Behavior Of Devices Research Articles

Numerical Simulation, Electrostatic and Physical Compact Modeling of C8-BTBT-C8 Organic Thin Film Transistor

This paper presents numerical simulation and compact modeling of 2,7-Dioctyl {1} benzothieno {3,2-b}{1} benzothiophene (C8-BTBT-C8) organic semiconductor-based TFT. It shows the entire modeling process flow of this organic semiconductor (OSC) and tests the device realization using a ring oscillator. The paper comprises OSC characterisation, band-gap modeling, electrostatic modeling, and capacitance modeling. The TCAD model consists of the Hopping and Pool Frenkel premise and characterizes the Density of State (DOS) for traps in deep and tail states. The findings from this research provide valuable information for improving OTFT models, enhancing their predictive accuracy, and advancing the understanding of organic semiconductor device behavior. The electrostatics demonstrate device structure dependency

Influence of Increased Online Information on Consumption Patterns during COVID-19: Case of Priority Medical Devices

Influence of Increased Online Information on Consumption Patterns during COVID-19: Case of Priority Medical Devices

Security intrusion monitoring model for Internet of Things (IoT) using sniffing tools on wireless sensor networks

The Internet of Things (IoT) has revolutionized the way devices interact and share data over wireless sensor networks (WSN), enabling seamless connectivity and automation. However, the proliferation of IoT devices has raised serious security and privacy risks concerns due to their inherent vulnerabilities. This paper proposes a model for security intrusion monitoring by analyzing the existing literature and providing insights into the design, implementation, and effective deployment of the proposed model to detect intrusion in IoT using sniffing tools for network traffic analysis in real-time within WSN. The model passively monitors network traffic and identifies anomalous patterns, unauthorized access attempts, and abnormal device behavior. The review findings highlight the significance of the proposed model in enhancing the security of IoT systems. By detecting anomalous behavior and potential security breaches. The model enables timely response and mitigation actions to ensure the confidentiality, integrity and availability (CIA) of IoT devices data. The model includes consideration of network architecture, deployment of intrusion detection algorithms, and establishment of response mechanisms. It identifies various types of security threats, such as unauthorized access attempts, Denial-of-service, Distributed DoS, Brute-force, Heartbleed, Botnet, Inside Infiltration and device tampering, thereby providing response mechanisms that include generating alerts, isolating compromised devices, or blocking suspicious network traffic. The model incorporates a feedback loop to continuously update the detection mechanisms and adapt to evolving security threats in real-time. Series of experiments and simulations to be conducted using various IoT devices and network scenarios to evaluate model effectiveness. The model to comprise of wireless Router, MatLab for Deep Neural Network (DNN) training, Raspberry Pi, Wireshark setup and an array of Internet of Things (IoT) devices. The researcher to use dataset by extracting intrinsic, host-based and time-based attributes from Wireshark Sniffing tool. The datasets generated shall be piped by tshark to an output text file saved as a csv. Under-sampling technique to be used to address class imbalance of datasets. The model shall then be trained using the dataset to be able to detect intrusion in IoTs. The results is expected to demonstrate the model's ability to detect a wide range of security intrusions with high accuracy and minimal false positives. In conclusion, the model offers a proactive approach to safeguard IoT deployment. By leveraging sniffing tools and advanced analysis techniques, the model enhances the detection and response capabilities, enabling efficient protection against emerging threats in IoT. However, challenges associated with the model are identified, including the complexity of network monitoring and potential privacy concerns.

Emission current enhancement from quasi-freestanding epitaxial graphene microstructure electron emitters through surface layered silicon dioxide

Enhanced electron emission from oxide-encapsulated quasi-freestanding bilayer epitaxial graphene devices is reported, including one emission current of 9.4 µA and successful emission even with oxide thicknesses of up to 1.25 µm. The low operating temperature (215 °C), and applied electric fields under which the devices operate indicate electron emission is due to phonon-assisted electron emission, wherein forward-scattering hot phonons impart the necessary energy for the electrons to escape the graphene as an emission current. A suite of device structures and behaviors are cataloged, and various emission behaviors are demonstrated through encapsulating oxide layers. Emission current enhancement due to electron multiplication in the oxide layers is observed across multiple devices and oxide thicknesses.

Automatically Discovering Mechanical Functions From Physical Behaviors via Clustering

Abstract Computational design methods provide opportunities to discover novel and diverse designs that traditional optimization approaches cannot find or that use physical phenomena in ways that engineers have overlooked. However, existing methods require supervised objectives to search or optimize for explicit behaviors or functions—e.g., optimizing aerodynamic lift. In contrast, this article unpacks what it means to discover interesting behaviors or functions we do not know about a priori using data from experiments or simulation in a fully unsupervised way. Doing so enables computers to invent or re-invent new or existing mechanical functions given only measurements of physical fields (e.g., fluid velocity fields) without directly specifying a set of objectives to optimize. This article explores this approach via two related parts. First, we study clustering algorithms that can detect novel device families from simulation data. Specifically, we contribute a modification to the hierarchical density-based spatial clustering of applications with noise algorithm via the use of the silhouette score to reduce excessively granular clusters. Second, we study multiple ways by which we preprocess simulation data to increase its discriminatory power in the context of clustering device behavior. This leads to an insight regarding the important role that a design’s representation has in compactly encoding its behavior. We test our contributions via the task of discovering simple fluidic devices and show that our proposed clustering algorithm outperforms other density-based algorithms, but that K-means clustering outperforms density-based algorithms, as measured by adjusted Rand score. However, the device types may have an even stronger impact on the clustering. This opens up new avenues of research wherein computers can automatically derive new device functions, behaviors, and structures without the need for human labels or guidance.

Computational design and experimental investigation of novel thiophene-based organic dyes with N,N-dimethylaminophenyl and morpholinophenyl donors for dye-sensitized solar cells

A new series of thiophene-based organic chromophores (4–7 a, b) has been developed through structural engineering of the electron donor (D) group, incorporating N,N-dimethylaminophenyl and morpholinophenyl, and acceptor moieties such as benzonitrile and flurobenzonitrile. Additionally, a π-linkage of phenylthiophene was inserted. The effects of various donating and accepting groups, as well as the spacer, on the geometrical, energetic, absorption, and photovoltaic properties of these sensitizers were investigated using Density Functional Theory (DFT) and Time-Dependent DFT. The sensitizers were optimized in their ground state using selected functional methods. Furthermore, key properties such as absorbance, frequency, energies of Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO), energy gap, dipole moment, and global reactivity descriptors were calculated using quantum chemical calculations. These spectral properties and quantum chemical calculations provide valuable insights into the potential use of these sensitizers in photonic devices. The results obtained from analyzing light harvesting efficiency (LHE), free energy injection (ΔGinj), and open circuit voltage (Voc) demonstrate that these dyes show promising characteristics for future experimental studies and offer guidelines for improving the performance of dye-sensitized solar cells. By employing structural modifications and investigating the electronic properties of these newly developed chromophores, this study contributes to the understanding of their behavior and potential applications in photonic devices. The findings provide a foundation for further experimental investigations and offer valuable insights for the development of more efficient dye-sensitized solar cells.

Reversible Redox‐Driven Crystallization in a Paracyclophane Monolayer at a Solid–Liquid Interface

AbstractThe development and integration of cyclophanes into future functional materials require a detailed understanding of the physicochemical principles that underlie their properties, phase behavior, and in particular the relationship between structure and function. Here, electrochemically switchable crystallization of a ferrocene‐bearing 3D Janus tecton (M‐Fc) at the interface between highly oriented pyrolytic graphite (HOPG) and an electrolyte solution is demonstrated. The M‐Fc adlayer is successfully visualized under both ambient and electrochemical conditions using scanning tunneling microscopy. Voltammetric measurements show a surface‐confined redox process for the M‐Fc modified surface that drives the phase transition between a visible 2D ordered linear phase (M‐Fc0, with ferrocene in the neutral state) and an invisible gas‐like adsorption layer with high mobility when ferrocene is oxidized, M‐Fc+, and a “square scheme” mechanism explains the data. Analogous experiments in a ferrocene‐free tecton adlayer show no phase transition and confirm that the dynamics in M‐Fc are redox‐driven. On‐surface 3D nanoarchitectures are also demonstrated by forming inclusion complexes between M‐Fc and β‐cyclodextrin and device behavior through electrochemical scanning tunneling spectroscopy (STS). These results showcase the functional potential of this class of cyclophanes, which can find use in actuators, optical crystals, and other smart materials.

Electronic and Structural Disorder of the Epitaxial La0.67Sr0.33MnO3 Surface.

Understanding and tuning epitaxial complex oxide films are crucial in controlling the behavior of devices and catalytic processes. Substrate-induced strain, doping, and layer growth are known to influence the electronic and magnetic properties of the bulk of the film. In this study, we demonstrate a clear distinction between the bulk and surface of thin films of La0.67Sr0.33MnO3 in terms of chemical composition, electronic disorder, and surface morphology. We use a combined experimental approach of X-ray-based characterization methods and scanning probe microscopy. Using X-ray diffraction and resonant X-ray reflectivity, we uncover surface nonstoichiometry in the strontium and lanthanum alongside an accumulation of oxygen vacancies. With scanning tunneling microscopy, we observed an electronic phase separation (EPS) on the surface related to this nonstoichiometry. The EPS is likely driving the temperature-dependent resistivity transition and is a cause of proposed mixed-phase ferromagnetic and paramagnetic states near room temperature in these thin films.

Improvement Performance of p-GaN Gate High-Electron-Mobility Transistors with GaN/AlN/AlGaN Barrier Structure.

This study demonstrates a particular composited barrier structure of high-electron-mobility transistors (HEMTs) with an enhancement mode composed of p-GaN/GaN/AlN/AlGaN/GaN. The purpose of the composite barrier structure device is to increase the maximum drain current, reduce gate leakage, and achieve lower on-resistance (Ron) performance. A comparison was made between the conventional device without the composited barrier and the device with the composited barrier structure. The maximum drain current is significantly increased by 37%, and Ron is significantly reduced by 23%, highlighting the synergistic impact of the composite barrier structure on device performance improvement. This reason can be attributed to the undoped GaN (u-GaN) barrier layer beneath p-GaN, which was introduced to mitigate Mg diffusion in the capping layer, thus addressing its negative effects. Furthermore, the AlN barrier layer exhibits enhanced electrical properties, which can be attributed to the critical role of high-energy-gap properties that increase the 2DEG carrier density and block leakage pathways. These traps impact the device behavior mechanism, and the simulation for a more in-depth analysis of how the composited barrier structure brings improvement is introduced using Synopsys Sentaurus TCAD.

Mathematical modeling of an electrostatic MEMS with tilted elastomeric micro-pillars

The aim of this study is to develop a comprehensive mathematical model for an electrostatic MEMS (Micro-Electro-Mechanical Systems) with gap-filling tilted micro-pillars. Elastomeric pillars are used due to their high dielectric constant, resulting in a higher equivalent permittivity of the gap medium. This leads to increased sensitivity and decreased required voltage. Although this system has gained popularity in recent years, a thorough understanding and accurate predictions of its behavior require a detailed mathematical model. Regrettably, the existing literature does not offer adequate information. The present model incorporates three coupled set of nonlinear differential equations that govern the longitudinal and transverse vibrations of the PDMS (Polydimethylsiloxane) pillars, as well as the transverse vibrations of the moving electrode. It demonstrates how the device's behavior is affected by varying tilt angles and numbers of micro-pillars. The viscoelastic properties of the PDMS pillars are also taken into account, following the Kelvin-Voigt model with non-linear strains. Numerical methods are used to solve the governing equations, enabling investigation of static deformation and vibrational responses under different DC (Direct Current) and DC+AC (Alternating Current) actuations. The nonlinear equations proposed in this study provide a valuable tool for accurately predicting the behavior of capacitive MEMS devices that use microstructured gaps with tilted micro-pillar arrays.

Analyzing the Temporal Behavior of Noisy Intermediate‐Scale Quantum Nodes and Algorithm Fidelity

AbstractIn the past decade, quantum computing has undergone rapid evolution, capturing the increasing interest of the scientific community, industry, and governments. This enthusiasm has resulted in ambitious growth plans which stimulate the development of more efficient quantum computing devices and programming environments. The easy accessibility of quantum platforms in the cloud has attracted individuals to explore quantum computing, prompting a comprehensive analysis and assessment of quantum device's behavior. The extensive benchmarking presented in this study involved all free available quantum computing devices within the IBM Quantum Platform. These devices are employed to execute tens of thousands of quantum program executions, with the objective of evaluating quantum computer behavior and performance over time and under different optimization options. Special emphasis has been placed on analyzing the transpile operation and the depth of generated quantum circuits. The machine analysis tests are conducted using Quantum Computing Run Assistant (QCRA), a versatile software tool specifically designed to streamline the effortless distribution of quantum programs across a range of quantum computing platforms. This software not only streamlines the optimization of benchmarking processes but also simplifies the assessment of different configurations and result quality through the collection of advanced job metadata.

Adaptive Multi-Layer Security Framework (AMLSF) for real-time applications in smart city networks

<p>This study introduces the Adaptive Multi-Layer Security Framework (AMLSF), a novel approach designed for real-time applications in smart city networks, addressing the current challenges in security systems. AMLSF innovatively incorporates machine learning algorithms for dynamic adjustment of security protocols based on real-time threat analysis and device behavior patterns. This approach marks a significant shift from static security measures, offering an adaptive encryption mechanism that scales according to application criticality and device mobility. Our methodology integrates hierarchical key management with real-time adaptability, further enhanced by an advanced rekeying strategy sensitive to device mobility and communication overhead. The paper’s findings reveal a substantial improvement in security efficiency. AMLSF outperforms existing models in encryption strength, rekeying time, communication overhead, and computational time by significant margins. Notably, AMLSF demonstrates an adaptability increase of over 30% compared to traditional models, with encryption strength and computational time efficiency improving by approximately 25%. These results underscore AMLSF’s capability in delivering robust, dynamic security without sacrificing performance. The achievements of AMLSF are significant, indicating a promising direction for smart city security frameworks. Its ability to adapt in real-time to various security needs, coupled with its performance efficiency, positions AMLSF as a superior choice for smart city networks facing diverse and evolving security threats. This framework sets a new benchmark in smart city security, paving the way for future developments in this rapidly advancing field.</p>

SCATTERING OF ELECTROMAGNETIC WAVES ON FLAT GRID TWO-PERIODIC STRUCTURES

Context. One of the scientific hypotheses for the creation of nonreciprocal optical metasurfaces is based on the use of a wave channel in which rays of the direct and reverse diffraction scenarios are realized on two-periodic flat structures with nonlinear elements. Such processes in the nanometer wavelength range of electronic devices require precise calculations of the interaction of waves and microstructures of devices. It is also important to describe the behavior of antenna devices in mobile communications. Expanding the wavelength range of stable communication is achieved by using prefractal structures in antenna devices in combination with periodic structuring. Similar modeling problems arise when electromagnetic waves penetrate materials with a crystalline structure (radio transparency).
 Objective. To test this hypothesis, it is necessary to carry out mathematical modeling of the process of scattering of electromagnetic waves by metasurfaces under conditions of excitation of several diffraction orders. It is known that among two-periodic flat lattices of different structures there are five types that fill the plane. These are the Bravais grilles. The problem of scattering of an incident monochromatic TE polarized wave on a metal screen with recesses in two-periodic structures filled with silicon was considered.
 Method. The paper builds mathematical models for the study of spatial-amplitude spectra of metasurfaces on Brave lattices and gives some results of their numerical study. The condition for determining the diffraction orders propagating over the grating is proposed. Scattered field amplitudes are from the solution of the boundary value problem for the Helmholtz equation in the COMSOL Multiphysics 5.4 package. Similar problem formulations are possible when studying the penetration of an electromagnetic field into a crystalline substance.
 Results. Obtained relations for diffraction orders of electromagnetic waves scattered by a diffraction grating. The existence of wavelengths incident on a two-periodic lattice for which there is no reflected wave is shown for different shapes (rectangular, square, hexagonal) of periodic elements in the center of which a depression filled with silicon was made. Distributions of reflection coefficients for different geometric sizes of colored elements and recesses are given. The characteristics of the electric field at resonant modes in the form of modulus isolines show the nature of the interaction of the field over the periodic lattice and the scatterersdepressions. At the resonant wavelengths of the incident waves, standing waves appear in the scatterers.
 Conclusions. A mathematical model of the set of diffraction orders propagating from a square and hexagonal lattice into halfspace is proposed z >= 0. It has been shown that flat periodic lattice with square or hexagonal periodicity elements and resonant scatterers in the form of cylindrical recesses filled with silicon can produce a non-mirrored scattered field in metal. The response of the lattices to changes in the wavelength of the incident field by the structure of diffraction orders of the scattered field and high sensitivity to the rotation of the incident plane were revealed. The two-periodic lattices have prospects for creating anti-reflective surfaces of various devices. Two-periodic lattices have prospects for creating anti-reflective surfaces for various devices, laser or sensor electronic devices, antennas in mobile communication elements, and radio transparency elements. They have more advanced manufacturing technologies in relation to spatial crystal structures.

Thermal dynamics assessment for multi-phase flow analysis with motile cilia and electric double layer effects: Application of Levenberg–Marquardt backpropagation NNs

Ciliary movement holds substantial importance in the realm of biomedical research. Disorders arising from impaired ciliary motion, such as primary ciliary dyskinesia and specific respiratory ailments, underscore the critical need to investigate both the normal functioning and dysfunction of cilia. Such research is fundamental to the development of effective treatments and therapies. This work explores the application of Levenberg–Marquardt backpropagation neural networks (LMBP-NNs) for simulating the flow of a non-Newtonian Jeffery fluid in peristaltic motion. Inspired by synchronous ciliary movement, microfluidic systems mimicking cilia functionality have been developed. These artificial cilia can generate controlled fluid flow patterns, enabling various applications such as drug delivery, mixing, and particle sorting in lab-on-a-chip devices. The present study focuses on measuring thermal and mass transfer in beating cilia during multiphase peristaltic pumping of Jeffery fluid in two- and three-dimensional channels using transverse magnetohydrodynamics in a porous medium. The governing equations for the non-Newtonian Jeffery fluid in both the fluid and dusty phases are formulated, and exact solutions are calculated for fluid phase velocity, particulate phase velocity, temperature, concentration, and pressure gradient. The physical behavior is investigated by graphing dimensionless parameters for velocity, temperature, and concentration profiles in both solid and liquid phases. The trapping phenomena is depicted by showing streamlines of various dimensionless characteristics such as porosity velocity and volume percentage. The LMBP-NNs approach is then used to train, test, and verify the neural network models using the reference datasets. The accuracy of the LMBP-NN is assessed using statistical data such as mean square error, curve fitting graphs, regression plots, and error histograms. The study also investigates flow model limits for momentum, temperature, and concentration profiles using visual representations. This study illustrates the computational capability of LMBP-NNs in modeling the peristaltic flow of Jeffery fluid and gives useful insights into fluid flow behavior in microfluidic devices containing artificial cilia. The findings help to better understand and optimize these systems for a variety of fluid manipulation and transportation applications.

Numerical studies of sCO2 Brayton cycle

This work describes the one-dimensional, thermo-hydraulic model of the sCO2 cycle Sofia developed to investigate optimal control methods and the behaviour of the cycle during operation. This dynamic model includes all devices such as turbomachinery, heat exchangers, valves, and piping including heat loss, in line with concept of the 1 MWe sCO2 cycle, to be realised in the site of a fossil power plant in the Czech Republic.The model assembly and calculations were conducted using the commercial Modelica-based library ClaRa + using the simulation environment Dymola and in combination with another Modelica-based library, UserInteraction; the real-time simulations, with some parameter changes during the calculation, are made and described in this paper.Nominal parameters were achieved during the steady-state simulation, except for the lower mass flow of sCO2. Transient simulation of power turbine start-up from standby state and results are also presented in this paper. The nominal state is achieved with the semi-automatic procedure in approx. 3 h. The simulation results allow more detailed analyses of control methods and a better understanding of real device control and behaviour during start-up, shutdown, or other transients.Careful manipulation of turbine valves in coordination with the pressuriser operation was identified as crucial for optimal control of the system. Also, the initial amount of CO2 in the pressuriser affects its behaviour during transients.

Evaluation of Fluid Behaviors in a Pushbutton-Activated Microfluidic Device for User-Independent Flow Control.

Although numerous studies have been conducted to realize ideal point-of-care testing (POCT), the development of a user-friendly and user-independent power-free microfluidic platform is still a challenge. Among various methods, the finger-actuation method shows a promising technique that provides a user-friendly and equipment-free way of delivering fluid in a designated manner. However, the design criteria and elaborate evaluation of the fluid behavior of a pushbutton-activated microfluidic device (PAMD) remain a critical bottleneck to be widely adopted in various applications. In this study, we have evaluated the fluid behavior of the PAMD based on various parameters, such as pressing velocity and depth assisted by a press machine. We have further developed a user-friendly and portable pressing block that reduces user variation in fluid behavior based on the evaluation.

Investigating the effects of sum-frequency conversions and surface impedance uniformity in traveling wave superconducting parametric amplifiers

Traveling wave parametric amplifiers (TWPAs) offer the most promising solution for high gain, broadband, and quantum noise limited amplification at microwave frequencies. Experimental realization of TWPAs has proved challenging with often major discrepancies between the theoretically predicted and the measured gain performance of the devices. Here, we extend the conventional modeling techniques to account for spatial variation in the surface impedance of the thin film and the parametric sum-frequency conversions effect, which subsequently results in accurate reproduction of experimental device behavior. We further show that such an analysis may be critical to ensure fabricated TWPAs can operate as designed.

A behavioral nonlinear modeling implementation for MEMS capacitive microphones

We are highlighting the behavioral nonlinear system-level modeling approach for MEMS capacitive microphones. The combination of finite element modeling and large signal nonlinear circuit modeling provides a strong capability to predict electroacoustical performance for capacitive transductions. Circuit simulation tools such as Cadence Virtuoso have emerged as powerful aids in designing behavioral models in both linear and nonlinear regimes. Typical small signal lumped element models fail to capture the MEMS behavior which changes over pressure and electrical bias conditions. The models described herein capture diaphragm displacement and capacitance change over large pressure ranges for a simply supported circular plate. This can be coupled with the electrostatic force, due to the applied bias voltage, and converted back to pressure, thereby realizing a feedback loop in the circuit model. The nonlinear model capability is extended to predict capacitance-bias behavior and estimate pull-in of the MEMS device. The microphone sensitivity, signal-to-noise ratio, and harmonic distortions are accurately predicted using the nonlinear large signal model when compared to measured microphone data.

Rapid detection of capture and emission processes in surface and buffer traps: Understanding dynamic degradation in GaN power devices

This study utilized a rapid detection technique, Isothermal Capture Transient Spectroscopy (ICTS), to identify and evaluate defects related to GaN device surface and buffer traps, and investigated the mechanisms by which they affect the dynamic performance of GaN based devices. Interface states and dielectric bulk traps were analyzed, unveiling distinctive device behavior under positive gate pulse-biased ICTS. The observed S-shaped threshold voltage (VTH) instability and dynamic gate-drain capacitance (CGD) issues were directly associated with these interface states and bulk traps. Negative gate pulse-biased ICTS revealed the presence of logarithmic electron trapping originating from extended electron traps, and spatial wide distributed hole traps within the GaN buffer layer, significantly impacting the dynamic on-resistance (RON) during off-state stress of the devices. The rapid assessment capabilities of this method provide a valuable tool for optimizing GaN epitaxy and manufacturing processes.

Advancing elderly healthcare: adapting UTAUT2 with inhibitors for wearable device continuance

PurposeWearable health devices (WHDs) have demonstrated significant potential in assisting elderly adults with proactive health management by utilizing sensors to record and monitor various aspects of their health, including physical activity, heart rate, etc. However, limited research has systematically explored older adults’ continued usage intention toward WHD. By utilizing the extended unified theory of acceptance and use of technology (UTAUT2), this paper aims to probe the precursors of elderly adults’ continuance intention to use WHD from an enabler–inhibitor perspective.Design/methodology/approachThe research model was developed based on UTAUT2 and examined utilizing the partial least squares technique (PLS). The research data were collected through in-person meetings with older people (n = 272) in four cities in China.FindingsResults reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic values and perceived complexity are the positive predictors of elderly adults’ continuance intention to use WHDs. Technology-related anxiety and usage cost negatively influence the formation of older people’s continuance intention.Originality/valueThis work is an original empirical investigation that draws on several theories as guiding frameworks. It adds to the existing literature on the usage of wearable technologies and offers insights into how the elderly’s intentions to continue using WHDs can be developed. This study broadens the scope of the UTAUT2 application and presents an alternative theoretical framework that can be utilized in future research on the usage behavior of wearable devices by individuals.