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Microribbon with meander type based on giant magnetoimpedance (GMI) effect has become a research hot spot due to their higher sensitivity and spatial resolution. The purpose of this paper is to further optimize the line spacing to improve the performance of meanders for sensor application.

The model of GMI effect of microribbon with meander type is established. The effect of line spacing (Ls) on GMI behavior in meanders is analyzed systematically.

Comparison of theory and experiment indicates that decreasing the line spacing increases the negative mutual inductance and a consequent increase in the GMI effect. The maximum value of the GMI ratio increases from 69% to 91.8% (simulation results) and 16.9% to 51.4% (experimental results) when the line spacing is reduced from 400 to 50 µm. The contribution of line spacing versus line width to the GMI ratio of microribbon with meander type was contrasted. This behavior of the GMI ratio is dominated by the overall negative contribution of the mutual inductance.

This paper explores the effect of line spacing on the GMI ratio of meander type by comparing the simulation results with the experimental results. The superior line spacing is found in the identical sensing area. The findings will contribute to the design of high-performance micropatterned ribbon with meander-type GMI sensors and the establishment of a ribbon-based magnetic-sensitive biosensing system.

This paper aims to investigate the influence of structure parameters on giant-magnetoimpedance (GMI) effect measured by non-contact method.

The GMI sensor contains a Co-based internal magnetic core fabricated by laser cutting and an external solenoid. The influences of magnetic permeability of magnetic core and structure parameters on GMI effect were calculated in theoretical model. The output impedance, resistance, reactance and GMI ratio were measured by non-contact method using impedance analyzer.

Enhancing external magnetic field intensity can decrease the magnetic permeability of core, which has vital influences on the magnetic property and the output response of GMI sensor. In addition, increasing the width of magnetic core and the number of solenoid turns can increase the maximum GMI ratio. The maximum GMI ratio is 3,230% with core width of 6 mm and solenoid turns of 200.

Comparing with traditional contact-measured GMI sensor, the maximum GMI ratio and the magnetic field sensitivity are improved and the power consumption is decreased in non-contact measured GMI sensor. GMI sensor measured by non-contact method has a wide range of potential applications in ultra-sensitive magnetic field detection.

The increased interest of the industrial sector in sustainable concepts and leadership has lagged behind conceptual advancement. Leaders are increasingly being pushed to encourage sustainable performance. In order to examine the relationship between responsible leadership and sustainable performance, this research creates a model based on the logic of RL performance, with the concurrent mediation of epistemic motivation and moderating role of sustainable climate.

The current research analyzed a sample of 520 respondents from employees recruited from public sector organizations in Pakistan who were full-time employees in Punjab province in three waves with an interval of two weeks in each wave. To collect data, the scales are adapted from past studies that were relevant to this study. The data received from the survey questionnaire are analyzed using SEM.

The study's findings demonstrate a significant as well as positive association between RL and SP with β = 0.298 and p < 0.001. Further, a significant mediating impact of epistemic motivation on the relationship between RL and sustainable performance with β = 0.238 and p < 0.001 is also evident. Epistemic motivation is an important mediator because transparency in knowledge held massive importance to get sustainable outcomes and is predominant factor to exert his/her efforts.

The research shows some theoretical and practical implications. To achieve the aims of sustainable development, organizations should first encourage responsible leadership behaviors. By establishing a shared vision and goals, top management can encourage responsible leadership techniques within their jurisdiction. In order to encourage responsible leadership behaviors, organizations should seek to create capacity at both organizational and social levels. It will change employee attitudes and provide the knowledge needed to achieve sustainable development objectives.

This is one of the initial studies to examine the relationship between responsible leadership and sustainable performance. Further, the concept of social exchange theory is used to understand sustainable performance from a comprehensive standpoint.

This paper aims to demonstrate the synthesis of polyesterification reaction of non-edible jatropha seed oil (JO) and acrylic acid, which leads to the production of acrylated epoxidised-based resin. To understand the physico-chemical characteristics when synthesis the JO-based epoxy acrylate, the effect of temperature on the reaction, concentration of acrylic acid and role of catalyst on reaction time and acid value were studied.

First, the double bond in JO was functionalised by epoxidation using the solvent-free performic method. The subsequent process was acrylation with acrylic acid using the base catalyst triethylamine and 4-methoxyphenol as an inhibitor respectively. The physico-chemical characteristics during the synthesis of the epoxy acrylate such as acid value was monitored and analysed. The formation of the epoxy and acrylate group was confirmed by a Fourier transform infrared spectroscopy spectra analysis and nuclear magnetic resonance analysis.

The optimum reaction condition was achieved at a ratio of epoxidised JO to acrylic acid of 1:1.5 and the reaction temperature of 110°C. This was indicated by the acid value reduction from 86 to 15 mg KOH/g sample at 6 hours.

The JO-based epoxy acrylate synthesised has a potential to be used in formulations the prepolymer resin for UV curable coating applications. The JO which is from natural resources and is sustainable raw materials that possible reduce the dependency on petroleum-based coating.

The epoxidised jatropha seed oil epoxy acrylate was synthesised, as a new type of oligomer resin that contains a reactive acrylate group, which can be alternative to petroleum-based coating and can used further in the formulation of the radiation curable coating.

Considering the vital role of resource-constraint innovation in developing countries, the aim of the study is to examine the mechanism of internal and external heterogeneous knowledge sharing (HKS) in the relationship between sustainable leadership (SL) and frugal innovation (FI). The social exchange theory was used to develop a research framework.

This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis to examine the relationship among several latent factors based on 263 participants from Pakistani SMEs.

The current findings support the significant positive impact of SL on both internal and external HKS. Moreover, this study also confirms the mediating effect of both types of HKS in the relationship between SL and FI.

To delve further into the benefits and vital role of HKS, it is recommended to conduct further research that would examine the potential impact of heterogeneous knowledge sources on the “SL–FI relationship” and to apply the presented research methodology in other countries and organizations beyond Pakistani SMEs.

This study is one of the first documented attempts to demonstrate HKS as a mechanism in the relationship between a specific type of leadership and FI.

Given innovation's significance, this research examines the link between empowered leadership and frugal innovation. The research also explores how collaborative cultures and organizational commitment mediate empowered leadership's effect on frugal innovation.

Quantitative method is used with the approach of hierarchical regression to test the hypotheses with data obtained from Pakistani small- and medium-sized enterprises (SMEs) through the questionnaire from 288 participants.

The results of this study show that empowered leadership has a considerable impact on the firm's capacity for frugal innovation. Additionally, this study shows that organizational commitment and collaborative culture significantly moderate the association between empowering leadership and frugal innovation.

Future studies should examine mediating factors, including employment experience, education and perceived organizational support, and moderating variables like employee psychological empowerment and leadership styles.

This research advises SMEs in developing nations to utilize frugal innovation since they cannot afford to spend extensively on technologies that add creativity and innovation to goods and services.

This study advances how leadership both directly and indirectly helps organizations strengthen their capacity for frugal innovation through the mediating roles of collaborative culture and organizational commitment.

Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.

In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.

The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.

In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.

The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.

The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.

In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.