Considering the surge in seismic events across the world, it is imperative to accurately predict the seismic response of Reinforced Concrete (RC) buildings. These buildings undergo inelastic deformation while vibrating due to seismic excitation and incur damage in various structural elements that lead to a reduction in their lateral stiffness and thus, alter the dynamic response of the building. However, seismic design codes adopt linear dynamic analysis to estimate seismic forces and response parameters. The present study aims to consider the effect of stiffness degradation of structural elements and structural damping modification on the seismic response of the RC shear building. A six-storey RC shear building, modelled as a Multi Degrees of Freedom (MDOF) system, was subjected to a series of strong and pulse-type seismic excitations. A nonlinear time history analysis using the Newmark-Beta numerical integration method was performed considering two cases; Stiffness Degradation Case (SDC) and Damping Modification Case (DMC). To quantify stiffness degradation and damping modification due to inelastic deformation, a post-yield behaviour of the RC frame with Aerated Autoclaved Concrete (AAC) blocks and without blocks was studied by performing quasi-static testing. Peak storey displacement, peak interstorey drift ratio and peak acceleration along with normalized ratios x_Roof/PGD , ẍ_Roof/PGA and V/W across each storey of the shear building were obtained and compared with the seismic response of the shear building with elastic initial stiffness and inherent damping. It has been found that neglecting the effect of stiffness degradation results in to underestimation of seismic response quantities considered.
Acceptance of bamboo as a building material is growing sharply due to its physical similarities with traditional structural elements. While strength and light weight make it a low-cost alternative to structural elements, bamboo is inherently sustainable. However, its use as a structural element is limited. The paper aims to study the strength and modulus properties of bamboo. A locally available bamboo identified as ‘Bambusa Vulgaris’ from the institute campus was collected and kept for sun-drying for about three weeks before preparing test specimens. Coupon specimens for strength and modulus; tension, compression, shear along with test specimens with and without notch for impact energy absorption were developed from bamboo culm adhering to relevant Indian and International Standards for the testing. These were tested using loading frame and impact testing machine. The ultimate tensile strength 166 MPa, and yield strength 124.5 MPa with an elastic modulus of 14.5 GPa derived for the dumbbell test specimen exhibiting ductile laminate failure. The compressive strength of test specimen was found to be 60 MPa with a height to diameter (h/D) ratio of 1. A shear strength of 148 MPa was achieved under single and 97 MPa under double shear on test specimens that sheared and elongated. Impact Energy Density for test specimens without a notch was evaluated to be 33.5 J/g/cm3, at par with steel material, demonstrating layer-by-layer fracture. These mechanical properties show good agreement with reported results for other species of bamboo.
The everyday rapid growth of the Internet of Things(IoT) across many domains like smart home, healthcare, agriculture, and industries has introduced increasingly complex security challenges. This paper presents a comprehensive survey of application-specific IoT security frameworks, highlighting their challenges and strategies, and strengths or limitations. The paper further surveys existing standards, regulations, as well as common attack vectors, including malware, DDoS, firmware tampering, and side channel analysis. In this review, the literature is organized into three stages:(i) IoT security frameworks covering layered architectures, general-purpose, AIML, or application-specific, (ii) security requirements and solutions across major IoT application domains; and (iii) Different IoT security mechanisms spanning hardware, network, and data layers. In addition to surveying and synthesis, this study introduces a novel LLM-based assistant architecture for domain-specific IoT security framework generation. The proposed expert assistant system leverages a fine-tuned open-source LLM or a custom-trained LLM to interpret high-level user intent, including IoT application, IoT device, Hardware, software, special concern objectives, and translates them into transparent, explainable, and actionable security framework suggestions. This review closes the gap between conventional static security models and one-size-fits-all security frameworks and the growing demand for customized, practical, and domain-specific IoT security design, providing both a critical synthesis of current research and a forward-looking AI-assisted framework for intelligent IoT security planning.
This study investigates how data science competencies, conceptualized as the micro-foundations of digital dynamic capabilities (DDCs), combine to influence the development of digital business capability (DBC). Using fuzzy-set qualitative comparative analysis (fsQCA), we examine configurations of competencies that enable DBC and identify necessary and sufficient conditions. The necessary-condition testing indicates no single competency is universally required, highlighting the configurational, micro-foundational nature of DDC development. The fsQCA uncovers three equifinal competency configurations that act as sufficient pathways to high DBC. Beyond capability building, the study demonstrates how distinct competency bundles facilitate business model renewal capabilities, translate analytics into data-enabled services, and reconfigure capabilities to embed servitized offerings into scalable architectures in the digital ecosystem business. These insights offer actionable guidance for practitioners, educators, and policymakers seeking to design data science competency systems that not only strengthen DDCs but also enable sustained business model innovation in AI, Industry 4.0, and other data-driven contexts.
This study explores the behavioral implications of financial stress on online pornography consumption across ten major Russian cities during the years 2019-2021. Using a panel data set reflecting urban digital behavior and macroeconomic uncertainty, the author uses fixed-effects regression techniques to estimate the impact of financial volatility on daily pornography viewership. The findings indicate a consistent and statistically significant positive relationship between financial stress and pornography consumption. The analysis incorporates pandemic-related controls, including lockdown periods, COVID-19 case surges, seasonal temperature effects and national holidays. These results underscore the role of digital escapism as a coping mechanism during times of economic uncertainty and crisis. The study contributes to a deeper understanding of stress-induced digital behaviors in transitional economies, presenting implications for public mental health frameworks, digital platform governance and socioeconomic policy design in Russia.
