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Early prediction of any type of cancer is important for the treatment of this type of disease, therefore, our target to evaluate whether monitoring early changes in plasma human epidermal growth factor receptor 2 (HER2) levels (using EIS), could help in the treatment of breast cancer or not? Human epidermal growth factor receptor 2 (HER2) overexpression is an important biomarker for treatment selection in earlier stages of cancers. The combined detection of the HER2 gene in plasma for blood cancer provides an important reference index for the prognosis of metastasis to other tissues. For this purpose, the authors fabricated and characterized a model wireless biosensor-based electrochemical impedance spectroscopy (EIS) for detecting HER2 plasma as therapeutics.

Most sensors generally are fabricated based on a connection between component of the sensors and the external circuits through wires. Although these types of sensors provide suitable sensitivities and also quick responses, the connection wires can be limited to the sensing ability in various devices approximately. Therefore, the authors designed a wireless sensor, which can provide the advantages of in vivo sensing and also long-distance sensing, quickly.

The biosensor structure was designed for detection of HER2, HER3 and HER-4 from lab-on-chip approach with six units of screen-printed electrode (SPE), which is built of an electrochemical device of gold/silver, silver/silver or carbon electrodes. The results exhibited that the biosensor is completely selective at low concentrations of the plasma and HER2 detection via the standard addition approach has a linearity plot, therefore, by using this type of biosensors HER2 in plasma can be detected.

This is then followed by detecting HER2 in real plasma using standard way which proved to have great linearity (R² = 0.991) proving that this technique can be used to detect HER2 solution in real patients.

The purpose of this study is to model the global dynamic hysteresis properties with an improved Jiles–Atherton (J-A) model through a unified set of parameters.

First, the waveform scaling parameters β, λ_k and λ_c are used to improve the calculation accuracy of hysteresis loops at low magnetic flux density. Second, the Riemann–Liouville (R-L) type fractional derivatives technique is applied to modified static inverse J-A model to compute the dynamic magnetic field considering the skin effect in wideband frequency magnetization conditions.

The proposed model is identified and verified by modeling the hysteresis loops whose maximum magnetic flux densities vary from 0.3 to 1.4 T up to 800 Hz using B30P105 electrical steel. Compared with the conventional J-A model, the global simulation ability of the proposed dynamic model is much improved.

Accurate modeling of the hysteresis properties of electrical steels is essential for analyzing the loss behavior of electrical equipment in finite element analysis (FEA). Nevertheless, the existing inverse Jiles–Atherton (J-A) model can only guarantee the simulation accuracy with higher magnetic flux densities, which cannot guarantee the analysis requirements of considering both low magnetic flux density and high magnetic flux density in FEA. This paper modifies the dynamic J-A model by introducing waveform scaling parameters and the R-L fractional derivative to improve the hysteresis loops’ simulation accuracy from low to high magnetic flux densities with the same set of parameters in a wide frequency range.

How does business model design play a role in enabling manufacturing firms’ services? This study aims to investigate the impact of two distinct types of business model design, namely, efficiency-centered business model design (EBMD) and novelty-centered business model design (NBMD), and their effects in balanced and imbalanced configurations, on two types of services: product- and customer-oriented services.

Using matched survey data of 390 top managers and objective performance data of 195 Chinese manufacturing firms, this study uses hierarchical regression, polynomial regression and response surface analysis to test the hypotheses.

The results show that while EBMD positively affects product-oriented services, NBMD positively affects customer-oriented services. Both types of services exert a significant influence on firm performance. Furthermore, the degree of product- and customer-oriented services increases with an increasing effort level with a balance between EBMD and NBMD. Asymmetrical, imbalanced configuration effects reveal that the degree of product-oriented services is higher when the EBMD effort exceeds the NBMD effort, and the degree of customer-oriented services is higher when the NBMD effort exceeds the EBMD effort.

This study enriches the understanding of designing business models to facilitate service growth in manufacturing firms, ultimately benefiting firm performance. In addition, exploring balanced and imbalanced configurations of EBMD and NBMD offers new insights into business model dual design research.

The purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.

A Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.

The proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.

The model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.

Mamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

We examined the dynamic volatility connectedness and diversification strategies among US real estate investment trusts (REITs) and green finance indices.

The DCC-GARCH dynamic connectedness framework and he DCC-GARCH t-copula model were employed in this study.

Using daily data from 2,206 observations spanning from 2 January 2015 to 31 January 2023 this paper presents the following findings: (1) cross-market spillovers exhibited a high correlation and significant fluctuations, particularly during extreme events; (2) our analysis confirmed that REIT acted as net receivers from other green indices, with the S&P North America Large-MidCap Carbon Efficient Index dominating the in-network volatility spillover; (3) this observation suggests asymmetric spillovers between the two markets and (4) a portfolio analysis was conducted using the DCC-GARCH t-copula framework to estimate hedging ratios and portfolio weights for these indices. When REIT and the Dow Jones US Select ESG REIT Index were simultaneously added to a risk-hedged portfolio, our findings indicated that no risk-hedging effect could be achieved. Moreover, the cost and performance of hedging green assets using REIT were found to be comparable.

We first examined the dynamic volatility connectedness and diversification strategies among US REITs and green finance indices. The outcomes of this study carry practical implications for market participants.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

This paper seeks to explore the sensitivity of these parameters and their impact on fiscal policy outcomes. We use the existing literature to establish possible ranges for each parameter, and we examine how changes within these ranges can alter the outcomes of fiscal policy. In this way, we aim to highlight the importance of these parameters in the formulation and evaluation of fiscal policy.

The role of fiscal policy, its effects and multipliers continues to be a subject of intense debate in macroeconomics. Despite adopting a New Keynesian approach within a macroeconomic model, the reactions of macroeconomic variables to fiscal shocks can vary across different contexts and theoretical frameworks. This paper aims to investigate these diverse reactions by conducting a sensitivity analysis of parameters. Specifically, the study examines how key variables respond to fiscal shocks under different parameter settings. By analyzing the behavioral dynamics of these variables, this research contributes to the ongoing discussion on fiscal policy. The findings offer valuable insights to enrich the understanding of the complex relationship between fiscal shocks and macroeconomic outcomes, thus facilitating informed policy debates.

This paper aims to investigate key elements of New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models. The focus is on the calibration of parameters and their impact on macroeconomic variables, such as output and inflation. The study also examines how different parameter settings affect the response of monetary policy to fiscal measures. In conclusion, this study has relied on theoretical exploration and a comprehensive review of existing literature. The parameters and their relationships have been analyzed within a robust theoretical framework, offering valuable insights for further research on how these factors influence model forecasts and inform policy recommendations derived from New Keynesian DSGE models. Moving forward, it is recommended that future work includes empirical analyses to test the reliability and effectiveness of parameter calibrations in real-world conditions. This will contribute to enhancing the accuracy and relevance of DSGE models for economic policy decision-making.

This study is motivated by the aim to provide a deeper understanding of the roles macroeconomic model parameters play concerning responses to expansionary fiscal policies and the subsequent reactions of monetary authorities. Comprehensive reviews that encompass this breadth of relationships within a single text are rare in the literature, making this work a valuable contribution to stimulating discussions on macroeconomic policies.

This study aims to explore and segment consumer preferences for electric and hybrid vehicles in Germany, Sweden, the Netherlands and Turkey, focusing on understanding the various factors that influence consumer decisions in these markets.

Using latent class analysis (LCA) on data collected through online surveys and discrete choice experiments, this research categorizes consumers into distinct segments. The approach allows for a nuanced understanding of how various factors such as income level, fuel cost, age, CO₂ emissions, purchase price, vehicle range, policy policies and environmental concerns interact with shape consumer preferences.

The analysis uncovers significant heterogeneity in consumer preferences for electric and hybrid vehicles across Germany, Sweden, the Netherlands and Turkey, revealing four key segments: “Eco-Driven Innovators,” “Value-Focused Pragmatists,” “Tech-Savvy Early Adopters” and “Reluctant Traditionalists.” “Eco-Driven Innovators” prioritize environmental benefits and are less sensitive to price, demonstrating a strong inclination toward vehicle CO₂ emissions and policy policies. “Value-Focused Pragmatists” weigh economic factors heavily, showing a sharp interest in fuel costs and purchase prices but are open to considering electric and hybrid vehicles if they present clear long-term savings. Technology-savvy early adopters are attracted by the latest technological advancements in vehicles, regardless of the type, and are motivated by factors beyond just environmental concerns or cost savings. Lastly, “Reluctant Traditionalists” exhibit minimal interest in electric and hybrid vehicles due to concerns over charging infrastructure and upfront costs. This detailed segmentation illustrates the diverse motivations and barriers influencing consumer choices, from governmental policies and environmental concerns to individual financial considerations and technological appeal.

This study stands out for its pioneering application of LCA to dissect the complexity of consumer preferences for electric and hybrid vehicles, a methodological approach not widely used in this research domain. Using LCA, the authors are able to uncover nuanced consumer segments, each with distinct preferences and motivations, providing a depth of insight into market dynamics that traditional analysis methods may overlook. This approach enables a more granular understanding of how diverse factors – ranging from environmental concerns to economic considerations and technological attributes – interact to shape consumer choices in different countries. The findings not only fill a critical gap in the existing literature by mapping the intricate landscape of consumer preferences, but also offer a novel perspective on strategizing market interventions. Therefore, the application of LCA enriches the discourse on sustainable transportation, offering stakeholders, manufacturers, policymakers and researchers – a refined toolkit for navigating the evolving market dynamics and fostering the adoption of electric and hybrid vehicles.

The purpose of this study is to empirically investigate the role of dynamic capabilities, specifically the sequence of sensing, seizing, and transforming capabilities, in highly uncertain, emerging technology environments. Focusing on the extended reality industry, the study aims to understand the antecedents to these dynamic capabilities, their sequential nature, and their subsequent impact on innovation and company performance.

Based on a survey of 130 German companies in the extended reality sector, we built a structural equation model that explores the relationship between dynamic capabilities, their antecedents, and their effect on innovation and company performance.

The analysis suggests that sensing capabilities positively influence seizing and transforming capabilities, while seizing directly contributes to transforming. Transforming capabilities are linked to improved innovation performance, which in turn boosts company performance. Organizational ambidexterity, market orientation, and technology orientation are found to be crucial antecedents, accounting for 33.1% of the variance in sensing capabilities.

This research illuminates the interdependence of dynamic capabilities in highly uncertain business environments, such as emerging technology markets. It contributes original insights by elucidating the sequential nature of dynamic capabilities and identifying their vital antecedents. It also enlarges the understanding of how dynamic capabilities impact firms’ innovation performance.