Although, the influence of the base-isolation (BI) system on the structural performance of building structures is well understood, but the influence of the soil-structure interaction (SSI) on the performance of the BI buildings is remains unclear. Additionally, rapid urbanization, the increasing rate of building construction, and the frequent occurrence of seismic events necessitate the investigation of the frequency-dependent behavior of typical urban building infrastructures equipped with seismic control systems such as base isolation (BI). The present study investigates the effect of the BI system on the seismic performance of low-, intermediate-, and high-rise base-isolated shear buildings considering the SSI. Three different buildings typically found in urban areas with different fundamental structural frequency are considered in the numerical investigation. For a comprehensive study of the earthquake on the building structures with different height (low-rise, intermediate-rise and high-rise buildings), the earthquake records are classified as low-intermediate- and high-frequency earthquakes. The numerical modelling of the building structures is done using lumped-mass model and the parameters of the linearized equivalent BI system are integrated into the model, considering that the BI system behaves like the bottom story of the structural system. For this study, the interaction between the soil and foundation is represented using springs and dashpots considering sway and rocking inertial responses. The seismic responses are compared between isolated and non-isolated cases, considering SSI and non-SSI conditions separately. It is found that, the SSI significantly affects the seismic response of each category of considered building structures, independent of isolated or non-isolated cases. Additionally, the sensitiveness of different types of buildings to different frequency-content earthquake is evident for both isolated and non-isolated scenarios. For all the considered earthquakes, the efficiency of the BI system is observed to be overestimated when SSI effect is neglected.
Clitoria ternatea (CT) polyphenols and anthocyanins are promising functional food ingredients but require protection against degradation and premature release. This study evaluated how alginate, xanthan gum, rapeseed oil, and ginger oil affect the structure and functionality of CT-loaded microcapsules. Six formulations were prepared: oil-free alginate controls with or without xanthan gum, and W/O/W double-emulsion microcapsules containing rapeseed oil or rapeseed–ginger oil in alginate or alginate–xanthan matrices. Alginate provided the ionically crosslinked gel network, while xanthan gum modified the matrix structure and increased water uptake within each formulation group. Incorporation of the oil phase markedly improved bead formation, producing more spherical and compact microcapsules compared with oil-free controls, which fragmented after drying and showed higher swelling. Rapeseed oil acted as a hydrophobic barrier and enhanced TPC loading capacity, reaching 160.77 ± 6.60 mg GAE/g in the alginate–xanthan formulation. The addition of ginger oil introduced a functional oil phase and, when combined with the alginate–xanthan matrix, contributed to improved color retention and slower phenolic release. FTIR spectra indicated the coexistence of polysaccharides, oils, and CT bioactives without evidence of chemical degradation. Encapsulated formulations containing ginger oil showed lower immediate antibacterial activity than free actives, consistent with controlled release from the microcapsule matrix. Overall, the alginate–xanthan system containing rapeseed–ginger oil provided the best balance between structural stability, bioactive retention, and controlled release, supporting its potential as a functional food delivery system.
Tsunami wave dynamics are inherently nonlinear and dispersive, making them challenging to model using conventional techniques. To address this, the present work develops a physics-informed Least Squares Support Vector Machine (PILSSVM) framework for approximating solutions of the geophysical Korteweg–de Vries (GeoKdV) equation, which describes long-wave propagation in equatorial regions. The proposed approach incorporates the governing partial differential equation along with initial and boundary conditions directly into the learning process, ensuring physical consistency and solution accuracy. The GeoKdV equation is considered as a benchmark problem to systematically validate the performance of the proposed framework under controlled conditions. For comparison, several data-driven models, including Support Vector Machine (SVM), Gaussian Process Regression (GPR), Multi-Layer Perceptron (MLP), Random Forest (RF), and Extreme Learning Machine (ELM), are also implemented to illustrate the distinction between data-driven and physics-informed learning. In addition, kernel function analysis (RBF, Laplacian, Polynomial, and Linear) is conducted to examine their influence on approximation accuracy and PDE residuals. The effect of key physical parameters, namely the Coriolis parameter and wave velocity, is analyzed to assess the capability of the proposed framework to capture the parametric dependence of the wave profile. Hyperparameter sensitivity and PDE residual analysis are further performed to evaluate training stability and solution consistency. The results demonstrate that the proposed physics-informed formulation provides stable and physically consistent approximations under varying modeling conditions.
In this study, the biophysical functionality of Boswellia serrata gum (BSG)—a complex oleo-gum resin comprising both water-soluble heteropolysaccharides and lipophilic constituents—was evaluated for its capacity to stabilize fiber-disrupted whole wheat cake (WWC) matrices through combined hydration-mediated and structural effects. BSG was incorporated at 0–2.0
This study aims to examine the impacts of country risks on outward foreign direct investment (OFDI) of emerging source countries (ESCs). This study disaggregates destinations as developed countries (DCs), emerging countries (ECs) and other developing countries (ODCs). Additionally, country risks are categorized as economic, political and natural risks. Hardly do there exist any earlier studies that have examined the source country's perspective and segregated destinations as per their level of development. This study uses a gravity model approach for a total of 166 countries and employs the Poisson Pseudo Maximum Likelihood (PPML) method, which is effective for estimating the FDI gravity model. The findings of this study show that the three risk factors are not responding in a similar manner to the FDI of both source and host countries. It is the nature of country risks of home countries that motivates outward FDI from emerging sources to select their destinations as DCs, ECs or ODCs. The results for bilateral export of ECs show that it is a potential complementary instrument that can be used as a learning mechanism to obtain foreign market-specific knowledge in the presence of country risks.
