A Context-Aware Framework for Secure Fintech APIs: Leveraging Java Spring Boot and OAuth 2.0 with Dynamic Token Adaptation

The rapid proliferation of internet-connected devices and the consequent evolution of cloud-computing infrastructure have created an unprecedented opportunity to redesign the way healthcare services are delivered. Traditional healthcare systems are burdened by geographical barriers, administrative overheads, fragmented record-keeping, and constrained access to specialist physicians—challenges that have been accentuated in the post-pandemic era. This paper presents TeleMed, a comprehensive, full-stack telemedicine web platform that integrates live video consultations, electronic health record (EHR) management, and artificial intelligence (AI)-based diagnostic assistance into a single, unified ecosystem. The platform is architected as a decoupled client-server application powered by a React.js single-page frontend, a Java 17 / Spring Boot 3.0 backend secured with JSON Web Token (JWT) authentication, and a hybrid database model comprising PostgreSQL for relational transactional data and MongoDB for unstructured medical records. The AI diagnostic module leverages the Meta Llama 3 large language model (LLM), accessed through the HuggingFace Inference Interface, employing advanced zero-shot and few-shot prompt engineering with dynamic patient-context injection to deliver highly personalized symptom analysis without the overhead of fine-tuning custom datasets. Real-time video consultations are facilitated through WebRTC standards via the Jitsi Meet API, enabling secure, low-latency virtual rooms. Experimental validation confirms that TeleMed successfully unifies intelligent triage, doctor discovery, appointment scheduling, and live consultation into a scalable platform, offering a practical blueprint for next-generation remote healthcare delivery

As web browsers have become more sophisticated at blocking unauthorized attempts to track users’ online activities (incognito mode, Do Not Track), so too have trackers evolved to trump those protections. In many cases, these new forms of tracking have turned features designed to improve the web experience against user privacy. We focus on browser fingerprinting as a testbed for proposing a novel approach to fighting back for user privacy. By intercepting and obfuscating the information generated by browser fingerprinting scripts, the user not only frustrates the attempt to track their movements, but, more importantly, wider usage degrades the quality of trackers’ databases, reducing the effective entropy of their metrics, thereby yielding privacy gains for all users, not just those employing this method of obfuscation.KeywordsUser PrivacyPrivacy ModeBrowser ExtensionEffective EntropyBrowser TrackingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We report on memsensors, a class of two terminal devices that combines features of memristive and sensor devices. Apart from a pinched hysteresis (memristive property) and stimulus dependent electrical resistance (sensing property) further properties like dynamic adaptation to an external stimulus emerge. We propose a three component equivalent circuit to model the memsensor electrical behaviour. In this model we find stimulus dependent hysteresis, a delayed response to the sensory signal and adaptation. Stimulus dependent IV hysteresis as a fingerprint of a memsensor device is experimentally shown for memristive ZnO microrods. Adaptation in memsensor devices as found in our simulations resembles striking similarities to the biology. Especially the stimulus dependency of the IV hysteresis and the adaptation to external stimuli are superior features for application of memsensors in neuromorphic engineering. Based on the simulations and experimental findings we propose design rules for memsensors that will facilitate further research on memsensitive systems.

We consider a stochastic individual-based model of adaptive dynamics on a finite trait graph G=(V,E)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G=(V,E)$$\\end{document}. The evolution is driven by a linear birth rate, a density dependent logistic death rate and the possibility of mutations along the directed edges in E. We study the limit of small mutation rates for a simultaneously diverging population size. Closing the gap between Bovier et al. (Ann Appl Probab 29(6):3541–358, 2019) and Coquille et al. (Electron J Probab 26:1–37, 2021) we give a precise description of transitions between evolutionary stable conditions (ESC), where multiple mutations are needed to cross a valley in the fitness landscape. The system shows a metastable behaviour on several divergent time scales, corresponding to the widths of these fitness valleys. We develop the framework of a meta graph that is constituted of ESCs and possible metastable transitions between them. This allows for a concise description of the multi-scale jump chain arising from concatenating several jumps. Finally, for each of the various time scales, we prove the convergence of the population process to a Markov jump process visiting only ESCs of sufficiently high stability.