The Internet of Things (IoT) and machine learning (ML) have various applications in different sectors of life, such as healthcare, agriculture, industries, transportation, smart cities, smart homes, etc., and their number is increasing with each passing day. The rapid development of IoT and its increasing demand in different fields of life create a serious problem of security for the IoT environment, which needs serious consideration to protect the IoT-enabled systems from external networks and cyber-attacks. Because of the open deployment environment and constrained resources, the IoT is prone to malicious assaults. Furthermore, the IoT’s diverse and dispersed properties make it difficult for conventional intrusion detection systems (IDS) to keep up with current technological developments. An ML-enabled IoT-based IDS is one of the most important security methods that can assist in defending computer networks and the IoT environment from numerous attacks and malicious activities. Keeping in mind the significant contribution of ML to securing the IoT environment, we proposed an ML-enabled IDS for securing the IoT networks and applications in this study. In the proposed system, we proposed a modified Random Forest (RF) algorithm and compared its performance with nine well-known ML algorithms for the detection of network attacks. Further, two of the most recent and well-known network datasets, i.e., TON-IoT and UNSW-NB15, are used to check the effectiveness of the ML-enabled IDS. The performance of the utilized ML algorithms was measured with the help of different performance measures such as accuracy, sensitivity, etc. The experimental outcomes illustrate the importance of the proposed ML-enabled IDS for securing the IoT environment and applications. The proposed system applies to almost all of the resource-constrained devices that use the IoT network.
The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use. This study examines cross-cultural differences in student perceptions of GenAI use, comparing responses from students at Canadian and South Korean universities. Using a scenario-based survey administered in Fall 2024, we analyzed how students judged the ethicality and rule compliance of AI-assisted coding practices. Results reveal that Canadian students were consistently more likely to perceive the use of GenAI as both unethical and against institutional policies compared to Korean students, despite functionally identical institutional policies. Statistical analysis, including Mann-Whitney U tests and correlation coefficients, demonstrated significant differences across nearly all scenarios. Analysis of the factors used in generating scenarios indicated that the amount of AI-generated code incorporated into assignments most strongly influenced ethical judgments. Findings were interpreted through Hofstede’s cultural dimensions framework, suggesting that cultural factors such as power distance, individualism, and uncertainty avoidance significantly shape students’ ethical reasoning regarding GenAI. Our results contribute to the growing body of evidence emphasizing that equitable AI integration in education must be culturally responsive, taking into account diverse conceptions of academic integrity. We advocate for the development of nuanced AI-use guidelines that are sensitive to local cultural contexts while upholding fundamental principles of academic honesty. This study highlights the need for ongoing cross-cultural research to inform ethical AI policies and support responsible GenAI use in global higher education settings.
Hematocrit levels and plasma protein concentrations are key determinants of blood viscosity and nanoparticle dispersion, but their combined influence under magnetic drug targeting (MDT) remains unclear. This study mathematically investigates their impact on MNP transport under MDT, alongside other physical parameters. A two-fluid model of unsteady blood flow in porous media was employed, using the Walburn-Schneck non-Newtonian constitutive equation to relate shear stress to hematocrit, plasma protein, and shear rate. The Brinkman model simulated the flow in the plasma layer. Velocities were computed analytically, and a numerical scheme of backward time-centered space solved the advection-diffusion equation for the dispersion of solute. Interestingly, the results reveal that lower hematocrit levels suppress the dispersion of MNP, contrary to the commonly held assumption, while elevated plasma protein concentrations enhance the dispersion. By incorporating variable diffusivity influenced by hematocrit, plasma protein, and local shear conditions, the model demonstrates improved predictive accuracy in drug transport and distribution patterns within the tumor microenvironment. The reduced slip velocity increases wall shear stress and intravascular resistance, while a larger nanoparticle size and enhanced magnetic properties improve pharmacokinetics. Stress jump, permeability, and plasma layer thickness significantly influence drug dispersion. The findings contribute to a deeper understanding of nanoparticle transport, aiding the optimization of MNP-based drug delivery systems for improved cancer therapy.
This study offers detailed analysis of modulation instability (MI) dynamics in inhomogeneous nonlinear Schr & ouml;dinger (NLS) media, including two-photon absorption (TPA) through analytical modeling and numerical simulations. In contrast to traditional MI studies conducted in uniform environments, our methodology systematically investigates how engineered spatial inhomogeneity through customized nonlinearity coefficients, group velocity dispersion (GVD) parameters, and distance-dependent profiles can be utilized to regulate nonlinear wave stability. Starting with a fixed TPA coefficient, we demonstrate that an increase in TPA not only mitigates instability but also causes significant pulse broadening, steering the system into a dissipative phase. Contour and surface analyses reveal a unique stabilization mechanism in the MI spectrum resulting from the interplay between non-linearity and TPA. When the TPA is established, fluctuations in GVD and nonlinearity coefficients yield a diverse array of structural behaviors, encompassing total sideband suppression, localized stability regions, and periodically recurring stable bands. By expanding this framework to inhomogeneous media, we illustrate that distance-dependent group velocity dispersion and nonlinearity profiles facilitate spatial localization and periodic modulation instability evolution, providing a customizable platform for wave manipulation. Numerical propagation studies indicate that elevated TPA values, in conjunction with graded GVD profiles, produce droplet-like spatiotemporal patterns, reflecting a sophisticated and manageable equilibrium between dissipation and dispersion. The results present novel opportunities for utilizing spatially designed optical medium to attain nonlinear light propagation, with potential applications in high-power pulse shaping, optical signal processing, and dissipative photonics.
Abstract The increasing frequency of natural hazards, intensified by climate change, poses substantial challenges to sustainable development worldwide. Northern Pakistan, particularly the Hunza district, is highly susceptible to multiple hazards, including landslides, earthquakes, glacier-induced floods, debris flows, and Glacier Lake Outburst Floods (GLOFs), driven by both climatic and tectonic factors. A multi-hazard assessment is essential to understand the complex interactions between these hazards, offering a comprehensive perspective on risk and facilitating more effective disaster preparedness and mitigation strategies. This study addresses the existing gap in multi-hazard assessments, which are often confined to single-hazard evaluations, by developing an integrated multi-hazard susceptibility map for the Hunza district in Northern Pakistan. The region’s complex topography, active tectonics, and accelerated glacier melting contribute to its high vulnerability to cascading and co-occurring hazards. The integrated assessment utilizes diverse data sources, including topographic attributes, geological, hydro-meteorological, environmental variables, and literature-derived hazard map for multi-hazard susceptibility analysis. A Machine Learning (ML) Forest-Based Classification and Regression (FBCR) model, Analytical Hierarchy Process (AHP), and Vs30-based site characterization was employed to classify and generate hazards individually and as integrated multi-hazard susceptibility map. The model incorporates eighteen geo-environmental variables for individual hazards assessment. The resulting multi-hazard susceptibility map indicates that 23.11% of the area is prone to landslides, 6.07% to flash floods, 4.66% to debris flows and flash floods, and 3.98% to a combination of flash floods, landslides, and debris flows. The highest multi-hazard zone, comprising seismic hazard, debris flows, landslides, and flash floods, covers 2.88% of the area, whereas low-hazard zones constitute 56.84% of the region. The landslide susceptibility model classifies 20% of the area as very high susceptible, while the flash flood, debris flow, and seismic hazard models indicate 5, 2, and 13% of the area, respectively, fall under very high susceptibility/hazard. This integrated multi-hazard approach provides a comprehensive risk assessment framework, supporting evidence-based disaster risk reduction policies and infrastructure planning in hazard-prone regions. The findings identify critical high-hazard zones, offering data-driven insights for targeted mitigation strategies and disaster risk reduction efforts.
