Improvements In Scalability Research Articles

Railway Cloud: Management and Orchestration Functionality Designed as Microservices

The cloudification and virtualisation of railway functions have the potential to improve railway operation efficiency, reliability, safety, and security, as well as to enhance passenger experience by offering innovative services. This paper considers issues related to the management and orchestration of railway clouds that host cloudified railway functions. A microservices-based approach to the design of railway cloud management and orchestration functionality is proposed. The basic railway cloud concepts were defined, and functionality related to the basic orchestration of the railway cloud and deployments is analysed in order to derive the requirements of platform resources and workload management. This functionality is further designed in the form of microservices, meaning that they could possibly be used in orchestration applications to enable improvements in scalability, fault isolation, and data security. The design of microservices follows the principles of the Representational State of Transfer (REST) application programming interface (API) as a set of interlinked resources. Resources related to railway cloud orchestration are identified with their associated data, relationships to other resources, and applicable methods. The resources’ methods are used in railway applications to implement the required orchestration functionality and to maintain the state of railway cloud orchestration processes. To verify the synthesised microservices, the common orchestration application logic and microservices’ logic were modelled, and it was proved that the orchestration processes, which run concurrently, expose equivalent behaviour. The proposed approach was validated using a simulation, aiming to evaluate injected latency as a key performance indicator for the reliability and safety of railway operations. Additionally, some safety and security issues related to railway cloud management and orchestration are considered.

Cyber–physical system architecture of autonomous robot ecosystem for industrial asset monitoring

Driven by advancements in Industry 4.0, the Internet of Things (IoT), digital twins (DT), and cyber–physical systems (CPS), there is a growing interest in the digitalizing of asset integrity management. CPS, in particular, is a pivotal technology for the development of intelligent and interconnected systems. The design of a scalable, low-latency communication network with efficient data management is crucial for connecting physical and digital twins in heterogeneous robot fleets. This paper introduces a generalized cyber–physical architecture aimed at governing an autonomous multi-robot ecosystem via a scalable communication network. The objective is to ensure accurate and near-real-time perception of the remote environment by digital twins during robot missions. Our approach integrates techniques such as downsampling, compression, and dynamic bandwidth management to facilitate effective communication and cooperative inspection missions. This allow for efficient bi-directional data exchange between digital and physical twins, thereby enhancing the overall performance of the system.This study contributes to the ongoing research on the deployment of cyber–physical systems for heterogeneous multi-robot fleets in remote inspection missions. The feasibility of the approach has been demonstrated through simulations in a representative environment. In these experiments, a fleet of robots is used to map an unknown building and generate a common 3D probabilistic voxel-grid map, while evaluating and managing bandwidth requirements. This study represents a step forward towards the practical implementation of continuous remote inspection with multi-robot systems through cyber–physical infrastructure. It offers potential improvements in scalability, interoperability, and performance for industrial asset monitoring.

The Role of Primary Healthcare in Improving the Maternal and Infant Mortality in Pakistan

Background: Maternal and neonatal mortality remains a significant public health challenge globally, particularly in countries like Pakistan, which exhibit some of the highest rates of such mortalities. Effective primary healthcare interventions are crucial for mitigating these rates, focusing on enhancing the quality of care provided to mothers and newborns. Understanding the scope and impact of these interventions can inform future strategies to improve maternal and neonatal health outcomes. Objective: This review aims to evaluate the existing literature on primary healthcare interventions targeting maternal and neonatal mortality, identifying the types of interventions employed, their focus areas, and the outcomes achieved, with a specific interest in the context of Pakistan. Methods: A scoping review methodology was employed, analyzing studies that investigated primary healthcare interventions designed to reduce maternal and neonatal mortality. The review included studies published in English, utilizing a mix of research methodologies, and focused on interventions such as morbidity and mortality review processes, healthcare worker training, and community engagement strategies. Databases searched included Google Scholar, OVID, and the WHO Global Health Library, among others. Results: Out of 25 studies reviewed, a majority concentrated on healthcare team and program-level interventions rather than systemic healthcare reforms. Key areas of focus included enhancing review processes for maternity and neonatal care, training and education for healthcare workers, and community development initiatives. However, there was a noted lack of interventions directly improving the healthcare experiences of mothers and newborns. Mixed methods were the most common research approach, aligning with the complexity of evaluating health system interventions. Conclusion: To improve maternal and neonatal health outcomes in Pakistan, a comprehensive approach addressing the entire health system is required. This involves not only focusing on the delivery of healthcare services but also incorporating community insights, continuous education programs, and leadership modifications in healthcare services. Future strategies should prioritize the application of standardized and relevant methods to enhance the quality of maternity and newborn care initiatives, aiming for scalable improvements in healthcare outcomes.

Implementasi Load Balancing dengan HAProxy di Sistem Informasi Akademik UIN Sunan Kalijaga

Efficiently managing academic information systems (AIS) is essential for educational institutions to provide reliable services to students and faculty. This research explores the integration of HAProxy load balancing and file synchronization techniques to optimize the performance of AIS. HAProxy is employed to distribute incoming requests across multiple backend servers, and the backend will call web service to access the data saved in the database to facilitate seamless data sharing and access. Additionally, file synchronization mechanisms are implemented to maintain consistency across scripts used in the backend system. The study conducts performance evaluations and benchmarks to assess the impact of HAProxy load balancing and file synchronization on AIS responsiveness and reliability. The results reveal significant system scalability and fault tolerance improvements, reducing downtime and enhancing user experience. This research contributes to optimizing academic information systems, enhancing their ability to handle increased loads, and ensuring the efficient delivery of educational services.

Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm

AbstractAssembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to $$\sim $$ ∼ 2.75$$\times $$ × , reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.

ICORPP: Interleaved commonsense reasoning and probabilistic planning on robots

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present an algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to “understand the world” and planning to “achieve the goal” respectively. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, and dialog management. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies.

Nanoscopic decoration of multivalent vanadium oxide on Laser-Induced graphene fibers via atomic layer deposition for flexible gel supercapacitors

Composite materials for high energy/power density supercapacitors are of very high importance. The precise well-defined nanoarchitectonics approach is a key element in the construction of high-performance devices. Surface redox reactions that frequently involve the exchange of oxygen atoms are fundamental to pseudocapacitive reactions mostly mediated by transition metal oxides (TMO). This process often changes the surface stoichiometry and atomic rearrangement in case of uncontrolled growth of TMO. Atomic layer deposition (ALD) has proven to be a facile process for smooth and uniform decoration of TMO on the electrochemically active carbon material to form a binder-free flexible composite material for supercapacitor (SC) applications. Although active carbon materials can be fabricated using various printing and lithographic techniques, continued improvement of cost and scalability, and low dimensional matrix are required to realize their full potential. Here, we demonstrate the scalable fabrication of laser-induced graphene fibers (LIGF) followed by ALD of multivalent vanadium oxide (VOx) films on the LIGF network (VOx-LIGF). The resultant VOx-LIGF shows a specific areal capacitance as high as 99 mF cm−2 at 1 mA cm−2 (aqueous solution, three-electrode cell) and 2 mF cm−2 at 0.25 mA cm−2 (gel electrolyte, two electrode cell). Moreover, the miniaturized supercapacitor device delivers a power density of 244 mW cm−3 as well as long-term cycling stability (93 % capacitance retention after 11,500 cycles) which is among the highest values achieved for any SC. Despite mechanical stress, these flexible supercapacitors maintain excellent electrochemical aspects and thus hold promise for high-power flexible and wearable electronics. Such a general, precise, well-defined, and low-cost route for atomic layer deposition-laser pulse-enhanced supercapacitor materials should find widespread applications.

A Comparative Analysis of Different Hydrogen Production Methods and Their Environmental Impact

This study emphasises the growing relevance of hydrogen as a green energy source in meeting the growing need for sustainable energy solutions. It foregrounds the importance of assessing the environmental consequences of hydrogen-generating processes for their long-term viability. The article compares several hydrogen production processes in terms of scalability, cost-effectiveness, and technical improvements. It also investigates the environmental effects of each approach, considering crucial elements such as greenhouse gas emissions, water use, land needs, and waste creation. Different industrial techniques have distinct environmental consequences. While steam methane reforming is cost-effective and has a high production capacity, it is coupled with large carbon emissions. Electrolysis, a technology that uses renewable resources, is appealing but requires a lot of energy. Thermochemical and biomass gasification processes show promise for long-term hydrogen generation, but further technological advancement is required. The research investigates techniques for improving the environmental friendliness of hydrogen generation through the use of renewable energy sources. Its ultimate purpose is to offer readers a thorough awareness of the environmental effects of various hydrogen generation strategies, allowing them to make educated judgements about ecologically friendly ways. It can ease the transition to a cleaner hydrogen-powered economy by considering both technological feasibility and environmental issues, enabling a more ecologically conscious and climate-friendly energy landscape.

Perfecting Liquid-State Theories with Machine Intelligence.

Recent years have seen a significant increase in the use of machine intelligence for predicting the electronic structure, molecular force fields, and physicochemical properties of various condensed systems. However, substantial challenges remain in developing a comprehensive framework capable of handling a wide range of atomic compositions and thermodynamic conditions. This perspective discusses potential future developments in liquid-state theories leveraging recent advancements in functional machine learning. By harnessing the strengths of theoretical analysis and machine learning techniques including surrogate models, dimension reduction, and uncertainty quantification, we envision that liquid-state theories will gain significant improvements in accuracy, scalability, and computational efficiency, enabling their broader applications across diverse materials and chemical systems.

Abstract 19161: EchoMap Automatically Maps Echocardiogram Report Text to Ontology

Background: Big data has the potential to revolutionize echocardiography by enabling novel research and rigorous, scalable quality improvement. Text reports are a key part of such analyses. Currently, echocardiogram reports include both structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both non-deep learning and deep-learning (e.g., large language model, or LLM) based techniques. Challenges to date in using echo text with LLMs include small size, domain-specific language, and high need for accuracy and clinical meaning in model results. Hypotheses: We tested whether we could map echocardiography text to a structured ontology using NLP. Methods: We developed a three-tier ontology for the echocardiographic anatomic structures, functional elements, and descriptive characteristics in an adult transthoracic echocardiogram using 919 sentences from UCSF’s structured echocardiogram report text. We tested LLM fine-tuning as well as non-LLM techniques to map echocardiography sentences to this ontology. Two-hundred twenty-eight UCSF sentences served as an internal test set. Additional test datasets included free text from UCSF reports; structured text sentences from two other hospitals; and sentences from reports representing 17 additional hospitals. Results: Despite all adhering to clinical guidelines for reporting, there were notable differences by institution in what structural and functional information was included in structured reporting. A non-LLM hierarchical model performed best in mapping sentences to the ontology, with internal test accuracy of 96% for the first level of the ontology, 91% for the second level, and 77% for the third level. Echomap retained good performance across diverse datasets and displayed the ability to extrapolate to ontological terms not initially included in training. Conclusions: We show that non-LLM NLP methods can achieve good performance and may be especially useful for small, specialized text datasets where clinical meaning is important. These results highlight the utility of a high-resolution, standardized cardiac ontology to harmonize reports across institutions.

Optimization and Scalability of Educational Platforms: Integration of Artificial Intelligence and Cloud Computing

The intersection between technology and education has taken on unprecedented relevance, driven by the promise of transforming teaching and learning through advanced digital tools. This study proposes a comprehensive exploration of how cloud computing and artificial intelligence converge to impact education, focusing on accessibility, efficiency, and quality of learning. A mixed-research design identified a 25% improvement in the personalization of educational content thanks to AI and a 60% increase in simultaneous user capacity through cloud computing. Additionally, a significant reduction in administrative errors and improvements in scalability were observed without sacrificing quality. The results demonstrate that these technologies not only improve efficiency and accessibility in education but also enrich the learning experience. By comparing these findings with previous research, this study highlights the synergistic value of these technologies and positions itself as a critical resource to guide future developments and improvements in the education sector in a digitally advanced world.

The Rise of Serverless Architectures: Security Challenges and Best Practices

The field of serverless computing has had significant growth and recognition in the past decade. This emerging area has garnered attention because to its notable impact on cost reduction, latency reduction, scalability improvement, and elimination of server-side management, among other benefits. Nevertheless, there is still a dearth of comprehensive study that would facilitate developers and academics in gaining a more profound comprehension of the importance of serverless computing in many scenarios. Therefore, it is imperative to provide scholarly study data that has been published within this particular field. This study conducted a comprehensive analysis of 275 scholarly articles retrieved from reputable literature sources, with the aim of extracting valuable insights pertaining to serverless computing. Subsequently, the acquired data underwent analysis in order to address many study inquiries pertaining to the contemporary advancements in serverless computing, encompassing its fundamental principles, available platforms, and patterns of utilization, among other relevant aspects. In addition, we analyze the current obstacles confronting serverless computing and explore potential avenues for future research to facilitate its deployment and utilization.

Design and Kinematic Modeling of a Soft Continuum Telescopic Arm for the Self-Assembly Mechanism of a Modular Robot.

In recent years, the development of mining robots has grown significantly, offering improved efficiency and safety in hazardous environments. However, there is still room for improvement in adaptability, scalability, and overall performance. The ROBOMINERS project, funded by the European Union's Horizon 2020 Research and Innovation Program, aims to facilitate Europe's access to mineral resources applying disruptive robotic concepts. One such concept is resilience, which can be achieved providing modular mining robots with the ability to reconfigure during operation. To address this challenge, this article presents the development and kinematic modeling of a soft, telescopic, continuum arm integrated into a modular robot. The arm serves as a mechanical interface for coupling different robotic modules or tools following the principle of the car crane. With a fully 3D-printed design, the arm features two sections of variable length that are driven by an innovative actuation method based on soft racks. It provides a 6 degrees of freedom (DoF) motion. The arm kinematic models are obtained by backbone parameterization assuming constant curvature and independent bending between sections for forward kinematics and applying a machine learning-based approach for inverse kinematics. The models are validated through the evaluation of two trajectories, measuring the deviation in each DoF and rack extension. Furthermore, a demonstration of the arm's coupling procedure between two robotic modules and one possible configuration of the robotic system showcases its functionality.

A Customized ADMM Approach for Large-Scale Nonconvex Semidefinite Programming

We investigate a class of challenging general semidefinite programming problems with extra nonconvex constraints such as matrix rank constraints. This problem has extensive applications, including combinatorial graph problems, such as MAX-CUT and community detection, reformulated as quadratic objectives over nonconvex constraints. A customized approach based on the alternating direction method of multipliers (ADMM) is proposed to solve the general large-scale nonconvex semidefinite programming efficiently. We propose two reformulations: one using vector variables and constraints, and the other further reformulating the Burer–Monteiro form. Both formulations admit simple subproblems and can lead to significant improvement in scalability. Despite the nonconvex constraint, we prove that the ADMM iterates converge to a stationary point in both formulations, under mild assumptions. Additionally, recent work suggests that in this matrix form, when the matrix factors are wide enough, the local optimum with high probability is also the global optimum. To demonstrate the scalability of our algorithm, we include results for MAX-CUT, community detection, and image segmentation.

Randomised trial of general practitioner online education for prescribing and test ordering

IntroductionPotentially inappropriate medicine prescriptions and low-value diagnostic testing pose risks to patient safety and increases in health system costs. The aim of the Clinical and Healthcare Improvement through My Health...

A Two-Stage Emotion Generation Model Combining CGAN and pix2pix

Computer vision has made significant advancements in emotional design. Designers can now utilize computer vision to create emotionally captivating designs that deeply resonate with people. This article aims at enhancing emotional design selection by separating appearance and color. A two-stage emotional design method is proposed, which yields significantly better results compared to classical single-stage methods.. In the Radboud face dataset (RaFD), facial expressions primarily rely on appearance, while color plays a relatively smaller role. Therefore, the two-stage model presented in this article can focus on shape design. By utilizing the SSIM image quality evaluation index, our model demonstrates a 31.63% improvement in generation performance compared to the CGAN model. Additionally, the PSNR image quality evaluation index shows a 10.78% enhancement in generation performance. The proposed model achieves superior design results and introduces various design elements.This article exhibits certain improvements in design effectiveness and scalability compared to conventional models.

Ab initio calculation of electron-phonon linewidths and molecular dynamics with electronic friction at metal surfaces with numeric atom-centred orbitals

Molecular motion at metallic surfaces is affected by nonadiabatic effects and electron-phonon coupling. The ensuing energy dissipation and dynamical steering effects are not captured by classical molecular dynamics simulations, but can be described with the molecular dynamics with electronic friction method and linear response calculations based on density functional theory. Herein, we present an implementation of electron-phonon response based on an all-electron numeric atomic orbital description in the electronic structure code FHI-aims. After providing details of the underlying approximations and numerical considerations, we present significant scalability and performance improvements of the new code compared to a previous implementation (Maurer et al 2016 Phys. Rev. B 94 115432). We compare convergence behaviour and results of our simulations for exemplary systems such as H2 adsorption on Cu(111), and CO on Ru(0001) against existing plane wave implementations. We examine different expressions to calculate electronic friction and vibrational lifetimes for their reliability and ease of convergence. Finally, we show the capabilities of the new code by studying the contribution of interband and intraband excitations to the vibrational lifetime of aperiodic adsorbate motion in large, previously unfeasible, periodic surface models.

Towards a Reversible Calcium Anode through Electrolyte and Interface Engineering for Calcium Metal Batteries

Lithium-ion batteries have played an important role in decarbonizing the transportation sector, but broader utilization is currently limited by practically-attainable energy densities, safety concerns, and supply chain issues. Batteries based on calcium (Ca) metal anodes offer higher theoretical energy densities, as well as significant potential improvements in scalability and sustainability. The field of Ca metal batteries is currently in its early stages, however, due to a limited number of electrolytes that can reversibly plate and strip Ca. Two important challenges to overcome are (1) the formation of passivating solid electrolyte interphases (SEI) between Ca and the electrolyte that prevent Ca2+ transport, and (2) Ca2+-anion interactions in the electrolyte that promote cluster formation and hinder electrochemistry.1,2 This talk will explore research performed in both aspects of Ca battery development and identify strategies for future advancements. First, the electrochemical signatures of three model interfaces on Ca foil electrodes were investigated in a baseline electrolyte using cyclic voltammetry;3 the long-term cycling behavior was governed to a greater extent by electrolyte properties than the initial surface layers. Subsequent efforts have focused on electrolyte engineering, and specifically, on studying the impact that the Ca2+ coordination environment has on reversible Ca plating and stripping. For example, strong coordination between Ca2+ and the anion TFSI- has been demonstrated to deactivate Ca plating behavior via TFSI- decomposition.4,5 However, competition between coordinating molecules can be exploited to favor specific Ca2+ speciation; preferential coordination between Ca2+ and BH4 - displaces bidentate TFSI---Ca2+, unlocking Ca plating behavior in tetrahydrofuran.5 Further studies into preferential coordination are ongoing in order to better identify the connections between Ca2+ coordination, SEI formation, and anode performance. We hope to identify specific electrolyte design criteria that can advance the functionality of Ca electrolytes and ultimately examine the feasibility of Ca as a novel battery chemistry.1 Aurbach, D., Skaletsky, R. & Gofer, Y. The Electrochemical Behavior of Calcium Electrodes in a Few Organic Electrolytes. Journal of The Electrochemical Society 138, 3536-3545 (1991).2 Arroyo-de Dompablo, M. E., Ponrouch, A., Johansson, P. & Palacín, M. R. Achievements, Challenges, and Prospects of Calcium Batteries. Chemical Reviews (2019).3 Melemed, A. M. & Gallant, B. M. Electrochemical Signatures of Interface-Dominated Behavior in the Testing of Calcium Foil Anodes. Journal of The Electrochemical Society 167, 140543 (2020).4 Hahn, N. T. et al. Influence of Ether Solvent and Anion Coordination on Electrochemical Behavior in Calcium Battery Electrolytes. ACS Applied Energy Materials 3, 8437-8447 (2020).5 Melemed, A. M., Skiba, D. A. & Gallant, B. M. Toggling Calcium Plating Activity and Reversibility through Modulation of Ca2+ Speciation in Borohydride-Based Electrolytes. The Journal of Physical Chemistry C 126, 892-902 (2022).

(Invited) Strategies for Characterization and Parameter Extraction of Vertical Junction-Less Nanowire FETs Dedicated to Design Technology Co-Optimization

In the era of emerging computing paradigms and artificial neural networks, hardware and functionality requirements are in the surge. In order to meet low power and latency criteria, new architectures for in-memory computing are being explored as alternatives to traditional von Neumann machines, which requires technological breakthrough at the semiconductor device level such as vertical gate-all-around junctionless nanowire field effect transistors (VNWFET), that can address many process challenges such as downscaling, short-channel effects, compactness and electrostatic control. Its integration in the mainstream design flow is not straightforward and requires design technology co-optimization (DTCO) at an early stage. This invited paper explores strategies for accurate characterization and parameter extraction of the VNWFETs to feed the DTCO compact models [1].As an important first step to access the intrinsic device characteristics, one must remove or ‘de-embed’ the parasitic contributions of external test structures and interconnections. However, for technologies such as the 3D VNWFET, precision of the S-parameter de-embedding step is crucial for accurate model parameter extraction. On the current fabricated VNWFET wafer, only open structures were available and the standard open de-embedding method led to inaccurate or even unphysical values of intrinsic capacitances. While one could design and fabricate additional de-embedding test structures to overcome this, to avoid another design cycle, a new dedicated de-embedding method was developed based on the modelling of the transistor interconnects, leveraging EM simulation [2]. After accurately calibrating the EM simulation against S-parameter measurements of the Open structure, virtual test structures of the same technological process, layout and material properties, were designed in the EM simulation environment. This included Open and Short structures at different reference planes that were simulated for extracting individual interconnect parasitic contributions. Next, the EM simulation served as a predictive tool to construct an electrical equivalent circuit of the entire 3D VNWFET interconnection network (Fig.1). Compared with conventional de-embedding techniques based on Open and Short measurements, the proposed interconnect network modelling-based approach relies on fewer test structures and can provide better accuracy, necessary for compact model parameter extraction.The DTCO is driven by the use of VNWFETs in highly compact 3D logic circuits. Design of these logic circuits relies heavily on compact models, containing a set of physics-based analytical equations for the juntionless transistors. The compact model developed for the 18 nm VNWFET technology [3] has been extensively validated against measurements depicting very good agreement over the entire bias range. To leverage the model for predictive design through DTCO, we further explored the scalability for all available transistor geometries that showed a visible improvement in current scalability for larger devices (Fig.2). However, process variation has limited linear current scaling in smaller nanowire diameters that are of more interest for junctionless transistor operation. In contrast, for a given number of parallel nanowires, maintaining gate electrostatic control over the channel in larger diameters becomes more difficult along with a degradation of the drain current on/off ratio, despite better current scaling (Fig.3). So as a design trade-off for the DTCO, we chose transistors with an optimal diameter (where junctionless functionality and gate electrostatic control are maintained for individual nanowires) with a higher number of nanowires in parallel to boost the drive current.Reliability assessment through electro-thermal measurements of VNWFETs is crucial when designing 3D logic circuits within DTCO framework, particularly for evaluating long-term performances and underlying transport mechanisms. Conventional methods for thermal investigation were not applicable for the VNWFETs as the drain current shows steady increase with temperature in contrast with classical devices (Fig.4). Since activation of self-heating could not be confirmed using traditional methods, a new methodology for thermal resistance estimation in VNWFETs is developed. This relies on the intersecting points on ID-VG curves at different measurement temperatures (TA) and power dissipations (Pdiss) corresponding to same VG and ID. From the ratio of junction temperatures (Tj=TA+RTH*Pdiss) for the two sets of intersecting curves related to different VD and threshold voltages and subsequent calculations, thermal resistances (RTH) were extracted. Theoretical calculations corroborated extracted results with RTH values ranging between 4 and 28×104 K/W. Other non-conventional methods were also explored such as low-frequency S-parameter measurements or RTH extraction using the slope of drain current versus temperature plot at different dissipated powers, all of which offered similar results even though measurement uncertainties related to each method should be carefully accounted for. A final verification using full device thermal simulation in COMSOL multiphysics, accounting for both ballistic and diffusive heat flux, showed fair agreement with experimental results. This is the first demonstration of thermal resistance extraction in highly-scaled VNWFETs. Figure 1

A research on cross‐chain and interoperation methods of fusion protocol

AbstractAt present, cross‐chain technology and cross‐chain system pay more attention to cross‐chain security and performance, and have shown great improvement in security, scalability and efficiency. However, the generality of cross‐chain technology in different scenarios is poor, and it may not fit in different business fields. In order to solve the generality problem of cross‐chain technology, this paper sorts out and analyzes the research of blockchain cross‐chain protocol, and proposes a fusion protocol cross‐chain method based on relay‐chain technology. This paper will introduce the cross‐chain framework and cross‐chain transaction process of fusion protocol. Cross‐chain framework includes blockchain cross‐chain interaction architecture, cross‐chain interaction governance mechanism and cross‐chain privacy protection scheme. The transaction process is divided into three different stages. Finally, combined with cross‐chain key technologies and landing application requirements, the future research direction of blockchain cross‐chain technology is given. The fusion protocol cross‐chain method based on relay‐chain technology proposed here is a groundbreaking innovation in the field of blockchain cross‐chain technology. By integrating the advantages of different blockchain systems and protocols, the fusion protocol is able to achieve a high degree of generality and compatibility, making it suitable for various business scenarios and applications.