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An optical biosensor is a specialized analytical device that utilizes the principles of optics and light in bimolecular processes. Localized surface plasmon resonance (LSPR) is a phenomenon in the realm of nanophotonics that occurs when metallic nanoparticles (NPs) or nanostructures interact with incident light. Conversely, surface-enhanced Raman spectroscopy (SERS) is an influential analytical technique based on Raman scattering, wherein it amplifies the Raman signals of molecules when they are situated near specific and specially designed nanostructures. A detailed exploration of the recent ground-breaking developments in optical biosensors employing LSPR and SERS technologies has been thoroughly discussed along with their underlying principles and the working mechanisms. A biosensor chip has been created, featuring a high-density deposition of gold nanoparticles (AuNPs) under varying ligand concentration and reaction duration on the substrate. An ordinary description, along with a visual illustration, has been thoroughly provided for concepts such as a sensogram, refractive index shift, surface plasmon resonance (SPR), and the evanescent field, Rayleigh scattering, Raman scattering, as well as the electromagnetic enhancement and chemical enhancement. LSPR and SERS both have advantages and disadvantages, but widely used SERS has some advantages over LSPR, like chemical specificity, high sensitivity, multiplexing, and versatility in different fields. This review confirms and elucidates the significance of different disease biomarker identification. LSPR and SERS both play a vital role in the detection of various types of cancer, such as cervical cancer, ovarian cancer, endometrial cancer, prostate cancer, colorectal cancer, and brain tumors. This proposed optical biosensor offers potential applications for early diagnosis and monitoring of viral disease, bacterial infectious diseases, fungal diseases, diabetes, and cardiac disease biosensing. LSPR and SERS provide a new direction for environmental monitoring, food safety, refining impurities from water samples, and lead detection. The understanding of these biosensors is still limited and challenging. [Display omitted] •Optical biosensors offer crucial insights into molecular interactions with their precise and sensitive detection capabilities.•We discussed plasmonic mode hybridization and multiresonant features in SERS enhance its sensitivity, contributing to advanced bio-plasmonics.•Recent progress in designing plasmonic hotspots for SERS involves a comparative analysis of fabrication techniques.•Exploring broad applications of LSPR and SERS, with ongoing advancements, challenges, and promising future prospects.

Telepharmacy is an emerging solution to address medicine access disparities in India, particularly in rural and underserved areas. However, its adoption is hindered by outdated legal provisions, limited administrative capacity, and infrastructure gaps. This study explores telepharmacy governance from a local administration perspective, examining legal frameworks, stakeholder insights, and state-level initiatives. A qualitative exploratory approach was used, involving analysis of 25 legal and policy documents, 25 stakeholder interviews (including regulators, municipal officers, pharmacists, and technology providers), and case studies from Kerala, Rajasthan, and Himachal Pradesh. Thematic analysis, supported by descriptive statistics, chi-square testing (χ² = 14.62, p = 0.023), Kruskal-Wallis comparisons (H = 6.12, p = 0.047), and a Weighted Barrier Index (WBI), identified five priority challenges: legal ambiguity (WBI = 4.5), administrative gaps (4.2), infrastructure limitations (4.0), unclear pharmacist oversight (3.6), and data privacy concerns (3.1). Kerala demonstrated the highest readiness, while Himachal Pradesh showcased innovative community-led models. Findings emphasize the need for telepharmacy-specific laws, integration with the Ayushman Bharat Digital Mission, AI-driven prescription validation, and targeted training for local administrators. Telepharmacy should be recognized as a governance innovation, not just a technological advancement, requiring coordinated central, state, and local reforms to scale services equitably and strengthen India’s healthcare delivery system.

We consider a Carrier Sense Multiple Access (CSMA) based scheduling algorithm for a single-hop wireless network under a realistic Signal-to-interference-plus-noise ratio (SINR) model for the interference. We propose two local optimization based approximation algorithms to efficiently estimate certain attempt rate parameters of CSMA called fugacities. It is known that adaptive CSMA can achieve throughput optimality by sampling feasible schedules from a Gibbs distribution, with appropriate fugacities. Unfortunately, obtaining these optimal fugacities is an NP-hard problem. Further, the existing adaptive CSMA algorithms use a stochastic gradient descent based method, which usually entails an impractically slow (exponential in the size of the network) convergence to the optimal fugacities. To address this issue, we first propose an algorithm to estimate the fugacities, that can support a given set of desired service rates. The convergence rate and the complexity of this algorithm are independent of the network size, and depend only on the neighborhood size of a link. Further, we show that the proposed algorithm corresponds exactly to performing the well-known Bethe approximation to the underlying Gibbs distribution. Then, we propose another local algorithm to estimate the optimal fugacities under a utility maximization framework, and characterize its accuracy. Numerical results indicate that the proposed methods have a good degree of accuracy, and achieve extremely fast convergence to near-optimal fugacities, and often outperform the convergence rate of the stochastic gradient descent by a few orders of magnitude.

Recent work has shown that adaptive CSMA algorithms can achieve throughput optimality. However, these adaptive CSMA algorithms assume a rather simplistic model for the wireless medium. Specifically, the interference is typically modelled by a conflict graph, and the channels are assumed to be static. In this work, we propose a distributed and adaptive CSMA algorithm under a more realistic signal-to-interference ratio (SIR) based interference model, with time-varying channels. We prove that our algorithm is throughput optimal under this generalized model. Further, we augment our proposed algorithm by using a parallel update technique. Numerical results show that our algorithm outperforms the conflict graph based algorithms, in terms of supportable throughput and the rate of convergence to steady-state.

CSMA (Carrier Sense Multiple Access) algorithms based on Gibbs sampling can achieve throughput optimality if certain parameters called the fugacities are appropriately chosen. However, the problem of computing these fugacities is NP-hard. In this work, we derive estimates of the fugacities by using a framework called the regional free energy approximations. In particular, we derive explicit expressions for approximate fugacities corresponding to any feasible service rate vector. We further prove that our approximate fugacities are exact for the class of chordal graphs. A distinguishing feature of our work is that the regional approximations that we propose are tailored to conflict graphs with small cycles, which is a typical characteristic of wireless networks. Numerical results indicate that the fugacities obtained by the proposed method are quite accurate and significantly outperform the existing Bethe approximation based techniques.