Engineering Domain Research Articles

Materials for semiconductor nanowires in nanoscale photonic and electronic devices

Abstract Many aspects of civilization are impacted by electronic and optoelectronic equipment, including medical instruments, communications, computing, multimedia systems, and basic home appliances. the development of nanoscale devices has garnered significant attention due to the increasing demand for smaller, more powerful, and efficient systems across various industries. Nanoscale devices operate at the nanometer scale, typically involving structures and components with dimensions on the order of 1 to 100 nanometers. As a potent class of materials, semiconductor nanowires are creating a lot of new possibilities for innovative nanoscale photonic and electrical devices through carefully regulated development and organization. the exploration of electronic and optoelectronic nanodevices, along with integrated arrays, opens up a rich landscape of possibilities across various technological domains. This research not only pushes the boundaries of miniaturization but also paves the way for innovative applications in electronics, photonics, sensing, and emerging fields like quantum and neuromorphic computing that hold the potential for numerous applications in the future.

Exploring the Electronic Properties of Doped Zirconia for Enhanced Optoelectronic Applications: A Quantum Chemical Approach

Zirconia (ZrO2) is a versatile material with applications in various fields due to its exceptional mechanical strength, thermal stability, and chemical resistance. In recent years, interest has surged in utilizing doped ZrO2 as photocatalysts. This study investigates the electronic properties of ZrO2 upon doping with non-metal elements sulfur (S), selenium (Se), and tellurium (Te) using first-principle calculations. The effects of different doping concentrations on the band structure and density of states (DOS) are examined. Calculations using GGA show significant reductions in the band gap upon doping, indicating potential for improved optoelectronic performance. Specifically, using accurate DFT+U approach we found that doping ZrO2 with 25% S led to a band gap reduction from 5.4 eV to 1.2 eV, demonstrating promising results for photovoltaic applications. This study provides valuable insights into the electronic properties of doped ZrO2 (ZrO2-xQx, Q=S, Se and Te, x=0.25,0.5 and 2) paving the way for tailored applications in various technological domains.

Cilia-assisted flow of electrically conducting micropolar fluid in a heated curved channel under Soret and Dufour effects

Cilia flow plays a crucial role in biological systems such as the movement of mucus in the respiratory tract, circulation of cerebrospinal fluid in the brain, and propulsion of sperm cells. Understanding the cilia flow of viscous fluids is essential for elucidating the biomechanics of these processes and their implications for health and disease. Motivated by such numerous biomedical applications, this article aims to exhibit the influence of heat and mass transfer on the ciliary flow of electrical conducting micropolar fluid in a curved channel. The energy and concentration equations are modulated with viscous dissipation, Soret, and Dufour effects. The constitutive equations are simplified by the hypothesis of lubrication approximation theory and then solved numerically using the implicit finite difference method (FDM). Results for velocity, pumping phenomenon, concentration, thermal field, rate of heat transfer, streamlines, skin friction coefficient, Nusselt number, and Sherwood number are analyzed subject to pertinent parameters. The study reveals that velocity is enhanced via both the micropolar parameter and curvature parameter. Temperature enhances for larger values of Dufour and Brickman numbers. Furthermore, the radial magnetic field plays a resistive role in the trapped bolus. The findings presented in this study should prove beneficial for researchers in the domains of medicine, engineering, science, and fluid mechanics. Further, it is found that the micropolar fluid model is more suitable for biofluids like blood.

Application of model predictive control scheme for piezoelectric ceramic micro-displacement

Abstract Currently, in the domain of micro-control technology, many operations need to meet the standard of nanometer-level positioning. As a high-precision micro-displacement element, piezoelectric ceramics are widely used in the field of micro-control due to their characteristics such as no need for transmission devices, high resolution, large output force, small size, and fast response speed. However, the inherent hysteresis nonlinearity of piezoelectric ceramics can lead to decreased displacement accuracy, oscillation, and even system instability. Therefore, eliminating the influence of nonlinear characteristics and improving displacement accuracy possess significant practical importance. In this paper, a model predictive control scheme is designed, which achieves faster response speed and higher accuracy compared to traditional control methods such as Proportional Integral Derivative control. Relevant simulations and tests have been conducted. In this scheme, hysteresis nonlinearity is first modeled and then compensated through feedforward, thereby simplifying the displacement control of piezoelectric ceramics into a more manageable linear problem. Subsequently, a linear model predictive control method is adopted to control the entire system. Both simulation and experimental results have verified the effectiveness of this control method.

Patient and care team experiences with an EHR-integrated symptom management program (eSyM) in routine oncology practice.

324 Background: Clinical trials have shown that collecting electronic patient-reported outcomes (ePROs) improves quality of life and survival in cancer patients. To bolster integration of ePROs into routine care, the SIMPRO Research Consortium developed an electronic symptom management (eSyM) program, deployed it at six cancer centers, and conducted a hybrid effectiveness-implementation study. eSyM, an ePRO toolkit integrated into the Epic EHR, enables patients to report and care teams to triage symptoms in real-time. To assess eSyM, we conducted structured qualitative interviews with patients and clinic staff, including doctors, nurses, advanced practice providers, and administrative staff. We sought to understand eSyM from the patient and staff perspectives. Methods: Using an iterative process, we developed semi-structured interview guides to query the experiences and perceptions of patients and staff exposed to eSyM. Questions were informed by the Consolidated Framework for Implementation Research. One year after eSyM was launched, sites identified and referred participants for interviews or focus groups. Participants must have either used eSyM to submit symptoms (patients) or had access to eSyM at their institution (staff). Patients provided written consent; a consent waiver was obtained for staff. Interviews were recorded and transcribed, and a common analytic codebook was developed. Transcripts were dual or triple-coded and underwent thematic analysis to identify key contexts and concepts within and across patient and staff groups. Results: From 9/28/20 to 4/14/23, 50 staff participated in individual interviews and 29 participated in focus groups (79 total). From 9/14/23 to 3/14/24, 14 patient interviews were conducted. Transcripts revealed that staff and patients held similar beliefs regarding eSyM compatibility, communication, and prioritization. 93% (13/14) of patients and 72% (57/79) of staff believed eSyM was easy to use and improved communication. Both cohorts (66% staff, 36% patients) noted that competing demands often needed to be prioritized over responding to symptom concerns. Discordance emerged in the domains of receptivity and technology; patients were more receptive and capable of using eSyM than staff perceived. Both groups believed eSyM was helpful but perceived that deeper engagement from patients and staff was necessary for sustainability. Conclusions: Most interviewees reported that eSyM added value and was easy to navigate. Competing clinical demands, however, frequently deprioritized use of eSyM. Staff tended to underestimate patients’ receptivity to use eSyM and were more likely to cite competing demands as a barrier to their own use of eSyM. Sites looking to adopt eSyM, or similar ePRO programs, will benefit from understanding the facilitators that need to be incorporated and the barriers that need to be surmounted to optimize success. Clinical trial information: NCT03850912 .

Utilization of Polymers for the Development of Nanomaterials

AbstractThis study highlights the significance of polymers in the progress of nanoparticles across various areas. The molecules of polymers are highly regarded for their ability to adapt and self‐assemble, making them essential components in the creation of nanomaterials. The variety of polymers include natural polymers such as chitosan, synthetic polymers like polyethylene, and biodegradable polymers like poly(lactic‐co‐glycolic acid) (PLGA). These kinds of polymers possess distinct advantages such as high strength, environmental sustainability, and biocompatibility. Incorporation of nanoscale fillers into polymer matrices, which enhances the mechanical, thermal, and electrical properties of materials, is crucial for the development of nanocomposites. Illustration instances encompass carbon nanotube‐polymer composites and polymer clay hybrids, which find application in the construction, automotive, and aerospace sectors. Composites can employ many synthetic methods to generate nanostructures. Nanofibers have utility in tissue engineering, whereas polymer nanoparticles function as carriers for medical delivery. Also, polymers enhance nanomaterials by modifying their surfaces, a crucial factor for their application in membrane technology, catalysis, and sensing. A collaborative synergy between polymers and nanoparticles fosters a wide range of applications, showcasing the versatility and potential of polymers in altering the characteristics of nanomaterials. The resulting partnership continues to generate pioneering breakthroughs that address complex challenges and unveil unprecedented prospects in the domains of science, technology, and business.

Exploring the mechanisms of enhanced piezoelectric properties in (K,Na)NbO3 single crystals

(K,Na)NbO3 (KNN)-based piezoelectric materials are candidates for replacing Pb-based materials. However, the piezoelectric properties of existing KNN-based single crystals are still inferior to those of Pb-based relaxor ferroelectric single crystals. Moreover, the piezoelectric response mechanism of KNN-based single crystals remains unclear. In this study, (Li,K,Na)(Nb,Sb,Ta)O3:Mn (KNNLST:Mn) single crystals with an excellent piezoelectric coefficient d33 of approximately 778 pC/N were prepared. Systematically studies of intrinsic and extrinsic piezoelectric responses have revealed that the high d33 of KNNLST:Mn single crystals is related to the shear piezoelectric response of a single-domain state and irreversible domain wall motion of the engineering domains. Furthermore, the effect of the orthorhombic (O)-tetragonal (T) phase boundary on intrinsic and extrinsic piezoelectric response is systematically studied, and the impact mechanism is elucidated. The results indicate that a lower dielectric response and elastic constant limit the intrinsic shear piezoelectric response of KNNLST:Mn single crystals, and approaching the O–T phase boundary can enhance both intrinsic and extrinsic piezoelectric responses. This study improves our understanding of the structure-performance relationship in KNN-based single crystals and offers insights for optimizing piezoelectric properties in KNN-based materials.

A transfer learning framework for the assessment of unconventional resources opportunities in the Middle East

An inadequate understanding of fluid transport processes in ultra-low permeability rocks has propelled data-driven modeling as an alternative and complementary tool to create recovery forecasts that honor the available data in the field. However, newly discovered fields lack data, which makes it challenging to apply traditional data-driven approaches to decision-making. Transfer learning provides an opportunity to start the early-stage analysis of the asset before adequate data becomes available. Here, we present a transfer learning framework that allows the basin-wide assessment of new shale gas and tight oil prospects. The proposed method is developed on real-world data from several thousand horizontal hydraulically fractured wells in the Eagle Ford super-basin in South Texas. A novel aspect of our method is integrating reservoir engineering domain expertise and the relationship among geologic and petrophysical variables, drilling and completion parameters, and well productivity in the data pre-processing and feature generation steps. We consider the temporal and spatial balancing of the training data to ensure that the resulting predictive models honor the real-world practices followed during unconventional field development. Our full-cycle transfer learning workflow consists of dimensionality reduction and unsupervised clustering, supervised learning, and hyperparameter fine-tuning. We test the workflow by examining the performance of a data-driven model of the Eagle Ford Basin on potential plays in the Middle East: Tuwaiq Mountain and Hanifa. The existence of multiple types of hydrocarbons–liquid oil, condensate, and dry gas–in the Eagle Ford results in a model flexible enough to be tested on various types of assets in a new location. We address the challenge of model overfitting by using data types with a relatively low resolution, which allows generalization to different geologies with basin-wide accuracy.

Static Magnetic Field Focusing With Neodymium Magnets For Wound Healing: A Numerical Study

Static magnetic fields (SMFs) find widespread applications in diverse scientific, technological, and medical domains. This study explores the potential of neodymium permanent magnets in focusing and controlling SMFs, specifically emphasizing wound healing applications. Numerical simulations using COMSOL Multiphysics create a uniform static magnetic field for wound healing. The study systematically increases the number of neodymium magnets, demonstrating enhanced magnetic flux density and a focused magnetic field. The results affirm the efficacy of neodymium magnets in generating a uniform static magnetic field between 160-600 m Tesla. This research proposes neodymium permanent magnets as a promising tool for wound healing applications, offering a non-invasive and focused therapeutic approach. While the study provides valuable insights, further experimental and clinical validations are necessary to establish the real-world efficacy of this method. The work contributes to the evolving understanding of static magnetic fields as a viable therapeutic modality for various medical conditions, particularly in the context of wound healing.

The impact of artificial intelligence on women's empowerment, and work-life balance in Saudi educational institutions.

Gender prejudice and stereotypes are prevalent in the workplace, particularly for women in the Artificial Intelligence (AI) industry, where they can significantly hinder professional development and limit prospects for growth. These challenges contribute to the underrepresentation of executives in AI. However, with the right measures, these barriers can be overcome, leading to a more inclusive and diverse AI industry. Women in this demanding technological domain often face additional difficulties in achieving a work-life balance, further constraining their professional advancement and engagement in the industry. This research aims to examine the implications of AI capabilities on work-life balance and the empowerment of female faculty members in enhancing the efficiency of educational institutions. The research performs a structural equation modeling (SEM) approach, using a survey conducted on female faculty of Saudi Arabian universities. The study specifically considers moderating variables such as age, education level, experience, and marital status. The findings, which reveal that AI managerial capability, as well as AI infrastructure agility, impacts work-life balance and empowerment of women faculties in educational institution efficiency, underscore the significance of considering demographic factors when analyzing women's empowerment and work-life balance as outcomes. By exploring these factors, the research provides a comprehensive understanding of how AI capabilities impact women's empowerment and their ability to maintain a work-life balance, ultimately contributing to the efficiency and effectiveness of educational institutions. These results emphasize the value of increasing women's empowerment and raising the standard of performance evaluation systems in educational sectors.

Promoting Synergies to Improve Manufacturing Efficiency in Industrial Material Processing: A Systematic Review of Industry 4.0 and AI

The manufacturing industry continues to suffer from inefficiency, excessively high prices, and uncertainty over product quality. This statement remains accurate despite the increasing use of automation and the significant influence of Industry 4.0 and AI on industrial operations. This review details an extensive analysis of a substantial body of literature on artificial intelligence (AI) and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document includes a summary of key information (i.e., various input tools, contributions, and application domains) on the current production system, as well as an in-depth study of relevant achievements made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance, AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time. These techniques help to increase the efficiency of material processing in the manufacturing process, based on empirical research conducted across different industrial sectors. The results indicate that Industry 4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The fourth industrial revolution was formed by AI, technology, industry, and convergence across different engineering domains. Based on the systematic study, this article critically explores the primary limitations and identifies potential prospects that are promising for greatly expanding the efficiency of smart factories of the future by merging Industry 4.0 and AI technology.

Biological Cell Response to Electric Field: A Review of Equivalent Circuit Models and Future Challenges.

Biological cells, characterized by complex and dynamic structures, demand precise models for comprehensive understanding, especially when subjected to external factors such as electric fields (EF) for manipulation or treatment. This interaction is integral to technologies like pulsed electric fields (PEF), inducing reversible and irreversible structural variations. Our study explores both simplified and sophisticated equivalent circuit models for biological cells under the influence of an external EF, covering diverse cell structures from single- to double-shell configurations. The paper highlights challenges in circuit modeling, specifically addressing the incorporation of reversible or irreversible pores in the membrane during external EF interactions, emphasizing the need for further research to refine technical aspects in this field. Additionally, we review a comparative analysis of the performance and applicability of the proposed circuit models, providing insights into their strengths and limitations. This contributes to a deeper insight of the complexities associated with modeling biological cells under external EF influences, paving the way for enhanced applications in medical and technological domains in future.&#xD.

Hybrid golden jackal and golden sine optimizer for tuning PID controllers

In the domain of control engineering, effectively tuning the parameters of proportional-integral-derivative (PID) controllers has persistently posed a challenge. This study proposes a hybrid algorithm (HGJGSO) that combines golden jackal optimization (GJO) and golden sine algorithm (Gold-SA) for tuning PID controllers. To accelerate the convergence of GJO, a nonlinear parameter adaptation strategy is incorporated. The improved GJO is combined with Gold-SA, capitalizing on the expedited convergence speed offered by the improved GJO, coupled with the global optimization and precise search capabilities of Gold-SA. HGJGSO maximizes the strengths of two algorithms, facilitating a comprehensive and balanced exploration and exploitation. The effectiveness of HGJGSO is assessed through tuning the PID controllers for three typical systems. The results indicate that HGJGSO surpasses the comparison tuning methods. To evaluate the applicability of HGJGSO, it is used to tune the cascade PID controllers for trajectory tracking in a quadrotor UAV. The results demonstrate the superiority of HGJGSO in addressing practical challenges.

Academic entrepreneurship in the humanities and social sciences: a systematic literature review and research agenda

The predominant empirical focus of academic entrepreneurship research has been in the science, technology and engineering domains. To date there has been a limited research and empirical focus on academic entrepreneurship in the humanities, arts and social sciences (HASS). This is against a background of the growing societal and policy recognition of the important contributions and impact of HASS based research. The purpose of this paper through a systematic literature review is to examine the current state of academic entrepreneurship research in HASS. Our systematic literature identified four key themes namely, actors,barriers,mechanismsandmeasurementandevaluation. From this, we develop and outline a research agenda for academic entrepreneurship in HASS.

Influence of external magnetic field on nanofluid dynamics using a two-phase Lattice Boltzmann Mixture Model at low Reynolds numbers

This work examines the behavior of an external magnetic field-induced Al2O3-water nanofluid flow between two parallel plates, with a particular emphasis on low Reynolds number scenarios. Understanding the dynamics of magnetohydrodynamic (MHD) flows in applications including energy systems, cooling systems, and medicinal devices depends on solving this problem. For the first time, a new two-phase lattice Boltzmann method was used to simulate the flow in conjunction with the mixture model. This methodology was specifically designed for nanofluid flow in the presence of magnetic field. Three coupled transport equations for flow velocity, concentration of nanoparticles, and magnetic field intensity are solved using this method. Three various cases were examined numerically. By comparing the simulated velocity profiles with analytical solutions in the first case, model validation was accomplished, demonstrating a strong agreement with less than 1 % variance. The impact of different inflow nanoparticle volume fractions (0.05 and 0.01) on the velocity field at various Reynolds numbers (0.5, 1, 10) and Hartmann numbers (0, 10, 20) was investigated in the second case. The findings showed that for Reynolds numbers of 0.5, 1, and 10, respectively, the velocity magnitude dramatically fell by approximately 75 %, 68 %, and 30 % as the Hartmann number grew from 0 to 20. Alongside this decrease, vortices began to form at Hartmann numbers of 10 and 20. Furthermore, the influence of the magnetic field diminished as the Reynolds number increased from 0.5 to 10, resulting in a noticeable reduction in vortex intensity. In the third case, where the nanoparticle volume fractions at the inlet were closer (e.g., 0.03 and 0.02), the intensity of the vortices decreased compared to the second case. The study demonstrates the robustness of the proposed model and its applicability across scientific and engineering domains. The novelty lies in quantifying MHD effects on nanofluid flow and particle distribution using a two-phase lattice Boltzmann approach, offering more precise and efficient simulations than existing methods. The findings provide new insights into the interaction between magnetic fields and nanofluids, especially at low Reynolds numbers, and emphasize the critical role of nanoparticle distribution in flow dynamics.

Disparity in employment in India: evidence from engineering graduates across social groups and gender

The study scrutinises caste and gender disparity in shaping employment outcomes and academic performances across 1406 students from a prestigious engineering college in Kerala, India, employing logistic regression of campus recruitment data. The study reveals that students from marginalised social groups experience significantly lower placement rates and exhibit substantially lower academic performance. Even after accounting for academic differences, marginalised community students are less likely to be employed, signalling labour market bias. Males within this marginalised group face even lower placement chances. When employed, marginalised group students are less likely to be employed in high-paying jobs. Interestingly, while females are preferred over males, especially in high-paying jobs, they are underrepresented in the engineering domain, particularly in the branches that are traditionally male-dominated. The study is among the first to comprehensively examine the interplay of multiple factors – employment disparity, wage levels, academic performances, caste and gender in campus recruitment.

Irreversibility analysis of CNT‐enhanced Williamson nanofluid flow in a stretching cylinder

AbstractThis study presents a novel exploration into the intricate interplay of slanted magnetohydrodynamic and radiative fluxes within a Williamson fluid incorporating single‐walled carbon nanotubes (SWCNTs) and multi‐walled carbon nanotubes (MWCNTs) in a water‐based system. Utilizing an exponentially stretching cylinder model, the research holistically integrates entropy production, encompassing thermal radiation, porosity, buoyant forces, and heat source/sink constraints within the governing equations. To solve the resulting governing equations with boundary conditions, the shooting technique is employed to convert them into initial value problems. Subsequently, the Runge–Kutta–Fehlberg 4–5th order method is used to obtain numerical solutions. Unveiling unprecedented insights, the investigation reveals distinctive parameter dependencies governing fluid behavior. It delineates how variations in different factors distinctly impact fluid velocity, thermal profiles, entropy generation, Bejan number, drag force, and heat transfer rates across different nanofluid compositions. Furthermore, the study highlights the transformative impact of nanocomposites on altering the thermal properties of the fluid and orchestrating nanofluid mobility. The results show that MWCNTs have an enhanced entropy generation rate when compared to SWCNTs. These findings bear substantial promise for real‐world applications involving water‐based nanofluids, shedding light on their manipulation and optimization in various technological domains.

Incorporation of machine learning into multiple stripe seismic fragility analysis of reinforced concrete wall structures

This study proposes a novel procedure that incorporates machine learning (ML) into the multiple stripe analysis (MSA) approach to efficiently produce seismic fragility curves for reinforced concrete (R/C) shear walls in building frame systems. The proposed procedure aims to mitigate computational challenges associated with the original MSA approach. In this context, ML models were developed for predicting the failure probability of R/C walls subjected to ground motions based on a specified threshold of maximum interstory drift ratio (MIDR). Specifically, the result of each numerical analysis, taken as the output variable of the ML models, was classified as either “B” (Below) or “E” (Exceeding) to indicate whether the MIDR of R/C walls was below or exceeding the specified threshold. This binary categorization was then used to calculate the failure probability points, which are necessary to derive fragility curves per the MSA approach. Data for training and testing the ML models were generated from nonlinear time history analyses of 46 distinct R/C walls subjected to 1000 ground motions. The R/C walls varied in height from four to 40 stories, and the ground motions included far-field, near-field pulse, and near-field no-pulse types. Four well-established ML methods, including random forest (RF), extreme gradient boosting, light gradient boosting machine, and categorical boosting, were considered. The performances of the ML models were compared using a confusion matrix. Based on this comparison, the RF model was selected and incorporated into the proposed procedure. Subsequently, the proposed approach was demonstrated to create the seismic fragility function of a new R/C wall structure. This study highlights the potential of ML applications in optimization problems within the earthquake engineering domain.

Extension of Meir-Keeler-Khan (ψ − α) Type Contraction in Partial Metric Space

In numerous scientific and engineering domains, fractional-order derivatives and integral operators are frequently used to represent many complex phenomena. They also have numerous practical applications in the area of fixed point iteration. In this article, we introduce the notion of generalized Meir-Keeler-Khan-Rational type (ψ−α)-contraction mapping and propose fixed point results in partial metric spaces. Our proposed results extend, unify, and generalize existing findings in the literature. In regards to applicability, we provide evidence for the existence of a solution for the fractional-order differential operator. In addition, the solution of the integral equation and its uniqueness are also discussed. Finally, we conclude that our results are superior and generalized as compared to the existing ones.

Fast Computer Model Calibration using Annealed and Transformed Variational Inference

Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models through statistical inference. While Bayesian inference is the standard approach for this task, employing Markov chain Monte Carlo methods often encounters computational hurdles due to the costly evaluation of likelihood functions and slow mixing rates. Although variational inference (VI) can be a fast alternative to traditional Bayesian approaches, VI has limited applicability due to boundary issues and local optima problems. To address these challenges, we propose flexible VI methods based on deep generative models that do not require parametric assumptions on the variational distribution. We embed a surjective transformation in our framework to avoid posterior truncation at the boundary. Additionally, we provide theoretical conditions that guarantee the success of the algorithm. Furthermore, our temperature annealing scheme and fine-tuning can prevent being trapped in local optima through a series of intermediate posteriors and weight adjustment. We apply our method to infectious disease models and a geophysical model, illustrating that the proposed method can provide fast and accurate inference compared to its competitors.