Smart Applications Research Articles

6G Technology for Indoor Localization by Deep Learning with Attention Mechanism

This paper explores 6G technology for indoor positioning, focusing on accuracy and reliability using convolutional neural networks (CNN) with channel state information (CSI). Indoor positioning is critical for smart applications and the Internet of Things (IoT). 6G is expected to significantly enhance positioning performance through the use of higher frequency bands, such as terahertz frequencies with wider bandwidth. Preliminary results show that 6G-based systems are expected to achieve centimeter-level positioning accuracy due to the integration of advanced artificial intelligence algorithms and terahertz frequencies. In addition, this paper also investigates the impact of self-attention (SA) and channel attention (CA) mechanisms on indoor positioning systems. The combination of these attention mechanisms with conventional CNNs has been proposed to further improve the accuracy and robustness of localization systems. CNN with SA demonstrates a 50% reduction in RMSE compared to CNN by capturing spatial dependencies more effectively.

Examining mobile medical supplies in supply chain: Exploring the impact of intention to use mobile medical apps

All sectors have an increasing interest in smart phone applications based on their many advantages that support business, especially the medical sector, which is constantly competing to develop the medical services provided, and accordingly in this research study we industrialized a mobile medical supplies and equipment ordering application (mobile medical app) classic and make an effort to authenticate it factually. When clients (hospitals doctors) create consumptions on the application, three dimensions can be identified: platform emotion stage, fear effect, and familiarity with product. This research designed to reinforce and brighten the most important magnitudes that improve a physician’s judgment of mobile medical app and the purpose to usage. Furthermore, this study inspected the availability of the model between hospital physicians in UAE. The classic ideal was observed by means of a model of 340 UAE clinic physicians and their personal assistant who utilize mobiles facilities in overall. The review technique, a calculable method, was applied; the fractional smallest cubes organizational calculation exhibiting systems was owned to inspect the planned agenda. The platform emotion dimension, especially fear and resistance to change, and the familiarity with the products were evaluated, and it was discovered that these factors positively influenced the objective to use the application. And the other side, the first dimension of emotion, fear, manifested as “apparent threat”, had no outcome on the purpose to using. These discoveries recommended that scholars should emphasis more on the facilities, merchandises, and the key task of the mobile medical app to control their inspirations on clients’ ordering purpose. This will progress the purchasing ways associated to acquiring medicinal materials utilizing mobile medical app and/or on other operational stages in unambiguously in UAE and the Central East at great.

Energy Challenges and Smart Applications in Production Systems

A key challenge of the modern world is addressing energy issues, especially in the context of growing public awareness of limited resources and the catastrophic effects of climate change, which are mainly caused by human activity and cumbersome industry [...]

Blockchain-Enabled Smart Contracts Revolutionizing Supply Chain Management: A Case Study

Inefficiencies, a lack of transparency, and trust issues are common challenges in today's globalized and complex supply chains. Smart contracts combined with the decentralized, immutable ledger system of blockchain technology have made it appear like a viable answer to these problems. The application of blockchain-enabled smart contracts to supply chain management is examined in this research paper's case study. This study explores the potential of blockchain technology to improve supply chain efficiency, traceability, and transparency by closely examining a real-world case. This case study explores the planning, execution, and results of incorporating smart contracts and blockchain technology into different phases of the supply chain, such as manufacturing, distribution, logistics, and procurement. The study also addresses the advantages, difficulties, and lessons discovered during the implementation of blockchain-enabled smart contracts, offering insightful information to enterprises thinking about implementing such systems. The results demonstrate how blockchain technology has the ability to completely transform supply chain management by increasing stakeholder collaboration, lowering costs, minimizing risks, and fostering trust. This case study adds to the expanding body of research on supply chain management applications of blockchain technology and provides useful advice for companies looking to use blockchain technology to improve their operations and gain a competitive edge in the fast-paced market of today

Bioinspired phosphorus functionalized polycarbazole as highly potent flame retardants and hydrophobic material for smart textile applications

Bioinspired phosphorus functionalized polycarbazole as highly potent flame retardants and hydrophobic material for smart textile applications

Smart molecular designs and applications of activatable organic photosensitizers.

Photodynamic therapy (PDT) - which combines light, oxygen and photosensitizers (PS) to generate reactive oxygen species - has emerged as an effective approach for targeted ablation of pathogenic cells with reduced risk of inducing resistance. Some organic PS are now being applied for PDT in the clinic or undergoing evaluation in clinical trials. A limitation of the first-generation organic PS was their potential off-target toxicity. This shortcoming prompted the design of constructs that can be activated by the presence of specific biomolecules - from small biomolecules to large enzymes - in the target cells. Here, we review advances in the design and synthesis of activatable organic PS and their contribution to PDT in the past decade. Important areas of research include novel synthetic methodologies to engineer smart PS with tuneable singlet oxygen generation, their integration into larger constructs such as bioconjugates, and finally, representative examples of their translational potential as antimicrobial and anticancer therapies.

UV ozone‐modified Fe3O4‐PDMS film for solvent‐driven soft actuators with controlled and multi‐stable deformations

AbstractIn recent years, solvent‐driven actuators have been capable of controlling deformations in response to external stimuli. However, a magnetic polydimethylsiloxane (PDMS) composite film with magnetic properties is traditionally developed using the micro‐electro‐mechanical system process. Although it has general actuation deformation, the composite film suffers from the problems of actuation and control. Here, this is done by co‐mixing PDMS and ferrosoferric oxide (Fe3O4) then waiting for curing and then undergoing ultraviolet‐ozone (UVO) surface treatment. By adjusting the time and orientation of the UVO treatment, a series of solvent‐driven actuators with multiform steady‐state structures (e.g., mono/bistable helix) is proposed. In addition, the deformation of the composite film under different geometrical parameters and surface constraints was visually predicted by incorporating finite element analysis methods. In addition, the controlled deformations of the composite film (such as box‐like, folded, and so on) are carried out by self‐morphing design. Finally, four bionic applications are also proposed to demonstrate the practical use of soft actuators based on Fe3O4‐PDMS films in bionic robots. This work provides new strategies for designing and fabricating programmable and controllable soft actuators and lays the foundation for a wide range of smart material applications.

Optimal scheduling of smart home appliances with a stochastic power outage: a two-stage stochastic programming approach

This study primarily concentrates on enhancing the scheduling of electric appliances within a smart home equipped with a photovoltaic solar array and a storage battery capable of redistributing excess electricity to the grid. Furthermore, the investigation takes into consideration the unpredictability of a power outage, analyzing the scheduling of these appliances to minimize consumer electricity expenses. To tackle this issue, a two-stage stochastic programming methodology is developed to effectively model the uncertainty surrounding both the onset time and duration of power outages. Additionally, a sample average approximation algorithm (SAA) is devised to efficiently address the problem. Through extensive computational experiments, the outcomes demonstrate that the SAA yields shorter CPU processing times, albeit without guaranteeing optimal solutions. The total average percent deviation of the SAA's upper bound from its lower bound and the optimal solution stands at 4.16% and 4.5%, respectively. Moreover, it is demonstrated that utilizing the stochastic approach, as opposed to the deterministic one, can enhance solution quality by 6.2%. Furthermore, a comprehensive sensitivity analysis is provided, focusing on probability distribution functions of outage start time and duration, alongside an analysis of the solar panel and battery storage capacities. It is revealed that when adopting a Normal distribution instead of a Uniform distribution, the performance of the SAA experiences a slight decline.

Experimental Evaluation Study of the Comfort and Energy Performance of Suspended Particle Device Smart Windows in a Residential Building: Achieving Optimal Control during the Cooling Season

In contrast to traditional windows, smart windows dynamically affect indoor visual and thermal environments through Visible light transmittance (VT) and Solar heat gain coefficient (SHGC) changes. Therefore, for enhancing smart window application effects, it is necessary to analyze comprehensively the impacts of smart window tinting on indoor visual and thermal environments, and to establish appropriate tinting control methods. This study aimed to determine optimal smart window control strategies for residential buildings during the cooling season by evaluating visual and thermal comfort and energy performance in an actual building equipped with SPD smart windows. An experimental building was located in Sejong, South Korea (36.5°N latitude, 127.3°E longitude; temperate climate zone), and the measurements were conducted from July to August 2023 to compare glare, indoor daylight illuminance, thermal comfort, and cooling and lighting energy between typical and smart windows. And, optimal control methods were proposed from general comfort and energy performance perspectives. The measurements indicated that it was difficult to satisfy all aspects of the visual and thermal environment, and comprehensively it was recommended to employing “always tinting” on summer. However, on a clear day, jointly applying smart windows and lighting dimming control and controlling automatically based on indoor daylight illuminance levels were found to be the most effective as it not only secured visual comfort performance but also reduced lighting energy consumption.

A multifunctional and tough lotus root starch-based bio-photonic hydrogel for stretchable fabric pattern color change and water rewriting.

Photonic crystal hydrogels (PCHs) are innovative materials that translate imperceptible deformations and humidity changes into visible colors, broadening the applications of photonics in bioengineering and smart materials. To overcome poor mechanical properties of traditional PCHs limited by weak intermolecular forces, we designed a PCH with a dual-network framework comprising N-isopropylacrylamide-co-acrylamide (NIPAM-co-AM) and biomass lotus root starch (LR). Since LR is rich in hydroxyl groups, it can undergo molecular linkage entanglement with the NIPAM-co-AM hydrogel matrix, forming hydrogen bonds that significantly enhance the mechanical properties of the PCH. We have prepared PCH films capable of conveying multidimensional information about the extent and distribution of transient deformation by encapsulating magnetic photonic crystal microspheres (MPCMs) within a hydrogel matrix. The PCH films were found to have ultrafast response time (< 10 s), full-color tunable range, high spatial resolution, excellent mechanical toughness (tensile up to 0.17 MPa) and sensitivity (2.52 nm %-1), and were able to sense relative humidity (RH) from 11 % to 98 %. Equipped with a dynamically reconfigurable lattice, this dual-responsive (tensile/humidity) PCH not only facilitates the creation of water-rewritable photonic films but also holds promise for integration into structural color elastic fabrics, thereby presenting novel prospects for smart textile applications.

Hybrid Photovoltaic and Gravity Energy Storage Integration for Smart Homes with Grid-Connected Management

This paper introduces a dynamic Smart Home Energy Management System (SHEMS) integrating a hybrid photovoltaic (PV) and gravity energy storage (GES) system aimed at minimizing environmental impacts and household energy consumption. The novel SHEMS features a one-week dynamic forecasting model that adapts to variable electricity prices, smart appliance schedules, solar output, and energy storage states. These results demonstrate that the system not only reduces household energy usage but also cuts electricity bills significantly, supplying power autonomously for up to 8.5 hours daily. By leveraging real-time data from the Dark Sky API on cloud cover and temperature, this model accurately predicts solar radiation and PV generation, aligning it with both grid and residential demands. The forecasting accuracy was assessed using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), which improved to 12.55% and 4.91% respectively, from initial values of 22.46% for RMSE and 11.78% for MAPE. These advancements enhance grid stability and optimize energy storage during peak periods, reducing dependence on fossil fuels. The integration of innovative renewable energy technologies and sophisticated forecast modeling significantly boosts the system's efficiency, promoting the sustainable use of energy resources in line with environmental and economic goals.

Investigation of Microstructure and Physical Characteristics of Eco-Friendly Piezoelectric Composite Thin Films Based on Chitosan and Ln2O3-Doped Na0.5Bi0.5TiO3-BaTiO3 Nanoparticles.

This paper investigates the synthesis and characterization of eco-friendly piezoelectric composite thin films composed of chitosan and Ln2O3-doped Na0.5Bi0.5TiO3-BaTiO3 (NBT-BT) nanoparticles. The films were fabricated using a solution-casting technique, successfully embedding the particles into the chitosan matrix, which resulted in enhanced piezoelectric properties compared to pure chitosan. Characterization methods, such as photoluminescence spectroscopy and piezo-response force microscopy (PFM) which revealed strong electromechanical responses, with notable improvements in piezoelectric performance due to the inclusion of NBT-BT nanoparticles. X-ray diffraction (XRD) analysis revealed a pure perovskite phase with the space group R3c for NBT-BT and NBT-BT-Ln particles. Scanning electron microscopy (SEM) images showed a non-uniform distribution of NBT-BT particles within the chitosan matrix. The results also suggest that the incorporation of rare earth elements further enhances the electrical and piezoelectric properties of the composites, highlighting their potential in flexible and smart device applications. Overall, these findings underscore the potential of chitosan-based composites in addressing environmental concerns while offering effective solutions for energy harvesting and biomedical applications.

Exploring the Mechanisms Driving Sense of Community in a Smart Society in Wuhan: A Mixed-Methods Multidimensional Comparative Study

In the context of China’s community governance, as a result of the insufficient willingness and ability of residents to participate, most communities have formed a “strong political–weak social” governance structure, which limits the effective expansion of social capital. To address this challenge and promote resident participation, achieving a “strong social and political” transformation in governance structure has become a core issue in current research. Meanwhile, “sense of community” serves as a crucial perspective in this research, which remains to be deepened in domestic studies and needs to consider the impact of emerging factors such as smart technologies. This study innovatively introduces affective events theory (AET), constructing a theoretical model and taking Wuhan as an example. By adopting quantitative and case comparison research methods, the paper delves into the factors influencing Wuhan residents’ sense of community in the context of a smart society and the promoting role of smart governance. On the basis of the findings, this paper presents the following conclusions: (1) Communities should attribute equal importance to the construction of both traditional and smart environments while reinforcing the leading and participatory roles of community administrators; (2) In the community’s daily governance processes, equal focus and importance must be attributed to both service delivery and engagement; (3) Communities must acknowledge the individual distinctions among residents and implement tailored governing strategies accordingly; (4) The government should prioritize the research and development of smart applications while boosting financial investments in the creation and operation of smart communities.

Flexible and highly selective NO2 gas sensor based on direct-ink-writing of eco-friendly graphene oxide for smart wearable application

Nitrogen dioxide (NO2) is a major cause of respiratory disorders in outdoor and indoor environments. Real-time NO2 monitoring using nonintrusive wearable devices can save lives and provide valuable health data. This study reports a room-temperature, wearable, and flexible smart NO2 gas sensor fabricated via cost-effective printing technology on a polyimide substrate. The sensor uses alkali lignin with edge-oxidised graphene oxide (EGO-AL) ink, demonstrating a sensitivity of 1.70% ppm⁻1 and a detection limit of 12.70 ppb, with excellent selectivity towards NO2. The high sensing properties are attributed to labile oxygen functional groups from GO and alkali lignin, offering abundant interacting sites for NO2 adsorption and electron transfer. The sensor fully recovers to the baseline after heat treatment at 150 °C, indicating its reusability. Integration into lab coats showcased its wearable application, utilising a flexible printed circuit board to wirelessly alert the wearer via cell phone to harmful NO2 levels (>3 ppm) in the environment. This smart sensing application underscores the potential for practical, real-time air quality monitoring, personal safety enhancement, and health management.

Decision Support Model for Selecting the Optimal Blockchain Oracle Platform: An Evaluation of Key Factors

Smart contract-based applications are executed in a blockchain environment, and they cannot directly access data from external systems, which is required for the service provision of these applications. Instead, smart contracts use agents known as blockchain oracles to collect and provide data feeds to the contracts. The functionality and compatibility with smart contract applications need to be considered when selecting the best-fit oracle platform. As the number of oracle alternatives and their features increases, the decision-making process becomes increasingly complex. Selecting the wrong or sub-optimal oracle is costly and may lead to severe security risks. This paper provides a decision support model for the oracle selection problem. The model supports smart contract decision-makers in selecting a secure, cost-effective, and feasible oracle platform for their applications. We interviewed oracle co-founders and smart contracts experts to refine and validate the decision model. Two real-world smart contract application case studies were used to evaluate the model. Our model prioritises and suggests more than one possible oracle platform based on the developer's required criteria, security assessment, and cost analysis. Moreover, this guided decision model serves to reveal issues that may go unnoticed if done haphazardly, reduce decision-making efforts, and provide a cost-effective solution.

An Introduction to Semantic Communication and Semantic-Aware Networking Standardization for 6G

Recent developments of networking technologies have witnessed a growing interest on human-oriented services such as Tactile Internet and VR/AR/XR, most of which are data-hungry and computationally expensive. Semantic communication and semantic-ware networking (SAN) are promising solutions to meet the above need and support emerging smart applications and services based on the network and user generated data. In this paper, we provide an overview of the ongoing standardization activity of semantic communication and semantic-aware networking at IMT-2030 (6G) standard promotion group, including the overview of the concepts, key functional components, reference architectures, and use cases.

A systematic review on artificial intelligence approaches for smart health devices

In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients’ health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare.

Valorization of nopal wastes to produce quantum dots: optimizing synthesis and exploring in smart textile applications

Quantum carbon dots (QCDs) were efficiently synthesized from post-extraction residues generated during nopal fabric production using a hydrothermal treatment. These QCDs were applied to nopal fabrics, enhancing their UV solar radiation absorption. The synthesized QCDs exhibited fluorescence emissions in the 200–300 nm range. An eco-friendly dispersion was created by incorporating QCDs into TiO2 for use in smart textiles, which underlines our commitment to maintaining a sustainable process. Bright and fluorescent patterns were successfully applied to commercial and nopal fabrics using a spray printing technique. Additionally, the QCDs demonstrated pH-sensitive color changes, paving the way for practical applications. This work represents an initial step towards a circular economy by utilizing residues from nopal fabric production to synthesize quantum dots, which may be employed in smart textiles applications with UV absorption capabilities.

Triboelectric Programmed Droplet Manipulation for Plug‐and‐Play Assembly

AbstractProgrammed droplet transport which is typically directed by surface energy gradients or external fields, is crucial in domains ranging from chemical reaction modulation to self‐powered intelligent sensing. However, droplet motion control remains constrained by path reconfiguration, requirements on chemical surface modification, and platform complexity. Here, a more universal plug‐and‐play droplet manipulation paradigm based on liquid‐solid contact electrification and triboelectricwetting on common dielectric surfaces is reported. By regulating the electrical double layer via asymmetric ion dynamics, the triboelectric charge polarity of droplets can be adjusted, enabling in situ manipulation without path reconfiguration dictated by the conventional droplet motion output. Droplets achieved an ultrahigh velocity of 450 mm s−1 on general surfaces, significantly exceeding the speeds observed in droplets subjected to constant electrostatic fields with chemical modifications. This flexible and modular functionally decoupled manipulation strategy offers an environmentally friendly, cost‐effective, and versatile paradigm, facilitating applications in chemical analysis and smart sensing.

PolyPhasic Sleep: Effects on a Human Body

The purpose of this paper is to assess the impact of polyphasic sleep on the level of productivity, cognitive skills and health in general. The studies were conducted for 6 months, during which we experimented on ourselves within the framework of three polyphasic sleep patterns: Uberman, Everyman and Dymaxion. The target was to find out if polyphasic sleep could increase efficiency without affecting health, both mentally and physically. Well-known personalities like Nikola Tesla, Leonardo da Vinci, Thomas Edison, etc. to name a few are frequently mentioned when discussing the benefits of polyphasic sleep, but there is scanty documentation backing up the effects of the phenomenon in the modern day. Therefore, this study attempted to do just that by assessing the burden placed on cognitive, emotional and physical health by each of those sleep schedules having employed lots of instruments such as smart watches, productivity applications, and cognitive tests.