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In this dissertation, we study temporally stochasticity in cellular automata and the behavior of such cellular automata. The work also explores the computational ability of such cellular automaton that illustrates the computability of solving the affinity classification problem. In addition to that, a cellular automaton, defined over Cayley tree, is shown as the classical searching problem solver. The proposed temporally stochastic cellular automata deals with two elementary cellular automata rules, say $f$ and $g$. The $f$ is the default rule, however, $g$ is temporally applied to the overall system with some probability $\tau$ which acts as a noise in the system. After exploring the dynamics of temporally stochastic cellular automata (TSCAs), we study the dynamical behavior of these temporally stochastic cellular automata (TSCAs) to identify the TSCAs that converge to a fixed point from any seed. We apply each of the convergent TSCAs to some standard datasets and observe the effectiveness of each TSCA as a pattern classifier. It is observed that the proposed TSCA-based classifier shows competitive performance in comparison with existing classifier algorithms. We use temporally stochastic cellular automata to solve a new problem in the field of cellular automata, named as, affinity classification problem which is a generalization of the density classification problem . We show that this model can be used in several applications, like modeling self-healing systems. Finally, we introduce a new model of computing unit developed around cellular automata to reduce the workload of the Central Processing Unit (CPU) of a machine to compute. Each cell of the computing unit acts as a tiny processing element with attached memory. Such a CA is implemented on the Cayley Tree to realize efficient solutions for diverse computational problems., Comment: This is my M.Tech thesis under the guidance of Dr. Sukanta Das, Associate Professor, Department of Information Technology, Indian Institute of Engineering Science and Technology

This work introduces a new problem, named as, affinity classification problem which is a generalization of the density classification problem. To solve this problem, we introduce temporally stochastic cellular automata where two rules are stochastically applied in each step on all cells of the automata. Our model is defined on 2-dimensional grid having affection capability. We show that this model can be used in several applications like modeling self-healing systems.

Bangladesh allocates a much smaller share of the government budget to health than countries of similar income level. Reallocation is politically challenging but could offer the biggest potential source of fiscal space for health, larger than the space that economic growth can bring. How those additional resources are used, is also important to improve financial protection. Improving the readiness of the public health care network to provide quality care, particularly at primary health care level, could reduce the use of alternative providers, thereby reducing the need for OOP payments. Ensuring the functionality of community clinics, union level facilities, and upazila health complexes, including a revision of their opening hours would also contribute towards this goal. Ensuring access to pharmaceutical products, particularly for essential NCD-related drugs, at affordable prices could also contribute to a reduction in households OOP payments. In addition to these supply-side interventions, the government could consider demand-side programs where resources follow the patient. For instance, given the large needs and the little resources available, an important pathway to reduce OOP payments while decreasing inequalities would be better targeting public subsidies to the poor and vulnerable. Finally, improving public financial management, policies, and governance will also contribute to the effective use of existing and any additional resources for health.

It is forecast that the single biggest challenge to medical care in the twenty-first century will be the control of antibiotic resistant bacteria, including multidrug resistant strains. Although numerous factors are associated with the rapid emergence of pathogen resistance, the limited number of core structures of currently used antibiotics is one of the reasons for this development. Unlike other conventional antibiotics, the recently discovered class of lipopeptide antibiotics consists of a cyclic peptide head group with an acyl chain that are synthesized by a non-ribosomal peptide synthetase in association with an acyl carrier protein (ACP). This dissertation presents the solution structure of an ACP, LipD that is involved in the acylation of the lipopeptide antibiotic friulimicin. Enhanced stability of holo-LipD (LipD with the phosphopantetheine group attached) was observed through biophysical investigations. This may originate from a subtle change in the helix angles, required for the attachment of the acyl chain. Moreover, interaction studies of ACPs with antimicrobial peptides (AMPs) revealed that bacterial ACPs can be novel targets for AMPs and that this may perturb the fatty acid synthesis to abrogate the pathogenesis of recalcitrant pathogens such as Pseudomonas aeruginosa. We also investigated the ligand binding properties of two selected periplasmic binding proteins (PBP). Understanding the mechanisms by which PBPs can bind and release various ligands has implications for designing biosensors and developing therapeutic compounds that will perturb the pathways mediated by PBPs. Detailed biophysical studies of the Escherichia.coli ferric citrate binding PBP, FecB, revealed that it could form complexes with a wide variety of different tricarboxylic acid-iron complexes. Moreover unexpectedly FecB was found to bind apo-citrate and other iron-free tricarboxylic acids with great avidity. Information regarding the plasticity of substrate binding by FecB will allow us to design new strategies to overcome bacterial resistance. On the other hand, we demonstrated that E. coli HisJ interacts with L-histidine with nM affinity and can also bind 3-methyl-L-histidine, which has been identified as a biomarker for muscle wasting. Therefore, HisJ can potentially be used to design a reagentless protein-based biosensor for the early detection of this muscle disease.