trusted execution environments (e.g., Intel SGX, ARM TrustZone), homomorphic encryption, and differential privacy. Each technique provides some degree of security, but differs with respect to threat coverage, performance overheads, as well as implementation and deployment challenges. In this paper, we present a systemization of knowledge (SoK) on these design considerations and trade-offs using several prominent security technologies. Our study exposes the need for \textit{software-hardware-security} codesign to realize efficient and effective solutions of securing user data. In particular, we explore how design considerations across applications, hardware, and security mechanisms must be combined to overcome fundamental limitations in current technologies so that we can minimize performance overhead while achieving sufficient threat model coverage. Finally, we propose a set of guidelines to facilitate putting these secure computing technologies into practice.
Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to novel sensing techniques and high-throughput technologies. In reference to biomedical image analysis, the advances in image acquisition modalities and high-throughput imaging experiments are creating new challenges. This huge information ensemble could overwhelm the analytic capabilities needed by physicians in their daily decision-making tasks as well as by biologists investigating complex biochemical systems. In particular, quantitative imaging methods convey scientifically and clinically relevant information in prediction, prognosis or treatment response assessment, by also considering radiomics approaches. Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications. In this regard, frameworks based on advanced Machine Learning and Computational Intelligence can significantly improve traditional Image Processing and Pattern Recognition approaches. However, conventional Artificial Intelligence techniques must be tailored to address the unique challenges concerning biomedical imaging data. This thesis aims at proposing novel and advanced computer-assisted methods for biomedical image analysis, also as an instrument in the development of Clinical Decision Support Systems, by always keeping in mind the clinical feasibility of the developed solutions. In conclusion, the ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies, leading towards biomedical data integration for personalized medicine. As a matter of fact, the proposed computer-assisted bioimage analysis methods can be beneficial for the definition of imaging biomarkers, as well as for quantitative medicine and biology.