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This paper aims to propose a single element, dual feed, polarisation diversity antenna. The proposed antenna operates from 2.9 to 10.6 GHz for covering the entire ultra-wideband (UWB) frequency range. The antenna is designed for usage in massive multiple input multiple output (MIMO) and closed packaging applications.

The size of the antenna is 24 × 24 × 1.6 mm³. The radiating element of the antenna is derived from the Sierpinski–Knopp (SK) fractal geometry for miniaturization of the antenna size. The antenna has a single reflecting stub placed between the two orthogonal feeds, to improve isolation.

The proposed antenna system exhibits S₁₁ < −10 dB, S₂₁ < −15 dB and stable radiation characteristics in the entire operating region. It also offers an envelope correlation coefficient < 0.01, a diversity gain > 9.9 dB and a capacity loss < 0.4 bps/Hz. The simulated and measured outputs were compared and results were found to be in similarity.

The proposed UWB-MIMO antenna has significant size reduction through usage of SK fractal geometry for radiating element. The antenna uses a single radiating element with dual feed. The stub is between the antenna elements which provide a compact and miniaturized MIMO solution for high density packaging applications. The UWB-MIMO antenna provides an isolation better than −20 dB in the entire UWB operating band.

Identification of the direction of the sound source is very important for human–machine interfacing in the applications such as target detection on military applications and wildlife conservation. Considering its vast applications, this study aims to design, simulate, fabricate and test a bidirectional acoustic sensor having two cantilever structures coated with piezoresistive material for sensing has been designed, simulated, fabricated and tested.

The structure is a piezoresistive acoustic pressure sensor, which consists of two Kapton diaphragms with four piezoresistors arranged in Wheatstone bridge arrangement. The applied acoustic pressure causes diaphragm deflection and stress in diaphragm hinge, which is sensed by the piezoresistors positioned on the diaphragm. The piezoresistive material such as carbon or graphene is deposited at maximum stress area. Furthermore, the Wheatstone bridge arrangement has been formed to sense the change in resistance resulting into imbalanced bridge and two cantilever structures add directional properties to the acoustic sensor. The structure is designed, fabricated and tested and the dimensions of the structure are chosen to enable ease of fabrication without clean room facilities. This structure is tested with static and dynamic calibration for variation in resistance leading to bridge output voltage variation and directional properties.

This paper provides the experimental results that indicate sensor output variation in terms of a Wheatstone bridge output voltage from 0.45 V to 1.618 V for a variation in pressure from 0.59 mbar to 100 mbar. The device is also tested for directionality using vibration source and was found to respond as per the design.

The fabricated devices could not be tested for practical acoustic sources due to lack of facilities. They have been tested for a vibration source in place of acoustic source.

The piezoresistive bidirectional sensor can be used for detection of direction of the sound source.

In defense applications, it is important to detect the direction of the acoustic signal. This sensor is suited for such applications.

The present paper discusses a novel yet simple design of a cantilever beam-based bidirectional acoustic pressure sensor. This sensor fabrication does not require sophisticated cleanroom for fabrication and characterization facility for testing. The fabricated device has good repeatability and is able to detect the direction of the acoustic source in external environment.

Fiber reinforced additive manufacturing (FRAM) is an emerging technology that combines additive manufacturing and composite materials. As a result, design freedom offered by the manufacturing process can be leveraged in design optimization. The purpose of the study is to propose a novel method that improves structural performance by optimizing 3D print orientation of FRAM components.

This work proposes a two-part design optimization method that optimizes 3D global print orientation and topology of a component to improve a structural objective function. The method considers two classes of design variables: (1) print orientation design variables and (2) density-based topology design variables. Print orientation design variables determine a unique 3D print orientation to influence anisotropic material properties. Topology optimization determines an optimal distribution of material within the optimized print orientation.

Two academic examples are used to demonstrate basic behavior of the method in tension and shear. Print orientation and sequential topology optimization improve structural compliance by 90% and 58%, respectively. An industry-level example, an aerospace component, is optimized. The proposed method is used to achieve an 11% and 15% reduction of structural compliance compared to alternative FRAM designs. In addition, compliance is reduced by 43% compared to an equal-mass aluminum design.

Current research surrounding FRAM focuses on the manufacturing process and neglects opportunities to leverage design freedom provided by FRAM. Previous FRAM optimization methods only optimize fiber orientation within a 2D plane and do not establish an optimized 3D print orientation, neglecting exploration of the entire orientation design space.

Polyurethane concrete (PUC), as a new type of steel bridge deck paving material, the bond-slip pattern at the interface with the steel plate is not yet clear. In this study, the mechanical properties of the PUC and steel plate interface under the coupled action of temperature, normal force and tangential force were explored through shear tests and numerical simulations. An analytical model for bond-slip at the PUC/steel plate interface and a predictive model for the shear strength of the PUC/steel plate interface were developed.

The new shear test device designed in this paper overcomes the defect that the traditional oblique shear test cannot test the interface shear performance under the condition of fixed normal force. The universal testing machine (UTM) test machine was used to adjust the test temperature conditions. Combined with the results of the bond-slip test, the finite element simulation of the interface is completed by using the COHENSIVE unit to analyze the local stress distribution characteristics of the interface. The use of variance-based uncertainty analysis guaranteed the validity of the simulation.

The shear strength (τ_f) at the PUC-plate interface was negatively correlated with temperature while it was positively correlated with normal stress. The effect of temperature on the shear properties was more significant than that of normal stress. The slip corresponding to the maximum shear (D₁) positively correlates with both temperature and normal stress. The interfacial shear ductility improves with increasing temperature.

Based on the PUC bond-slip measured curves, the relationship between bond stress and slip at different stages was analyzed, and the bond-slip analytical model at different stages was established; the model was defined by key parameters such as elastic ultimate shear stress τ₀, peak stress τ_f and interface fracture energy G_f.

The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins.

The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm.

The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers.

A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.

Firms are driven to ride on the digital wave in today’s open innovation ecosystem. This study aims to explore the effect of digital transformation (DT) on knowledge-intensive business services (KIBS) firms’ innovation ambidexterity, namely, radical versus incremental innovation, respectively. Meanwhile, the authors evaluated the moderating role of the complexity of R&D collaboration portfolio (i.e. organizational diversity and geographic diversity) in the above relationships.

Using a panel data set of 171 Chinese listed firms in the information and communications technology services industry from 2010 to 2018, the proposed hypotheses were empirically attested.

It is found that DT has a positive relationship with radical innovation and an inverted U-shaped relationship with incremental innovation. In terms of the R&D collaboration portfolio, organizational diversity positively moderates the relationships between DT and innovation ambidexterity, respectively. The geographic diversity weakens the inverted U-shaped effect of DT on incremental innovation; however, its moderating role in the link between DT and radical innovation is not empirically verified.

Extant scholars mainly addressed the interplay between KIBS firms and their manufacturing clients, while this study reveals the different consequences of DT on KIBS firms’ innovation ambidexterity to highlight the role of KIBS firms is an independent and essential innovator in a knowledge-driven economy. Notably, the findings contribute to knowledge management (KM) and R&D literature by confirming the diversity of the R&D collaboration portfolio is a critical KM strategy for KIBS firms to develop and promote external knowledge resources.

The paper aims to enrich current debate about human–technology dichotomy in socio-economic settings by decoding and systematizing the main phases through which it has been approached in managerial and social studies.

A multi-interpretative framework is built thanks to the adoption of a qualitative approach inspired by the inductive logic, and for analyzing the historical evolution of the approach to human–technology dichotomy and for explaining them through an innovative conceptual model.

An innovative conceptual model is proposed for depicting connections and evolutions among the main four phases in the evolution of the approaches to human–technology dichotomy.

Reflections and conceptual model herein can support researchers in rereading the multiple theoretical and practical contributions provided with reference to human and technology relations in socio-economic settings.

The paper can support managers and entrepreneurs in defining and evaluating managerial approaches for efficiently enhancing human–technology interaction.

The paper proposes an innovative conceptual model based on a multi-interpretative framework for decoding the historical evolution of the approaches to human–technology dichotomy in business settings. New variables are added to the current debate about the topic for building an original interpretive viewpoint.

This research aims at the theory of planned behavior (TPB) and the global entrepreneurship monitor (GEM) framework – How can cognitive traits for entrepreneurship be used by incubators and accelerators?

In this research the authors analyze the factors that catalyze the founding of new technology-based firms. From a practitioner stand-point, the GEM posits that these factors can be classified as contextual, social and individual factors. The present study focuses on the latter, looking into how demographic characteristics, possession of human capital and cognitive traits interrelate. The authors rely on a sample of 141 technological new ventures being incubated in Madrid, Spain, which is analyzed with the multilayer perceptron technique.

The results show that cognitive traits, as defined in the TPB, act as the “last mile” in the entrepreneurial decision process, while demographic and human capital factors appear to antecede them. These results are relevant for incubators and accelerators, which now gain a better, more complete understanding of success factors of their incubatees.

This research deals both with practitioners' view of entrepreneurship and with scientific literature, intertwining both with the purpose of providing valuable information for incubators and accelerators.

Sustainable development involves companies on an individual, organizational and social level requiring the adoption of business models or innovations capable of privileging the co-creation of mutual value with a view to sustainability. From an organizational perspective, this paper aims to show that knowledge brokers, by making explicit their roles as mediators of interactions and acting on dynamic capabilities (DCs), can generate a proactive approach to the three dimensions of sustainability and specifically allows capabilities to positively impact the propensity toward sustainable supply chain management (SSCM) practices.

This study offers an empirical analysis of 200 companies in the agro-food sector participating in a knowledge brokerage system activated by protection consortia. It uses a multiple regression technique that allows for observing relationships between DCs and SSCM.

Absorptive, adaptive and innovative capabilities, when understood and brokered, have a positive and direct impact on the SSCM.

As there have rarely been frameworks developed that correlate knowledge brokerage, DCs and sustainability, this paper suggests that DCs, when adequately valued by the knowledge broker, allow for identifying the requirements of the various stakeholders regarding sustainability and changes in market scenarios to generate sustainability practices along the supply chain.

Doctoral students are promising entrepreneurial actors in university-based ventures, which positively impact the external environment and create value for their universities. In this article, the authors extend current research on academic entrepreneurship by shedding light on the role of university support in the early stage of Ph.D. entrepreneurship. Based on social information processing theory, the authors posit that academic entrepreneurship results from the interplay between doctoral students' human capital and university-level support. A multilevel model is proposed and empirically tested to shed light on the cradle of doctoral students' entrepreneurship by explaining the variance of their entrepreneurial alertness and intentions.

A model is proposed that explains the combined effect of specific human capital and different forms of university support on doctoral students' cognitive transition from entrepreneurial alertness to intentions. The model was then tested through structural equation modeling (SEM) and multigroup analysis (MGA) on a sample of 187 doctoral students enrolled in Italian universities.

The SEM results reveal that doctoral students' entrepreneurial alertness is influenced by perceived educational support and human capital. The MGA demonstrates that those who perceive a higher level of support for concept and business development from universities are more likely to convert their alertness into intentions than those who perceive lower support.

The present paper brings to the stage doctoral students as an extremely promising entrepreneurial target. In doing so, it extends academic entrepreneurship studies by detailing how and when the different forms of university support influence their entrepreneurial decisions, along with individual dimensions.