Democratic Development and the Role of Citizenship Education in Sub-Saharan African with a Case Focus on Zambia

In addressing issues related to problems of democratisation in Africa, this paper attempts to relate the issue to the need for citizenship education and the role that can play in social development. Citizenship should be central to the formation of viable civil societies that claim a tangible stake in national public spaces in post-Cold War Africa. These and related topics are discussed relative to new possibilities that could lead to the full realisation of the concept as well as the practice of enfranchised citizenship and inclusive social development in aspiring democracies in the Sub Saharan African context. The complexity of the development ‘problematique’ that Sub-Saharan Africa is facing is unique in that it is multi-dimensional, but above all else, politically located. It is, therefore, central to our discussions here that to correct the continent’s current schemes of underdevelopment, pragmatic schemes of governance must be achieved. To do that, we are suggesting, new possibilities of citizenship education should be formulated for the general African scene in general, and for democratising but still both institutionally and economically weakened Zambia

Lift & Project Systems Performing on the Partial-Vertex-Cover Polytope

We study integrality gap (IG) lower bounds on strong LP and SDP relaxations derived by the Sherali-Adams (SA), Lovasz-Schrijver-SDP (LS+), and Sherali-Adams-SDP (SA+) lift-and-project (L&P) systems for the t-Partial-Vertex-Cover (t-PVC) problem, a variation of the classic Vertex-Cover problem in which only t edges need to be covered. t-PVC admits a 2-approximation using various algorithmic techniques, all relying on a natural LP relaxation. Starting from this LP relaxation, our main results assert that for every epsilon > 0, level-Theta(n) LPs or SDPs derived by all known L&P systems that have been used for positive algorithmic results (but the Lasserre hierarchy) have IGs at least (1-epsilon)n/t, where n is the number of vertices of the input graph. Our lower bounds are nearly tight. Our results show that restricted yet powerful models of computation derived by many L&P systems fail to witness c-approximate solutions to t-PVC for any constant c, and for t = O(n). This is one of the very few known examples of an intractable combinatorial optimization problem for which LP-based algorithms induce a constant approximation ratio, still lift-and-project LP and SDP tightenings of the same LP have unbounded IGs. We also show that the SDP that has given the best algorithm known for t-PVC has integrality gap n/t on instances that can be solved by the level-1 LP relaxation derived by the LS system. This constitutes another rare phenomenon where (even in specific instances) a static LP outperforms an SDP that has been used for the best approximation guarantee for the problem at hand. Finally, one of our main contributions is that we make explicit of a new and simple methodology of constructing solutions to LP relaxations that almost trivially satisfy constraints derived by all SDP L&P systems known to be useful for algorithmic positive results (except the La system).Comment: 26 page

A Metric for Linear Temporal Logic

We propose a measure and a metric on the sets of infinite traces generated by a set of atomic propositions. To compute these quantities, we first map properties to subsets of the real numbers and then take the Lebesgue measure of the resulting sets. We analyze how this measure is computed for Linear Temporal Logic (LTL) formulas. An implementation for computing the measure of bounded LTL properties is provided and explained. This implementation leverages SAT model counting and effects independence checks on subexpressions to compute the measure and metric compositionally

The Case for the Precision Timed (PRET) Machine

It is time for a new era of processors whose temporal behavior is as easily controlled as their logical function. We call them precision timed (PRET) machines. Our basic argument is that real-time systems, in which temporal behavior is as important as logical function, are an important and growing application; processor architecture needs to follow suit

Economics of Managing Invasive Species in Tropical and Sub-Tropical Areas of the U.S.A.: Case Study Development

Resource /Energy Economics and Policy,

The asymptotic relative efficiency of mixed statistical tests

Mixed statistical tests are described. It is shown that these tests have a much higher efficiency than conventionally used statistics such as the sign test and polarity coincidence correlation without the high operational complexity of the Wilcoxon, Mann-Whitney, Kendall\tau, or Fisher-Yates: Terry-Hoeffding tests

Efficient Parallel Reinforcement Learning Framework using the Reactor Model

Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads to multiple computational resources, allowing for faster generation of samples, estimation of values, and policy improvement. These computational paradigms require a seamless integration of training, serving, and simulation workloads. Existing frameworks, such as Ray, are not managing this orchestration efficiently, especially in RL tasks that demand intensive input/output and synchronization between actors on a single node. In this study, we have proposed a solution implementing the reactor model, which enforces a set of actors to have a fixed communication pattern. This allows the scheduler to eliminate work needed for synchronization, such as acquiring and releasing locks for each actor or sending and processing coordination-related messages. Our framework, Lingua Franca (LF), a coordination language based on the reactor model, also supports true parallelism in Python and provides a unified interface that allows users to automatically generate dataflow graphs for RL tasks. In comparison to Ray on a single-node multi-core compute platform, LF achieves 1.21x and 11.62x higher simulation throughput in OpenAI Gym and Atari environments, reduces the average training time of synchronized parallel Q-learning by 31.2%, and accelerates multi-agent RL inference by 5.12x.Comment: 10 pages, 11 figure

Predictive Models for Maximum Recommended Therapeutic Dose of Antiretroviral Drugs

A novel method for predicting maximum recommended therapeutic dose (MRTD) is presented using quantitative structure property relationships (QSPRs) and artificial neural networks (ANNs). MRTD data of 31 structurally diverse Antiretroviral drugs (ARVs) were collected from FDA MRTD Database or package inserts. Molecular property descriptors of each compound, that is, molecular mass, aqueous solubility, lipophilicity, biotransformation half life, oxidation half life, and biodegradation probability were calculated from their SMILES codes. A training set (n = 23) was used to construct multiple linear regression and back propagation neural network models. The models were validated using an external test set (n = 8) which demonstrated that MRTD values may be predicted with reasonable accuracy. Model predictability was described by root mean squared errors (RMSEs), Kendall's correlation coefficients (tau), P-values, and Bland Altman plots for method comparisons. MRTD was predicted by a 6-3-1 neural network model (RMSE = 13.67, tau = 0.643, P = 0.035) more accurately than by the multiple linear regression (RMSE = 27.27, tau = 0.714, P = 0.019) model. Both models illustrated a moderate correlation between aqueous solubility of antiretroviral drugs and maximum therapeutic dose. MRTD prediction may assist in the design of safer, more effective treatments for HIV infection

Quantifying the energetic contributions of desolvation and π-electron density during translesion DNA synthesis

This report examines the molecular mechanism by which high-fidelity DNA polymerases select nucleotides during the replication of an abasic site, a non-instructional DNA lesion. This was accomplished by synthesizing several unique 5-substituted indolyl 2′-deoxyribose triphosphates and defining their kinetic parameters for incorporation opposite an abasic site to interrogate the contributions of π-electron density and solvation energies. In general, the Kd, app values for hydrophobic non-natural nucleotides are ∼10-fold lower than those measured for isosteric hydrophilic analogs. In addition, kpol values for nucleotides that contain less π-electron densities are slower than isosteric analogs possessing higher degrees of π-electron density. The differences in kinetic parameters were used to quantify the energetic contributions of desolvation and π-electron density on nucleotide binding and polymerization rate constant. We demonstrate that analogs lacking hydrogen-bonding capabilities act as chain terminators of translesion DNA replication while analogs with hydrogen bonding functional groups are extended when paired opposite an abasic site. Collectively, the data indicate that the efficiency of nucleotide incorporation opposite an abasic site is controlled by energies associated with nucleobase desolvation and π-electron stacking interactions whereas elongation beyond the lesion is achieved through a combination of base-stacking and hydrogen-bonding interactions