Multiple Devices Research Articles

Production of large quantity of plasma activated water using multiple plasma device setup

This study focuses on large-scale production of plasma-activated water (PAW) using batch processes with circulating and non-circulating modes. A multi-plasma device setup, powered by a self-developed high-voltage high-frequency supply. This plasma is electrically characterized and the produced radicals/species are identified using emission spectroscopy, with resulting effluent gases containing polluting species. The PAW setup utilizes these gases to create substantial quantities of PAW, decreasing environmental pollutants. The non-circulating mode produces highly reactive PAW (2L/batch) while the circulating mode yields larger volumes (20L/batch) with lower reactivity. Applications range from microbial inactivation to agriculture. Both batch setups are scalable for industrial PAW production.

Microservice instances selection and load balancing in fog computing using deep reinforcement learning approach

Fog-native computing is an emerging paradigm that makes it possible to build flexible and scalable Internet of Things (IoT) applications using microservice architecture at the network edge. With this paradigm, IoT applications are decomposed into multiple fine-grained microservices, strategically deployed on various fog nodes to support a wide range of IoT scenarios, such as smart cities and smart farming. Nonetheless, the performance of these IoT applications is affected by their limited effectiveness in processing offloaded IoT requests originating from multiple IoT devices. Specifically, the requested IoT services are composed of multiple dependent microservice instances collectively referred to as a service plan (SP). Each SP comprises a series of tasks designed to be executed in a predefined order, with the objective of meeting heterogeneous Quality of Service (QoS) requirements (e.g., low service delays). Different from the cloud, selecting the appropriate service plan for each IoT request can be a challenging task in dynamic fog environments due to the dependency and decentralization of microservice instances, along with the instability of network conditions and service requests (i.e., change quickly over time). To deal with this challenge, we study the microservice instances selection problem for IoT applications deployed on fog platforms and propose a learning-based approach that employs Deep Reinforcement Learning (DRL) to compute the optimal service plans. The latter optimizes the delay of application requests while effectively balancing the load among microservice instances. In our selection process, we carefully address the plan-dependency to efficiently select valid service plans for every request by introducing two distinct approaches; an action masking approach and an adaptive action mapping approach. Additionally, we propose an improved experience replay to address delayed action effects and enhance our model training efficiency. A series of experiments were conducted to assess the performance of our Microservice Instances Selection Policy (MISP) approach. The results demonstrate that our model reduces the average failure rate by up to 65% and improves load balance by up to 45% on average when compared to the baseline algorithms.

Ensuring cross-device portability of electromagnetic side-channel analysis for digital forensics

Investigation on smart devices has become an essential subdomain in digital forensics. The inherent diversity and complexity of smart devices pose a challenge to the extraction of evidence without physically tampering with it, which is often a strict requirement in law enforcement and legal proceedings. Recently, this has led to the application of non-intrusive Electromagnetic Side-Channel Analysis (EM-SCA) as an emerging approach to extract forensic insights from smart devices. EM-SCA for digital forensics is still in its infancy, and has only been tested on a small number of devices so far. Most importantly, the question still remains whether Machine Learning (ML) models in EM-SCA are portable across multiple devices to be useful in digital forensics, i.e., cross-device portability. This study experimentally explores this aspect of EM-SCA using a wide set of smart devices. The experiments using various iPhones and Nordic Semiconductor nRF52-DK devices indicate that the direct application of pre-trained ML models across multiple identical devices does not yield optimal outcomes (under 20 % accuracy in most cases). Subsequent experiments included collecting distinct samples of EM traces from all the devices to train new ML models with mixed device data; this also fell short of expectations (still below 20 % accuracy). This prompted the adoption of transfer learning techniques, which showed promise for cross-model implementations. In particular, for the iPhone 13 and nRF52-DK devices, applying transfer learning techniques resulted in achieving the highest accuracy, with accuracy scores of 98 % and 96 %, respectively. This result makes a significant advancement in the application of EM-SCA to digital forensics by enabling the use of pre-trained models across identical or similar devices.

PaSTG: A Parallel Spatio-Temporal GCN Framework for Traffic Forecasting in Smart City

Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these spatio-temporal models have mainly been evaluated on small-scale datasets. In light of the rapid growth of the Internet of Things and urbanization, cities are witnessing an increased deployment of sensors, resulting in the collection of extensive sensor data to provide more accurate insights into citywide traffic dynamics. Spatio-temporal graph modeling on large-scale traffic data is challenging due to the memory constraint of the computing device. For traffic forecasting, subgraph sampling from road networks onto multiple devices is feasible. Many GCN sampling methods have been proposed recently. However, combining these with STGCNs degrades performance. This is primarily due to prediction biases introduced by each sampled subgraph, which analyze traffic states from a regional perspective. Addressing these challenges, we introduce a parallel STGCN framework called PaSTG. PaSTG divides the road network into regions, each processed by an individual STGCN in a device. To mitigate regional biases, Aggregation Blocks in PaSTG merge spatial-temporal features from each STBlock. This collaboration enhances traffic forecasting. Furthermore, PaSTG implements pipeline parallelism and employs a graph partition algorithm for optimized pipeline efficiency. We evaluate PaSTG on various STGCNs using three traffic datasets on multiple GPUs. Results demonstrate that our parallel approach applies widely to diverse STGCN models, surpassing existing GCN samplers by up to 57.4% in prediction accuracy. Additionally, the parallel framework achieves speedups of up to 2.87x and 4.70x in training and inference compared to GCN samplers.

Optimal configuration and power scheduling algorithm of distributed power system with large range power flow transfer

The distributed power system is composed of multiple distributed energy sources, loads and energy storage devices, which have the characteristics of decentralization and complexity. Therefore, a distributed power system optimal allocation and power dispatching algorithm considering large-scale power flow transfer is proposed. Calculate the power flow values of different nodes, and construct an optimization model according to the results to complete power flow transmission. In the inner loop, the current dispatching is set as the objective, and in the outer loop, the voltage dispatching is set as the objective. Distributed power system scheduling is completed by double closed-loop vectors. The experimental results show that the power oscillation amplitude of this method is small and the duration is short.

An Online Adaptive Damping Controller for Converter-Interfaced Generation

The increasing integration of converter-interfaced generation within large-scale synchronous power systems is presenting both new challenges and opportunities in how we operate these dynamical networks. One of these challenges is designing and parameterizing the digital control loops that dictate the dynamical behavior of these fast-acting resources. Improperly tuned gains, or unexpected controller couplings through the network, can lead to poorly damped oscillations during disturbance conditions and/or decrease the system stability margin. Within this work, we present an approach for adaptively tuning a damping controller to improve the dynamic response of converter-interfaced generation, based only on local measurements. We show that, with low-amplitude probing, we can identify a subset set of observable system modes. We then propose a linear MISO state-feedback controller to improve the damping ratio and stability margin of the system. We show the effectiveness of the proposed controller through both eigenvalue analysis and time-domain simulations. We first demonstrate the proposed approach on a simple 3-bus system followed by a larger test case, the IEEE 14 bus system, with multiple devices simultaneously probing the network.

Descriptive study of the challenges when implementing an app for patients with neovascular age-related macular degeneration to monitor their vision at home

ObjectivesRemote monitoring of health has the potential to reduce the burden to patients of face-to-face appointments and make healthcare more efficient. Apps are available for patients to self-monitor vision at...

Stationery Management System

The Stationery Management System is a comprehensive software solution designed to streamline and automate the processes involved in managing stationery inventory for organizations. This system aims to improve efficiency, reduce costs, and enhance overall productivity by providing a centralized platform to track, control, and replenish stationery supplies. The system offers a user-friendly interface that allows authorized personnel, such as office administrators or procurement managers, to manage all aspects of stationery inventory effectively. It provides the following key features: Inventory Management: The system maintains a comprehensive database of stationery items, including details such as item name, description, quantity, and unit price. It allows users to easily add new items, update existing ones, and track stock levels in real-time. Purchase and Requisition: Users can generate purchase orders or requisitions directly from the system, ensuring a streamlined procurement process. The system can also integrate with external vendors or suppliers to automate order placement and tracking. Stock Monitoring: The system provides real-time visibility into the current stock levels of stationery items. Users can set up automated alerts to notify them when stock levels fall below a predefined threshold, ensuring timely reordering and preventing stock outs. Issue and Return Tracking: The system facilitates the tracking of stationery items issued to employees or departments. It enables users to record item issuances, monitor their usage, and manage returns efficiently. This feature helps prevent misuse and ensures accountability. Reporting and Analytics: The system generates comprehensive reports and analytics, offering valuable insights into stationery usage, expenditure, and inventory trends. These reports assist in making data-driven decisions, optimizing procurement processes, and identifying cost-saving opportunities. Integration and Accessibility: The system can integrate with other existing systems within the organization, such as accounting or Enterprise Resource Planning (ERP) software, to ensure seamless data flow and minimize manual data entry. It also provides accessibility across multiple devices, enabling users to manage stationery inventory remotely. Overall, the Stationery Management System provides organizations with a centralized and efficient solution to manage their stationery inventory effectively. By automating key processes and providing real-time visibility, the system optimizes procurement, reduces waste, and enhances productivity, leading to cost savings and improved operational efficiency.

Gradient pyramid mechanism and Nelder-Mead simplex enhanced Colony Predation Algorithm for optimal power flow problems

The escalating human energy demand, particularly in the realm of electricity consumption, imposes a substantial burden on power system planning, resulting in the emergence of the optimal power flow (OPF) problem. To address this challenge, researchers have implemented a Flexible AC Transmission System (FACTS) in modern power systems to optimize network congestion. This paper presents a new application of the Colony Predation Algorithm (CPA) in the domain of Optimal Power Flow (OPF) through the introduction of an enhanced Colony Predation Algorithm (PNMCPA). PNMCPA incorporates the gradient pyramid mechanism and the Nelder-Mead simplex (NMs), marking the first utilization of CPA in the OPF field. The proposed approach presents a novel hierarchical gradient pyramid mechanism that combines the approximate gradient descent method with the construction of a pyramid fall rate, thereby optimizing the population as a whole within a framework that enhances population diversity. Additionally, the NMs algorithm is employed to optimize the optimal set of solutions for the population, thereby accelerating the convergence efficiency of the algorithm. The primary objective of this study is to optimize multiple factors, including the generation cost and transmission loss of the power system when multiple FACT components are present. Utilizing the Weibull probability density function, the model captures the uncertain and unstable nature of wind energy resources, encompassing direct, penalty, and standby costs. The proposed algorithm is applied to the IEEE30 bus test system, and the experimental results demonstrate that PNMCPA achieves a final optimization cost of 806.0132 $/h, surpassing other optimization algorithms. Concerning the power loss matter within the bus system, the PNMCPA algorithm significantly effectively mitigates total power loss, reducing from 1.44 MW to 1.41 MW instead of the initial CPA and substantially enhancing convergence speed. Empirical simulation outcomes clearly endorse PNMCPA as a highly efficient approach to tackling optimal power flow problems in the presence of multiple FACTS devices. The significant advancements presented in this paper mark a significant contribution to the field, offering a novel and superior solution to optimize power systems in the face of escalating energy demands.

FloBP: a model-driven approach for developing and executing IoT-enhanced business processes

The capability to integrate Internet of Things (IoT) technologies into business processes (BPs) has emerged as a transformative paradigm, offering unprecedented opportunities for organisations to enhance their operational efficiency and productivity. Interacting with the physical world and leveraging real-world data to make more informed business decisions is of greatest interest, and the idea of IoT-enhanced BPs promises to automate and improve business activities and permit them to adapt to the physical environment of execution. Nonetheless, combining these two domains is challenging, and it requires new modelling methods that do not increase notation complexity and provide independent execution between the process and the underlying device technology. In this work, we propose FloBP, a model-driven engineering approach separating concerns between the IoT and BPs, providing a structured and systematic approach to modelling and executing IoT-enhanced BPs. Applying the separation of concerns through an interdisciplinary team is needed to ensure that the approach covers all necessary process aspects, including technological and modelling ones. The FloBP approach is based on modelling tools and a microservices architecture to deploy BPMN models, and it facilitates integration with the physical world, providing flexibility to support multiple IoT device technologies and their evolution. A smart canteen scenario describes and evaluates the approach’s feasibility and its possible adoption by various stakeholders. The performed evaluation concludes that the application of FloBP facilitates the modelling and development of IoT-enhanced BPs by sharing and reusing knowledge among IoT and BP experts.

Ultrafast Quasi‐2D/3D Perovskite Photodetectors Conferred Using Interfacial Engineering of Self‐Assembled Monolayers

AbstractQuasi‐2D (q‐2D) perovskites are extensively studied due to their hybrid properties of 3D and 2D phases and have proven to be applied to a wide range of optoelectronics. Multiple device engineering methods for perovskite photodetectors are developed. However, the treatment of self‐assembled monolayer (SAM) is rarely discussed. Herein, a series of silane‐coupling agents of (3‐chloropropyl)trichlorosilane (CPTS), phenyltrichlorosilane, and (3‐aminopropyl)triethoxysilane is adopted as SAM materials and elucidated their influence on q‐2D/3D CsPbBr3 films and their photodetector performance. The optical properties and morphology results reveal that perovskite film on CPTS‐modified surface can prolong excitons’ lifetime, produce a larger grain size and better size distribution, while also present better crystallinity and united orientation. As for the fabricated photodetectors, the device on a CPTS‐modified surface has the highest photocurrent of 5.46 × 10−8 A, a responsivity of 0.16 A W−1, and an 18‐times‐higher specific detectivity than the pristine counterpart, which comes from the higher surface energy and favorable energy level alignment. The photodetector on the CPTS‐modified surface also shows better stability than that on the pristine surface. This study pioneers by using SAMs on q‐2D perovskite photodetectors and provides a facile route of device fabrication for perovskite photodetectors.

Rapid, contamination-less, and efficient environmental DNA filtration system

Due to the sporadic distribution and trace amount of environmental DNA (eDNA) in deep-sea water, in the context of biodiversity monitoring, large volumes of filtration and multiple filtration replicates are required for eDNA metabarcoding. To address issues tied to the use of multiple filtration devices and large filtration volumes (e.g., contamination, time consumption, etc.), we have developed two systems for simple, rapid, and contamination-less filtration simultaneously that allow for the processing of multiple sample replicates from large volumes of water. First, the water from a Niskin bottle was filtered directly using a solenoid pump. Second, the pumped deep-sea water, using the siphon effect, was directly filtered by a filtration device driven by water pressure. This system can process 24 replicates simultaneously without the need for expensive equipment and active driving force. Compared with conventional filtering methods, e.g., peristaltic pumps, the proposed systems reduce filtration time, minimizing contamination, and enabling the simultaneous acquisition of multiple replicates. Overall, the systems presented here provide an effective approach for eDNA metabarcoding analysis, particularly for the filtration of large volumes of water containing small amounts of eDNA, such as deep-sea water.•The present systems reduce filtration time and contamination without water having to be transferred.•Simultaneous multiple replicates improve the efficiency and reliability of biodiversity assessments.

Intelligent conflict detection of IoT services using high-level Petri nets

AbstractInternet of Things has become a common paradigm for various domains. To meet a user’s complex requirement, we should compose multiple IoT devices (IoT services) to provide comprehensive services to the user. However, these services usually coexist, which is likely to lead to conflicts. Moreover, each user may have different kinds of needs. Suppose that in a smart home there are more than one person, a conflict may occur when they request the same service in this environment. Actually, even though they request different IoT services, among which if there exists function impact or QoS impact, a conflict could still occur. In this paper, we propose to employ high-level Petri nets to detect conflicts among IoT services. We first model the formal methods for conflict policies. Then, we present a Petri nets-based mechanism for modeling and detecting conflicts. Finally, we expand the previous model and use high-level Petri nets for handling fuzzy IoT conflict policies modeling and reasoning. The experimental results show that the proposed approach performs well in accuracy.

Resource-aware multi-criteria vehicle participation for federated learning in Internet of vehicles

Federated learning (FL), as a safe distributed training mode, provides strong support for the edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and safe data sharing. However, due to the resource limitation and the instability of training environment in the complex IoV, ideal performance of FL cannot be achieved. Since considering the actual resource constraints and federated task requirements, the diversified device selection criteria make the resource-aware vehicle selection problem become a multi-criteria selection problem. To effectively support FL for IoV, the resource-aware multi-criteria vehicle selection problem was described as a many-objective optimization problem, and a resource-aware many-objective vehicle selection model (RA-MaOVSM) is proposed to optimize resource efficiency. The RA-MaOVSM considering heterogeneous resources (like computation resources, communication resources, energy resources and data resources) of on-board devices in IoV, and realizes the joint optimization of learning efficiency, energy cost and global performance. Additionally, a novel probability distribution combination game strategy is applied to many-objective evolutionary algorithm (MaOEA) for improving the model solving performance. Simulation results demonstrate that RA-MaOVSM can effectively optimize the IoV resources and FL model performance, and the designed algorithm exhibits good convergence and distribution, achieving a good balance among multiple device selection criteria.

Programming memristor arrays with arbitrarily high precision for analog computing.

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.

Fulminant Myocarditis and Venoarterial Extracorporeal Membrane Oxygenation: A Systematic Review.

This systematic review aimed to look at the effectiveness of venoarterial extracorporeal membrane oxygenation (VA-ECMO) therapy in treating fulminant myocarditis and evaluating the optimal length of time a patient should be placed on VA-ECMO. Fulminant myocarditis is a potentially life-threatening medical conditionmost commonly brought on by cardiogenic shock, which often progresses to severe circulatory compromise, requiring the patient to be placed on some form of mechanical circulatory assistanceto maintain adequate tissue perfusion. Medical centers have multiple mechanical assistive devices available for treatment at their disposal, but our area of focus was placed on one system in particular: VA-ECMO therapy. Although the technology has been around for more than 30 years, there is limited information on how effective VA-ECMO is regarding the treatment of fulminant myocarditis. Due to the lack of data regarding the treatment administration of VA-ECMO for fulminant myocarditis, standard treatment duration guidelines do not exist, resulting in a wide variation of treatment administrations among medical centers.In regardto short-term outcomes, VA-ECMO has shown to be effective in treating fulminant myocarditis, with a one-year post-hospital survival rate ranging from 57.1% to 78% at discharge. For long-term health and survival, the studies that recorded long-term survival ranged from 65% to 94.1%. However, given the small number of studies that pursue this, more research is needed to prove the efficacy of VA-ECMO for thetreatment of fulminant myocarditis.

THE IMPACT OF E-SERVICE QUALITY ON E-LOYALTY THROUGH THE MEDIATING EFFECTS OF E-SATISFACTION AND E-TRUST IN LEBANON

Purpose: This study seeks to explore the relationships between e-service quality, e-satisfaction, e-trust, and e-loyalty within the context of Lebanon's digital landscape. By investigating the mediating effects of e-satisfaction and e-trust, the research aims to provide nuanced insights into the intricate dynamics shaping e-loyalty in the Lebanese online environment. The findings are anticipated to contribute valuable knowledge for businesses and policymakers seeking to enhance electronic service offerings and foster customer loyalty in the digital realm. Design/methodology/data analysis: In this comprehensive quantitative study, a validated questionnaire was deployed to survey Lebanon's diverse online shopper demographic. The participation of 2378 individuals from varied backgrounds added depth and representativeness to the data. The analytical phase employed T-test, ANOVA, and Structural Equation Modeling (SEM) techniques, leveraging SPSS software for robust statistical analysis and interpretation. This methodological approach ensures a thorough examination of the dynamics influencing online shopping behavior in the Lebanese context. Findings: Highlighting critical insights for E-commerce and e-retail, this study emphasizes the pivotal role of website efficiency and information quality. The findings underscore that a streamlined website contributes significantly to fostering customer trust, satisfaction, and loyalty. These essential factors identified in the study illuminate the pathway for businesses to enhance their online platforms, ultimately strengthening the bonds with customers and cultivating sustained loyalty in the digital realm. Originality/value: This study is important because the pandemic and lockdowns have changed consumer purchasing behaviour. Customers are more critical of e-commerce platforms and more comfortable using multiple devices to access online stores and apps as they shop more online. Practical implications: This study is timely and relevant for businesses trying to stay competitive in the changing e-commerce landscape due to consumer behaviour changes.

Flexible and Ultra Low Weight Energy Harvesters Based on 2D Phosphorene or Black phosphorus (BP): Current and Futuristic Prospects.

Phosphorene, or two-dimensional (2D) black phosphorus, has recently emerged as a competitor of graphene as it offers several advantages, including a tunable band gap, higher on/off current ratio, piezoelectric nature, and biocompatibility. Researchers have succeeded in obtaining several forms of phosphorene, such as nanosheets, nanorods, nanoribbons, and quantum dots, with satisfactory yields. Nanostructures with various controlled properties have been fabricated in multiple devices for energy production. These phosphorene-based devices are lightweight, flexible, and efficient, demonstrating great potential for energy-harvesting applications in sensors and nanogenerators. While ongoing exploration and advancements continue for these lightweight energy harvesters, it is essential to review the current progress in order to develop a future roadmap for the potential use of these phosphorene-based energy harvesters in space programs. They could be employed in applications such as wearable devices for astronauts, where ultralow weight is a vital component of any integrated device. This review also anticipates the growing significance of phosphorene in various emerging applications such as robots, information storage devices, and artificial intelligence.

Inorganic perovskite-based active multifunctional integrated photonic devices

The development of highly efficient active integrated photonic circuits is crucial for advancing information and computing science. Lead halide perovskite semiconductors, with their exceptional optoelectronic properties, offer a promising platform for such devices. In this study, active micro multifunctional photonic devices were fabricated on monocrystalline CsPbBr3 perovskite thin films using a top-down etching technique with focused ion beams. The etched microwire exhibited a high-quality micro laser that could serve as a light source for integrated devices, facilitating angle-dependent effective propagation between coupled perovskite-microwire waveguides. Employing this strategy, multiple perovskite-based active integrated photonic devices were realized for the first time. These devices included a micro beam splitter that coherently separated lasing signals, an X-coupler performing transfer matrix functions with two distinguishable light sources, and a Mach-Zehnder interferometer manipulating the splitting and coalescence of coherent light beams. These results provide a proof-of-concept for active integrated functionalized photonic devices based on perovskite semiconductors, representing a promising avenue for practical applications in integrated optical chips.

Porous Conductive Textiles for Wearable Electronics.

Over the years, researchers have made significant strides in the development of novel flexible/stretchable and conductive materials, enabling the creation of cutting-edge electronic devices for wearable applications. Among these, porous conductive textiles (PCTs) have emerged as an ideal material platform for wearable electronics, owing to their light weight, flexibility, permeability, and wearing comfort. This Review aims to present a comprehensive overview of the progress and state of the art of utilizing PCTs for the design and fabrication of a wide variety of wearable electronic devices and their integrated wearable systems. To begin with, we elucidate how PCTs revolutionize the form factors of wearable electronics. We then discuss the preparation strategies of PCTs, in terms of the raw materials, fabrication processes, and key properties. Afterward, we provide detailed illustrations of how PCTs are used as basic building blocks to design and fabricate a wide variety of intrinsically flexible or stretchable devices, including sensors, actuators, therapeutic devices, energy-harvesting and storage devices, and displays. We further describe the techniques and strategies for wearable electronic systems either by hybridizing conventional off-the-shelf rigid electronic components with PCTs or by integrating multiple fibrous devices made of PCTs. Subsequently, we highlight some important wearable application scenarios in healthcare, sports and training, converging technologies, and professional specialists. At the end of the Review, we discuss the challenges and perspectives on future research directions and give overall conclusions. As the demand for more personalized and interconnected devices continues to grow, PCT-based wearables hold immense potential to redefine the landscape of wearable technology and reshape the way we live, work, and play.