South Indian Education Society (SIES), founded 1932, is one of the oldest educational societies in Mumbai. SIES has established a high school, a group of arts, science and commerce colleges, along with academic and professional institutions of higher learning, with altogether, more than 18,000 students.
The rise of the Internet of Things (IoT) has created a problem of abundance in digital forensics, presenting unique challenges that require novel solutions – including more advanced and scalable tools and techniques. In this paper, we introduce Digital Forensics 4.0 Framework which is a complete data acquisition, analysis and preservation mechanism from various levels to overcome the challenges mentioned above. The platform also bundles in tools to pull forensic data from IoT, analyse network traffic and collect cloud-based evidence providing a comprehensive picture of incidents with an IoT component. The platform leverages machine learning algorithms to automate detection of anomalies, makes investigations much faster and more accurate with a sophisticated framework. This methodology was demonstrated by the development and model’s framework through prototype implementation and case situations in real world to show feasibility of effectively using this approach for monitoring and actionable analytics for malicious events, illegal transmission of data, or security breaches. According to the results, the framework managed to record and analyse traces gathered from an IoT device pool including valuable insights for forensic investigators. But issues of large IoT network s callability and legal access to cloud data still need to be resolved. The project’s future work will focus on scalability, privacy-preserving properties and AI-driven techniques beyond what is already included in the framework. In conclusion, the proposed Digital Forensics 4.0 Framework seems a good alternative scenario for IoT forensics as an illustrative lead to more efficient and powerful digital investigations in this developing IoT environment.
In this paper, a distribution called quaternion inverse Gaussian distribution (QIGD) is introduced. The fundamental properties of probability density function, raw moments, moments generating function, skewness, and kurtosis are discussed. The maximum likelihood estimators of parameters in quaternion algebra are studied. Examples based on quaternion algebra and applications to quantum mechanics are illustrated.
Benzene (BENZ) is classified as a Class 1 residual solvent according to ICH Q3C guidelines due to its established classification as a Group 1 human carcinogen. During the chemical production of Cetirizine Dihydrochloride (CTZ) API, Cetirizine Dihydrochloride tablet (CTZT), Levocetirizine Dihydrochloride (LCTZ) API, and Levocetirizine Dihydrochloride tablet (LCTZT), BENZ can appear as a residual process impurity with carcinogenic risk; development of a highly sensitive quantification method is a critical requirement for pharmaceutical quality control. This study focused on developing and validating a method for detecting trace-level BENZ in CTZ and LCTZ drug substance and drug product. The method demonstrated exceptional sensitivity, with a least detectable concentration of 0.04 ppm and a quantifiable concentration of 0.12 ppm, significantly lower than the ICH-mandated safety limit of 2 ppm. Linearity and accuracy studies yielded an outstanding percentage of samples spiked BEN in the drug substance and drug product of CTZ and LCTZ and found within acceptance limit, approving the method's validity in presence of the drug atmosphere. Conventional headspace GC-FID/GC-MS are established techniques for benzene analysis; the proposed HPLC method offers a simpler, cost-effective, and validated method, making it well suited for routine quality control laboratories where GC facilities may not be readily available.
Urbanization has resulted in a high rise in the use of vehicles, thus increasing parking problems like extended search times, fuel consumption, traffic congestion, and user frustration. To counter these problems, this paper introduces ParkSense, an IoT-based smart parking system that combines hardware and software elements for real-time parking space monitoring and management. It uses NodeMCU microcontrollers and IR sensors for car presence detection and an LCD display for real-time on-site updates. It has connectivity with ThingSpeak cloud to provide remote data access and visualization. The frontend is built with the MERN stack (MongoDB, Express, React, Node.js), and the Tailwind CSS provides a user-friendly and responsive interface on devices. ParkSense functionalities include real-time slot monitoring, access to historical data, administrative dashboards, and secure online payments. The system has proven to be highly efficient, reliable, scalable, and easy to use during testing and implementation. It saves considerable parking search time and fuel consumption, thus helping to create a more sustainable city environment. Future developments involve AI-based predictive analytics, dynamic pricing, personalized recommendations, and integration with EV charging stations.
