Scalable Applications Research Articles

PlutoAR: a scalable marker-based augmented reality application for interactive and inclusive education

PlutoAR: a scalable marker-based augmented reality application for interactive and inclusive education

VT-IO: A Virtual Time System Enabling High-fidelity Container-based Network Emulation for I/O Intensive Applications

Network emulation allows unmodified code execution on lightweight containers to enable accurate and scalable networked application testing. However, such testbeds cannot guarantee fidelity under high workloads, especially when many processes concurrently request resources (e.g., CPU, disk I/O, GPU, and network bandwidth) that are more than the underlying physical machine can offer. A virtual time system enables the emulated hosts to maintain their own notion of virtual time. A container can stop advancing its time when not running (e.g., in an idle or suspended state). The existing virtual time systems focus on precise time management for CPU-intensive applications but are not designed to handle other operations, such as disk I/O, network I/O, and GPU computation. In this paper, we develop a lightweight virtual time system that integrates precise I/O time for container-based network emulation. We model and analyze the temporal error during I/O operations and develop a barrier-based time compensation mechanism in the Linux kernel. We also design and implement Dynamic Load Monitor (DLM) to mitigate the temporal error during I/O resource contention. VT-IO enables accurate virtual time advancement with precise I/O time measurement and compensation. The experimental results demonstrate a significant improvement in temporal error with the introduction of DLM. The temporal error is reduced from 7.889 seconds to 0.074 seconds when utilizing the DLM in the virtual time system. Remarkably, this improvement is achieved with an overall overhead of only 1.36% of the total execution time.

Abstract C024: Room temperature stabilization of fecal samples supports important microbiome insights to expand access

Abstract Analysis of microbial signatures associated with colorectal cancer has potential to improve access and adherence to cancer screening programs through non-invasive sampling, particularly in underserved communities. Lack of reproducibility between studies remains a challenge and has limited the ability to translate research findings into scalable clinical applications. The common practice of storing samples at -80°C/-112°F is inconvenient, often not feasible, and may introduce bias if samples are not handled carefully and consistently. Large-scale screening programs would benefit from room temperature stabilization and sample homogenization at the point of collection, without the logistical considerations for rapid and long-term freezing. To evaluate the potential of room temperature stability, fecal samples from adult and pediatric individuals were self-collected at home using the OMNIgene™•GUT Dx device with corresponding unstabilized samples stored at -80°C/-112°F. Metagenomics sequencing was utilized to generate microbiome profiles and identify taxa-associated biomarkers relevant to gastrointestinal health and aging. Stabilized samples were compared to freshly collected unstabilized samples (n=18) to evaluate any bias introduced by the stabilizing solution. The same stabilized samples were compared to unstabilized samples stored at -80°C/-112°F to evaluate stability over time. In addition, reproducibility was evaluated by performing DNA extractions in triplicate from ten stabilized fecal samples. Finally, the ability of the room temperature stabilization to capture expected differences between groups was evaluated by comparing adult and pediatric (n=30 per group) samples and looking for known signatures specific to each group. This research demonstrated equivalent stability over time between stabilized samples at room temperature and unstabilized samples stored at -80°C/-112°F, showing preservation of both highly abundant and rare members of the microbial community. Findings indicate that room temperature stabilization does not introduce bias relative to freshly collected unstabilized samples and produces sufficient sample homogenization to yield reproducible microbial profiles. The comparison between adult and pediatric stabilized fecal samples highlights an enrichment for pediatric-specific signatures, such as Bifidobacterium and Veillonella species, and further captures the lower diversity of pediatric samples relative to more developed adult microbiota. These results demonstrate that the OMNIgene™•GUT Dx device enables the preservation of microbial community structure with room temperature stabilization and sample homogenization for use in the detection of biomarkers in fecal samples. Simple logistics of room temperature storage, combined with an easy-to-use self-collection design and compatibility with standard postal shipping assist integration into clinical workflows and enable at-home collections from diverse populations, especially at-risk, minority populations without easy access to clinical services. Citation Format: Savannah Colameco, Jean M. Macklaim, Ashlee Brown, Tara Crawford Parks. Room temperature stabilization of fecal samples supports important microbiome insights to expand access [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C024.

Implementing Serverless Architecture: Discuss the practical aspects and challenges

Function as a Service (FaaS) has garnered significant attention for its approach to deploying computations to serverlessbackends across various cloud environments. It simplifies the complexity of provisioning and managing resources for applications by leveraging cloud providers' capabilities, offering users an illusion of perpetual resource availability. Among these providers, the AWS serverless platform stands out, offering a novel paradigm for cloud application development, abstracting away concerns about underlying hardware infrastructure while ensuring scalability, security, and cost-effectiveness.However, due to the absence of standardized benchmarks, serverless functions often rely on ad-hoc solutions for building cost-efficient and scalable applications. The development of the SeBS framework has addressed this gap, enabling comprehensive testing, evaluation, and performance analysis across different cloud providers. While previous research has explored serverless platforms among various providers, little attention has been given to AWS Lambda service's performance within the ARM64 architecture and its comparison with traditional x86 architectures.

Creating Serverless Web Applications with AWS

Abstract: Serverless computing has emerged as a groundbreaking innovation, allowing organizations to alleviate the complexities associated with provisioning, scaling, and managing infrastructure. The surge in serverless adoption poses challenges in choosing optimal solutions amid a myriad of proposed designs by experts. This paper categorizes serverless patterns using a multivocal literature review, highlighting benefits and challenges in coordination, aggregation, event management, availability, communication, and validation. To illustrate the practical application of serverless computing, we present a case study involving the use of AWS Lambda, Amazon DynamoDB, and other services to process real-time data streams for a fictional ride-sharing company. The study showcases the advantages of serverless computing, emphasizing its ability to free developers from server-related concerns, enabling them to focus on building scalable and reliable applications. This paper contributes to the understanding of serverless computing's role in the broader context of cloud technologies and its application in real-world scenarios

Secure smart city application using webservice model and mayfly optimization‐based lightweight CNN

AbstractIoT infrastructures and web services have primarily been deployed in sustainable smart cities and societies. Users are highly attracted to the personalized web services by the IoT infrastructure due to the improved quality of service (QoS) it offers. To acquire service personalization, the complex distribution systems (smart cities) collect users' sensitive data leading to different privacy issues such as unauthorized user access. The heavy security schemes designed for complex architectures cannot be directly implemented in resource‐constrained IoT devices. The existing models designed cannot offer customization to user privacy where the user has little or no control over their data. However, there are other issues in such a system, including centralization, security, privacy, scalability, and communication latency. To overcome these issues, in this paper, we proposed a web service‐based adaptive optimal lightweight CNN (AOLCNN) for a secure smart city. The use of a lightweight convolutional neural network (LCNN) combined with the mayfly optimization (MO) method improves the production process in the smart industry. The MO algorithm not only optimizes the LCNN weights but also enhances the model's performance by selecting the appropriate web service parameters for the user to get rid of irrelevant web services, resulting in increased smart industrial productivity. Blockchain technology is incorporated to enhance the security, privacy, and transparency of IoT systems and also help them to create and verify web services. The built‐in key store system particularly intended for Android smartphones is used in the proposed security‐based web service application. The NIST Curve P‐256 is used to generate key pairs and verify them using the elliptic curve digital signature algorithm (ECDSA). The performance evaluation of the smart web service‐based AOLCNN model in terms of security, privacy, latency, and memory usage shows our proposed model offers significant performance when compared to the conventional techniques. The experimental findings suggest that the AOLCNN satisfies the needs of scalable IoT applications and the security scheme incorporated also helps the device to safeguard it from malicious entities and unauthorized user access.

Moderating manipulation: Demystifying extremist tactics for gaming the (regulatory) system

AbstractDue to its ease of scalability and broad applicability, the use of artificial intelligence (AI) and machine learning in platform management has gained prominence. This has led to widespread debates about the use of deplatforming as the default tool for repeated or severe violations of terms or service. But technologically deterministic approaches are not infallible and can be predictable based on their actions. This opens the door for manipulation of media content and technological affordances to become tactical options for actors seeking to subvert regulation. Existing discussions often neglect the topic of manipulation of content, algorithms, or platform affordances as a primary aspect of the strategies used by extremists in relation to the difficulties of moderation from a policy perspective. This study argues that it is essential to understand how extremists and conspiracy theorists use manipulation tactics to ‘game’ the current policy, regulatory and legislative systems of content moderation. Developing approaches that attend to manipulation as a strategy and focus on platform and context‐specific tactics will generate more effective policies, platform rules, AI developments and moderation procedures. This study analyses and demystifies three primary tactics, which the authors categorize as numerology, borderlands and merchandising, regularly used by extremists online in their strategies to ‘game’ content moderation. We provide case examples from a variety of ideologies including far‐right, QAnon and male supremacism to highlight the tactics rather than ideological nature of such manipulation. We conclude with a discussion of how demystification processes could be incorporated into content moderation settings. This study contributes new insights about evasion tactics to the content moderation discussion and expands current understanding of how platforms can develop sociotechnical remedial measures.

A scalable modified deep reinforcement learning algorithm for serverless IoT microservice composition infrastructure in fog layer

Nowadays many modern and Artificial Intelligence (AI) enabled Internet of Things (IoT) applications consist of chains connecting microservices distributed across the fog and cloud layers to achieve a certain functionality. These microservices should be assigned to the available processing instances placed on the things, fog and cloud layers while considering the resource utilization, Quality of Service (QoS) parameters and requirements of the user. Despite several existing optimization approaches, IoT microservice composition remains a challenge in serverless computing architecture due to the large-scale environment, its dynamic nature and the heterogeneous nodes with limited capacity. To address these challenges, a novel IoT microservice composition based on serverless architecture is proposed in the fog layer. Serverless computing is a new paradigm which offers the ability to build scalable applications without worrying about back-end management making this paradigm particularly effective in IoT environment. We specifically propose a modified Deep Reinforcement Learning (DRL)-based Microservice Chaining at Fog Layer (DRLMCF) algorithm to improve the distributed chained microservice placement in order to minimize resource utilization and delay in a fog based serverless architecture. Unlike existing methods, the proposed DRLMCF is built on realistic assumptions and does not require expert tuning or a large amount of labeled data to make optimal decisions. Furthermore, our method is not entirely dependent on a central controller and fog nodes take part in the decision making thus making the algorithm scalable in IoT environment. Compared to state-of-the art methods including load balance, shortest path and random schedule algorithms, evaluation by simulating several real-world scenarios demonstrates the proposed DRLMCF improves end to end delay up to 57.3% and the number of successfully chained microservices by up to 84%.

Thin paints for durable and scalable radiative cooling

Passive daytime radiative cooling (PDRC) is environment-friendly without energy input by enhancing the coating’s solar reflectance (R¯solar) and thermal emittance (ε¯LWIR) in the atmosphere’s long-wave infrared transmission window. However, high R¯solar is usually achieved by increasing the coating’s thickness, which not only increases materials’ cost but also impairs heat transfer. Additionally, the desired high R¯solar is vulnerable to dust pollution in the outdoors. In this work, a thin paint was designed by mixing hBN plates, PFOTS, and IPA.R¯solar = 0.963 andε¯LWIR = 0.927 was achieved at a thickness of 150 μm due to the high backscattering ability of scatters. A high through-plane thermal conductivity (∼1.82 W m−1 K−1) also can be obtained. In addition, the porous structure coupled with the binder PFOTS resulted in a contact angle of 154°, demonstrating excellent durability under dust contamination. Outdoor experiments showed that the thin paint can obtain a 2.3 °C lower temperature for sub-ambient cooling than the reference PDRC coating in the daytime. Furtherly, the above-ambient heat dissipation performance can be enhanced by spraying the thin paint on a 3D heat sink, which was 15.7 °C lower than the reference 1D structure, demonstrating excellent performance for durable and scalable PDRC applications.

A scalable and durable polydimethylsiloxane-coated nanoporous polyethylene textile for daytime radiative cooling

Abstract Radiative cooling technology with zero-energy consumption and zero-carbon emission has drawn enormous attention. However, the high-cost manufacture, limited scalability, and narrow application scopes remain major impediments to radiative cooling commercialization. Here, we present a bilayer PDMS/nanoPE fabricated by an automatic film applicator for high-performance passive daytime radiative cooling. The nanoPE underlayer maximizes the reflection of sunlight and the transparent PDMS top-layer dramatically enhances the infrared emissivity of pristine nanoPE across the atmospheric transparency window (∆E 8–13 μm = 0.85). The obtained PDMS/nanoPE simultaneously allows a high solar reflectance of 0.94 and a thermal emittance of 0.94, enabling a sub-ambient cooling of 4.5 °C with a maximum of 7.6 °C in rooftop test and a theoretical net cooling power of 65 W/m2. A distinct temperature reduction of more than 10 °C can be achieved in comparison with pristine PDMS film. Integration of the hydrophobicity, durability, robust mechanical strength, and industrial scalability, we believe this work will provide practical and efficient solutions to cooling vehicles, buildings, and the human body in a simple and low-cost manner.

High-precision fiber-optic time transfer with an unlimited compensation range.

We present a fiber-optic time transfer system with high transfer stability and an unlimited compensation range of the delay variation. We first stably transmit a frequency signal from a voltage-controlled oscillator to the remote site. The time signal is then embedded in the frequency signal by simply selecting its one cycle per second with a tunable gate signal. Therefore, the proposed time transfer system inherits both the stability and the unlimited adjustment range of the frequency transfer yet with no need for demodulation. The time deviation of 1.93 ps is achieved at 1000-s averaging. This simple and demodulation-free time transfer system is applicable for scalable distributed applications that require high-precision time synchronization and wide-range delay compensation.

FLBP: A Federated Learning-enabled and Blockchain-supported Privacy-Preserving of Electronic Patient Records for the Internet of Medical Things

The evolution of the computing paradigms and the Internet of Medical Things (IoMT) have transfigured the healthcare sector with an alarming rise of privacy issues in healthcare records. The rapid growth of medical data leads to privacy and security concerns to protect the confidentiality and integrity of the data in the feature-loaded infrastructure and applications. Moreover, the sharing of medical records of a patient among hospitals rises security and interoperability issues. This article, therefore, proposes a Federated Learning-and-Blockchain-enabled framework to protect electronic medical records from unauthorized access using a deep learning technique called Artificial Neural Network (ANN) for a collaborative IoMT-Fog-Cloud environment. ANN is used to identify insiders and intruders. An Elliptical Curve Digital Signature (ECDS) algorithm is adopted to devise a secured Blockchain-based validation method. To process the anti-malicious propagation method, a Blockchain-based Health Record Sharing (BHRS) is implemented. In addition, an FL approach is integrated into Blockchain for scalable applications to form a global model without the need of sharing and storing the raw data in the Cloud. The proposed model is evident from the simulations that it improves the operational cost and communication (latency) overhead with a percentage of 85.2% and 62.76%, respectively. The results showcase the utility and efficacy of the proposed model.

Java in Action : AI for Fraud Detection and Prevention

In today's increasingly digital world, the financial and e-commerce sectors face a growing threat from fraudulent activities. Fraudsters are becoming more sophisticated, making it essential to employ advanced tools and technologies to combat this menace effectively. This paper presents a comprehensive exploration of using Java-based Artificial Intelligence (AI) systems for fraud detection and prevention. Java has long been a trusted choice for building scalable and robust applications, and AI is revolutionizing how businesses safeguard their financial transactions. By combining these two powerful technologies, organizations can develop intelligent systems that analyze vast amounts of data in real time, detect suspicious patterns, and take swift action to prevent fraudulent activities. This paper delves into the principles and techniques of AI, machine learning, and deep learning, demonstrating how these methodologies can be harnessed within the Java ecosystem. We explore the development and deployment of predictive models, anomaly detection algorithms, and behavioral analysis using Java libraries and tools. Moreover, we will discuss the challenges and considerations when implementing AI-driven fraud detection systems, including data privacy, model accuracy, and scalability. By the end of this presentation, the audience will gain valuable insights into how Java-based AI can be a game-changer in the battle against fraud, enhancing the security and trustworthiness of financial and e-commerce platforms. This abstract provides an overview of the paper's content, emphasizing the significance of Java and AI in the context of fraud detection and prevention, and inviting the audience to learn more about the topic.

Advancing reproducibility can ease the 'hard truths' ofsynthetic biology.

Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.

A Federated Consensus for Proof of Authority in IoT-Blockchain Applications

The growing adoption of Internet of Things (IoT) devices and the need for secure and scalable blockchain applications pose significant challenges in the realm of consensus protocols. This paper proposes a novel consensus mechanism called Federated Consensus for Proof of Authority (Fed-PoA), which combines the advantages of Proof of Authority (PoA) and federated learning to achieve secure and scalable IoT-Blockchain applications. The Fed-PoA ensures efficient data sharing, privacy preservation, and decentralized operation. Performance evaluation of this model in a simulated environment demonstrates superior convergence and memory usage compared to a representative work in this context.

Shape-Memory Three-Dimensional Evaporators with High Portability for Efficient Solar-Driven Freshwater Production.

Solar-driven water evaporation can alleviate the severe water scarcity situation in a nonpolluting and sustainable manner. Although the design of integrated three-dimensional (3D) solar evaporators has been proven to be effective in achieving ultrahigh evaporation rates and energy efficiency, their scalable application is still hindered by complex manufacturing processes and poor portability. Herein, we report a highly portable shape-memory 3D solar evaporator by depositing MXene on low-cost lignin-cellulosic sponges for freshwater production. When not in use, the 3D evaporator can be compressed into a thin film with up to 89.3% volume reduction, ensuring minimal space occupation and high portability. When needed, due to the shape-memory effect, the 3D structure can be rapidly restored by swelling the compressed film in water, resulting in an efficient 3D solar evaporator. This 3D evaporator exhibits not only a high evaporation rate of 2.48 kg m-2 h-1 under 1 sun illumination but also excellent long-term stability and recyclability. In addition, the 3D evaporator itself can serve as a water reservoir without requiring a continuous water supply during evaporation, showing remarkable application flexibility. This work opens a new perspective for manufacturing highly portable and efficient 3D solar evaporators and may facilitate their progress from the laboratory to commercial applications.

Large-Area MoS2 Films Grown on Sapphire and GaN Substrates by Pulsed Laser Deposition.

In this paper, we present the preparation of few-layer MoS2 films on single-crystal sapphire, as well as on heteroepitaxial GaN templates on sapphire substrates, using the pulsed laser deposition (PLD) technique. Detailed structural and chemical characterization of the films were performed using Raman spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction measurements, and high-resolution transmission electron microscopy. According to X-ray diffraction studies, the films exhibit epitaxial growth, indicating a good in-plane alignment. Furthermore, the films demonstrate uniform thickness on large areas, as confirmed by Raman spectroscopy. The lateral electrical current transport of the MoS2 grown on sapphire was investigated by temperature (T)-dependent sheet resistance and Hall effect measurements, showing a high n-type doping of the semiconducting films (ns from ~1 × 1013 to ~3.4 × 1013 cm-2 from T = 300 K to 500 K), with a donor ionization energy of Ei = 93 ± 8 meV and a mobility decreasing with T. Finally, the vertical current injection across the MoS2/GaN heterojunction was investigated by means of conductive atomic force microscopy, showing the rectifying behavior of the I-V characteristics with a Schottky barrier height of ϕB ≈ 0.36 eV. The obtained results pave the way for the scalable application of PLD-grown MoS2 on GaN in electronics/optoelectronics.

Metasurface Absorbers with Film-Coupled Au Nanoclusters to Achieve Broadband and Polarization-Independent Absorption of UV-Vis Light.

Metasurface absorbers (MAs) have attracted widespread interest in the recent study of subwavelength artificial optical metasurfaces, although most reported MAs suffer from the actualities of costly and time-consuming fabrications, narrow working bandwidth, polarization-dependent responses, etc., somewhat limiting their practical applications. Herein, we introduce a facile and low-cost method to fabricate MAs with excellent absorption performances via the self-assembly of synthesized Au nanoclusters (NCs) on a Au film spaced by a nanoscale-thick dielectric SiO2. Interestingly, the proposed MAs with well-designed Au film-coupled Au NCs (i.e., an appropriate surface coverage of Au NCs and the compatible thickness of the SiO2 spacer) exhibited a measured average absorbance above 90% within a broad UV-vis wavelength band (200-800 nm). In addition, owing to the MAs' topological symmetry, their UV-vis absorption behaviors presented polarization insensitivity with the incident light angles ranging from 20 to 50°. It has been demonstrated that the excited different surface plasmon resonance modes between Au NCs and the adjacent Au film were vital; in addition, the light-trapping effects from "V"-shaped structures of Au NCs were favorable for the designed MAs with enhanced light absorption. We believe that such MAs and the potential self-assembly fabrication strategy may facilitate scalable optical applications such as photothermalvoltaics, ultraviolet protection, optical storage, and sensing.

Ultra-Dispersed α-MoC1-x Embedded in a Plum-Like N-Doped Carbon Framework as a Synergistic Adsorption-Electrocatalysis Interlayer for High-Performance Li-S Batteries.

The shuttle effect and sluggish redox kinetics of lithium polysulfides (LiPSs) severely hinder the scalable application of lithium-sulfurr (Li-S) batteries. Herein, the highly dispersed α-phase molybdenum carbide nano-crystallites embedded in a porous nitrogen-doped carbon framework (α-MoC1-x @NCF) are developed via a simple metal-organic frameworks (MOFs) assisted strategy and proposed as the multifunctional separator interlayer for Li-S batteries. The inlaid MoC1-x nanocrystals and in situ doped nitrogen atoms provide a strong chemisorption and outstanding electrocatalytic conversion toward LiPSs, whereas the unique plum-like carbon framework with hierarchical porosity enables fast electron/Li+ transfer and can physically suppress LiPSs shuttling. Benefiting from the synergistic trapping-catalyzing effect of the MoC1-x @NCF interlayer toward LiPSs, the assembled Li-S battery achieves high discharge capacities (1588.1mAhg-1 at 0.1C), impressive rate capability (655.8mAhg-1 at 4.0C) and ultra-stable lifespan (a low capacity decay of 0.059% per cycle over 650 cycles at 1.0C). Even at an elevated sulfur loading (6.0mgcm-2 ) and lean electrolyte (E/S is ≈5.8µLmg-1 ), the battery can still achieve a superb areal capacity of 5.2mAhcm-2 . This work affords an effective design strategy for the construction of muti-functional interlayer in advanced Li-S batteries.

300 mm CMOS-compatible superconducting HfN and ZrN thin films for quantum applications

The rising interest in increased manufacturing maturity of quantum processing units is pushing the development of alternative superconducting materials for semiconductor fab process technology. However, these are often facing CMOS process incompatibility. In contrast to common CMOS materials, such as Al, TiN, and TaN, reports on the superconductivity of other suitable transition-metal nitrides are scarce, despite potential superiority. Here, we demonstrate fully CMOS-compatible fabrication of HfN and ZrN thin films on state-of-the-art 300 mm semiconductor process equipment, utilizing reactive DC magnetron sputtering on silicon wafers. Measurement of mechanical stress and surface roughness of the thin films demonstrates process compatibility. We investigated the materials phase and stoichiometry by structural analysis. The HfN and ZrN samples exhibit superconducting phase transitions with critical temperatures up to 5.84 and 7.32 K, critical fields of 1.73 and 6.40 T, and coherence lengths of 14 and 7 nm, respectively. A decrease in the critical temperature with decreasing film thickness indicates mesoscopic behavior due to geometric and grain-size limitations. The results promise a scalable application of HfN and ZrN in quantum computing and related fields.