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.
With the advent of nanotechnology, combined drug therapies employing dual-drug delivery provide an efficient way to overcome the drawbacks of conventional chemotherapy, such as lack of specificity, multidrug resistance and low aqueous solubility. Herein, a dual-drug delivery system based on mesoporous silica nanoparticles (MSNs) was developed to target tumours with the dual-responsive co-delivery of two anticancer drugs, camptothecin (CPT) and 5-fluorouracil (5-FU), in a sequential manner. The mesopores were loaded with CPT, and subsequently, a pegylated-biotin polymer was used to coat them. The disulphide link and acid group present in the polymer contribute to the stimuli-triggered release of the drugs. Since cancer cells need biotin to continue proliferating, it functions as a targeting ligand. Mathematical modelling studies revealed that the drug-release kinetics followed a diffusion mechanism for both the hydrophobic and hydrophilic drugs. Beyond in vitro release and cytotoxicity assays, extensive in vivo biological evaluations, including liver function markers, serum biochemistry and histopathological examinations, demonstrated pronounced tumour suppression with reduced hepatic toxicity. The nanocarrier downregulated key tumour biomarkers, effectively lowered serum transaminases and restored normal liver architecture. Collectively, these findings affirm the translational potential of this smart dual-drug delivery platform for cancer therapy.
Speed bumps are widely implemented as physical traffic-calming measures because of their effectiveness in reducing vehicle speeds. However, they can impose significant discomfort and long-term health risks, particularly for riders of motorized two-wheelers, who are directly exposed to mechanical shocks. This study evaluates these impacts using two physiological metrics: vibration dose value (VDV), representing short-term discomfort, and static compressive stress (S e ), indicative of spinal stress and long-term health risks. As an exploratory pilot study, field experiments conducted on three speed-bump geometries-350 mm, 500 mm, and 750 mm widths (each 50 mm in height)-suggest a trend whereby VDV decreases with increasing speed because of shorter exposure durations, while S e increases, reflecting heightened spinal stress. To balance these opposing trends, a risk factor (R) is introduced to quantify long-term health risk. The study proposes a framework for identifying optimum crossing speeds for each bump geometry, with our study suggesting values of 10 km/h for the 350 mm bump, 15 km/h for the 500 mm bump, and 20 km/h for the 750 mm bump under the stated commuter-exposure scenario. These optimum speeds correspond to a risk factor value below 0.8, indicating a low probability of adverse health outcomes. By integrating comfort and health metrics into a unified risk-based framework, this research provides actionable insights for safer and more inclusive speed-bump design-ensuring speed control without compromising rider well-being.
