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The coronavirus disease 2019 (COVID-19) has resulted in an ongoing pandemic worldwide. Countries have adopted non-pharmaceutical interventions (NPI) to slow down the spread. This study proposes an agent-based model that simulates the spread of COVID-19 among the inhabitants of a city. The agent-based model can be accommodated for any location by integrating parameters specific to the city. The simulation gives the number of total COVID-19 cases. Considering each person as an agent susceptible to COVID-19, the model causes infected individuals to transmit the disease via various actions performed every hour. The model is validated by comparing the simulation to the real data of Ford County, KS, USA. Different interventions, including contact tracing, are applied on a scaled-down version of New York City, USA, and the parameters that lead to a controlled epidemic are determined. Our experiments suggest that contact tracing via smartphones with more than 60% of the population owning a smartphone combined with city-wide lockdown results in the effective reproduction number (Rt) to fall below 1 within 3 weeks of intervention. For 75% or more smartphone users, new infections are eliminated, and the spread is contained within 3 months of intervention. Contact tracing accompanied with early lockdown can suppress the epidemic growth of COVID-19 completely with sufficient smartphone owners. In places where it is difficult to ensure a high percentage of smartphone ownership, tracing only emergency service providers during a lockdown can go a long way to contain the spread. [ABSTRACT FROM AUTHOR]

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kinds of materials. This sort of molecule fingerprinting has thus led to the widespread application of Raman spectrum in various fields like medical diagnosis, forensics, mineralogy, bacteriology, virology, etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods targeted toward Raman spectra analysis. We examine, experiment, and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both have their perks and pitfalls; therefore, we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to the invariance property in convolutional neural networks (CNNs) and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. This has been achieved by incorporating shifted multi-layer perceptrons (MLP) at the earlier levels of the network to extract significant features across the entire spectrum, which are further refined by the inclusion of triplet loss in the hidden layers. Our experiments on 4 public datasets demonstrate superior performance over the much more complex state-of-the-art methods, and thus, RamanNet has the potential to become the de facto standard in Raman spectra data analysis. [ABSTRACT FROM AUTHOR]

The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the 'language of life', has been analyzed for a multitude of applications and inferences. Owing to the rapid development of deep learning, in recent years there have been a number of breakthroughs in the domain of Natural Language Processing. Since these methods are capable of performing different tasks when trained with a sufficient amount of data, off-the-shelf models are used to perform various biological applications. In this study, we investigated the applicability of the popular Skip-gram model for protein sequence analysis and made an attempt to incorporate some biological insights into it. We propose a novel k-mer embedding scheme, Align-gram, which is capable of mapping the similar k-mers close to each other in a vector space. Furthermore, we experiment with other sequence-based protein representations and observe that the embeddings derived from Align-gram aids modeling and training deep learning models better. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis. [ABSTRACT FROM AUTHOR]

In Bangladesh, groundwater is the main source of both drinking water and irrigation. Suction lift pumps and force mode of operation are the predominant technologies for groundwater abstraction in Bangladesh. For a sustainable usage policy, it is thus important to identify which technology would be more appropriate for which area in Bangladesh. With that aim in mind, this paper proposes a methodology that leverages the power of machine learning that can potentially learn intricate relationships between the (annual maximum) groundwater level (GWL) and the relevant hydrogeological factors (HGFs). A number of machine learning algorithms—both classification and regression models—was trained. Our classification models were trained as a binary classifier to predict the abstraction technology of a particular point. Notably, our best classification model was based on the Random Forest algorithm, which achieved an accuracy of 91% and an excellent value of 96% for the area under receiver operating characteristics curve, indicating its strong discriminant capability. We also identified (elevation derived from) digital elevation model, specific yield and lithology as the three most important HGFs for GWL in Bangladesh. On the other hand, to predict the actual (annual maximum) GWL, we employed a two-stage approach, where we first employed the above-mentioned classification model to identify the suitable abstraction technology for the point of interest and subsequently predict the actual GWL using the appropriate Random Forest regressor. This also had a reasonable accuracy (minimum absolute error was less than 1 for suction mode and less than 5 for the force mode). Finally, using our prediction models, we prepared groundwater (technology) maps for the whole Bangladesh. [ABSTRACT FROM AUTHOR]

Recently, a number of researchers have suggested light-based devices to solve combinatorially interesting problems. In this paper, we design a light based device to solve a generalized version of the Subset Sum problem which was previously handled by Oltean and Muntean. We further design a system which is capable of providing us with the solution subset of the problem in addition to the YES/NO answer to the question of whether there exists a solution or not. [ABSTRACT FROM AUTHOR]

The Longest Common Subsequence (LCS) problem is a classic and well-studied problem in computer science. The LCS problem is a common task in DNA sequence analysis with many applications to genetics and molecular biology. In this paper, we present a new and efficient algorithm for solving the LCS problem for two strings. Our algorithm runs in O(ℛlog log n+ n) time, where ℛ is the total number of ordered pairs of positions at which the two strings match. [ABSTRACT FROM AUTHOR]

This paper presents a multiprocessor based heuristic algorithm for the Multi-dimensional Multiple Choice Knapsack Problem (MMKP). MMKP is a variant of the classical 0–1 knapsack problem, where items having a value and a number of resource requirements are divided into groups. Exactly one item has to be picked up from each group to achieve a maximum total value without exceeding the resource constraint of each type. MMKP has many real world applications including admission control in adaptive multimedia server system. Exact solution to this problem is NP-Hard, and hence is not feasible for real time applications like admission control. Therefore, heuristic solutions have been developed to solve the MMKP. M-HEU is one such heuristic, which solves the MMKP achieving a reasonable percentage of optimality. In this paper, we present a multiprocessor algorithm based on M-HEU, which runs in O( T/ p+ s( p)) time, where T is the time required by the algorithm using single processor, p is the number of processors and s( p), a function of p, is the synchronization overhead. We also present the worst-case analysis of our algorithm, the computation of the optimal number of processors as well as the lower bound of the total value that can be achieved by the heuristic. [ABSTRACT FROM AUTHOR]

The number of entries in a structural database of proteins is increasing day by day. Methods for retrieving protein tertiary structures from such a large database have turn out to be the key to comparative analysis of structures that plays an important role to understand proteins and their functions. In this paper, we present fast and accurate methods for the retrieval of proteins having tertiary structures similar to a query protein from a large database. Our proposed methods borrow ideas from the field of computer vision. The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure. Experimental results clearly indicate the superiority of our approach in both running time and accuracy. Our method is readily available for use from this website: http://research.buet.ac.bd:8080/Comograd/. [ABSTRACT FROM AUTHOR]