Computer Processing Unit Research Articles

Photo-enhanced precious metal recovery enabled by trithiocyanuric-linked covalent triazine frameworks

Precious metals recovery from secondary resources is crucial for ensuring the sustainable supply of resources and development of circular economy. Herein, the trithiocyanuric-linked covalent triazine frameworks (denoted as TTC-CTFs) are proposed and constructed for efficient recovery of precious metals. As expected, the proposed TTC-CTFs afford ultrahigh uptake capacities of 3365 mg g−1 for Au(III) and 1125 mg g−1 for Pt(IV) under light irradiation, achieving the respective 2.3-fold and 1.5-fold enhancement relative to those in the dark and outperforming the previously reported materials. Meanwhile, fast kinetics and superior recyclability are achieved, as reflecting by 15 min of the equilibrium time and neglect decline in recovery efficiency during ten cycles. Post-recovery characterizations and theoretical calculations reveal the combination of adsorption, photoreduction/chemical reduction, and crystallization is responsible for the photo-enhanced Au recovery. Significantly, TTC-CTFs are also competent for selective and efficient gold recovery from the computer processing unit (CPU) leachate, as companied by 99% of recovery efficiency and considerable profit, highlighting the feasibility of the proposed TTC-CTFs for the precious metal recovery from the actual e-waste.

MODELING STOCHASTIC ADVERSE EFFECTS OF CBN 2023 REDESIGNED NAIRA NOTES POLICY ON RURAL FARMERS IN NIGERIA

The recent Central Bank of Nigeria (CBN) 2023 redesigned naira notes is of good benefits to strengthen the economy of the country by checking counterfeiting and hoarding of large volume of banknotes by the public. Despite all the efforts made by the CBN for citizens to enjoy the benefits of this implementation, most rural farmers were faced with adverse effects of uncertainties in the production and marketing of their agricultural produce due to lack of redesigned new naira notes in circulation. The adverse effects of these uncertainties are modeled as Advanced Stochastic Time-Delay Differential Equation (ASTDDE). The modeled equation is solved using Extended Second Derivative Block Backward Differentiation Formulae Method (ESDBBDFM) without the use of interpolation techniques in the evaluations of the delay term and noise term. In comparing the numerical results of this method with other existing methods in literature, the newly developed mathematical expressions for the evaluations of the delay term and the noise term in solving ASDDEs with the discrete schemes of ESDBBDFM gives better results for step number than step numbers and 3 by producing Least Minimum Absolute Random Error (LMARE) in a Lower Computational Processing Unit Time (LCPUT) faster than other existing methods that applied interpolation techniques in evaluations of the delay term and the noise term.

A GPU based accelerated solver for simulation of heat transfer during metal casting process

The metal casting process, which is one of the key drivers of the manufacturing industry, involves several physical phenomena occurring simultaneously like fluid flow, phase change, and heat transfer which affect the casting yield and quality. Casting process modeling involves numerical modeling of these phenomena on a computer. In recent decades, this has become an inevitable tool for foundry engineers to make defect-free castings. To expedite computational time graphics processing units (GPUs) are being increasingly used in the numerical modeling of heat transfer and fluid flow. Initially, in this work a CPU based implicit solver code is developed for solving the 3D unsteady energy equation including phase change numerically using finite volume method which predicts the thermal profile during solidification in the metal casting process in a completely filled mold. To address the computational bottleneck, which is identified as the linear algebraic solver based on the bi-conjugate gradient stabilized method, a GPU-based code is developed using Compute Unified Device Architecture toolkit and was implemented on the GPU. The CPU and GPU based codes are then validated against a commercial casting simulation code FLOW-3D CAST® for a simple casting part and against in-house experimental results for gravity die casting of a simple geometry. Parallel performance is analyzed for grid sizes ranging from 10 × 10 × 10 to 210 × 210 × 210 and for three time-step sizes. The performance of the GPU code based on occupancy and throughput is also investigated. The GPU code exhibits a maximum speedup of 308× compared to the CPU code for a grid size of 210 × 210 × 210 and a time-step size of 2 s.

Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications

Abstract. The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration.

Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure with disbond and delamination type of damages, validated using data generated by finite element simulations and experiments performed at various temperatures in the range 0–90 °C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage. Despite the limited number of features, the lightweight model shows reasonably high accuracy, thereby enabling detection of small size defects with improved sensitivity on an edge device for online GW-SHM.

Impact of surfactant on Al2O3/water nanofluids stability for cooling the central processing unit of computer

In order for a computer to perform well, it is important to keep the central processing unit (CPU) cool, and this can be accomplished by using a liquid coolant. This study was conducted to investigate how effective nanofluids could be as a coolant for a water block used in CPU cooling. The nanofluids used in the experiment contained Al2O3 nanoparticles and SDS surfactant in a mixture in water at volume fractions of 0.5%. They were prepared using an ultrasonic cleaner, and then tested to determine how well they transferred heat and the amount of pump power needed. The findings suggest that nanofluids with higher concentrations have better convection coefficient values and are more efficient at reducing the temperature of the water block, but they require more pumping power. It was concluded that the Al2O3 nanofluid with SDS surfactant could be used as a coolant for CPU cooling because of its effectiveness in lowering the temperature of the water block. The present work results are agreed with data available in the literature.

Efficient Recycling of Gold and Copper from Electronic Waste by Selective Precipitation.

The recycling of metals from electronic waste (e-waste) using efficient, selective, and sustainable processes is integral to circular economy and net-zero aspirations. Herein, we report a new method for the selective precipitation of metals such as gold and copper that offsets the use of organic solvents that are traditionally employed in solvent extraction processes. We show that gold can be selectively precipitated from a mixture of metals in hydrochloric acid solution using triphenylphosphine oxide (TPPO), as the complex [(TPPO)4 (H5 O2 )][AuCl4 ]. By tuning the acid concentration, controlled precipitation of gold, zinc and iron can be achieved. We also show that copper can be selectively precipitated using 2,3-pyrazinedicarboxylic acid (2,3-PDCA), as the complex [Cu(2,3-PDCA-H)2 ]n ⋅ 2n(H2 O). The combination of these two precipitation methods resulted in the recovery of 99.5 % of the Au and 98.5 % of the Cu present in the connector pins of an end-of-life computer processing unit. The selectivity of these precipitation processes, combined with their straightforward operation and the ability to recycle and reuse the precipitants, suggests potential industrial uses in the purification of gold and copper from e-waste.

Efficient Recycling of Gold and Copper from Electronic Waste by Selective Precipitation

AbstractThe recycling of metals from electronic waste (e‐waste) using efficient, selective, and sustainable processes is integral to circular economy and net‐zero aspirations. Herein, we report a new method for the selective precipitation of metals such as gold and copper that offsets the use of organic solvents that are traditionally employed in solvent extraction processes. We show that gold can be selectively precipitated from a mixture of metals in hydrochloric acid solution using triphenylphosphine oxide (TPPO), as the complex [(TPPO)4(H5O2)][AuCl4]. By tuning the acid concentration, controlled precipitation of gold, zinc and iron can be achieved. We also show that copper can be selectively precipitated using 2,3‐pyrazinedicarboxylic acid (2,3‐PDCA), as the complex [Cu(2,3‐PDCA‐H)2]n ⋅ 2n(H2O). The combination of these two precipitation methods resulted in the recovery of 99.5 % of the Au and 98.5 % of the Cu present in the connector pins of an end‐of‐life computer processing unit. The selectivity of these precipitation processes, combined with their straightforward operation and the ability to recycle and reuse the precipitants, suggests potential industrial uses in the purification of gold and copper from e‐waste.

Direct numerical simulation of slender cones with variable nose bluntness based on graphics processing unit computation

Direct numerical simulation with up to 10×109 scale grid points based on graphics processing unit computation is carried out to investigate the bluntness effect on the hypersonic boundary-layer transition over a slender cone with zero angle of attack at Mach 6. Four cases with the nose radii of 1, 10, 20, and 40 mm are conducted, and the corresponding Reynolds number based on the nose radius varies from 1.0×104 to 4.0×105. Random disturbances with a broad spectrum of frequencies and a wide range of azimuthal wavenumbers were applied to the wall to simulate disturbances caused by wall roughness. The numerical results show that as the nose tip radius increases, the transition position gradually moves downstream with increased transition region. For the case with a nose radius of 1 mm, the flow transition and entropy swallowing occur almost simultaneously, while for other cases, the transition takes place earlier than the entropy swallowing. In consequence, the disturbance amplitude upstream of the transition in the 1 mm case is much larger than that of other cases. To further study the mechanism of the transition, the frequency spectrum analysis is carried out. It is found that all cases exhibit two characteristic frequencies within the transition region, i.e., the high frequency and extremely low frequency. Owing to the influence of the entropy layer, the characteristic high frequency of the 1 mm case is significantly higher than that of other cases. With the increase in the nose radius, the characteristic frequency of the high frequency decreases gradually.

Solving the Neutron Transport Equation for Microreactor Modeling Using Unstructured Meshes and Exascale Computing Architectures

The Microreactor Exascale eZ CALculation (MEZCAL) tool has been developed to accurately and efficiently solve the neutron transport equation in general, unstructured meshes to support the design and modeling of microreactors. MEZCAL solves the self-adjoint angular flux form of the neutron transport equation using the finite element method. As the neutron transport equation is computationally expensive to solve, MEZCAL is designed to efficiently use exascale computing architectures, with an emphasis on graphics processing unit computing. To leverage existing tools, MEZCAL is built using the MFEM library and uses solvers from HYPRE, PETSc, and SLEPc. Verification of the neutron transport solver in MEZCAL is demonstrated with the solution to a one-dimensional cylindrical problem that has a semi-analytic solution. After verification, a realistic microreactor based on the MARVEL microreactor design is modeled using MEZCAL. Spatial and angular refinement results are presented for a two-dimensional model of the MARVEL microreactor, and the eigenvalue is converged to approximately 60 pcm. This convergence required a very fine mesh and more than 3.76 Billion Degrees Of Freedom (BDOF). Preliminary results are also presented for a three-dimensional model of the MARVEL microreactor. Finally, a weak scaling study is performed to investigate how the methods in MEZCAL will scale for larger problems with the next generation of exascale computing architectures.

Efficient parallelization for 3D-3V sparse grid Particle-In-Cell: Single GPU architectures

In the present paper, an efficient General Purpose Graphical Processing Unit (GPGPU)-based implementation of sparse grid Particle-In-Cell (PIC) methods is proposed. The parallelization, implementing novel strategies specific to sparse-PIC methods and tailored to GPU architectures, provides speed-ups1 as large as 100 on a single Tesla V100 GPU, with respect to sequential Computing Processing unit (CPU) execution; and a four order of magnitude reduction of the computational time in comparison with a standard PIC sequential CPU simulation. In addition, the simple implementation of the parallelization with the OpenACC framework offers portability to a large class of accelerators.

Interfacial Coordinational Bond Triggered Photoreduction Membrane for Continuous Light-Driven Precious Metals Recovery.

Chemical/electric energy-driven processes dominate the traditional precious metal (PM) recovery market. The renewable energy-driven selective PM recycling approach crucial for carbon neutrality is under exploration. Herein, via an interfacial structure engineering approach, coordinational-active pyridine groups are covalently integrated onto the photoactive semiconductor SnS2 surface to construct Py-SnS2. Triggered by the preferred coordinational binding force between PMs and pyridine groups, together with the photoreduction capability of SnS2, Py-SnS2 shows significantly enhanced selective PM-capturing performance toward Au3+, Pd4+, and Pt4+ with recycling capacity up to 1769.84, 1103.72, and 617.61 mg/g for Au3+, Pd4+, and Pt4+, respectively. Further integrating the Py-SnS2 membrane into a homemade light-driven flow cell, 96.3% recovery efficiency was achieved for continuous Au recycling from a computer processing unit (CPU) leachate. This study reported a novel strategy to fabricate coordinational bonds triggered photoreductive membranes for continuous PM recovery, which could be expanded to other photocatalysts for broad environmental applications.

Interfacial Coordination Bonding-Assisted Redox Mechanism-Driven Highly Selective Precious Metal Recovery on Covalent-Functionalized Ultrathin 1T-MoS2.

Rational design of functional material interfaces with well-defined physico-chemical-driven forces is crucial for achieving highly efficient interfacial chemical reaction dynamics for resource recovery. Herein, via an interfacial structure engineering strategy, precious metal (PM) coordination-active pyridine groups have been successfully covalently integrated into ultrathin 1T-MoS2 (Py-MoS2). The constructed Py-MoS2 shows highly selective interfacial coordination bonding-assisted redox (ICBAR) functionality toward PM recycling. Py-MoS2 shows state-of-the-art high recovery selectivity toward Au3+ and Pd4+ within 13 metal cation mixture solutions. The related recycling capacity reaches up to 3343.00 and 2330.74 mg/g for Au3+ and Pd4+, respectively. More importantly, above 90% recovery efficiencies have been achieved in representative PMs containing electronic solid waste leachate, such as computer processing units (CPU) and spent catalysts. The ICBAR mechanism developed here paves the way for interface engineering of the well-documented functional materials toward highly efficient PM recovery.

OpenMP Fortran programs for solving the time-dependent dipolar Gross-Pitaevskii equation

OpenMP Fortran programs for solving the time-dependent dipolar Gross-Pitaevskii equation

Инженерный расчет разноглубинного рыболовного трала

This paper presents a method for calculating the power and geometric properties of a midwater fishing trawl, which is a complicated engineering structure made up of tied flexible (threads, ropes, flap) and rigid (rigging, trawl boards) elements. A computational model, the functional relations for internal forces and geometric characteristics of the trawl, and a calculation procedure based on the finite element method are proposed. The computational algorithm is implemented on a graphics processing unit of a personal computer with the use of DirectCompute, Shader Model 5, and high-level shader language. The performance and resource intensity of the algorithm applied in the computer-aided design system for industrial fishing gear are assessed in terms of the number of trawl nodes and computing equipment. A comparative analysis of the calculated and experimentally obtained characteristics of the trawl physical model is presented. According to the results, the computational error in geometric characteristics and internal forces is less than 3 and 5%, respectively.

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy.

Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist's skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

INTERNET OF THINGS-ASSISTED LORAWAN-ENABLED HAND HYGIENE MONITORING AND TRACKING SYSTEM FOR PREVENTING COMMUNITY-ACQUIRED PNEUMONIA AND HEALTHCARE-ASSOCIATED INFECTIONS

Healthcare-associated infections (HAI) are a primary concern in critical care units, dialysis centers, and nursing stations. Practicing Alcohol-Based Hand Hygiene (ABH) is predicted to reduce the risk of disease transmission. Recent technological breakthroughs in the Internet of Things (IoT) and Long Range communication (LoRa) protocols provide cutting-edge solutions to reduce HAIs, particularly community and hospital-acquired pneumonia. This research uses an IoT-enabled LoRa network to monitor and track hand hygiene practices to avoid pneumonia and other HAI infections. The ABH dispenser can recognize the subjects, activate the hand sanitizer, permit the subject admission, and record hand-washing activities to a server powered by an NVIDIA Jetson Nano Graphical Processing Unit computer. All the data, with a user ID and GPS location, is deployed in a cloud server and an application server for storage and display and relayed to a LoRa gateway using the ESP32 IoT platform equipped with a LoRaWAN and short-range Bluetooth Low Energy. A series of field tests was conducted in various hospital buildings and simulated scenarios. Real-world LoRa network situations have brought an overall success rate of 92%, whereas laboratory testing has an overall success rate of 98%. As individuals grew more conscious of the need for personal and institutional hygiene during the Covid-19 pandemic, the frequency of HAIs increased. The IoT-enabled intelligent ABH network is a cost-effective infection prevention and control mechanism, and it reduces pneumonia, HAI rate, and stress of healthcare workers and critical care unit patients.

Neuromorphic devices based on fluorite‐structured ferroelectrics

AbstractA continuous exponential rise has been observed in the storage and processing of the data that may not curtail in the foreseeable future. The required data processing speed and power consumption are restricted by the buses between the logic and memory devices that are characteristic of the von Neumann computing architecture. Bio‐mimicking neuromorphic computing has garnered considerable academic and industrial interest to resolve these challenges. Additionally, devices based on emerging nonvolatile memories capable of mimicking the behaviors of synapses and neurons, which are the main elements in biological computing systems (brains), are attracting significant interest from the device community. With the discovery of ferroelectricity in fluorite‐structured oxides, such as HfO2 and ZrO2, which are compatible with the state‐of‐the‐art complementary‐metal‐oxide‐semiconductor processes, ferroelectric devices have rapidly evolved as the main direction of these research and development activities. Fundamental science related to fluorite‐structured ferroelectrics has been intensively studied over the last decade. At present, the focus is gradually moving to practical applications, including neuromorphic computing and advanced classical processing or memory units in the conventional von Neumann architecture. However, despite its rapid development, the wealth of recent progress in neuromorphic computing devices based on fluorite‐structured ferroelectrics has not been reviewed and systemized. This progress report comprehensively reviews and systemizes the recent progress in artificial synaptic and spiking neuron devices for neuromorphic computing based on fluorite‐structured ferroelectrics.image

Bayesian optimization using multiple directional objective functions allows the rapid inverse fitting of parameters for chromatography simulations

The modeling of chromatographic separations can speed up downstream process development, reducing the time to market and corresponding development costs for new products such as pharmaceuticals. However, calibrating such models by identifying suitable parameter values for mass transport and sorption is a major, time-consuming challenge that can hinder model development and improvement. We therefore designed a new approach based on Bayesian optimization (BayesOpt) and Gaussian processes that reduced the time required to compute relevant chromatography parameters by up to two orders of magnitude compared to a multistart gradient descent and a genetic algorithm. We compared the three approaches side by side to process several internal and external datasets for ion exchange chromatography (based on a steric mass action isotherm) and hydrophobic interaction chromatography (a modified version of a recently published five-parameter isotherm) as well as different input data types (gradient elution data alone vs gradient elution and breakthrough data). We found that BayesOpt computation was consistently faster than the other approaches when using either single-core or 12-cores computer processing units. The error of the BayesOpt parameter estimates was higher than that of the competing algorithms, but still two orders of magnitude less than the variability of our experimental data, indicating BayesOpts applicability for chromatography modeling. The low computational demand of BayesOpt will facilitate rapid model development and improvement even for large datasets (e.g., > 100 proteins) and increase its suitability for research laboratories or small and medium enterprises lacking access to dedicated mainframe computers.

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.