Multiple Smart Research Articles

Model-based design of a mechanically intelligent shape-morphing structure

Soft robotics has significant interest within the industrial applications due to its advantages in flexibility and adaptability. Nevertheless, its potential is challenged by low stiffness and limited deformability, particularly in large-scale application scenarios such as underwater and offshore engineering. The integration of smart materials and morphing structures presents a promising avenue for enhancing the capabilities of soft robotic systems, especially in large deformation and variations in stiffness. In this study, we propose a multiple smart materials based mechanically intelligent structure devised through a model-based design framework. Specifically, the intelligent structure incorporates smart hydrogel and shape memory polymer (SMP). Employing the finite element method (FEM), we simulated the complex interactions among smart material to analyze the performance characteristics of the intelligent structure. The results demonstrate that, utilizing smart hydrogel and shape memory polymer (SMP) can effectively attain large deformation and exhibit variable stiffness due to the shape memory effect. Besides, the shape-morphing structures exhibit customized behaviours including bending, curling, and elongation, all while reducing reliance on external power sources. In conclusion, utilizing multiple smart materials within the model-based design framework offers an efficient approach for developing mechanically intelligent structure capable of complex deformations and variable stiffness, thereby providing support for underwater or offshore engineering applications.

MRNA-seq-based analysis predicts: AEG-1 is a therapeutic target and immunotherapy biomarker for pan-cancer, including OSCC.

The aberrant expression of AEG-1 is significantly correlated with tumorigenesis, development, neurodegeneration and inflammation. However, the relationship between AEG-1 expression and immune infiltration in OSCC, as well as other tumor types, has yet to be comprehensively analyzed. The expression levels, prognostic and clinicopathological characteristics, mutation patterns and methylation landscapes of AEG-1 in various tumors were obtained from multiple databases, including TIMER, GEPIA, HPA, TCGA, UALCAN, cBioPortal, SMART and TISIDB, in addition to single-cell RNA-seq data. The integration of these datasets facilitated the elucidation of the relationships among pan-cancer cellular heterogeneity, immune infiltration and AEG-1 expression levels. In vitro experiments created AEG-1 overexpressing cell lines, and mRNA-seq analyzed AEG-1-related differential genes in OSCC. RT-PCR validated these findings in vivo using xenograft tumors. Tumor cell lines were developed to study AEG-1's effects through H&E, Masson, and PAS staining. Immunohistochemistry examined AEG-1-related gene expression patterns. Our analysis demonstrated that AEG-1 is highly expressed across various cancer types and is associated with tumor grade and patient prognosis. Additionally, AEG-1 amplification was observed in multiple cancers. Notably, we identified a significant elevation of AEG-1 expression in OSCC, which strongly correlated with patient prognosis and immune infiltration. Through mRNA-seq analysis of differentially expressed genes and immune-related gene sets, we identified a strong correlation between AEG-1 and immune infiltration markers such as LCP2, CD247, HLA-DPA1, HLA-DRA, HLA-DRB1, CIITA and CD74 in OSCC. Additionally, AEG-1 was found to regulate Th1/Th2 immune homeostasis, promote glycogen accumulation, and contribute to tumor fibrosis. In conclusion, AEG-1 significantly correlates with prognosis and immune infiltration across various cancer types and holds potential as a novel prognostic immune biomarker for OSCC. This finding may facilitate the identification of patients who are most likely to benefit from adjuvant immunotherapy.

Smart contract languages: A comparative analysis

Smart contracts have played a pivotal role in the evolution of blockchains and Decentralized Applications (DApps). As DApps continue to gain widespread adoption, multiple smart contract languages have been and are being made available to developers, each with its distinctive features, strengths, and weaknesses. In this paper, we examine the smart contract languages used in major blockchain platforms, with the goal of providing a comprehensive assessment of their main properties. Our analysis targets the programming languages rather than the underlying architecture: as a result, while we do consider the interplay between language design and blockchain model, our main focus remains on language-specific features such as usability, programming style, safety and security. To conduct our assessment, we propose an original benchmark which encompasses a wide, yet manageable, spectrum of key use cases that cut across all the smart contract languages under examination.

DApps ecosystems: mapping the network structure of smart contract interactions

Decentralized applications (DApps) built on blockchain platforms such as Ethereum and coded in languages such as Solidity, have recently gained attention for their potential to disrupt traditional centralized systems. Despite their rapid adoption, limited research has been conducted to understand the underlying code structure of these applications. In particular, each DApp is composed of multiple smart contracts, each containing a number of functions that can be called to trigger a specific event, e.g., a token transfer. In this paper, we reconstruct and analyse the network of contracts and functions calls within the DApp, which is helpful to unveil vulnerabilities that can be exploited by malicious attackers. We show how decentralization is architecturally implemented, identifying common development patterns and anomalies that could influence the system’s robustness and efficiency. We find a consistent network structure characterized by modular, self-sufficient contracts and a complex web of function interactions, indicating common coding practices across the blockchain community. Critically, a small number of key functions within each DApp play a central role in maintaining network connectivity, making them potential targets for cyber attacks and highlighting the need for robust security measures.

Impacts of environmental regulation perceptions on farmers’ intentions to adopt multiple smart hog breeding technologies: Evidence from rural Hubei, China

Smart hog breeding technologies have been viewed as practical solutions for environmental pollution, pork production inefficiency and food insecurity in rural areas. With difficulty in promoting these technologies, little is known about the role environmental regulation plays. Using 703 household survey data from rural Hubei, China, this paper identifies the impacts of different environmental regulation perceptions (i.e., supervisory, incentive and guiding regulation perceptions) on hog breeding farmers' intentions to adopt smart hog breeding technologies and on the intensity of their adoption intentions. Both multivariate probit model and ordered probit model are employed. Results show that smart hog breeding technologies are strong complementary. Farmers' environmental regulation perceptions greatly promote their intentions to adopt smart hog breeding technologies and their adoption intention intensity. Only the intensity of healthy farmers' adoption intentions is promoted by supervisory regulation perception. Compared to those without village cadres in family, the intensity of village-cadre group's adoption intentions is strengthened more by supervisory and guiding regulation perceptions. The greater incentive regulation perception is, the stronger the intensity of large-scale farmers' adoption intentions would be. The impacts of different environmental regulation perceptions on the intensity of adoption intentions are all partially mediated by value perception of smart agriculture. These findings suggest promoting smart hog breeding technologies jointly and underscore the importance of local environmental regulations in accelerating the diffusion of smart hog breeding technologies.

An adaptive and late multifusion framework in contextual representation based on evidential deep learning and Dempster–Shafer theory

AbstractThere is a growing interest in multidisciplinary research in multimodal synthesis technology to stimulate diversity of modal interpretation in different application contexts. The real requirement for modality diversity across multiple contextual representation fields is due to the conflicting nature of data in multitarget sensors, which introduces other obstacles including ambiguity, uncertainty, imbalance, and redundancy in multiobject classification. This paper proposes a new adaptive and late multimodal fusion framework using evidence-enhanced deep learning guided by Dempster–Shafer theory and concatenation strategy to interpret multiple modalities and contextual representations that achieves a bigger number of features for interpreting unstructured multimodality types based on late fusion. Furthermore, it is designed based on a multifusion learning solution to solve the modality and context-based fusion that leads to improving decisions. It creates a fully automated selective deep neural network and constructs an adaptive fusion model for all modalities based on the input type. The proposed framework is implemented based on five layers which are a software-defined fusion layer, a preprocessing layer, a dynamic classification layer, an adaptive fusion layer, and an evaluation layer. The framework is formalizing the modality/context-based problem into an adaptive multifusion framework based on a late fusion level. The particle swarm optimization was used in multiple smart context systems to improve the final classification layer with the best optimal parameters that tracing 30 changes in hyperparameters of deep learning training models. This paper applies multiple experimental with multimodalities inputs in multicontext to show the behaviors the proposed multifusion framework. Experimental results on four challenging datasets including military, agricultural, COIVD-19, and food health data provide impressive results compared to other state-of-the-art multiple fusion models. The main strengths of proposed adaptive fusion framework can classify multiobjects with reduced features automatically and solves the fused data ambiguity and inconsistent data. In addition, it can increase the certainty and reduce the redundancy data with improving the unbalancing data. The experimental results of multimodalities experiment in multicontext using the proposed multimodal fusion framework achieve 98.45% of accuracy.

A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber Model

The Publisher-Subscriber model of data exchange has been a popular method for many Internet-based applications, including the Internet of Things (IoT). A traditional PS system consists of publishers, subscribers, and a broker. The publishers create new data for a registered topic, and the data broker relays the data to the corresponding subscribers. This paper introduces a blockchain-based smart broker for the publisher-subscriber (PS) framework for the IoT network. As IoT data comes from devices operating in various environments, it may suffer from multiple challenges, such as hardware failures, connectivity issues, and external vulnerabilities, thereby impacting data quality in terms of accuracy and timeliness. It is important to monitor this data and inform subscribers about its quality. The proposed smart broker is composed of multiple smart contracts that continuously monitor the quality of the topic data by assessing its relationship with other related topics and its drift or delay in publishing intervals. It assigns a reputation score to each topic computed based on its quality and drifts, and it passes both the original data and the reputation score as a measure of quality to the subscriber. Furthermore, the smart broker can suggest substitute topics to subscribers when the requested topic data are unavailable or of very poor quality. The evaluation shows that a smart broker efficiently monitors the reputation of the topic data, and its efficiency increases notably when the data quality is worse. As the broker is run inside the blockchain, it automatically inherits the advantages of the blockchain, and the quality scoring is indisputable based on immutable data.

An Integrated Event-Driven Real-Time Tactical–Operational Optimization Framework for Smart Port Operations Planning

The ongoing issues in global supply chain disruptions have raised many concerns of port productivity, among which port congestion is a key issue. This article implements an integrated tactical–operational optimization framework which raises the capabilities of port information systems to deliver smarter decision-making processes in ports through a decision support system. To this end, we developed a library of multiple smart models for the optimization of port operations, independently engaged in parallel but mathematically coordinated to achieve autonomous real-time distributed optimization, using a novel event-driven structure to enable future implementations using digital twins. The framework was tested to benchmark different commercial solvers on several real instances for the port under study. The results show a strong improvement in port operational planning.

Development of an energy-autonomous eddy current sensor system for in-situ component monitoring

Energy-autonomous non-destructive testing (NDT) systems provide new opportunities for structural health monitoring. An NDT system that is energy-autonomous and transmits the recorded data wirelessly can be used in places that are not easily accessible for inspection. In addition, it can provide for continuous monitoring, which can improve the operational safety of various components and structures. This study presents a concept that enables energy-autonomous and wireless structural health monitoring using a splined shaft as a demonstrator. To monitor mechanical overloads, a material sensor is applied to critical areas of these components by a laser heat treatment. The structural change that occurs as a result of an overload can be monitored by the developed smart sensor system. This system consists of a material sensor, a low-cost eddy current sensor, an energy-efficient evaluation unit with an ultra-low-power microcontroller unit (MCU) and a Long-Range Wide Area Network (LoRaWAN) data transmission unit. Wireless data transmission via LoRaWAN provides access to the Industrial Internet of Things network, enabling the collection of NDT data from multiple smart sensors in a cloud and the processing of these data to assess the condition of the component from any location. It is shown that the MCU-based eddy current testing system is able to generate a similar signal quality for material characterisation compared to an industrial eddy current testing system, despite a much more compact design and a significantly higher energy efficiency. The energy autonomy of the smart sensor system is achieved by an energy harvesting system integrated into the splined shaft. In addition, three different testing scenarios are presented, which differ mainly in the scope of data processing and evaluation on the MCU, and thus also in the energy required.

Improving Manufacturing Efficiency with Industry 4.0 Technologies: A Comparative Analysis of Smart Factories

The advent of Industry 4.0 technologies has ushered in a new era of manufacturing marked by the integration of cyber-physical systems, data-driven decision-making, and automation. This study presents a comprehensive comparative analysis of the impact of Industry 4.0 technologies on manufacturing efficiency through a focus on smart factories. The research examines how these technologies, such as the Internet of Things (IoT), artificial intelligence, and advanced robotics, contribute to operational improvements across diverse manufacturing environments. The methodology involves a comparative case study approach, analyzing multiple smart factories from various industries to identify common trends and distinct challenges. Key performance indicators (KPIs) related to efficiency, productivity, and resource utilization are assessed, providing a nuanced understanding of the transformative effects of Industry 4.0. The findings reveal that the implementation of smart technologies leads to significant improvements in production processes, reduced downtime, and enhanced product quality. Furthermore, the study explores the challenges encountered during the adoption of Industry 4.0, including cybersecurity concerns, workforce upskilling, and the integration of legacy systems. Insights gained from successful implementations serve as a guide for manufacturers seeking to optimize their operations through the adoption of Industry 4.0 technologies. The research contributes to the existing literature by offering a comparative perspective on smart factories, shedding light on best practices, and facilitating a deeper understanding of the factors influencing successful Industry 4.0 adoption.

Too Much Is Never Enough: An Analysis of Smart Device Purchase Intention

ABSTRACT This study investigates users’ willingness to purchase additional smart devices to enhance their personal digital ecosystems. Specifically, it delves into how information system continuance intention and media system dependency influence the intention to purchase additional smart devices. Drawing on the assemblage theory, the study also examines the potential moderating effects of the satisfaction level and the number of smart devices already owned. Based on analysis of a sample comprising 400 smart device users, the study reveals that usage continuance intention and dependency have a significant and positive relation to purchase intention. Furthermore, this study sheds light on the positive impact of owning multiple smart devices on these relations. It is noteworthy that among users with a large number of smart devices, the link between continuance intention and purchase intention is significantly stronger when the satisfaction level is low. This finding underscores the potential risk of fostering a form of addiction.

Energy Harvest of Multiple Smart Sensors With Real-Time Fault-Detection

For multiple smart sensors with limited energy supply, the relationship between the energy supply and sensor fault-free region is often unknown, and neither of the interior structure nor exterior circumstance modeling the smart sensors is easy to accurately achieve, so how to guarantee the fault-free smart sensors to harvest the most energy in the fault-free way is a very challenging topic. To address this issue, this paper first formulates the individual smart sensor as a single-input single-output (SISO) model-free system (MFS), with its energy supply and sensing error as the input and output, respectively, then makes use of Lyapunov function to deduce an upper bound of the fault-free region to realize the fault-detection of any smart sensor and disclose the relationship between the energy supply and fault-free region, and finally achieves the optimal energy supply guiding the overall energy harvest of all fault-free smart sensors to converge to the maximum with the convergence rate no larger than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa\in[0,1]$</tex-math> </inline-formula> , while enjoying the real-time fault-detection. An algorithm based on the sound theoretical foundations is further proposed to implement the optimal energy supply. Theoretical analysis, simulations and field experiments jointly verify the performance of our method. To our best knowledge, it is the initial work towards this issue. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper addresses the interesting issue of how to guarantee multiple smart sensors to harvest the most energy in the free-fault way. Through the insightful disclosure of relationship between the energy supply and sensor fault-free region, and the optimal control of energy supply, this paper facilitates the overall energy harvest of all fault-free smart sensors that work under the environments, where the available energy is limited, to converge to the maximum while enjoying the real-time fault-detection, which we believe could push the development of Internet of Things (IoT) or Cyber-Physical System (CPS) that employs multiple smart sensors to sense the physical world. Simulations and field experimental investigations jointly show that the proposed solution outperforms the existing solutions.

Optimized scheduling of smart community energy systems considering demand response and shared energy storage

Integrated energy systems within communities play a pivotal role in addressing the diverse energy requirements of the system, emerging as a central focus in contemporary research. This paper contributes to exploring optimal scheduling in a smart community featuring multiple smart buildings equipped with a substantial share of distributed photovoltaic sources, shared energy storage, and controllable loads. The study formulates a two-stage scheduling optimization model incorporating multiple stakeholders, explicitly examining the game dynamics between the smart community operator and the customer load aggregator. The weights assigned to each optimization objective are determined using an analytic hierarchy process-entropy weight method to establish a balanced and nuanced approach. The ensuing optimal scheduling encompasses unit output and energy trading considerations, focusing on enhancing the system's economic viability, safety measures, and environmental sustainability. A comprehensive evaluation is conducted to validate the proposed model by comparing its performance with alternative operational strategies. The results show that the designed strategy can reduce the operating cost by 40.22% and increase the level of PV consumption by 22.57% compared to the traditional heat-based strategy, which is effective in mitigating power fluctuations, smartly responding to dispatch peak demand, enhancing new energy integration, and ensuring the security of grid operation.

Optimal resource allocation and operation for smart energy hubs considering hydrogen storage systems and electric vehicles

Energy hubs (EHs) have substantially paved the way for the coordinated operation of various energy carriers, converters, and storage. However, the establishment of optimal planning and operation of the EH include several challenges, e.g., the stochastic nature of non-dispatchable generation assets, obtaining a satisfactory performance from the storage assets with reasonable energy and power density costs, and long-term load growth. Accordingly, this paper proposes a two-stage stochastic optimal resource allocation and operation approach for multiple smart energy hubs-based microgrids. The first stage optimizes the location of each EH and different assets' capacities. On the other hand, the second stage optimizes the charging/discharging rates of each asset and parking lot power. These microgrids combine renewable energy sources. Moreover, for research gap and unlike existing research, the cooperative operation of hybrid storage systems (i.e., solar-powered compressed air energy storage, hydrogen storage systems, battery energy storage systems, thermal energy storage systems) along with plug-in electric vehicles (PEV) has been investigated. Furthermore, annual load growth and price-based demand response (PBDR) are employed in this study. The simulation results demonstrate that the controlled charging/discharging of the PEVs reduces the total annualized cost by 5.49%, while the operation and emission costs are reduced by 21.3% and 19.86%, respectively. Furthermore, employing PBDR has led to a reduction of 10.1% in capital and operational costs.

Evolutionary Multi-Objective Feature Selection Algorithms on Multiple Smart Sustainable Community Indicator Datasets

The conceptual fusion of smart city and sustainability indicators has inspired the emergence of the smart sustainable city (SSC). Given the early stage of development in this field, most SSC studies have been primarily theoretical. Notably, existing empirical studies have overlooked the crucial aspect of feature engineering in the context of SSC, despite its significance in advancing SSC initiatives. This paper introduces an approach advocating for feature subset selection to maximize prediction accuracy and minimize computational time across diverse SSC indicators encompassing socio-cultural, economic, environmental, and governance categories. The study systematically collected multiple datasets on SSC indicators, covering various themes within the SSC framework. Employing six carefully chosen multiple-objective evolutionary feature selection algorithms, the research selected feature subsets. These subsets were then utilized in modeling algorithms to predict SSC indicators. The proposal enhanced prediction accuracy for life expectancy, online shopping intentions, energy consumption, air quality, water quality, and traffic flow for a smart and sustainable city by minimizing the subset features. The findings underscore the efficacy of feature subset selection in generating minimal features, thereby enhancing both prediction accuracy and computational efficiency in the realm of SSC indicators. For researchers aiming to develop sustainable systems for real-time data monitoring within SSC, the identified subset features offer a valuable resource, negating the necessity for extensive dataset collection. The provided SSC datasets are anticipated to serve as a catalyst, inspiring researchers to embark on empirical studies that explore SSC development from diverse perspectives, ultimately contributing to a more profound understanding of the SSC dynamics.

Gold nanorod-chitosan based nanocomposites for photothermal and chemoembolization therapy of breast cancer

Gold nanorods (AuNR) have received significant attention in tumor thermo-chemotherapy. However, insufficient thermal availability limits the in vivo highly efficient applications of AuNR in photothermal therapy. In this study, we have fabricated N-isopropylacrylamide grafted O-carboxymethyl chitosan nanoparticles (NCMC NPs) with thermo-responsive properties for co-encapsulating AuNR and doxorubicin (DOX), forming AuNR@NCMC/DOX nanocomposites (NCs). As a result of the thermo- and photothermal-responsiveness, AuNR@NCMC/DOX NCs exhibited irreversible aggregation at high temperature and under near-infrared (NIR) irradiation with an increase of size to 3 μm. When AuNR@NCMC/DOX NCs reached tumor sites following intravenous administration, they were located in the tumor vessels under NIR irradiation due to an embolization effect. This response enhanced tumor targeting, on-demand release, and the thermal performance of AuNR@NCMC/DOX NCs. We have observed higher tumor accumulation of DOX and AuNR with subsequent stronger inhibition of tumor growth than that achieved without NIR irradiation. The development of AuNR-based NCs with multiple smart responsivenesses at tumors can provide a promising paradigm for solid tumor treatment via the cooperative effects of photothermal therapy and chemoembolization.

Energy Management Scheme for Optimizing Multiple Smart Homes Equipped with Electric Vehicles

The rapid advancement in technology and rise in energy consumption have motivated research addressing Demand-Side Management (DSM). In this research, a novel design for Home Energy Management (HEM) is proposed that seamlessly integrates Battery Energy Storage Systems (BESSs), Photovoltaic (PV) installations, and Electric Vehicles (EVs). Leveraging a Mixed-Integer Linear Programming (MILP) approach, the proposed system aims to minimize electricity costs. The optimization model takes into account Real-Time Pricing (RTP) tariffs, facilitating the efficient scheduling of household appliances and optimizing patterns for BESS charging and discharging, as well as EV charging and discharging. Both individual and multiple Smart Home (SH) case studies showcase noteworthy reductions in electricity costs. In the case of multiple SHs, a remarkable cost reduction of 46.38% was achieved compared to a traditional SH scenario lacking integration of a PV, BESS, and EV.

Energy-Optimal Control of Multiple Smart Sensors With Faulty External Signal

Energy-Optimal Control of Multiple Smart Sensors With Faulty External Signal

Smart Agriculture system based on IoT reference architecture and service choreography

This paper presents a monitoring, control, and management system that allows the interoperability of multiple smart gateways in a greenhouse to bolster automatic decision making on water supplied to the crops in real-time. The system consists of several sensor nodes interconnected by Zigbee to measure ambient and soil parameters to control the greenhouse irrigation process. In addition, a web service is included to help the system manager to visualize and operate the greenhouse on a web platform. The system also supports human decision making to cope with undesirable and unexpected environmental events. Smart gateways enable data reading, storage and edge computing using automatic rules to manage actions in the greenhouse, as well as data transmission for centralized processing in cloud services. The service communication system layer is supported by choreography middleware for messaging distribution, which enables agile scalability. This research proposes a hybrid scheme and compares to a centralized scheme. The latency times in the choreography system were analyzed to find a suitable solution adapted to infrastructure deployment features. Numerical results shows that such an adaptive deployment of the irrigation services among edge and fog layers can reduce the computational load on the gateways, improve system response times, and enhance device performance.

Toward optimal voltage/VAR control with smart PVs in active distribution networks

Rapidly increasing penetration of distributed PVs brings great challenges to distribution networks. Driven by the revised IEEE 1547 standard, inverter-based smart PVs provide customized control and voltage regulation capabilities. This paper presents a comprehensive study investigating voltage/VAR control considering network reconfiguration, VAR optimization, and smart PVs. To do so, a second-order cone programming-based VAR optimization and network reconfiguration (VONR) model is formulated. The enhanced necessary radiality condition, and the extensions for multiple smart PV operation modes and multi-period operation are provided. A comprehensive case study considering various network and smart PV operation modes, and hourly VONR operation is conducted. The results show that implementing VONR with smart PV reactive power dispatching optimizes the scheduling plans, resulting in a reduction of network loss by 59.97 % and 30.37 % for the 33-bus and 123-bus test systems, respectively, compared to the no-control operation.