Electronic Processes Research Articles

CELERIDADE DO PROCESSO ELETRÔNICO NA GESTÃO PÚBLICA

The public sector faces a wide range of accumulated documents and processes, which makesit essential to implement changes and innovations to improve services and increase theefficiency of both the organization and the professionals involved. This article presents astudy on the speed of the electronic information process in public management, a technologythat facilitates communication and information in a dynamic and flexible way. The research aims to evaluate the system's performance based on the experience of different bodies, as wellas analyzing critical points. The methodology applied was qualitative in nature through theinterpretation of existing texts, theories and concepts on the topic, through a bibliographicreview. It is concluded that despite there being some cases of resistance on the part ofemployees during the system implementation process, the results are positive in the use ofelectronic information processes, demonstrating that the technology brings benefits andefficiency to the public sector.

System Dynamics Modeling For Co2 Emission Reduction Through Paper Replacement By Electronic Processes In A Public Institution

Background: The study addresses the importance of reducing CO2 emissions to mitigate climate change, focusing on replacing paper use with electronic processes in a public institution. Paper usage significantly contributes to CO2 emissions, while electronic management can reduce these emissions. The study is a proposal that encouraged the electronic processing of administrative tasks to ensure speed. Decree 8.539/2015 made the electronic processing of documents in federal public administration mandatory. The Judiciary branch was a pioneer in implementing electronic processes in Brazilian public institutions. For this article, the Systems Dynamics (SD) methodology was used, and a simulation model was developed to assess the environmental impact of process automation at the Federal University of Santa Maria (UFSM). Key results highlighted the reduction of CO2 emissions and cost savings. The implementation of digital processes resulted in a significant reduction in CO2 emissions, and the adoption of electronic processes generated financial savings. Therefore, System Dynamics modeling is a useful tool for institutions to plan scenarios and promote environmental sustainability. The transition to electronic processes in public institutions can bring significant environmental and economic benefits.

Dual S-Scheme g-C3N4/Ag3PO4/g-C3N5 photocatalysts for removal of tetracycline pollutants through enhanced molecular oxygen activation

A dual S-scheme g-C3N4/Ag3PO4/g-C3N5 heterojunction was prepared by decomposition methods, and it displayed enhanced performance to degrade tetracycline hydrochloride with the ideal stability under different water substrates and ions. Comparing with three single components, as g-C3N4, g-C3N5, and Ag3PO4, the dual S-scheme g-C3N4/Ag3PO4/g-C3N5 heterojunction displayed 4.4-, 3.4-, and 2.5-times enhancements in the tetracycline hydrochloride removal. Based on the dynamics analyses for charge carriers and band structure calculations, two channels of molecular oxygen activation (MOA) between Ag3PO4 and g-C3N4 (and g-C3N5) were confirmed. More importantly, according to this double consumption process of excited electrons, dual S-scheme g-C3N4/Ag3PO4/g-C3N5 could suppress the charge recombination, which was the key point to boosting photocatalytic activity. Moreover, the determination of intermediates also supported the vital role of MOA during these photocatalytic reactions. this report of two reactive sites in MOA that generate reactive oxygen species in a “V” type band structure. The electronic dynamic in the reaction was also testified by several detections, indicating the enhanced charge separation and migration from internal field effect and electron trapping from dual S-scheme mechanism. This work provides a new research direction for the design and mechanism analysis of dual S-scheme photocatalysts

Atomically dispersed Ru on flower-like In2O3 to boost CO2 hydrogenation to methanol

Metal-based catalysts are prevalent in the CO2 hydrogenation to methanol owing to their remarkable catalytic activity. Herein, Ru/In2O3 catalysts with different morphologies obtained by doping Ru into In2O3 with irregular, rod-like, and flower-like morphologies are used for catalytic CO2 hydrogenation to methanol. Results indicate that the flower-like Ru/In2O3 (Ru/In2O3-F) exhibits higher catalytic performance than Ru/In2O3 with other morphologies, achieving a 12.9% CO2 conversion, 74.02% methanol selectivity, and 671.36 mgMeOH·h−1·gcat−1 methanol spatiotemporal yield. Furthermore, Ru/In2O3-F maintains its catalytic stability over 200 h at 5 MPa and 290 °C. The promotional effect mainly stems from the fact that electronic structure of Ru can be effectively adjusted by modulating the morphology of In2O3. The strong interaction between atomically dispersed Ru and In2O3-F enhances the structural stability of Ru, inhibiting the agglomeration of the catalyst during the reaction process. Furthermore, density-functional theory calculations reveal that highly dispersed Ru atoms not only perform efficient and rapid electronic gain and loss processes, facilitating the catalytic activation of H2 into H intermediates. It also enables the generated reactive H to rapidly overflow to the surrounding In sites to participate in CO2 reduction. These findings provide a theoretical basis for the development of high-performance catalysts for CO2 hydrogenation.

A theoretical exploration of the electronic structure and single photoionization of the many-electron system confined in Gaussian potential

This manuscript investigates the electronic structures, spectral properties, and photoionization processes of the confined atomic system. For this purpose, a relativistic methodology employing the Dirac–Coulomb Hamiltonian within the context of relativistic configuration interaction is suggested, utilizing independent particle basis wavefunctions. The key idea of this approach is to place the atom inside a Gaussian potential, which gives a realistic description of the spatial confinement in quantum dots due to a smooth change at the quantum dot boundaries and has a finite range and depth for the spatial confinement. As a result, the local central potential is modified, which is determined by a self-consistent process. The solutions to the Dirac equation, incorporating the aforementioned central potential, yield both the continuous and bound state wave functions. The photoionization process is determined through the application of the distorted wave approach within the context of relativistic Dirac theory. As an application, the electronic structures of the confined Li atom, including energies, ionization potentials, transition rates, and photoionization dynamical properties such as wave functions, cross sections, and photoelectron angular distributions, are systematically investigated within the dipole approximation for a wide range of potential depths and confining radii. A systematic comparison of the present outcomes is made with other available results. The present study is not only meaningful for fundamental research in atomic and molecular physics, but also has implications for a range of disciplines, such as nanochemistry, materials science, and other related fields.

Utilizing multi-point temperature sensing to evaluate the low frequency noise of phasemeter for intersatellite laser interferometer.

High-precision phasemeters are a key technology in intersatellite laser interferometers used for detecting gravitational waves (GWs) in space. As the core of the readout system, the phasemeter must operate in the bandwidth of 5-25MHz, and its resolution needs to reach the order of μrad/Hz at mHz. It presents significant challenges to electronic signal processing technology. To investigate the primary noise source in the low-frequency band, a mathematical model of thermal drift to phase noise was established, and a multi-point temperature sensing scheme for critical electronic components was proposed. In particular, we evaluated a phasemeter based on a commercial platform and assessed the thermal drift noise according to the proposed model. This study identifies and explains the effects of temperature linear drift and overcorrection in components, demonstrating that thermal drift noise is the main noise source for the phasemeter at frequencies from 0.1 to 1 mHz. In addition, the proposed scheme is universal in its applicability and may be implemented in any circuit for the evaluation of temperature effects on the components of interest.

Interplay of Na Substitution in Magnetic Interaction and Photocatalytic Properties of Ca1-xNaxTi0.5Ta0.5O3 Perovskite Nanoparticles.

This research paper delves into the enhancement of wastewater treatment through the design and synthesis of advanced photocatalytic materials, focusing on the effects of sodium (Na) substitution in Ca1-xNaxTa0.5Ti0.5O3 perovskites. By employing various analytical techniques such as X-ray diffraction, Field Emission Scanning Electron Microscopy, Transmission Electron Microscopy and UV-vis spectroscopy, the study examines the transition of these perovskites from tetragonal to orthorhombic structures and observes a reduction in Ca content with Na substitution, which also favors the cubic phase formation and inhibits secondary phases. Significantly, magnetic property analysis uncovers an unexpected ferromagnetic ordering in these perovskites, including compositions traditionally viewed as non-magnetic. The photocatalytic tests reveal a significant improvement in degrading Rhodamine B dye under visible light, particularly in samples with higher Na levels, attributed to enhanced light absorption and efficient electron processes. The study highlights the optimal Na substitution level for peak photocatalytic performance, offering valuable insights into the complex interplay between structural, magnetic, and photocatalytic properties of these perovskites, and their potential in various applications, thereby contributing to the advancement of wastewater treatment technologies.

Critical study of type-2 fuzzy logic control from theory to applications: A state-of-the-art comprehensive survey

The review systematically explores the theoretical foundations of type-2 fuzzy sets and the associated mathematical frameworks of the advancements in type-2 fuzzy logic systems (T2-FLS), as well as a comprehensive overview of the state-of-the-art techniques and applications of type-2 fuzzy logic systems. The review's application-oriented section looks at a number of different areas where type-2 fuzzy logic has made a big difference. These areas include robotics, autonomous vehicle control, electric and electronic control, image processing, and railway control. This overview mainly discusses the past, present, and future trends in type-2 fuzzy logic applications. The primary contribution summarizes the most essential type-2 fuzzy logic research with theoretical and practical implications. This article will provide a critical path and a strong foundation for individuals interested in this field to pursue future research.

Drug Interactions between Androgen Receptor Axis-Targeted Therapies and Antithrombotic Therapies in Prostate Cancer: Delphi Consensus.

Background/Objectives: Abiraterone acetate, apalutamide, darolutamide, and enzalutamide, which make up the androgen receptor axis-targeted therapies (ARATs) drug class, are commonly used in the management of prostate cancer. Many patients on ARATs also receive oral antithrombotic therapy (i.e., anticoagulants or antiplatelets). The concomitant use of ARATs and antithrombotic therapies creates the potential for clinically relevant drug-drug interactions, but the literature regarding the actual consequences of these interactions, and guidance for co-prescribing, is limited. We assembled a multidisciplinary panel of experts and provided them with clinical information derived from a comprehensive literature review regarding the drug-drug interactions between ARATs and antithrombotic therapies. Methods: A three-stage modified electronic Delphi process was used to gather and consolidate opinions from the panel. Each stage consisted of up to three rounds of voting to achieve consensus on which ARAT/antithrombotic therapy drug pairs warrant attention, the possible clinical consequences of drug-drug interactions, and suggested actions for management. Results: The panel achieved consensus to avoid 11 ARAT/antithrombotic therapy drug pairs and modify therapy for eight pairs. Assessments relied heavily on pharmacokinetic data and extrapolation from drug-drug interaction studies of similarly metabolized drugs. Conclusions: This e-Delphi process highlights the need for further research into the clinical impact of ARAT/antithrombotic drug interactions. Nonetheless, the suggested actions aim to provide clinicians with a practical framework for therapeutic decision making.

LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification

Lung auscultation is essential for early lung condition detection. Categorizing adventitious lung sounds requires expert discrimination by medical specialists. This paper details the features of LungNeXt, a novel classification model specifically designed for lung sound analysis. Furthermore, we propose two auxiliary methods: RandClipMix (RCM) for data augmentation and Enhanced Mel-Spectrogram for Feature Extraction (EMFE). RCM addresses the issue of data imbalance by randomly mixing clips within the same category to create new adventitious lung sounds. EMFE augments specific frequency bands in spectrograms to highlight adventitious features. These contributions enable LungNeXt to achieve outstanding performance. LungNeXt optimally integrates an appropriate number of NeXtblocks, ensuring superior performance and a lightweight model architecture. The proposed RCM and EMFE methods, along with the LungNeXt classification network, have been evaluated on the SPRSound dataset. Experimental results revealed a commendable score of 0.5699 for the lung sound five-category task on SPRSound. Specifically, the LungNeXt model is characterized by its efficiency, with only 3.804M parameters and a computational complexity of 0.659G FLOPS. This lightweight and efficient model is particularly well-suited for applications in electronic stethoscope back-end processing equipment, providing efficient diagnostic advice to physicians and patients.

Research on Multiplication Routine Based on Reconfigurable Four-Valued Logic Processor

Despite the indispensable role of traditional electronic computers in modern society, their limitations in parallel processing capabilities, bit-width constraints, and processor bit-width are becoming increasingly apparent, especially when handling large-scale datasets and complex computational tasks. Although hardware technology and algorithm optimization continue to advance, the arithmetic units of traditional computers—adders—remain constrained by carry delay and bit-width limitations. This bottleneck is particularly pronounced in multiplication operations, mainly when adders are used for partial product accumulation. However, since 2018, the emergence of a new type of Reconfigurable Four-Valued Logic Electronic Processor (RFLEP) has provided a potential solution to these traditional limitations. With its large processor bit-width, flexible bit grouping capabilities, and dynamic hardware function reconfiguration features, this processor has brought revolutionary changes to the field of computing. In this context, this paper proposes and implements a Reconfigurable Four-Valued Logic Multiplication Routine (RFLMR) tailored explicitly for the RFLEP. The RFLMR utilizes the Modified Signed-Digit (MSD) representation method in multi-valued logic combined with the M transformation in four-valued logic to generate partial products. These partial products are then efficiently summed in parallel using the JW-MSD parallel adder, achieving the rapid execution of multiplication operations. Experimental results demonstrate that the multiplication routine based on the RFLEP performs multiplication operations accurately and meets theoretical expectations regarding implementation efficiency and performance. This research not only provides new ideas for developing next-generation high-performance computing systems but also paves the way for exploring more efficient and powerful computing models, heralding a profound transformation in future computing technology.

Dirac Electrons with Molecular Relaxation Time at Electrochemical Interface between Graphene and Water.

The time dynamics of charge accumulation at the electrochemical interface between graphene and water is important for supercapacitors, batteries, and chemical and biological sensors. By using impedance spectroscopy, we have found that measured capacitance (Cm) at this interface with the gate voltage Vgate ≈ 0.1 V follows approximate laws Cm~T1.2 and Cm~T0.11 (T is Vgate period) in frequency ranges (1000-50,000) Hz and (0.02-300) Hz, respectively. In the first range, this dependence demonstrates that the interfacial capacitance (Cint) is only partially charged during the charging period. The observed weaker frequency dependence of the measured capacitance (Cm) at frequencies below 300 Hz is primarily determined by the molecular relaxation of the double-layer capacitance (Cdl) and by the graphene quantum capacitance (Cq), and it also implies that Cint is mostly charged. We have also found a voltage dependence of Cm below 10 Hz, which is likely related to the voltage dependence of Cq. The observation of this effect only at low frequencies indicates that Cq relaxation time is much longer than is typical for electron processes, probably due to Dirac cone reconstruction from graphene electrons with increased effective mass as a result of their quasichemical bonding with interfacial molecular charges.

Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques

This study presents an advanced methodology for improving electronic assembly quality through real-time, inline inspection utilizing state-of-the-art artificial intelligence (AI) and deep learning technologies. The primary goal is to ensure compliance with stringent manufacturing standards, notably IPC-A-610 and IPC-J-STD-001. Employing the existing infrastructure of pick-and-place machines, this system captures high-resolution images of electronic components during the assembly process. These images are analyzed instantly by AI algorithms capable of detecting a variety of defects, including damage, corrosion, counterfeit, and structural irregularities in components and their leads. This proactive approach shifts from conventional reactive quality assurance methods by integrating real-time defect detection and strict adherence to industry standards into the assembly process. With an accuracy rate exceeding 99.5% and processing speeds of about 5 ms per component, this system enables manufacturers to identify and address defects promptly, thereby significantly enhancing manufacturing quality and reliability. The implementation leverages big data analytics, analyzing over a billion components to refine detection algorithms and ensure robust performance. By pre-empting and resolving defects before they escalate, the methodology minimizes production disruptions and fosters a more efficient workflow, ultimately resulting in considerable cost reductions. This paper showcases multiple case studies of component defects, highlighting the diverse types of defects identified through AI and deep learning. These examples, combined with detailed performance metrics, provide insights into optimizing electronic component assembly processes, contributing to elevated production efficiency and quality.

Strong Electron-Electron-Nuclei Correlations in Two-Photon Double Ionization of H_{2}.

Two-photon double ionization is a paradigmatic example of how electron correlation manifests. In molecular targets, its coupling with the slower nuclear motion introduces an additional complication and induces electron-electron-nuclei correlations. Experimentally, momentum-coincident measurements can provide a complete kinematical image of the molecular full Coulomb breakup. Previous theoretical studies have described this process by ignoring nuclear motion and the subsequent Coulomb explosion of the dication. Here we show, by means of a full-dimensional treatment of two-photon double ionization of the H_{2} molecule, that nuclear motion plays a decisive role even for pulses as short as 1.5fs, a time during which the nuclei are not expected to move significantly. We find strong correlations between nuclear and electronic degrees of freedom, giving access to different electronic processes as a function of nuclear kinetic energy. In particular, we observe unexpectedly strong back-to-back asymmetry in the photoelectron angular distributions, as well as novel interferences resulting from the coherent contributions from two-photon sequential absorption paths via different molecular cationic states.

Iron-Catalyzed Laser-Induced Graphitization - Multiscale Analysis of the Structural Evolution and Underlying Mechanism.

The transition to sustainable materials and eco-efficient processes in commercial electronics is a driving force in developing green electronics. Iron-catalyzed laser-induced graphitization (IC-LIG) has been demonstrated as a promising approach for rendering biomaterials electrically conductive. To optimize the IC-LIG process and fully exploit its potential for future green electronics, it is crucial to gain deeper insights into its catalyzation mechanism and structural evolution. However, this is challenging due to the rapid nature of the laser-induced graphitization process. Therefore, multiscale preparation techniques, including ultramicrotomy of the cross-sectional transition zone from precursor to fully graphitized IC-LIG electrode, are employed to virtually freeze the IC-LIG process in time. Complementary characterization is performed to generate a 3D model that integrates nanoscale findings within a mesoscopic framework. This enabled tracing the growth and migration behavior of catalytic iron nanoparticles and their role during the catalytic laser-graphitization process. A three-layered arrangement of the IC-LIG electrode is identified including a highly graphitized top layer with an interplanar spacing of 0.343nm. The middle layer contained γ-iron nanoparticles encapsulated in graphitic shells. A comparison with catalyst-free laser graphitization approaches highlights the unique opportunities that IC-LIG offers and discuss potential applications in energy storage devices, catalysts, sensors, and beyond.

The N,N,N′,N′-tetraphenylbenzidine-based conjugated hypercrosslinked polymers for adsorping iodine and fluorescence sensing 2,4-dinitrophenol, iodine

Although progress has been made in the development of hypercrosslinked polymers (HCPs) for iodine uptake, their fluorescence performance and fluorescence sensing performance are rare due to the lack of conjugated behavior in common HCPs. Herein, the N,N,N′,N′-tetraphenylbenzidine-based conjugated hyper-crosslinked polymers (conjugated TPB-based HCPs) were efficiently fabricated via a one-step Friedel-Crafts arylation reaction by selecting N,N,N′,N′-tetraphenylbenzidine (TPB) and N,N′-diphenyl-N,N′-di(m-tolyl)benzidine (DPDB) as the basic building blocks and p-dimethoxybenzene (DMB) as an external crosslinker which will lead to the formation of the conjugated structures. The derived TPB-based HCPs (denoted as, TPB-DMB and DPDB-DMB) possessed excellent stability and high surface areas, Their iodine adsorption capacities are up to 4.25 and 3.56 g·g−1. The high iodine adsorption is caused by chemical adsorption. The fully conjugated structure and the 3D aryl network give the conjugated TPB-based HCPs excellent luminescence properties and fluorescence sensing properties for 2,4-dinitrophenol and iodine. The fluorescence sensing mechanisms of the conjugated TPB-based HCPs for DNP and I2 includes photo-induced electron process and the resonance energy transfer process. This study provides a viable approach for the development of highly efficient iodine sorbents and fluorescence sensors of DNP and iodine for addressing environmental issues.

Achieving Electron Capture Dissociation in the Radio Frequency Linear Ion Trap without the Assistance of a Magnetic Field─A Simulation Study.

It is extremely difficult to inject a low-energy electron beam into a conventional radiofrequency (RF) linear ion trap for electron capture dissociation (ECD) without using a magnetic field to focus the electrons. In this study, the dynamic process of electrons in an RF field during their injection and transmission through a linear ion trap was simulated to determine the range of the RF phase where the electrons can be decelerated to meet the energy requirement for ECD. The ECD time window was expanded by applying a time-dependent compensation voltage to the cathode. The relationship between the cathode voltage and the phase of the RF voltage was determined. The ECD time window was increased to 49.4% of the total RF cycle after applying a compensation voltage. Between the phases of RF voltage of 0 and 0.975 π, at least 98.7% of electrons can be injected into the ECD reaction zone, and 94% of them had an energy less than 3 eV. The range of electron energy can also easily be shifted upward to enable hot electron capture dissociation.

GO@β-Ag2MoO4 Composite: One-Step Synthesis, Characterization, and Photocatalytic Performance against RhB Dye.

The combination of materials to improve properties of interest has become one of the strategies widely used for numerous applications, including new catalysts, over the last few decades. In this study, silver molybdate (β-Ag2MoO4) microcrystals were efficiently obtained by the hydrothermal method, obtaining composites with different amounts of graphene oxide (GO) (1, 2.5, 5, 7.5, and 10%, w/w) using the conventional hydrothermal method. The incorporation of GO on silver molybdate was confirmed by X-ray diffraction (XRD) and Raman spectroscopy, where the vibrational modes and crystallographic planes characteristic of the materials of interest were highlighted. The images collected by scanning electron microscopy (SEM) revealed the occurrence of plate-shaped structures (shells) anchored to the surface of the silver molybdate microcrystals (core). The optical properties showed that the materials presented E gap between 3.34 and 3.39 eV, where the sample with 7.5% of GO (GO@β-AgMo_7.5) was the one that presented energy for the conduction band, largely favorable to the formation of superoxide radicals through the photoexcitation process of electrons. The catalytic tests demonstrated that, among the samples obtained in this study, the sample with 7.5% of GO (GO@β-AgMo_7.5) exhibits superior photocatalytic performance against the dye rhodamine B (RhB) in an aqueous medium. Thus, the kinetics constant for photolysis (absence of catalysts) and for the sample β-AgMo and the sample with 7.5% of GO (GO@β-AgMo_7.5) are 0.38 × 10-3, 12 × 10-3, and 23.72 × 10-3 min-1, respectively. Therefore, it is 62.5 times more efficient in the degradation of the RhB dye, which confirms the promising photocatalytic properties of the obtained composite.

EP537 - Revolutionizing informed consent: A quantum leap in precision for chronic pain awareness in inguinal hernia operations through electronic consent

Abstract Aim The study aimed to assess the extent to which clinicians provide information to patients about the occurrence of chronic pain during consultations, recognizing it's significance as a prevalent postoperative complication following inguinal hernia repair. Methods Data were gathered by two teams at distinct time periods within the same trust. One team utilized the standard paper-based consenting method, while the other employed a recently introduced electronic consenting process. A comparison of these approaches was undertaken to derive the study's results, making it a retrospective and comparative analysis. Results An investigation on this subject conducted initially uncovered that 84% of patients were not informed about the risk of chronic pain and did not provide consent for this common occurrence via standard paper based method. Consequently, the study recommended the implementation of an e-consent facility in the Trust. This facility would feature a template encompassing all pertinent risks associated with inguinal hernia surgery. Following the introduction of e-consent later, we conducted a subsequent analysis on patients who underwent inguinal hernia surgery. The findings indicated a notable improvement, with 98% of patients having given their consent for chronic pain.Manner of consentingPatient who was informed about chronic pain during consentingPatient who wasn't informed about chronic pain during consentingPaper consent16%84%e-Consent982% Conclusion The incorporation of chronic pain as a potential complication in the consenting process for inguinal hernia surgery has seen improvement since the introduction of the e-consent software. However, to achieve full compliance, there is a need for further enhancement and increased utilization of the e-consent software.

Photonic probabilistic machine learning using quantum vacuum noise

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element – a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~1 Gbps and energy consumption of ~5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.