Raman spectroscopy offers a powerful, non-destructive tool for pharmaceutical quantification, particularly in environments where traditional techniques like HPLC are limited by throughput and sample preparation demands. However, the quantification of low-concentration compounds remains challenging due to weak Raman scattering and high background interference. This study evaluates the use of portable Raman instrumentation coupled with Support Vector Regression (SVR) to quantify clindamycin across various concentrations. Spectral preprocessing steps included Savitzky–Golay smoothing, Standard Normal Variate (SNV) normalisation, and blank subtraction (ΔSNV) to enhance analyte-specific signal fidelity. Three SVR-based models were developed using full spectra, chemically meaningful fingerprint bands, and coefficient-filtered features. Models were evaluated through grouped cross-validation, bootstrapping, and external testing on formulations prepared in different solvent matrix and derived from distinct clindamycin sources (commercial tablet vs. analytical-grade standard). The top performing model achieved R² values exceeding 0.98 with root mean squared errors below 2.85 mg/mL. Blind sample predictions, made on fully unseen data, fell within 95% confidence intervals of the true concentrations, demonstrating strong model robustness.

The integration of machine learning (ML) techniques with Raman spectroscopy has emerged as a promising strategy for advancing cancer diagnostics through label-free, high-resolution molecular analysis. This review aims to map and synthesize current research directions in this rapidly evolving field by conducting a structured review of existing review articles. Using a curated dataset of 70 reviews retrieved from Scopus and Web of Science, we applied Latent Dirichlet Allocation (LDA) topic modeling to uncover dominant thematic clusters across the literature. Our findings reveal five key research axes: (1) instrumentation and signal acquisition, (2) data preprocessing and spectral denoising, (3) classification models and algorithmic pipelines, (4) biomedical applications in oncology, and (5) emerging trends including deep learning and hybrid methods. This thematic structure highlights both the maturity and fragmentation of the current knowledge landscape. We also discuss the limitations of our approach, including database and article-type restrictions, and the use of LDA as a single modeling method. By identifying underexplored areas and recurring methodological challenges, this review contributes to a clearer understanding of the research gaps and future opportunities at the intersection of ML and Raman spectroscopy for cancer research.

To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.

We define a variant of accepting splicing system that can be used as a problem solver. A condition for halting the computation on a given input as well as a condition for making a decision as soon as the computation has stopped is considered. An algorithm based on this accepting splicing system that solves a well-known NP-complete problem, namely the 3-colorability problem is presented. We discuss an efficient solution in terms of running time and additional resources (axioms, supplementary symbols, number of splicing rules. More precisely, for a given graph with n vertices and m edges, our solution runs in O(nm) time, and needs O(mn2) other resources. Two variants of this algorithm of a reduced time complexity at an exponential increase of the other resources are finally discussed.

The Tile Assembly Model, and its many variants, is one of the most fundamental algorithmic assembly formalism within DNA nanotechnology. Most of the research in this field is focused on the complexity of assembling different shapes and patterns. In many cases, the assembly process is intrinsically deterministic and the final product is unique, while the assembly process might evolve through several possible assembly strategies. In this study we consider the controlled assembly of one dimensional tile structures according to predefined assembly graphs. We provide algorithmic approaches for developing such controlled assembly protocols, using the signal-passing Tile Assembly Model, as well as probabilistic approaches for investigating the assembly of such tile-based one-dimensional structures. As a byproduct, we build a generalized TAS (tile assembly system) which generate specific non-local non-associative algebraic computations and we assamble n × n squares using only one tile, which is a better efficiency compared to the staged assembly model.

For Infrastructure-as-a-Service, Cloud service providers, such as Amazon EC2 and Rackspace, allow users to lease their computing resources over the Internet, and invest their money into developing and maintaining the infrastructure. Hence, maximizing profit, right pricing, and rightsizing are vital elements to their business. To address these issues, we propose in this article an economic model for cloud service providers that can be used to maximize profit based on right pricing and rightsizing in the Cloud data centre. Total cost is a key element in the model and it is analyzed by considering the Total Cost of Ownership (TCO) of the Cloud.

Reliability estimation of High Performance Computing (HPC) systems enables resource allocation, and fault tolerance frameworks to minimize the performance loss due to unexpected failures. Recent studies have shown that compute nodes in HPC systems follow a time varying failure rate distribution such as Weibull, instead of the exponential distribution. In this paper, we propose a model for the Time to Failure (TTF) distribution of a system of k s-independent nodes when individual nodes exhibit time varying failure rates. We also present the system reliability, failure rates, Mean Time to Failure (MTTF), and derivations of the proposed system TTF model. The model is validated using observed data on time to failure.

In order to address anticipated high failure rates, resiliency characteristics have become an urgent priority for next-generation extreme-scale high-performance computing (HPC) systems. This poster describes our past and ongoing efforts in novel fault resilience technologies for HPC. Presented work includes proactive fault resilience techniques, system and application reliability models and analyses, failure prediction, transparent process- and virtual-machine-level migration, and trade-off models for combining preemptive migration with checkpoint/restart. This poster summarizes our work and puts all individual technologies into context with a proposed holistic fault resilience framework.

For full checkpoint on a large-scale HPC system, huge memory contexts must potentially be transferred through the network and saved in a reliable storage. As such, the time taken to checkpoint becomes a critical issue which directly impacts the total execution time. Therefore, incremental checkpoint as a less intrusive method to reduce the waste time has been gaining significant attentions in the HPC community. In this paper, we built a model that aims to reduce full checkpoint overhead by performing a set of incremental checkpoints between two consecutive full checkpoints. Moreover, a method to find the number of those incremental checkpoints is given. Furthermore, most of the comparison results between the incremental checkpoint model and the full checkpoint model (Liu et al., 2007) on the same failure data set show that the total waste time in the incremental checkpoint model is significantly smaller than the waste time in the full checkpoint model.

The increase in the physical size of High Performance Computing (HPC) platform makes system reliability more challenging. In order to minimize the performance loss (rollback and checkpoint overheads) due to unexpected failures or unnecessary overhead of fault tolerant mechanisms, we present a reliability-aware method for an optimal checkpoint/restart strategy. Our scheme aims at addressing fault tolerance challenge, especially in a large-scale HPC system, by providing optimal checkpoint placement techniques that are derived from the actual system reliability. Unlike existing checkpoint models, which can only handle Poisson failure and a constant checkpoint interval, our model can deal with a varying checkpoint interval and with different failure distributions. In addition, the approach considers optimality for both checkpoint overhead and rollback time. Our validation results suggest a significant improvement over existing techniques.

The increase in the physical size of High Performance Computing (HPC) platform makes system reliability more challenging. In order to minimize the performance loss due to unexpected failures or unnecessary overhead of fault tolerant mechanisms, we present a reliability-aware method for an optimal checkpoint/restart strategy towards minimizing rollback and checkpoint overheads. Our scheme aims to address fault tolerance challenge especially in a large-scale HPC system by providing optimal checkpoint placement techniques that are derived from the actual system reliability. Unlike existing checkpoint models, which can only handle Poisson failure and a constant checkpoint interval, our model can perform a varying checkpoint interval and deal with different failure distributions. In addition, the approach considers optimality for both checkpoint overhead and rollback time. Our validation results suggest a significant improvement over existing techniques.