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Background: The development of innovative approaches in drug analysis is a challenging task for medicine, pharmacy, and engineering sciences. For instance, the requirement of proper dosage forms in releasing active ingredients is crucial. It is essential to analyze drugs in biological liquids for early diagnosis and treatment purposes. Drug analysis is also of great importance to control the quality of pharmaceutical products, test their efficacy, and develop novel drug formulation. The present review is aimed to highlight the most recent spectrophotometric approaches applied to analyze various classes of drugs in biological media and/or dosage forms. Methods: Direct and derivative UV/UV-Vis spectrophotometry and a combination of various techniques with spectrophotometry, such as injection analysis and chemometrics, have most widely been applied in the analysis of dosage forms. In addition, emerging technologies, such as UV imaging allows to obtain the distribution of drug concentration in time-resolved 2D images based on UV light absorption, utilization of nanotechnology, self-assembled nanomaterials, and aptamer-based nanoparticles have gained interests to investigate drug assays and to quantify the proper drug release. Results: Due to their high versatility, ease of application, low cost, and fast response, spectrophotometric methods are one of the most preferable methods providing high accuracy and precision with a wide linear range in drug analysis. Conclusion: Selected examples demonstrating the applicability of spectrophotometric methods in pharmaceutical assays in this review might contribute to the overall importance of the analytical test used in modern pharmaceutical analysis.

Nose to brain drug delivery system is an interesting approach to deliver a drug directly in the brain through the nose. Intranasal drug delivery is very beneficial because it avoids first-pass metabolism and achieves a greater concentration of drugs in the central nervous system (CNS) at a low dose. This delivery system is used for the treatment of various neurological disorders such as Parkinson's disease, Alzheimer's disease, schizophrenia, dementia, brain cancer, etc. To treat such types of diseases, different formulations like nanoparticles (NPs), microemulsions, in situ gel, etc. can be used depending on the physiochemical properties of the drug. In this review, some essential characteristics related to the delivery of nose to the brain and their possible obstacles are underlined, which include anatomy and physiology of nose to brain delivery. This review also summarizes innovations from the past three to five years.

Mutual exclusion (ME) is one of the most commonly used techniques to handle conflicts in concurrent systems. Traditionally, mutual exclusion algorithms have been designed under the assumption that a process does not fail while acquiring/releasing a lock or while executing its critical section. However, failures do occur in real life, potentially leaving the lock in an inconsistent state. This gives rise to the problem of \emph{recoverable mutual exclusion (RME)} that involves designing a mutual exclusion algorithm that can tolerate failures, while maintaining safety and liveness properties. One of the important measures of performance of any ME algorithm, including an RME algorithm, is the number of \emph{remote memory references (RMRs)} made by a process (for acquiring and releasing a lock as well as recovering the lock structure after a failure). The best known RME algorithm solves the problem for $n$ processes in sub-logarithmic number of RMRs, given by $\mathcal{O}(\frac{\log n}{\log \log n})$, irrespective of the number of failures in the system. In this work, we present a new algorithm for solving the RME problem whose RMR complexity gradually \emph{adapts} to the number of failures that have occurred in the system "recently". In the absence of failures, our algorithm generates only $\mathcal{O}(1)$ RMRs. Furthermore, its RMR complexity is given by $\mathcal{O}(\min\{ \sqrt{F}, \frac{\log n}{\log \log n} \})$ where $F$ is the total number of failures in the "recent" past. In addition to read and write instructions, our algorithm uses compare-and-swap (\CAS{}) and fetch-and-store (\FAS{}) hardware instructions, both of which are commonly available in most modern processors.

We develop an inductive proof-technique to generate imperative programs for pointer data structures from behavioural specifications expressed in the Answer Set Programming (ASP) formalism. ASP is a non-monotonic logic based formalism that employs negation-as-failure which helps emulate the human thought process, allowing domain experts to model desired system behaviour succinctly. We argue in this paper that ASP's reliance on negation-as-failure makes it a better formalism than those based on first-order logic for writing formal specifications. We assume the a domain expert provides the representation of inductively defined data structures along with a specification of its operations. Our procedures combined with our novel proof-technique reason over the specifications and automatically generate an imperative program. Our proof-technique leverages the idea of partial deduction to simplify logical specifications. By algebraically simplifying logical specifications we arrive at a residual specification which can be interpreted as an appropriate imperative program. This work is in the realm of constructing programs that are correct according to a given specification.

In this work, we consider the problem of placing replicas in a data center or storage area network, represented as a digraph, so as to lexico-minimize a previously proposed reliability measure which minimizes the impact of all failure events in the model in decreasing order of severity. Prior work focuses on the special case in which the digraph is an arborescence. In this work, we consider the broader class of multitrees: digraphs in which the subgraph induced by vertices reachable from a fixed node forms a tree. We parameterize multitrees by their number of "roots" (nodes with in-degree zero), and rule out membership in the class of fixed-parameter tractable problems (FPT) by showing that finding optimal replica placements in multitrees with 3 roots is NP-hard. On the positive side, we show that the problem of finding optimal replica placements in the class of \emph{untangled} multitrees is FPT, as parameterized by the replication factor $\rho$ and the number of roots $k$. Our approach combines dynamic programming (DP) with a novel tree decomposition to find an optimal placement of $\rho$ replicas on the leaves of a multitree with $n$ nodes and $k$ roots in $O(n^2\rho^{2k+3})$ time., Comment: 18 pages, 7 figures accepted for publication in COCOA 2017

A new model of causal failure is presented and used to solve a novel replica placement problem in data centers. The model describes dependencies among system components as a directed graph. A replica placement is defined as a subset of vertices in such a graph. A criterion for optimizing replica placements is formalized and explained. In this work, the optimization goal is to avoid choosing placements in which a single failure event is likely to wipe out multiple replicas. Using this criterion, a fast algorithm is given for the scenario in which the dependency model is a tree. The main contribution of the paper is an $O(n + \rho \log \rho)$ dynamic programming algorithm for placing $\rho$ replicas on a tree with $n$ vertices. This algorithm exhibits the interesting property that only two subproblems need to be recursively considered at each stage. An $O(n^2 \rho)$ greedy algorithm is also briefly reported., Comment: 22 pages, 7 figures, 4 algorithm listings